From 2696a49c8538457aac1fb06d048df88c0019ec5d Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Tue, 7 Oct 2025 15:09:27 +0200 Subject: [PATCH 01/43] use inspect-ai to evaluate aime25 and gsm8k --- src/lighteval/main_inspect.py | 75 +++++++++++++++++++ src/lighteval/metrics/dynamic_metrics.py | 2 +- .../metrics/utils/extractive_match_utils.py | 4 +- src/lighteval/tasks/default_prompts.py | 28 +++---- 4 files changed, 88 insertions(+), 21 deletions(-) create mode 100644 src/lighteval/main_inspect.py diff --git a/src/lighteval/main_inspect.py b/src/lighteval/main_inspect.py new file mode 100644 index 000000000..9743c03cf --- /dev/null +++ b/src/lighteval/main_inspect.py @@ -0,0 +1,75 @@ +from inspect_ai import Task, eval, task +from inspect_ai.dataset import hf_dataset +from inspect_ai.scorer import Score, Target, accuracy, scorer, stderr +from inspect_ai.solver import TaskState, generate, self_critique, system_message + +from lighteval.metrics.utils.extractive_match_utils import ( + ExprExtractionConfig, + LatexExtractionConfig, + extract_target_from_pred, + get_extraction_regexes, +) +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.registry import Registry +from lighteval.utils.language import Language + + +MATH_SYSTEM_PROMPT = """Solve the following math problem efficiently and clearly. The last line of your response should be of the following format: 'Therefore, the final answer is: $\\boxed{{ANSWER}}$. I hope it is correct' (without quotes) where ANSWER is just the final number or expression that solves the problem. Think step by step before answering.""" + +@scorer(metrics=[accuracy(), stderr()]) +def extractive_math_scorer(): + gold_extraction_target = (ExprExtractionConfig(),) + pred_extraction_target = (ExprExtractionConfig(), LatexExtractionConfig(boxed_match_priority=0)) + language = Language.ENGLISH + fallback_mode = "first_match" + extraction_mode = "first_match" + timeout_seconds = 5 + + gold_extraction_regexes = get_extraction_regexes(gold_extraction_target, language) + pred_extraction_regexes = get_extraction_regexes(pred_extraction_target, language) + + async def score(state: TaskState, target: Target): + extracted_predictions = extract_target_from_pred( + state.output.completion, pred_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds + ) + extracted_gold = extract_target_from_pred( + target.text, gold_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds + ) + return Score( + value="C" if extracted_predictions == extracted_gold else "I", + explanation=state.output.completion, + answer=str(extracted_predictions), + ) + + return score + + +@task +def get_task(lighteval_task_config: LightevalTaskConfig): + name = lighteval_task_config.name + sample_fields = lighteval_task_config.prompt_function + split = lighteval_task_config.evaluation_splits[0] + dataset = hf_dataset( + lighteval_task_config.hf_repo, name=lighteval_task_config.hf_subset, split=split, sample_fields=sample_fields + ) + solver = [ + system_message(MATH_SYSTEM_PROMPT), + generate(), + ] + scorer = [extractive_math_scorer()] + + return Task(dataset=dataset, solver=solver, scorer=scorer, name=name) + + +def main(): + TASK = "lighteval|aime25|0" + MODEL = "openai/gpt-4o" + + registry = Registry(tasks=TASK) + config = registry._update_task_configs()[TASK.rsplit("|", 1)[0]][0] + + eval(get_task(config), model=MODEL) + + +if __name__ == "__main__": + main() diff --git a/src/lighteval/metrics/dynamic_metrics.py b/src/lighteval/metrics/dynamic_metrics.py index 66ed91c3a..06f364a91 100644 --- a/src/lighteval/metrics/dynamic_metrics.py +++ b/src/lighteval/metrics/dynamic_metrics.py @@ -217,7 +217,7 @@ def add_to_specifics_with_timeout( ] formatted_doc.specific["extracted_golds"] = [str(gold) for golds in extracted_golds for gold in golds] - def compute(self, doc: Doc, model_response: ModelResponse) -> float: + def compute(self, doc: str, model_response: str) -> float: golds = doc.get_golds() predictions = model_response.final_text diff --git a/src/lighteval/metrics/utils/extractive_match_utils.py b/src/lighteval/metrics/utils/extractive_match_utils.py index cce2b1793..d0a71fd42 100644 --- a/src/lighteval/metrics/utils/extractive_match_utils.py +++ b/src/lighteval/metrics/utils/extractive_match_utils.py @@ -345,14 +345,14 @@ def lazy_indices_regex( def get_extraction_regexes( - formatted_doc: Doc, target_types: Sequence[ExtractionTarget], language: Language + target_types: Sequence[ExtractionTarget], language: Language, len_choices: int = 1 ) -> list[tuple[list[tuple[re.Pattern[str], int]], ExtractionTarget]]: extraction_regexes: list[tuple[list[tuple[re.Pattern[str], int]], ExtractionTarget]] = [ (lazy_latex_regex(target_type, language), target_type) if isinstance(target_type, LatexExtractionConfig) else (lazy_expr_regex(target_type, language), target_type) if isinstance(target_type, ExprExtractionConfig) - else (lazy_indices_regex(target_type, len(formatted_doc.choices), language), target_type) + else (lazy_indices_regex(target_type, len_choices, language), target_type) for target_type in target_types ] diff --git a/src/lighteval/tasks/default_prompts.py b/src/lighteval/tasks/default_prompts.py index a78860168..537b022ee 100644 --- a/src/lighteval/tasks/default_prompts.py +++ b/src/lighteval/tasks/default_prompts.py @@ -30,6 +30,7 @@ import numpy as np import pycountry +from inspect_ai.dataset import Sample from lighteval.tasks.requests import Doc from lighteval.utils.utils import as_list @@ -130,21 +131,14 @@ def simpleqa(line, task_name: str = None): ) -def aime_prompt_fn(line, task_name: str = None): +def aime_prompt_fn(record): # Prompt template adapted from # - simple-evals: https://github.com/openai/simple-evals/blob/6e84f4e2aed6b60f6a0c7b8f06bbbf4bfde72e58/math_eval.py#L17 # - Llama 3: https://huggingface.co/datasets/meta-llama/Llama-3.2-1B-Instruct-evals/viewer/Llama-3.2-1B-Instruct-evals__math__details?views%5B%5D=llama_32_1b_instruct_evals__math__details # Note that it is important to have the final answer in a box for math-verify to work correctly - MATH_QUERY_TEMPLATE = """ -Solve the following math problem efficiently and clearly. The last line of your response should be of the following format: 'Therefore, the final answer is: $\\boxed{{ANSWER}}$. I hope it is correct' (without quotes) where ANSWER is just the final number or expression that solves the problem. Think step by step before answering. - -{Question} -""".strip() - return Doc( - task_name=task_name, - query=MATH_QUERY_TEMPLATE.format(Question=line["problem"]), - choices=[line["answer"]], - gold_index=0, + return Sample( + input=record["problem"], + target=record["answer"], ) @@ -936,13 +930,11 @@ def gsm_plus(line, task_name: str = None): ) -def gsm8k(line, task_name: str = None): - # Has special analysis in metric for number decomposition - return Doc( - task_name=task_name, - query=f"Question: {line['question']}\nAnswer:", - choices=[f" {line['answer']}"], - gold_index=0, + +def gsm8k(record): + return Sample( + input=record['question'], + target=record['answer'], ) From 578d5308ae8c245280a234264ca83810870227f6 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Tue, 7 Oct 2025 15:14:08 +0200 Subject: [PATCH 02/43] revert file --- src/lighteval/metrics/dynamic_metrics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/lighteval/metrics/dynamic_metrics.py b/src/lighteval/metrics/dynamic_metrics.py index 06f364a91..66ed91c3a 100644 --- a/src/lighteval/metrics/dynamic_metrics.py +++ b/src/lighteval/metrics/dynamic_metrics.py @@ -217,7 +217,7 @@ def add_to_specifics_with_timeout( ] formatted_doc.specific["extracted_golds"] = [str(gold) for golds in extracted_golds for gold in golds] - def compute(self, doc: str, model_response: str) -> float: + def compute(self, doc: Doc, model_response: ModelResponse) -> float: golds = doc.get_golds() predictions = model_response.final_text From 21fa870d5a392c5c1e33a3ef3551ff0cfd0a8210 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Tue, 7 Oct 2025 16:36:50 +0200 Subject: [PATCH 03/43] working for 3 tasks --- src/lighteval/main_inspect.py | 90 +++++++++++++++---- .../metrics/utils/extractive_match_utils.py | 1 - src/lighteval/tasks/default_prompts.py | 1 - src/lighteval/tasks/extended/ifeval/main.py | 55 ++++++------ 4 files changed, 100 insertions(+), 47 deletions(-) diff --git a/src/lighteval/main_inspect.py b/src/lighteval/main_inspect.py index 9743c03cf..55d0e2c96 100644 --- a/src/lighteval/main_inspect.py +++ b/src/lighteval/main_inspect.py @@ -1,7 +1,10 @@ -from inspect_ai import Task, eval, task -from inspect_ai.dataset import hf_dataset +from dataclasses import dataclass +from typing import Callable + +from inspect_ai import Epochs, Task, eval, task +from inspect_ai.dataset import Sample, hf_dataset from inspect_ai.scorer import Score, Target, accuracy, scorer, stderr -from inspect_ai.solver import TaskState, generate, self_critique, system_message +from inspect_ai.solver import TaskState, generate, system_message from lighteval.metrics.utils.extractive_match_utils import ( ExprExtractionConfig, @@ -9,12 +12,29 @@ extract_target_from_pred, get_extraction_regexes, ) -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.registry import Registry +from lighteval.tasks.default_prompts import aime_prompt_fn, gsm8k +from lighteval.tasks.extended.ifeval.main import ifeval_prompt, ifeval_scorer from lighteval.utils.language import Language +@dataclass +class TaskConfig: + name: str + prompt_function: Callable[[dict], Sample] + hf_repo: str + hf_subset: str + split: str + metrics: list + system_prompt: str + epochs: int = 1 + generation_size: int | None = None + num_samples: list[int] | None = None + epochs_reducer: str | None = None + + MATH_SYSTEM_PROMPT = """Solve the following math problem efficiently and clearly. The last line of your response should be of the following format: 'Therefore, the final answer is: $\\boxed{{ANSWER}}$. I hope it is correct' (without quotes) where ANSWER is just the final number or expression that solves the problem. Think step by step before answering.""" +IFEVAL_SYSTEM_PROMPT = """FOLLOW THE INSTRUCTIONS STRICTLY.""" + @scorer(metrics=[accuracy(), stderr()]) def extractive_math_scorer(): @@ -45,30 +65,64 @@ async def score(state: TaskState, target: Target): @task -def get_task(lighteval_task_config: LightevalTaskConfig): +def get_task(lighteval_task_config: TaskConfig): name = lighteval_task_config.name sample_fields = lighteval_task_config.prompt_function - split = lighteval_task_config.evaluation_splits[0] + split = lighteval_task_config.split + system_prompt = lighteval_task_config.system_prompt + metrics = lighteval_task_config.metrics + hf_repo = lighteval_task_config.hf_repo + hf_subset = lighteval_task_config.hf_subset + dataset = hf_dataset( - lighteval_task_config.hf_repo, name=lighteval_task_config.hf_subset, split=split, sample_fields=sample_fields + hf_repo, name=hf_subset, split=split, sample_fields=sample_fields ) solver = [ - system_message(MATH_SYSTEM_PROMPT), - generate(), + system_message(system_prompt), + generate(cache=True), ] - scorer = [extractive_math_scorer()] - - return Task(dataset=dataset, solver=solver, scorer=scorer, name=name) + scorer = metrics + epochs = lighteval_task_config.epochs + epochs_reducer = lighteval_task_config.epochs_reducer + + return Task(dataset=dataset, solver=solver, scorer=scorer, name=name, epochs=Epochs(epochs, epochs_reducer)) + + +gsm8k_task_config = TaskConfig( + name="gsm8k", + prompt_function=gsm8k, + hf_repo="openai/gsm8k", + hf_subset="main", + split="train", + metrics=[extractive_math_scorer()], + system_prompt=MATH_SYSTEM_PROMPT, + epochs=4, +) +aime25_task_config = TaskConfig( + name="aime25", + prompt_function=aime_prompt_fn, + hf_repo="yentinglin/aime_2025", + hf_subset="default", + split="train", + metrics=[extractive_math_scorer()], + system_prompt=MATH_SYSTEM_PROMPT, + epochs=4, +) +ifeval_task_config = TaskConfig( + name="ifeval", + prompt_function=ifeval_prompt, + hf_repo="google/IFEval", + split="train", + hf_subset="default", + metrics=[ifeval_scorer()], + system_prompt=IFEVAL_SYSTEM_PROMPT, +) def main(): - TASK = "lighteval|aime25|0" MODEL = "openai/gpt-4o" - registry = Registry(tasks=TASK) - config = registry._update_task_configs()[TASK.rsplit("|", 1)[0]][0] - - eval(get_task(config), model=MODEL) + eval(get_task(gsm8k_task_config), model=MODEL, display="rich", limit=10) if __name__ == "__main__": diff --git a/src/lighteval/metrics/utils/extractive_match_utils.py b/src/lighteval/metrics/utils/extractive_match_utils.py index d0a71fd42..a5ef53a6f 100644 --- a/src/lighteval/metrics/utils/extractive_match_utils.py +++ b/src/lighteval/metrics/utils/extractive_match_utils.py @@ -31,7 +31,6 @@ from sympy.parsing import parse_expr from lighteval.metrics.utils.math_comparison import should_treat_as_complex -from lighteval.tasks.requests import Doc from lighteval.tasks.templates.utils.formulation import ChoicePrefix, get_prefix from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS from lighteval.utils.imports import requires diff --git a/src/lighteval/tasks/default_prompts.py b/src/lighteval/tasks/default_prompts.py index 537b022ee..5b4581bf8 100644 --- a/src/lighteval/tasks/default_prompts.py +++ b/src/lighteval/tasks/default_prompts.py @@ -930,7 +930,6 @@ def gsm_plus(line, task_name: str = None): ) - def gsm8k(record): return Sample( input=record['question'], diff --git a/src/lighteval/tasks/extended/ifeval/main.py b/src/lighteval/tasks/extended/ifeval/main.py index ae7d42809..dc9bae556 100644 --- a/src/lighteval/tasks/extended/ifeval/main.py +++ b/src/lighteval/tasks/extended/ifeval/main.py @@ -22,12 +22,13 @@ import numpy as np +from inspect_ai.dataset import Sample +from inspect_ai.scorer import Score, Target, accuracy, scorer, stderr +from inspect_ai.solver import TaskState import lighteval.tasks.extended.ifeval.instructions_registry as instructions_registry from lighteval.metrics.metrics_sample import SampleLevelComputation -from lighteval.metrics.utils.metric_utils import ( - SampleLevelMetricGrouping, -) +from lighteval.metrics.utils.metric_utils import SampleLevelMetricGrouping from lighteval.models.model_output import ModelResponse from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.requests import Doc, SamplingMethod @@ -36,37 +37,33 @@ # Very specific task where there are no precise outputs but instead we test if the format obeys rules @requires("langdetect") -def ifeval_prompt(line, task_name: str = ""): - return Doc( - task_name=task_name, - query=line["prompt"], - choices=[""], - gold_index=0, - instruction="", - specific={"instructions_id_list": line["instruction_id_list"], "kwargs": line["kwargs"]}, +def ifeval_prompt(record): + metadata = {"instruction_id_list": record["instruction_id_list"], "kwargs": record["kwargs"]} + + return Sample( + input=record["prompt"], + metadata=metadata, ) submetric_names = [ "prompt_level_strict_acc", - "inst_level_strict_acc", "prompt_level_loose_acc", - "inst_level_loose_acc", ] -REASONING_TAG_PAIRS = [ - ("", ""), -] - -class IFEvalMetrics(SampleLevelComputation): - def compute(self, doc: Doc, model_response: ModelResponse, **kwargs) -> dict: - response = model_response.final_text[0] +@scorer(metrics={ + "prompt_level_strict_acc": [accuracy(), stderr()], + "prompt_level_loose_acc": [accuracy(), stderr()], +}) +def ifeval_scorer(): + async def score(state: TaskState, target: Target): + response = state.output.completion # Strict instructions - instruction_list = doc.specific["instructions_id_list"] - all_kwargs = doc.specific["kwargs"] - prompt = doc.query + instruction_list = state.metadata["instruction_id_list"] + all_kwargs = state.metadata["kwargs"] + prompt = state.input # Loose instructions r = response.split("\n") @@ -117,12 +114,16 @@ def compute(self, doc: Doc, model_response: ModelResponse, **kwargs) -> dict: is_following_list_loose.append(is_following) - return { + return Score(value={ "prompt_level_strict_acc": int(all(is_following_list_strict)), - "inst_level_strict_acc": is_following_list_strict, "prompt_level_loose_acc": int(all(is_following_list_loose)), - "inst_level_loose_acc": is_following_list_loose, - } + }) + return score + + +class IFEvalMetrics(SampleLevelComputation): + def compute(self, doc: Doc, model_response: ModelResponse, **kwargs) -> dict: + pass @requires("langdetect") From 27b2af115a69f869894244fc9dee30b3bedde7b4 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Tue, 7 Oct 2025 16:41:54 +0200 Subject: [PATCH 04/43] parallel evals of tasks --- src/lighteval/main_inspect.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/lighteval/main_inspect.py b/src/lighteval/main_inspect.py index 55d0e2c96..590257f23 100644 --- a/src/lighteval/main_inspect.py +++ b/src/lighteval/main_inspect.py @@ -121,8 +121,9 @@ def get_task(lighteval_task_config: TaskConfig): def main(): MODEL = "openai/gpt-4o" + all_tasks = [gsm8k_task_config, aime25_task_config, ifeval_task_config] - eval(get_task(gsm8k_task_config), model=MODEL, display="rich", limit=10) + eval([get_task(task) for task in all_tasks], model=MODEL, display="rich", limit=10, max_tasks=3) if __name__ == "__main__": From b9a610dc7940946986eb547425c1e640a55d3ee3 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Wed, 8 Oct 2025 12:30:50 +0000 Subject: [PATCH 05/43] adds gpqa diamond to inspect --- src/lighteval/main_inspect.py | 147 ++++++-------------- src/lighteval/metrics/metrics.py | 68 ++++++++- src/lighteval/tasks/default_prompts.py | 31 ++--- src/lighteval/tasks/default_tasks.py | 83 ++++------- src/lighteval/tasks/extended/ifeval/main.py | 38 ++--- src/lighteval/tasks/lighteval_task.py | 22 +++ 6 files changed, 186 insertions(+), 203 deletions(-) diff --git a/src/lighteval/main_inspect.py b/src/lighteval/main_inspect.py index 590257f23..23b33b837 100644 --- a/src/lighteval/main_inspect.py +++ b/src/lighteval/main_inspect.py @@ -1,82 +1,47 @@ -from dataclasses import dataclass -from typing import Callable +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + from inspect_ai import Epochs, Task, eval, task -from inspect_ai.dataset import Sample, hf_dataset -from inspect_ai.scorer import Score, Target, accuracy, scorer, stderr -from inspect_ai.solver import TaskState, generate, system_message - -from lighteval.metrics.utils.extractive_match_utils import ( - ExprExtractionConfig, - LatexExtractionConfig, - extract_target_from_pred, - get_extraction_regexes, -) -from lighteval.tasks.default_prompts import aime_prompt_fn, gsm8k -from lighteval.tasks.extended.ifeval.main import ifeval_prompt, ifeval_scorer -from lighteval.utils.language import Language - - -@dataclass -class TaskConfig: - name: str - prompt_function: Callable[[dict], Sample] - hf_repo: str - hf_subset: str - split: str - metrics: list - system_prompt: str - epochs: int = 1 - generation_size: int | None = None - num_samples: list[int] | None = None - epochs_reducer: str | None = None - - -MATH_SYSTEM_PROMPT = """Solve the following math problem efficiently and clearly. The last line of your response should be of the following format: 'Therefore, the final answer is: $\\boxed{{ANSWER}}$. I hope it is correct' (without quotes) where ANSWER is just the final number or expression that solves the problem. Think step by step before answering.""" -IFEVAL_SYSTEM_PROMPT = """FOLLOW THE INSTRUCTIONS STRICTLY.""" - - -@scorer(metrics=[accuracy(), stderr()]) -def extractive_math_scorer(): - gold_extraction_target = (ExprExtractionConfig(),) - pred_extraction_target = (ExprExtractionConfig(), LatexExtractionConfig(boxed_match_priority=0)) - language = Language.ENGLISH - fallback_mode = "first_match" - extraction_mode = "first_match" - timeout_seconds = 5 - - gold_extraction_regexes = get_extraction_regexes(gold_extraction_target, language) - pred_extraction_regexes = get_extraction_regexes(pred_extraction_target, language) - - async def score(state: TaskState, target: Target): - extracted_predictions = extract_target_from_pred( - state.output.completion, pred_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds - ) - extracted_gold = extract_target_from_pred( - target.text, gold_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds - ) - return Score( - value="C" if extracted_predictions == extracted_gold else "I", - explanation=state.output.completion, - answer=str(extracted_predictions), - ) - - return score +from inspect_ai.dataset import hf_dataset +from inspect_ai.solver import generate, system_message + +from lighteval.tasks import default_tasks +from lighteval.tasks.lighteval_task import LightevalTaskConfig_inspect as LightevalTaskConfig @task -def get_task(lighteval_task_config: TaskConfig): +def get_task(lighteval_task_config: LightevalTaskConfig): name = lighteval_task_config.name sample_fields = lighteval_task_config.prompt_function - split = lighteval_task_config.split + + dataset_repo = lighteval_task_config.dataset_repo + dataset_subset = lighteval_task_config.dataset_subset + dataset_split = lighteval_task_config.dataset_split + system_prompt = lighteval_task_config.system_prompt metrics = lighteval_task_config.metrics - hf_repo = lighteval_task_config.hf_repo - hf_subset = lighteval_task_config.hf_subset - dataset = hf_dataset( - hf_repo, name=hf_subset, split=split, sample_fields=sample_fields - ) + dataset = hf_dataset(dataset_repo, name=dataset_subset, split=dataset_split, sample_fields=sample_fields) solver = [ system_message(system_prompt), generate(cache=True), @@ -88,42 +53,18 @@ def get_task(lighteval_task_config: TaskConfig): return Task(dataset=dataset, solver=solver, scorer=scorer, name=name, epochs=Epochs(epochs, epochs_reducer)) -gsm8k_task_config = TaskConfig( - name="gsm8k", - prompt_function=gsm8k, - hf_repo="openai/gsm8k", - hf_subset="main", - split="train", - metrics=[extractive_math_scorer()], - system_prompt=MATH_SYSTEM_PROMPT, - epochs=4, -) -aime25_task_config = TaskConfig( - name="aime25", - prompt_function=aime_prompt_fn, - hf_repo="yentinglin/aime_2025", - hf_subset="default", - split="train", - metrics=[extractive_math_scorer()], - system_prompt=MATH_SYSTEM_PROMPT, - epochs=4, -) -ifeval_task_config = TaskConfig( - name="ifeval", - prompt_function=ifeval_prompt, - hf_repo="google/IFEval", - split="train", - hf_subset="default", - metrics=[ifeval_scorer()], - system_prompt=IFEVAL_SYSTEM_PROMPT, -) - - def main(): - MODEL = "openai/gpt-4o" - all_tasks = [gsm8k_task_config, aime25_task_config, ifeval_task_config] + MODEL = ["openai/gpt-4o"] + all_tasks = [ + default_tasks.gsm8k_lighteval, + default_tasks.aime25, + default_tasks.gpqa_diamond, + ] # default_tasksifeval] + all_tasks = [get_task(task) for task in all_tasks] + + # eval_set(all_tasks, model=MODEL, display="rich", limit=10, max_tasks=3, bundle_dir="./log_static", log_dir="./log_dynamic-1") - eval([get_task(task) for task in all_tasks], model=MODEL, display="rich", limit=10, max_tasks=3) + eval(all_tasks[-1], model=MODEL, display="rich", limit=10, max_tasks=3) if __name__ == "__main__": diff --git a/src/lighteval/metrics/metrics.py b/src/lighteval/metrics/metrics.py index 167919974..4fad36b8b 100644 --- a/src/lighteval/metrics/metrics.py +++ b/src/lighteval/metrics/metrics.py @@ -25,6 +25,8 @@ import numpy as np from aenum import Enum +from inspect_ai.scorer import Score, Target, accuracy, scorer, stderr +from inspect_ai.solver import TaskState from lighteval.metrics.dynamic_metrics import MultilingualExtractiveMatchMetric from lighteval.metrics.harness_compatibility.drop import DropMetrics @@ -66,6 +68,8 @@ ExprExtractionConfig, IndicesExtractionConfig, LatexExtractionConfig, + extract_target_from_pred, + get_extraction_regexes, ) from lighteval.metrics.utils.metric_utils import ( CorpusLevelMetric, @@ -77,6 +81,66 @@ from lighteval.utils.language import Language +@scorer(metrics=[accuracy(), stderr()]) +def extractive_math_scorer(): + gold_extraction_target = (ExprExtractionConfig(),) + pred_extraction_target = (ExprExtractionConfig(), LatexExtractionConfig(boxed_match_priority=0)) + language = Language.ENGLISH + fallback_mode = "first_match" + extraction_mode = "first_match" + timeout_seconds = 5 + + gold_extraction_regexes = get_extraction_regexes(gold_extraction_target, language) + pred_extraction_regexes = get_extraction_regexes(pred_extraction_target, language) + + async def score(state: TaskState, target: Target): + extracted_predictions = extract_target_from_pred( + state.output.completion, pred_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds + ) + extracted_gold = extract_target_from_pred( + target.text, gold_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds + ) + return Score( + value="C" if extracted_predictions == extracted_gold else "I", + explanation=state.output.completion, + answer=str(extracted_predictions), + ) + + return score + + +@scorer(metrics=[accuracy(), stderr()]) +def multichoice_scorer(): + language = Language.ENGLISH + gold_extraction_target = ( + IndicesExtractionConfig(prefix_for_extraction="NativeLetters", try_extract_without_anchor=True), + ) + pred_extraction_target = ( + IndicesExtractionConfig(prefix_for_extraction="NativeLetters", try_extract_without_anchor=True), + ) + fallback_mode = "first_match" + extraction_mode = "first_match" + timeout_seconds = 5 + + gold_extraction_regexes = get_extraction_regexes(gold_extraction_target, language) + pred_extraction_regexes = get_extraction_regexes(pred_extraction_target, language) + + async def score(state: TaskState, target: Target): + extracted_predictions = extract_target_from_pred( + state.output.completion, pred_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds + ) + extracted_gold = extract_target_from_pred( + target.text, gold_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds + ) + return Score( + value="C" if extracted_predictions == extracted_gold else "I", + explanation=state.output.completion, + answer=str(extracted_predictions), + ) + + return score + + class Metrics(Enum): acc_golds_likelihood = SampleLevelMetric( # todo: we need a better name for this! metric_name="acc", @@ -85,14 +149,14 @@ class Metrics(Enum): corpus_level_fn=np.mean, higher_is_better=True, ) - avg_at_k = SampleLevelMetric( + avg_at_k = SampleLevelMetric( # metric_name="avg@k", sample_level_fn=AvgAtK(strip_strings=True), category=SamplingMethod.GENERATIVE, corpus_level_fn=np.mean, higher_is_better=True, ) - avg_at_k_math = SampleLevelMetric( + avg_at_k_math = SampleLevelMetric( # metric_name="avg@k", sample_level_fn=AvgAtK( sample_scoring_function=MultilingualExtractiveMatchMetric( diff --git a/src/lighteval/tasks/default_prompts.py b/src/lighteval/tasks/default_prompts.py index 5b4581bf8..6358c0cfe 100644 --- a/src/lighteval/tasks/default_prompts.py +++ b/src/lighteval/tasks/default_prompts.py @@ -888,29 +888,16 @@ def gpqa(line, task_name: str = None): ) -def gpqa_instruct(line, task_name: str = None): +def gpqa_instruct(record): """Prompt template adapted from simple-evals: https://github.com/openai/simple-evals/blob/83ed7640a7d9cd26849bcb3340125002ef14abbe/common.py#L14""" gold_index = random.randint(0, 3) - choices = [line["Incorrect Answer 1"], line["Incorrect Answer 2"], line["Incorrect Answer 3"]] - choices.insert(gold_index, line["Correct Answer"]) - instruction = "Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering." - query_template = "{Instruction}\n\n{Question}\n\nA) {A}\nB) {B}\nC) {C}\nD) {D}" - query = query_template.format( - # Stripping to avoid accidental extra whitespaces, present in GPQA - A=choices[0].strip(), - B=choices[1].strip(), - C=choices[2].strip(), - D=choices[3].strip(), - Question=line["Question"].strip(), - Instruction=instruction, - ) + choices = [record["Incorrect Answer 1"], record["Incorrect Answer 2"], record["Incorrect Answer 3"]] + choices.insert(gold_index, record["Correct Answer"]) - return Doc( - task_name=task_name, - query=query, - choices=LETTER_INDICES[: len(choices)], - gold_index=gold_index, - instruction=instruction, + return Sample( + input=record["Question"].strip(), + choices=choices, + target=LETTER_INDICES[gold_index], ) @@ -932,8 +919,8 @@ def gsm_plus(line, task_name: str = None): def gsm8k(record): return Sample( - input=record['question'], - target=record['answer'], + input=record["question"], + target=record["answer"], ) diff --git a/src/lighteval/tasks/default_tasks.py b/src/lighteval/tasks/default_tasks.py index 7092264ad..07e4f5ac1 100644 --- a/src/lighteval/tasks/default_tasks.py +++ b/src/lighteval/tasks/default_tasks.py @@ -19,16 +19,17 @@ # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. +from inspect_ai.scorer import choice + import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics +from lighteval.metrics.metrics import Metrics, extractive_math_scorer, multichoice_scorer from lighteval.metrics.normalizations import ( LogProbCharNorm, - gsm8k_normalizer, harness_triviaqa_normalizer, helm_normalizer, math_normalizer, ) -from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.lighteval_task import LightevalTaskConfig, LightevalTaskConfig_inspect from lighteval.tasks.templates.qa import get_qa_prompt_function from lighteval.utils.language import Language @@ -441,19 +442,17 @@ metrics=[Metrics.g_pass_at_k_math(sample_params={"k": 16, "n": 48})], version=1, ) -aime25 = LightevalTaskConfig( +aime25 = LightevalTaskConfig_inspect( name="aime25", - suite=["lighteval"], prompt_function=prompt.aime_prompt_fn, - hf_repo="yentinglin/aime_2025", - hf_subset="default", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=10000, - metrics=[Metrics.pass_at_k_math(sample_params={"k": 1, "n": 1})], - version=2, + dataset_repo="yentinglin/aime_2025", + dataset_subset="default", + dataset_split="train", + dataset_revision="main", + metrics=[extractive_math_scorer()], + system_prompt="ASNWER USING THE FORMAT $ANSWER$", + epochs=4, + epochs_reducer="pass_at_4", ) aime25_gpassk = LightevalTaskConfig( name="aime25_gpassk", @@ -8494,20 +8493,14 @@ stop_sequence=["\n"], version=0, ) -gpqa_diamond_instruct_lighteval = LightevalTaskConfig( +gpqa_diamond = LightevalTaskConfig_inspect( name="gpqa:diamond", - suite=["lighteval"], prompt_function=prompt.gpqa_instruct, - hf_repo="Idavidrein/gpqa", - hf_subset="gpqa_diamond", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=32768, # needed for reasoning models like R1 - metrics=[Metrics.gpqa_instruct_pass_at_k(sample_params={"k": 1})], - stop_sequence=[], # no stop sequence, will use eos token - version=1, + dataset_repo="Idavidrein/gpqa", + dataset_subset="gpqa_diamond", + dataset_split="train", + metrics=[multichoice_scorer(), choice()], + system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", ) gpqa_extended_instruct_lighteval = LightevalTaskConfig( name="gpqa:extended", @@ -8569,39 +8562,15 @@ stop_sequence=None, version=0, ) -gsm8k_leaderboard = LightevalTaskConfig( +gsm8k_lighteval = LightevalTaskConfig_inspect( name="gsm8k", - suite=["leaderboard"], prompt_function=prompt.gsm8k, - hf_repo="gsm8k", - hf_subset="main", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling_from_train", - generation_size=256, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": gsm8k_normalizer, "normalize_pred": gsm8k_normalizer}) - ], - stop_sequence=[], - version=0, -) -gsm8k_lighteval = LightevalTaskConfig( - name="gsm8k", - suite=["lighteval"], - prompt_function=prompt.gsm8k, - hf_repo="openai/gsm8k", - hf_subset="main", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling_from_train", - generation_size=256, - metrics=[ - Metrics.expr_gold_metric, - ], - stop_sequence=["Question:"], - version=0, + dataset_repo="openai/gsm8k", + dataset_subset="main", + dataset_split="train", + dataset_revision="main", + metrics=[extractive_math_scorer()], + system_prompt="ANSWER USING THE FORMAT $ANSWER$", ) headqa_en_lighteval = LightevalTaskConfig( name="headqa:en", diff --git a/src/lighteval/tasks/extended/ifeval/main.py b/src/lighteval/tasks/extended/ifeval/main.py index dc9bae556..50867c5e3 100644 --- a/src/lighteval/tasks/extended/ifeval/main.py +++ b/src/lighteval/tasks/extended/ifeval/main.py @@ -52,10 +52,12 @@ def ifeval_prompt(record): ] -@scorer(metrics={ - "prompt_level_strict_acc": [accuracy(), stderr()], - "prompt_level_loose_acc": [accuracy(), stderr()], -}) +@scorer( + metrics={ + "prompt_level_strict_acc": [accuracy(), stderr()], + "prompt_level_loose_acc": [accuracy(), stderr()], + } +) def ifeval_scorer(): async def score(state: TaskState, target: Target): response = state.output.completion @@ -114,10 +116,13 @@ async def score(state: TaskState, target: Target): is_following_list_loose.append(is_following) - return Score(value={ - "prompt_level_strict_acc": int(all(is_following_list_strict)), - "prompt_level_loose_acc": int(all(is_following_list_loose)), - }) + return Score( + value={ + "prompt_level_strict_acc": int(all(is_following_list_strict)), + "prompt_level_loose_acc": int(all(is_following_list_loose)), + } + ) + return score @@ -150,17 +155,12 @@ def agg_inst_level_acc(items): ifeval = LightevalTaskConfig( name="ifeval", prompt_function=ifeval_prompt, - suite=["extended"], - hf_repo="google/IFEval", - hf_subset="default", - metrics=[ifeval_metrics], - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split="train", - few_shots_select="random_sampling", - generation_size=1280, - stop_sequence=[], # no stop sequence, will use eot token - version="0.1", + dataset_repo="google/IFEval", + dataset_subset="default", + dataset_split="train", + dataset_revision="main", + metrics=[], + system_prompt="FOLLOW THE INSTRUCTIONS STRICTLY.", ) diff --git a/src/lighteval/tasks/lighteval_task.py b/src/lighteval/tasks/lighteval_task.py index 7eb6c1f16..989f2192c 100644 --- a/src/lighteval/tasks/lighteval_task.py +++ b/src/lighteval/tasks/lighteval_task.py @@ -27,6 +27,7 @@ from datasets import DatasetDict, load_dataset from huggingface_hub import TextGenerationInputGrammarType +from inspect_ai.dataset import Sample from multiprocess import Pool from pytablewriter import MarkdownTableWriter @@ -43,6 +44,27 @@ logger = logging.getLogger(__name__) +@dataclass +class LightevalTaskConfig_inspect: + """Configuration dataclass for a LightevalTask. + + This class stores all the configuration parameters needed to define and run + an evaluation task, including dataset information, prompt formatting, + evaluation metrics, and generation parameters. + """ + + name: str + prompt_function: Callable[[dict], Sample] + dataset_repo: str + dataset_subset: str + dataset_split: str + metrics: list + system_prompt: str | None = None + dataset_revision: str | None = None + epochs: int = 1 + epochs_reducer: str | None = None + + @dataclass class LightevalTaskConfig: """Configuration dataclass for a LightevalTask. From 25c112859928d628c0dd477b1ac234302bcf7cf6 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Mon, 13 Oct 2025 16:28:00 +0200 Subject: [PATCH 06/43] move tasks to individual files --- src/lighteval/main_inspect.py | 28 +- src/lighteval/metrics/metrics.py | 8 +- src/lighteval/tasks/default_prompts.py | 24 +- src/lighteval/tasks/default_tasks.py | 22840 ---------------- src/lighteval/tasks/lighteval_task.py | 3 +- src/lighteval/tasks/multilingual/tasks.py | 4368 --- .../tasks/multilingual/tasks/acva.py | 154 + .../tasks/multilingual/tasks/afri_mgsm.py | 116 + .../tasks/multilingual/tasks/afri_mmlu.py | 138 + .../tasks/multilingual/tasks/afri_xnli.py | 122 + .../tasks/multilingual/tasks/arabic_arc.py | 95 + .../tasks/multilingual/tasks/arabic_mmlu.py | 148 + .../tasks/multilingual/tasks/arcd.py | 97 + .../tasks/multilingual/tasks/belebele.py | 225 + src/lighteval/tasks/multilingual/tasks/c3.py | 106 + .../tasks/multilingual/tasks/ceval.py | 158 + .../tasks/multilingual/tasks/chegeka.py | 93 + .../tasks/multilingual/tasks/chinese_squad.py | 97 + .../tasks/multilingual/tasks/cmath.py | 92 + .../tasks/multilingual/tasks/cmmlu.py | 174 + .../tasks/multilingual/tasks/cmnli.py | 105 + .../tasks/multilingual/tasks/cmrc2018.py | 97 + .../tasks/multilingual/tasks/copa_indic.py | 122 + .../tasks/multilingual/tasks/enem.py | 106 + .../tasks/multilingual/tasks/exams.py | 111 + .../tasks/multilingual/tasks/faquad.py | 99 + .../tasks/multilingual/tasks/flores200.py | 310 + .../tasks/multilingual/tasks/fquad_v2.py | 97 + .../tasks/multilingual/tasks/french_boolq.py | 92 + .../multilingual/tasks/french_triviqa.py | 93 + .../tasks/multilingual/tasks/germanquad.py | 99 + .../tasks/multilingual/tasks/global_mmlu.py | 217 + .../tasks/multilingual/tasks/hellaswag_hin.py | 98 + .../tasks/multilingual/tasks/hellaswag_tel.py | 97 + .../tasks/multilingual/tasks/hellaswag_tha.py | 104 + .../tasks/multilingual/tasks/hellaswag_tur.py | 107 + .../tasks/multilingual/tasks/hindi_arc.py | 105 + .../tasks/multilingual/tasks/hindi_boolq.py | 99 + .../tasks/multilingual/tasks/indicqa.py | 112 + .../tasks/multilingual/tasks/kenswquad.py | 97 + .../tasks/multilingual/tasks/m3exams.py | 113 + .../multilingual/tasks/mathlogicqa_rus.py | 107 + .../tasks/multilingual/tasks/meta_mmlu.py | 182 + .../tasks/multilingual/tasks/mgsm.py | 107 + .../tasks/multilingual/tasks/mintaka.py | 104 + .../tasks/multilingual/tasks/mkqa.py | 131 + .../multilingual/tasks/mlmm_arc_challenge.py | 142 + .../multilingual/tasks/mlmm_hellaswag.py | 144 + .../tasks/multilingual/tasks/mlmm_mmlu.py | 197 + .../multilingual/tasks/mlmm_truthfulqa.py | 149 + .../tasks/multilingual/tasks/mlqa.py | 108 + .../tasks/multilingual/tasks/oab_exams.py | 105 + .../tasks/multilingual/tasks/ocnli.py | 106 + .../tasks/multilingual/tasks/openai_mmlu.py | 182 + .../tasks/multilingual/tasks/openbook_ara.py | 102 + .../tasks/multilingual/tasks/openbook_es.py | 104 + .../tasks/multilingual/tasks/openbook_rus.py | 104 + .../tasks/multilingual/tasks/parus.py | 103 + .../tasks/multilingual/tasks/paws_x.py | 115 + .../tasks/multilingual/tasks/piqa_ar.py | 103 + src/lighteval/tasks/multilingual/tasks/rcb.py | 106 + .../tasks/multilingual/tasks/sber_squad.py | 97 + .../tasks/multilingual/tasks/soqal.py | 96 + .../tasks/multilingual/tasks/squad_es.py | 98 + .../tasks/multilingual/tasks/squad_it.py | 98 + .../tasks/multilingual/tasks/swahili_arc.py | 108 + .../tasks/multilingual/tasks/thai_exams.py | 97 + .../tasks/multilingual/tasks/thaiqa.py | 96 + .../tasks/multilingual/tasks/tquad_v2.py | 96 + .../tasks/multilingual/tasks/turkish_arc.py | 108 + .../tasks/multilingual/tasks/turkish_mmlu.py | 116 + .../tasks/multilingual/tasks/tydiqa.py | 111 + .../tasks/multilingual/tasks/worldtree_rus.py | 106 + .../tasks/multilingual/tasks/xcodah.py | 113 + .../tasks/multilingual/tasks/xcopa.py | 119 + .../tasks/multilingual/tasks/xcsqa.py | 130 + .../tasks/multilingual/tasks/xnli.py | 129 + .../tasks/multilingual/tasks/xnli2.py | 133 + .../tasks/multilingual/tasks/xnli_indic.py | 118 + .../tasks/multilingual/tasks/xquad.py | 116 + .../tasks/multilingual/tasks/xstory.py | 123 + .../tasks/multilingual/tasks/xwinograd.py | 103 + src/lighteval/tasks/tasks/agieval.py | 346 + src/lighteval/tasks/tasks/aime.py | 52 + src/lighteval/tasks/tasks/anli.py | 76 + src/lighteval/tasks/tasks/arc.py | 74 + .../__init__.py => tasks/arc_agi_2.py} | 32 +- src/lighteval/tasks/tasks/arithmetic.py | 191 + src/lighteval/tasks/tasks/asdiv.py | 42 + src/lighteval/tasks/tasks/babi_qa.py | 42 + src/lighteval/tasks/tasks/bbq.py | 224 + src/lighteval/tasks/tasks/bigbench.py | 2714 ++ src/lighteval/tasks/tasks/bigbench_hard.py | 318 + src/lighteval/tasks/tasks/blimp.py | 1107 + src/lighteval/tasks/tasks/bold.py | 128 + src/lighteval/tasks/tasks/boolq.py | 63 + src/lighteval/tasks/tasks/civil_comments.py | 176 + src/lighteval/tasks/tasks/commonsenseqa.py | 42 + src/lighteval/tasks/tasks/coqa.py | 42 + src/lighteval/tasks/tasks/covid_dialogue.py | 42 + src/lighteval/tasks/tasks/drop_qa.py | 41 + src/lighteval/tasks/tasks/dyck_language.py | 76 + .../tasks/tasks/entity_data_imputation.py | 68 + src/lighteval/tasks/tasks/entitymatching.py | 240 + src/lighteval/tasks/tasks/ethics.py | 112 + src/lighteval/tasks/tasks/glue.py | 298 + src/lighteval/tasks/tasks/gpqa.py | 64 + src/lighteval/tasks/tasks/gsm8k.py | 37 + src/lighteval/tasks/tasks/gsm_plus.py | 38 + src/lighteval/tasks/tasks/headqa.py | 74 + src/lighteval/tasks/tasks/hellaswag.py | 50 + .../tasks/{extended => tasks}/hle/main.py | 0 .../ifbench/evaluation_lib.py | 0 .../ifbench/instructions.py | 0 .../ifbench/instructions_registry.py | 0 .../tasks/{extended => tasks}/ifbench/main.py | 0 .../ifeval/instructions.py | 0 .../ifeval/instructions_registry.py | 0 .../ifeval/instructions_utils.py | 0 .../tasks/{extended => tasks}/ifeval/main.py | 0 src/lighteval/tasks/tasks/imdb.py | 70 + src/lighteval/tasks/tasks/jeopardy.py | 54 + src/lighteval/tasks/tasks/lambada.py | 64 + .../lcb/codegen_metrics.py | 0 .../tasks/{extended => tasks}/lcb/main.py | 0 .../tasks/tasks/legal_summarization.py | 103 + src/lighteval/tasks/tasks/legalsupport.py | 42 + src/lighteval/tasks/tasks/lexglue.py | 144 + src/lighteval/tasks/tasks/lextreme.py | 319 + src/lighteval/tasks/tasks/logiqa.py | 42 + src/lighteval/tasks/tasks/lsat_qa.py | 106 + src/lighteval/tasks/tasks/math.py | 147 + src/lighteval/tasks/tasks/math_500.py | 38 + src/lighteval/tasks/tasks/mathqa.py | 42 + src/lighteval/tasks/tasks/me_q_sum.py | 42 + src/lighteval/tasks/tasks/med.py | 88 + src/lighteval/tasks/tasks/med_dialog.py | 63 + src/lighteval/tasks/tasks/mgsm.py | 212 + .../mix_eval/judge_prompts.py | 0 .../{extended => tasks}/mix_eval/main.py | 0 .../{extended => tasks}/mix_eval/prompts.py | 0 src/lighteval/tasks/tasks/mmlu.py | 938 + src/lighteval/tasks/tasks/mmlu_redux.py | 167 + src/lighteval/tasks/tasks/mmmu_pro.py | 84 + .../mt_bench/judge_prompt_templates.py | 0 .../{extended => tasks}/mt_bench/main.py | 0 src/lighteval/tasks/tasks/musr.py | 82 + src/lighteval/tasks/tasks/narrativeqa.py | 42 + .../tasks/tasks/natural_questions.py | 51 + src/lighteval/tasks/tasks/numeracy.py | 154 + .../olympiade_bench/main.py | 0 src/lighteval/tasks/tasks/openbookqa.py | 58 + src/lighteval/tasks/tasks/piqa.py | 55 + src/lighteval/tasks/tasks/prost.py | 42 + src/lighteval/tasks/tasks/pubmedqa.py | 50 + src/lighteval/tasks/tasks/qa4mre.py | 88 + src/lighteval/tasks/tasks/qasper.py | 54 + src/lighteval/tasks/tasks/quac.py | 42 + src/lighteval/tasks/tasks/race_high.py | 42 + src/lighteval/tasks/tasks/raft.py | 346 + .../tasks/tasks/real_toxicity_prompts.py | 42 + src/lighteval/tasks/tasks/sacrebleu.py | 2890 ++ src/lighteval/tasks/tasks/sciq.py | 42 + src/lighteval/tasks/tasks/simpleqa.py | 42 + src/lighteval/tasks/tasks/siqa.py | 42 + src/lighteval/tasks/tasks/squad_v2.py | 56 + src/lighteval/tasks/tasks/storycloze.py | 65 + src/lighteval/tasks/tasks/summarization.py | 106 + src/lighteval/tasks/tasks/swag.py | 42 + .../tasks/tasks/synthetic_reasoning.py | 123 + src/lighteval/tasks/tasks/the_pile.py | 331 + .../tiny_benchmarks/main.py | 0 src/lighteval/tasks/tasks/toxigen.py | 42 + src/lighteval/tasks/tasks/triviaqa.py | 42 + src/lighteval/tasks/tasks/truthfulqa.py | 48 + src/lighteval/tasks/tasks/twitterAAE.py | 65 + src/lighteval/tasks/tasks/unscramble.py | 113 + src/lighteval/tasks/tasks/webqs.py | 42 + src/lighteval/tasks/tasks/wikifact.py | 2349 ++ src/lighteval/tasks/tasks/wikitext.py | 50 + src/lighteval/tasks/tasks/winogrande.py | 42 + src/lighteval/tasks/tasks/wsc273.py | 42 + src/lighteval/tasks/tasks/xcopa.py | 226 + src/lighteval/tasks/tasks/xstory_cloze.py | 208 + src/lighteval/tasks/tasks/xwinograd.py | 122 + 185 files changed, 26982 insertions(+), 27248 deletions(-) delete mode 100644 src/lighteval/tasks/default_tasks.py delete mode 100644 src/lighteval/tasks/multilingual/tasks.py create mode 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100644 src/lighteval/tasks/tasks/triviaqa.py create mode 100644 src/lighteval/tasks/tasks/truthfulqa.py create mode 100644 src/lighteval/tasks/tasks/twitterAAE.py create mode 100644 src/lighteval/tasks/tasks/unscramble.py create mode 100644 src/lighteval/tasks/tasks/webqs.py create mode 100644 src/lighteval/tasks/tasks/wikifact.py create mode 100644 src/lighteval/tasks/tasks/wikitext.py create mode 100644 src/lighteval/tasks/tasks/winogrande.py create mode 100644 src/lighteval/tasks/tasks/wsc273.py create mode 100644 src/lighteval/tasks/tasks/xcopa.py create mode 100644 src/lighteval/tasks/tasks/xstory_cloze.py create mode 100644 src/lighteval/tasks/tasks/xwinograd.py diff --git a/src/lighteval/main_inspect.py b/src/lighteval/main_inspect.py index 23b33b837..a9e0eaa9f 100644 --- a/src/lighteval/main_inspect.py +++ b/src/lighteval/main_inspect.py @@ -39,32 +39,44 @@ def get_task(lighteval_task_config: LightevalTaskConfig): dataset_split = lighteval_task_config.dataset_split system_prompt = lighteval_task_config.system_prompt - metrics = lighteval_task_config.metrics dataset = hf_dataset(dataset_repo, name=dataset_subset, split=dataset_split, sample_fields=sample_fields) - solver = [ + solvers = lighteval_task_config.solvers or [ system_message(system_prompt), generate(cache=True), ] - scorer = metrics + scorers = lighteval_task_config.scorers epochs = lighteval_task_config.epochs epochs_reducer = lighteval_task_config.epochs_reducer - return Task(dataset=dataset, solver=solver, scorer=scorer, name=name, epochs=Epochs(epochs, epochs_reducer)) + return Task(dataset=dataset, solver=solvers, scorer=scorers, name=name, epochs=Epochs(epochs, epochs_reducer)) + + +model_args = { + "max-tokens", "system-message", "temperature", "top-p", "top-k", "frequence-penalty", + "presence-penalty", "logit-bias", "seed", "stop-seqs", "num-choices", "best-of", "log-probs", "top-logprobs", + "cache-prompt", "reasoning-effort", "reasoning-tokens", "reasoning-history", "response-format", "parallel-tool-calls", "max-tool-output", + "internal-tools", "max-retries", "timeout" +} def main(): MODEL = ["openai/gpt-4o"] all_tasks = [ - default_tasks.gsm8k_lighteval, - default_tasks.aime25, - default_tasks.gpqa_diamond, + #default_tasks.gsm8k_lighteval, + #default_tasks.aime25, + #default_tasks.aime24, + #default_tasks.math_500, + default_tasks.gsm_plus, + #default_tasks.gpqa_diamond, + #default_tasks.gpqa_extended, + #default_tasks.gpqa_main, ] # default_tasksifeval] all_tasks = [get_task(task) for task in all_tasks] # eval_set(all_tasks, model=MODEL, display="rich", limit=10, max_tasks=3, bundle_dir="./log_static", log_dir="./log_dynamic-1") - eval(all_tasks[-1], model=MODEL, display="rich", limit=10, max_tasks=3) + eval(all_tasks, model=MODEL, display="rich", limit=10, max_tasks=3, log_probs=True) if __name__ == "__main__": diff --git a/src/lighteval/metrics/metrics.py b/src/lighteval/metrics/metrics.py index 4fad36b8b..2fc5ec0b4 100644 --- a/src/lighteval/metrics/metrics.py +++ b/src/lighteval/metrics/metrics.py @@ -25,7 +25,7 @@ import numpy as np from aenum import Enum -from inspect_ai.scorer import Score, Target, accuracy, scorer, stderr +from inspect_ai.scorer import Score, Target, accuracy, exact, scorer, stderr from inspect_ai.solver import TaskState from lighteval.metrics.dynamic_metrics import MultilingualExtractiveMatchMetric @@ -83,7 +83,7 @@ @scorer(metrics=[accuracy(), stderr()]) def extractive_math_scorer(): - gold_extraction_target = (ExprExtractionConfig(),) + gold_extraction_target = (ExprExtractionConfig(), LatexExtractionConfig(boxed_match_priority=0)) pred_extraction_target = (ExprExtractionConfig(), LatexExtractionConfig(boxed_match_priority=0)) language = Language.ENGLISH fallback_mode = "first_match" @@ -122,8 +122,8 @@ def multichoice_scorer(): extraction_mode = "first_match" timeout_seconds = 5 - gold_extraction_regexes = get_extraction_regexes(gold_extraction_target, language) - pred_extraction_regexes = get_extraction_regexes(pred_extraction_target, language) + gold_extraction_regexes = get_extraction_regexes(gold_extraction_target, language, len_choices=4) + pred_extraction_regexes = get_extraction_regexes(pred_extraction_target, language, len_choices=4) async def score(state: TaskState, target: Target): extracted_predictions = extract_target_from_pred( diff --git a/src/lighteval/tasks/default_prompts.py b/src/lighteval/tasks/default_prompts.py index 6358c0cfe..cb48c936c 100644 --- a/src/lighteval/tasks/default_prompts.py +++ b/src/lighteval/tasks/default_prompts.py @@ -901,19 +901,15 @@ def gpqa_instruct(record): ) -def gsm_plus(line, task_name: str = None): +def gsm_plus(record): # GSM8K with 8 prompt variations per sample # Some prompts require critical thinking (around 1k/10k), we skip them as # they are a bit trickier to eval with regular text extraction. - if line["perturbation_type"] == "critical thinking": - return None - return Doc( - task_name=task_name, - query=f"Question: {line['question']}\n\nAnswer:", - choices=[line["answer"]], - gold_index=0, + return Sample( + input=record['question'], + target=record["answer"], ) @@ -1421,22 +1417,20 @@ def lsat_qa(line, task_name: str = None): ) -def math_500(line, task_name: str = None): +def math_500(record): # Prompt template adapted from # - simple-evals: https://github.com/openai/simple-evals/blob/6e84f4e2aed6b60f6a0c7b8f06bbbf4bfde72e58/math_eval.py#L17 # - Llama 3: https://huggingface.co/datasets/meta-llama/Llama-3.2-1B-Instruct-evals/viewer/Llama-3.2-1B-Instruct-evals__math__details?views%5B%5D=llama_32_1b_instruct_evals__math__details # Note that it is important to have the final answer in a box for math-verify to work correctly + MATH_QUERY_TEMPLATE = """ Solve the following math problem efficiently and clearly. The last line of your response should be of the following format: 'Therefore, the final answer is: $\\boxed{{ANSWER}}$. I hope it is correct' (without quotes) where ANSWER is just the final number or expression that solves the problem. Think step by step before answering. -{Question} """.strip() - return Doc( - task_name=task_name, - query=MATH_QUERY_TEMPLATE.format(Question=line["problem"]), - gold_index=0, - choices=[line["solution"]], + return Sample( + input=record["problem"], + target=record["solution"], ) diff --git a/src/lighteval/tasks/default_tasks.py b/src/lighteval/tasks/default_tasks.py deleted file mode 100644 index 07e4f5ac1..000000000 --- a/src/lighteval/tasks/default_tasks.py +++ /dev/null @@ -1,22840 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. -from inspect_ai.scorer import choice - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics, extractive_math_scorer, multichoice_scorer -from lighteval.metrics.normalizations import ( - LogProbCharNorm, - harness_triviaqa_normalizer, - helm_normalizer, - math_normalizer, -) -from lighteval.tasks.lighteval_task import LightevalTaskConfig, LightevalTaskConfig_inspect -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.utils.language import Language - - -mmmu_pro_standard_4_options = LightevalTaskConfig( - name="mmmu_pro:standard-4", - suite=["lighteval"], - prompt_function=prompt.mmmu_pro, - hf_repo="MMMU/MMMU_pro", - hf_subset="standard (4 options)", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, # expected an answer in a format 'Answer: B' - metrics=[Metrics.gpqa_instruct_metric], - stop_sequence=None, - version=0, -) -mmmu_pro_standard_10_options = LightevalTaskConfig( - name="mmmu_pro:standard-10", - suite=["lighteval"], - prompt_function=prompt.mmmu_pro, - hf_repo="MMMU/MMMU_pro", - hf_subset="standard (10 options)", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, # expected an answer in a format 'Answer: B' - metrics=[Metrics.gpqa_instruct_metric], - stop_sequence=None, - version=0, -) -mmmu_pro_vision = LightevalTaskConfig( - name="mmmu_pro:vision", - suite=["lighteval"], - prompt_function=prompt.mmmu_pro_vision, - hf_repo="MMMU/MMMU_pro", - hf_subset="vision", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, # expected an answer in a format 'Answer: B' - metrics=[Metrics.gpqa_instruct_metric], - stop_sequence=None, - version=0, -) -abstract_narrative_understanding_bigbench = LightevalTaskConfig( - name="abstract_narrative_understanding", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="abstract_narrative_understanding", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -agieval_aqua_rat_lighteval = LightevalTaskConfig( - name="agieval:aqua-rat", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-aqua-rat", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_gaokao_biology_lighteval = LightevalTaskConfig( - name="agieval:gaokao-biology", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-gaokao-biology", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_gaokao_chemistry_lighteval = LightevalTaskConfig( - name="agieval:gaokao-chemistry", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-gaokao-chemistry", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_gaokao_chinese_lighteval = LightevalTaskConfig( - name="agieval:gaokao-chinese", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-gaokao-chinese", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_gaokao_english_lighteval = LightevalTaskConfig( - name="agieval:gaokao-english", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-gaokao-english", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_gaokao_geography_lighteval = LightevalTaskConfig( - name="agieval:gaokao-geography", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-gaokao-geography", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_gaokao_history_lighteval = LightevalTaskConfig( - name="agieval:gaokao-history", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-gaokao-history", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_gaokao_mathqa_lighteval = LightevalTaskConfig( - name="agieval:gaokao-mathqa", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-gaokao-mathqa", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_gaokao_physics_lighteval = LightevalTaskConfig( - name="agieval:gaokao-physics", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-gaokao-physics", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_logiqa_en_lighteval = LightevalTaskConfig( - name="agieval:logiqa-en", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-logiqa-en", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_logiqa_zh_lighteval = LightevalTaskConfig( - name="agieval:logiqa-zh", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-logiqa-zh", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_lsat_ar_lighteval = LightevalTaskConfig( - name="agieval:lsat-ar", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-lsat-ar", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_lsat_lr_lighteval = LightevalTaskConfig( - name="agieval:lsat-lr", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-lsat-lr", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_lsat_rc_lighteval = LightevalTaskConfig( - name="agieval:lsat-rc", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-lsat-rc", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_sat_en_lighteval = LightevalTaskConfig( - name="agieval:sat-en", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-sat-en", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_sat_en_without_passage_lighteval = LightevalTaskConfig( - name="agieval:sat-en-without-passage", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-sat-en-without-passage", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -agieval_sat_math_lighteval = LightevalTaskConfig( - name="agieval:sat-math", - suite=["lighteval"], - prompt_function=prompt.agieval, - hf_repo="dmayhem93/agieval-sat-math", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=None, - version=0, -) -aime24 = LightevalTaskConfig( - name="aime24", - suite=["lighteval"], - prompt_function=prompt.aime_prompt_fn, - hf_repo="HuggingFaceH4/aime_2024", - hf_subset="default", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.pass_at_k_math(sample_params={"k": 1})], - version=2, -) -aime24_avg = LightevalTaskConfig( - name="aime24_avg", - suite=["lighteval"], - prompt_function=prompt.aime_prompt_fn, - hf_repo="HuggingFaceH4/aime_2024", - hf_subset="default", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.avg_at_k_math(sample_params={"k": 64})], - version=2, -) -aime24_gpassk = LightevalTaskConfig( - name="aime24_gpassk", - suite=["lighteval"], - prompt_function=prompt.aime_prompt_fn, - hf_repo="HuggingFaceH4/aime_2024", - hf_subset="default", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=8192, - metrics=[Metrics.g_pass_at_k_math(sample_params={"k": 16, "n": 48})], - version=1, -) -aime25 = LightevalTaskConfig_inspect( - name="aime25", - prompt_function=prompt.aime_prompt_fn, - dataset_repo="yentinglin/aime_2025", - dataset_subset="default", - dataset_split="train", - dataset_revision="main", - metrics=[extractive_math_scorer()], - system_prompt="ASNWER USING THE FORMAT $ANSWER$", - epochs=4, - epochs_reducer="pass_at_4", -) -aime25_gpassk = LightevalTaskConfig( - name="aime25_gpassk", - suite=["lighteval"], - prompt_function=prompt.aime_prompt_fn, - hf_repo="yentinglin/aime_2025", - hf_subset="default", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=8192, - metrics=[Metrics.g_pass_at_k_math(sample_params={"k": 16, "n": 48})], - version=1, -) -anachronisms_bigbench = LightevalTaskConfig( - name="anachronisms", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="anachronisms", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -analogical_similarity_bigbench = LightevalTaskConfig( - name="analogical_similarity", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="analogical_similarity", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -analytic_entailment_bigbench = LightevalTaskConfig( - name="analytic_entailment", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="analytic_entailment", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -anli_r1_lighteval = LightevalTaskConfig( - name="anli:r1", - suite=["lighteval", "anli"], - prompt_function=prompt.anli, - hf_repo="anli", - hf_subset="plain_text", - hf_avail_splits=["train_r1", "dev_r1", "test_r1"], - evaluation_splits=["test_r1"], - few_shots_split="train_r1", - few_shots_select="random_sampling_from_train", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -anli_r2_lighteval = LightevalTaskConfig( - name="anli:r2", - suite=["lighteval", "anli"], - prompt_function=prompt.anli, - hf_repo="anli", - hf_subset="plain_text", - hf_avail_splits=["train_r2", "dev_r2", "test_r2"], - evaluation_splits=["test_r2"], - few_shots_split="train_r2", - few_shots_select="random_sampling_from_train", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -anli_r3_lighteval = LightevalTaskConfig( - name="anli:r3", - suite=["lighteval", "anli"], - prompt_function=prompt.anli, - hf_repo="anli", - hf_subset="plain_text", - hf_avail_splits=["train_r3", "dev_r3", "test_r3"], - evaluation_splits=["test_r3"], - few_shots_split="train_r3", - few_shots_select="random_sampling_from_train", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -arc_agi_2 = LightevalTaskConfig( - name="arc_agi_2", - suite=["lighteval"], - prompt_function=prompt.arc_agi_2, - hf_repo="arc-agi-community/arc-agi-2", - hf_subset="default", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[Metrics.exact_match], - stop_sequence=None, - version=0, -) -arc_c_letters_original = LightevalTaskConfig( - name="arc:c:letters", - suite=["original", "arc"], - prompt_function=prompt.arc_with_options_letters_predict, - hf_repo="ai2_arc", - hf_subset="ARC-Challenge", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc, Metrics.exact_match], - stop_sequence=["\n"], - version=0, -) -arc_c_options_original = LightevalTaskConfig( - name="arc:c:options", - suite=["original", "arc"], - prompt_function=prompt.arc_with_options, - hf_repo="ai2_arc", - hf_subset="ARC-Challenge", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -arc_c_simple_original = LightevalTaskConfig( - name="arc:c:simple", - suite=["original", "arc"], - prompt_function=prompt.arc, - hf_repo="ai2_arc", - hf_subset="ARC-Challenge", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -arc_challenge_leaderboard = LightevalTaskConfig( - name="arc:challenge", - suite=["leaderboard", "arc"], - prompt_function=prompt.arc, - hf_repo="ai2_arc", - hf_subset="ARC-Challenge", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling_from_train", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -arc_easy_lighteval = LightevalTaskConfig( - name="arc:easy", - suite=["lighteval", "arc"], - prompt_function=prompt.arc, - hf_repo="ai2_arc", - hf_subset="ARC-Easy", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select="random_sampling_from_train", - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -arithmetic_1dc_lighteval = LightevalTaskConfig( - name="arithmetic:1dc", - suite=["lighteval", "arithmetic"], - prompt_function=prompt.arithmetic, - hf_repo="EleutherAI/arithmetic", - hf_subset="arithmetic_1dc", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.acc_golds_likelihood], - stop_sequence=["\n"], - version=0, -) -arithmetic_2da_lighteval = LightevalTaskConfig( - name="arithmetic:2da", - suite=["lighteval", "arithmetic"], - prompt_function=prompt.arithmetic, - hf_repo="EleutherAI/arithmetic", - hf_subset="arithmetic_2da", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.acc_golds_likelihood], - stop_sequence=["\n"], - version=0, -) -arithmetic_2dm_lighteval = LightevalTaskConfig( - name="arithmetic:2dm", - suite=["lighteval", "arithmetic"], - prompt_function=prompt.arithmetic, - hf_repo="EleutherAI/arithmetic", - hf_subset="arithmetic_2dm", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.acc_golds_likelihood], - stop_sequence=["\n"], - version=0, -) -arithmetic_2ds_lighteval = LightevalTaskConfig( - name="arithmetic:2ds", - suite=["lighteval", "arithmetic"], - prompt_function=prompt.arithmetic, - hf_repo="EleutherAI/arithmetic", - hf_subset="arithmetic_2ds", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.acc_golds_likelihood], - stop_sequence=["\n"], - version=0, -) -arithmetic_3da_lighteval = LightevalTaskConfig( - name="arithmetic:3da", - suite=["lighteval", "arithmetic"], - prompt_function=prompt.arithmetic, - hf_repo="EleutherAI/arithmetic", - hf_subset="arithmetic_3da", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.acc_golds_likelihood], - stop_sequence=["\n"], - version=0, -) -arithmetic_3ds_lighteval = LightevalTaskConfig( - name="arithmetic:3ds", - suite=["lighteval", "arithmetic"], - prompt_function=prompt.arithmetic, - hf_repo="EleutherAI/arithmetic", - hf_subset="arithmetic_3ds", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.acc_golds_likelihood], - stop_sequence=["\n"], - version=0, -) -arithmetic_4da_lighteval = LightevalTaskConfig( - name="arithmetic:4da", - suite=["lighteval", "arithmetic"], - prompt_function=prompt.arithmetic, - hf_repo="EleutherAI/arithmetic", - hf_subset="arithmetic_4da", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.acc_golds_likelihood], - stop_sequence=["\n"], - version=0, -) -arithmetic_4ds_lighteval = LightevalTaskConfig( - name="arithmetic:4ds", - suite=["lighteval", "arithmetic"], - prompt_function=prompt.arithmetic, - hf_repo="EleutherAI/arithmetic", - hf_subset="arithmetic_4ds", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.acc_golds_likelihood], - stop_sequence=["\n"], - version=0, -) -arithmetic_5da_lighteval = LightevalTaskConfig( - name="arithmetic:5da", - suite=["lighteval", "arithmetic"], - prompt_function=prompt.arithmetic, - hf_repo="EleutherAI/arithmetic", - hf_subset="arithmetic_5da", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.acc_golds_likelihood], - stop_sequence=["\n"], - version=0, -) -arithmetic_5ds_lighteval = LightevalTaskConfig( - name="arithmetic:5ds", - suite=["lighteval", "arithmetic"], - prompt_function=prompt.arithmetic, - hf_repo="EleutherAI/arithmetic", - hf_subset="arithmetic_5ds", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.acc_golds_likelihood], - stop_sequence=["\n"], - version=0, -) -arithmetic_bb_bigbench = LightevalTaskConfig( - name="arithmetic_bb", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="arithmetic", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -ascii_word_recognition_bigbench = LightevalTaskConfig( - name="ascii_word_recognition", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="ascii_word_recognition", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -asdiv_lighteval = LightevalTaskConfig( - name="asdiv", - suite=["lighteval"], - prompt_function=prompt.asdiv, - hf_repo="EleutherAI/asdiv", - hf_subset="asdiv", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.acc_golds_likelihood], - stop_sequence=["\n"], - version=0, -) -authorship_verification_bigbench = LightevalTaskConfig( - name="authorship_verification", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="authorship_verification", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -auto_categorization_bigbench = LightevalTaskConfig( - name="auto_categorization", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="auto_categorization", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu], - stop_sequence=["\n"], - version=0, -) -auto_debugging_bigbench_lite = LightevalTaskConfig( - name="auto_debugging", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_linefeed_before_and_after_query, - hf_repo="tasksource/bigbench", - hf_subset="auto_debugging", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=None, - version=0, -) -babi_qa_helm = LightevalTaskConfig( - name="babi_qa", - suite=["helm"], - prompt_function=prompt.babi_qa, - hf_repo="facebook/babi_qa", - hf_subset="en-valid-qa1", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_causal_judgment_lighteval = LightevalTaskConfig( - name="bigbench:causal_judgment", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="causal_judgement", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_date_understanding_lighteval = LightevalTaskConfig( - name="bigbench:date_understanding", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="date_understanding", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_disambiguation_qa_lighteval = LightevalTaskConfig( - name="bigbench:disambiguation_qa", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="disambiguation_qa", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_geometric_shapes_lighteval = LightevalTaskConfig( - name="bigbench:geometric_shapes", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="geometric_shapes", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_logical_deduction_five_objects_lighteval = LightevalTaskConfig( - name="bigbench:logical_deduction_five_objects", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="logical_deduction_five_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_logical_deduction_seven_objects_lighteval = LightevalTaskConfig( - name="bigbench:logical_deduction_seven_objects", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="logical_deduction_seven_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_logical_deduction_three_objects_lighteval = LightevalTaskConfig( - name="bigbench:logical_deduction_three_objects", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="logical_deduction_three_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_movie_recommendation_lighteval = LightevalTaskConfig( - name="bigbench:movie_recommendation", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="movie_recommendation", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_navigate_lighteval = LightevalTaskConfig( - name="bigbench:navigate", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="navigate", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_reasoning_about_colored_objects_lighteval = LightevalTaskConfig( - name="bigbench:reasoning_about_colored_objects", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="reasoning_about_colored_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_ruin_names_lighteval = LightevalTaskConfig( - name="bigbench:ruin_names", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="ruin_names", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_salient_translation_error_detection_lighteval = LightevalTaskConfig( - name="bigbench:salient_translation_error_detection", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="salient_translation_error_detection", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_snarks_lighteval = LightevalTaskConfig( - name="bigbench:snarks", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="snarks", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_sports_understanding_lighteval = LightevalTaskConfig( - name="bigbench:sports_understanding", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="sports_understanding", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_temporal_sequences_lighteval = LightevalTaskConfig( - name="bigbench:temporal_sequences", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="temporal_sequences", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_tracking_shuffled_objects_five_objects_lighteval = LightevalTaskConfig( - name="bigbench:tracking_shuffled_objects_five_objects", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="tracking_shuffled_objects_five_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_tracking_shuffled_objects_seven_objects_lighteval = LightevalTaskConfig( - name="bigbench:tracking_shuffled_objects_seven_objects", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="tracking_shuffled_objects_seven_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_tracking_shuffled_objects_three_objects_lighteval = LightevalTaskConfig( - name="bigbench:tracking_shuffled_objects_three_objects", - suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, - hf_repo="lighteval/bbh", - hf_subset="tracking_shuffled_objects_three_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bigbench_causal_judgment_harness = LightevalTaskConfig( - name="bigbench:causal_judgment", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="causal_judgement", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_date_understanding_harness = LightevalTaskConfig( - name="bigbench:date_understanding", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="date_understanding", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_disambiguation_qa_harness = LightevalTaskConfig( - name="bigbench:disambiguation_qa", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="disambiguation_qa", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_geometric_shapes_harness = LightevalTaskConfig( - name="bigbench:geometric_shapes", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="geometric_shapes", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_logical_deduction_five_objects_harness = LightevalTaskConfig( - name="bigbench:logical_deduction_five_objects", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="logical_deduction_five_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_logical_deduction_seven_objects_harness = LightevalTaskConfig( - name="bigbench:logical_deduction_seven_objects", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="logical_deduction_seven_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_logical_deduction_three_objects_harness = LightevalTaskConfig( - name="bigbench:logical_deduction_three_objects", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="logical_deduction_three_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_movie_recommendation_harness = LightevalTaskConfig( - name="bigbench:movie_recommendation", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="movie_recommendation", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_navigate_harness = LightevalTaskConfig( - name="bigbench:navigate", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="navigate", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_reasoning_about_colored_objects_harness = LightevalTaskConfig( - name="bigbench:reasoning_about_colored_objects", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="reasoning_about_colored_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_ruin_names_harness = LightevalTaskConfig( - name="bigbench:ruin_names", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="ruin_names", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_salient_translation_error_detection_harness = LightevalTaskConfig( - name="bigbench:salient_translation_error_detection", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="salient_translation_error_detection", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_snarks_harness = LightevalTaskConfig( - name="bigbench:snarks", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="snarks", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_sports_understanding_harness = LightevalTaskConfig( - name="bigbench:sports_understanding", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="sports_understanding", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_temporal_sequences_harness = LightevalTaskConfig( - name="bigbench:temporal_sequences", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="temporal_sequences", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_tracking_shuffled_objects_five_objects_harness = LightevalTaskConfig( - name="bigbench:tracking_shuffled_objects_five_objects", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="tracking_shuffled_objects_five_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_tracking_shuffled_objects_seven_objects_harness = LightevalTaskConfig( - name="bigbench:tracking_shuffled_objects_seven_objects", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="tracking_shuffled_objects_seven_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bigbench_tracking_shuffled_objects_three_objects_harness = LightevalTaskConfig( - name="bigbench:tracking_shuffled_objects_three_objects", - suite=["harness"], - prompt_function=prompt.bbh_harness, - hf_repo="lighteval/bbh", - hf_subset="tracking_shuffled_objects_three_objects", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["", "Q=", "\n\n"], - must_remove_duplicate_docs=True, - version=0, -) -bbh_boolean_expressions_harness = LightevalTaskConfig( - name="bbh:boolean_expressions", - suite=["harness"], - prompt_function=prompt.bbh_boolean_expressions, - hf_repo="lukaemon/bbh", - hf_subset="boolean_expressions", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_causal_judgment_harness = LightevalTaskConfig( - name="bbh:causal_judgment", - suite=["harness"], - prompt_function=prompt.bbh_causal_judgment, - hf_repo="lukaemon/bbh", - hf_subset="causal_judgement", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_date_understanding_harness = LightevalTaskConfig( - name="bbh:date_understanding", - suite=["harness"], - prompt_function=prompt.bbh_date_understanding, - hf_repo="lukaemon/bbh", - hf_subset="date_understanding", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_disambiguation_qa_harness = LightevalTaskConfig( - name="bbh:disambiguation_qa", - suite=["harness"], - prompt_function=prompt.bbh_disambiguation_qa, - hf_repo="lukaemon/bbh", - hf_subset="disambiguation_qa", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_dyck_languages_harness = LightevalTaskConfig( - name="bbh:dyck_languages", - suite=["harness"], - prompt_function=prompt.bbh_dyck_languages, - hf_repo="lukaemon/bbh", - hf_subset="dyck_languages", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_formal_fallacies_harness = LightevalTaskConfig( - name="bbh:formal_fallacies", - suite=["harness"], - prompt_function=prompt.bbh_formal_fallacies, - hf_repo="lukaemon/bbh", - hf_subset="formal_fallacies", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_geometric_shapes_harness = LightevalTaskConfig( - name="bbh:geometric_shapes", - suite=["harness"], - prompt_function=prompt.bbh_geometric_shapes, - hf_repo="lukaemon/bbh", - hf_subset="geometric_shapes", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_hyperbaton_harness = LightevalTaskConfig( - name="bbh:hyperbaton", - suite=["harness"], - prompt_function=prompt.bbh_hyperbaton, - hf_repo="lukaemon/bbh", - hf_subset="hyperbaton", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_logical_deduction_five_objects_harness = LightevalTaskConfig( - name="bbh:logical_deduction_five_objects", - suite=["harness"], - prompt_function=prompt.bbh_logical_deduction_five_objects, - hf_repo="lukaemon/bbh", - hf_subset="logical_deduction_five_objects", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_logical_deduction_seven_objects_harness = LightevalTaskConfig( - name="bbh:logical_deduction_seven_objects", - suite=["harness"], - prompt_function=prompt.bbh_logical_deduction_seven_objects, - hf_repo="lukaemon/bbh", - hf_subset="logical_deduction_seven_objects", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_logical_deduction_three_objects_harness = LightevalTaskConfig( - name="bbh:logical_deduction_three_objects", - suite=["harness"], - prompt_function=prompt.bbh_logical_deduction_three_objects, - hf_repo="lukaemon/bbh", - hf_subset="logical_deduction_three_objects", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_movie_recommendation_harness = LightevalTaskConfig( - name="bbh:movie_recommendation", - suite=["harness"], - prompt_function=prompt.bbh_movie_recommendation, - hf_repo="lukaemon/bbh", - hf_subset="movie_recommendation", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_multistep_arithmetic_two_harness = LightevalTaskConfig( - name="bbh:multistep_arithmetic_two", - suite=["harness"], - prompt_function=prompt.bbh_multistep_arithmetic_two, - hf_repo="lukaemon/bbh", - hf_subset="multistep_arithmetic_two", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_navigate_harness = LightevalTaskConfig( - name="bbh:navigate", - suite=["harness"], - prompt_function=prompt.bbh_navigate, - hf_repo="lukaemon/bbh", - hf_subset="navigate", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_object_counting_harness = LightevalTaskConfig( - name="bbh:object_counting", - suite=["harness"], - prompt_function=prompt.bbh_object_counting, - hf_repo="lukaemon/bbh", - hf_subset="object_counting", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_penguins_in_a_table_harness = LightevalTaskConfig( - name="bbh:penguins_in_a_table", - suite=["harness"], - prompt_function=prompt.bbh_penguins_in_a_table, - hf_repo="lukaemon/bbh", - hf_subset="penguins_in_a_table", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_reasoning_about_colored_objects_harness = LightevalTaskConfig( - name="bbh:reasoning_about_colored_objects", - suite=["harness"], - prompt_function=prompt.bbh_reasoning_about_colored_objects, - hf_repo="lukaemon/bbh", - hf_subset="reasoning_about_colored_objects", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_ruin_names_harness = LightevalTaskConfig( - name="bbh:ruin_names", - suite=["harness"], - prompt_function=prompt.bbh_ruin_names, - hf_repo="lukaemon/bbh", - hf_subset="ruin_names", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_salient_translation_error_detection_harness = LightevalTaskConfig( - name="bbh:salient_translation_error_detection", - suite=["harness"], - prompt_function=prompt.bbh_salient_translation_error_detection, - hf_repo="lukaemon/bbh", - hf_subset="salient_translation_error_detection", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_snarks_harness = LightevalTaskConfig( - name="bbh:snarks", - suite=["harness"], - prompt_function=prompt.bbh_snarks, - hf_repo="lukaemon/bbh", - hf_subset="snarks", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_sports_understanding_harness = LightevalTaskConfig( - name="bbh:sports_understanding", - suite=["harness"], - prompt_function=prompt.bbh_sports_understanding, - hf_repo="lukaemon/bbh", - hf_subset="sports_understanding", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_temporal_sequences_harness = LightevalTaskConfig( - name="bbh:temporal_sequences", - suite=["harness"], - prompt_function=prompt.bbh_temporal_sequences, - hf_repo="lukaemon/bbh", - hf_subset="temporal_sequences", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_tracking_shuffled_objects_five_objects_harness = LightevalTaskConfig( - name="bbh:tracking_shuffled_objects_five_objects", - suite=["harness"], - prompt_function=prompt.bbh_tracking_shuffled_objects_five_objects, - hf_repo="lukaemon/bbh", - hf_subset="tracking_shuffled_objects_five_objects", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_tracking_shuffled_objects_seven_objects_harness = LightevalTaskConfig( - name="bbh:tracking_shuffled_objects_seven_objects", - suite=["harness"], - prompt_function=prompt.bbh_tracking_shuffled_objects_seven_objects, - hf_repo="lukaemon/bbh", - hf_subset="tracking_shuffled_objects_seven_objects", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_tracking_shuffled_objects_three_objects_harness = LightevalTaskConfig( - name="bbh:tracking_shuffled_objects_three_objects", - suite=["harness"], - prompt_function=prompt.bbh_tracking_shuffled_objects_three_objects, - hf_repo="lukaemon/bbh", - hf_subset="tracking_shuffled_objects_three_objects", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_web_of_lies_harness = LightevalTaskConfig( - name="bbh:web_of_lies", - suite=["harness"], - prompt_function=prompt.bbh_web_of_lies, - hf_repo="lukaemon/bbh", - hf_subset="web_of_lies", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbh_word_sorting_harness = LightevalTaskConfig( - name="bbh:word_sorting", - suite=["harness"], - prompt_function=prompt.bbh_word_sorting, - hf_repo="lukaemon/bbh", - hf_subset="word_sorting", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["", "Q=", "\n\n"], - version=0, -) -bbq_helm = LightevalTaskConfig( - name="bbq", - suite=["helm"], - prompt_function=prompt.bbq, - hf_repo="lighteval/bbq_helm", - hf_subset="all", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -bbq_Age_helm = LightevalTaskConfig( - name="bbq:Age", - suite=["helm"], - prompt_function=prompt.bbq, - hf_repo="lighteval/bbq_helm", - hf_subset="Age", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -bbq_Disability_status_helm = LightevalTaskConfig( - name="bbq:Disability_status", - suite=["helm"], - prompt_function=prompt.bbq, - hf_repo="lighteval/bbq_helm", - hf_subset="Disability_status", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -bbq_Gender_identity_helm = LightevalTaskConfig( - name="bbq:Gender_identity", - suite=["helm"], - prompt_function=prompt.bbq, - hf_repo="lighteval/bbq_helm", - hf_subset="Gender_identity", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -bbq_Nationality_helm = LightevalTaskConfig( - name="bbq:Nationality", - suite=["helm"], - prompt_function=prompt.bbq, - hf_repo="lighteval/bbq_helm", - hf_subset="Nationality", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -bbq_Physical_appearance_helm = LightevalTaskConfig( - name="bbq:Physical_appearance", - suite=["helm"], - prompt_function=prompt.bbq, - hf_repo="lighteval/bbq_helm", - hf_subset="Physical_appearance", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -bbq_Race_ethnicity_helm = LightevalTaskConfig( - name="bbq:Race_ethnicity", - suite=["helm"], - prompt_function=prompt.bbq, - hf_repo="lighteval/bbq_helm", - hf_subset="Race_ethnicity", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -bbq_Race_x_SES_helm = LightevalTaskConfig( - name="bbq:Race_x_SES", - suite=["helm"], - prompt_function=prompt.bbq, - hf_repo="lighteval/bbq_helm", - hf_subset="Race_x_SES", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -bbq_Race_x_gender_helm = LightevalTaskConfig( - name="bbq:Race_x_gender", - suite=["helm"], - prompt_function=prompt.bbq, - hf_repo="lighteval/bbq_helm", - hf_subset="Race_x_gender", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -bbq_Religion_helm = LightevalTaskConfig( - name="bbq:Religion", - suite=["helm"], - prompt_function=prompt.bbq, - hf_repo="lighteval/bbq_helm", - hf_subset="Religion", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -bbq_SES_helm = LightevalTaskConfig( - name="bbq:SES", - suite=["helm"], - prompt_function=prompt.bbq, - hf_repo="lighteval/bbq_helm", - hf_subset="SES", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -bbq_Sexual_orientation_helm = LightevalTaskConfig( - name="bbq:Sexual_orientation", - suite=["helm"], - prompt_function=prompt.bbq, - hf_repo="lighteval/bbq_helm", - hf_subset="Sexual_orientation", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -bbq_lite_json_bigbench_lite = LightevalTaskConfig( - name="bbq_lite_json", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="bbq_lite_json", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -bigbench_auto_debugging_helm = LightevalTaskConfig( - name="bigbench:auto_debugging", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="auto_debugging", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_age_ambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:age_ambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-age_ambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_age_disambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:age_disambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-age_disambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_disability_status_ambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:disability_status_ambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-disability_status_ambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_disability_status_disambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:disability_status_disambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-disability_status_disambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_gender_identity_ambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:gender_identity_ambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-gender_identity_ambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_gender_identity_disambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:gender_identity_disambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-gender_identity_disambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_nationality_ambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:nationality_ambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-nationality_ambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_nationality_disambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:nationality_disambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-nationality_disambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_physical_appearance_ambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:physical_appearance_ambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-physical_appearance_ambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_physical_appearance_disambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:physical_appearance_disambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-physical_appearance_disambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_race_ethnicity_ambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:race_ethnicity_ambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-race_ethnicity_ambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_race_ethnicity_disambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:race_ethnicity_disambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-race_ethnicity_disambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_religion_ambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:religion_ambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-religion_ambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_religion_disambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:religion_disambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-religion_disambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_ses_ambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:ses_ambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-ses_ambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_ses_disambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:ses_disambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-ses_disambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_sexual_orientation_ambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:sexual_orientation_ambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-sexual_orientation_ambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_bbq_lite_json_sexual_orientation_disambig_helm = LightevalTaskConfig( - name="bigbench:bbq_lite_json:sexual_orientation_disambig", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="bbq_lite_json-sexual_orientation_disambig", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_code_line_description_helm = LightevalTaskConfig( - name="bigbench:code_line_description", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="code_line_description", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_conceptual_combinations_contradictions_helm = LightevalTaskConfig( - name="bigbench:conceptual_combinations:contradictions", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conceptual_combinations-contradictions", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_conceptual_combinations_emergent_properties_helm = LightevalTaskConfig( - name="bigbench:conceptual_combinations:emergent_properties", - suite=["helm"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conceptual_combinations-emergent_properties", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_conceptual_combinations_fanciful_fictional_combinations_helm = LightevalTaskConfig( - name="bigbench:conceptual_combinations:fanciful_fictional_combinations", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conceptual_combinations-fanciful_fictional_combinations", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_conceptual_combinations_homonyms_helm = LightevalTaskConfig( - name="bigbench:conceptual_combinations:homonyms", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conceptual_combinations-homonyms", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_conceptual_combinations_invented_words_helm = LightevalTaskConfig( - name="bigbench:conceptual_combinations:invented_words", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conceptual_combinations-invented_words", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_adna_from_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:adna_from", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-adna_from", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_adna_to_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:adna_to", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-adna_to", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_atikampe_from_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:atikampe_from", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-atikampe_from", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_atikampe_to_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:atikampe_to", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-atikampe_to", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_gornam_from_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:gornam_from", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-gornam_from", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_gornam_to_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:gornam_to", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-gornam_to", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_holuan_from_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:holuan_from", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-holuan_from", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_holuan_to_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:holuan_to", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-holuan_to", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_mkafala_from_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:mkafala_from", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-mkafala_from", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_mkafala_to_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:mkafala_to", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-mkafala_to", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_postpositive_english_from_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:postpositive_english_from", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-postpositive_english_from", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_postpositive_english_to_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:postpositive_english_to", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-postpositive_english_to", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_unapuri_from_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:unapuri_from", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-unapuri_from", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_unapuri_to_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:unapuri_to", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-unapuri_to", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_vaomi_from_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:vaomi_from", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-vaomi_from", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_conlang_translation_vaomi_to_helm = LightevalTaskConfig( - name="bigbench:conlang_translation:vaomi_to", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="conlang_translation-vaomi_to", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], - stop_sequence=["\n"], - version=0, -) -bigbench_emoji_movie_helm = LightevalTaskConfig( - name="bigbench:emoji_movie", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="emoji_movie", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_formal_fallacies_syllogisms_negation_helm = LightevalTaskConfig( - name="bigbench:formal_fallacies_syllogisms_negation", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="formal_fallacies_syllogisms_negation", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_hindu_knowledge_helm = LightevalTaskConfig( - name="bigbench:hindu_knowledge", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="hindu_knowledge", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_known_unknowns_helm = LightevalTaskConfig( - name="bigbench:known_unknowns", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="known_unknowns", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_language_identification_helm = LightevalTaskConfig( - name="bigbench:language_identification", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="language_identification", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_linguistics_puzzles_helm = LightevalTaskConfig( - name="bigbench:linguistics_puzzles", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="linguistics_puzzles", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_logic_grid_puzzle_helm = LightevalTaskConfig( - name="bigbench:logic_grid_puzzle", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="logic_grid_puzzle", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_logical_deduction_five_objects_helm = LightevalTaskConfig( - name="bigbench:logical_deduction-five_objects", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="logical_deduction-five_objects", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_logical_deduction_seven_objects_helm = LightevalTaskConfig( - name="bigbench:logical_deduction-seven_objects", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="logical_deduction-seven_objects", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_logical_deduction_three_objects_helm = LightevalTaskConfig( - name="bigbench:logical_deduction-three_objects", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="logical_deduction-three_objects", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_misconceptions_russian_helm = LightevalTaskConfig( - name="bigbench:misconceptions_russian", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="misconceptions_russian", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_novel_concepts_helm = LightevalTaskConfig( - name="bigbench:novel_concepts", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="novel_concepts", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_operators_helm = LightevalTaskConfig( - name="bigbench:operators", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="operators", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_parsinlu_reading_comprehension_helm = LightevalTaskConfig( - name="bigbench:parsinlu_reading_comprehension", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="parsinlu_reading_comprehension", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_play_dialog_same_or_different_helm = LightevalTaskConfig( - name="bigbench:play_dialog_same_or_different", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="play_dialog_same_or_different", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_repeat_copy_logic_helm = LightevalTaskConfig( - name="bigbench:repeat_copy_logic", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="repeat_copy_logic", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_strange_stories_boolean_helm = LightevalTaskConfig( - name="bigbench:strange_stories-boolean", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="strange_stories-boolean", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_strange_stories_multiple_choice_helm = LightevalTaskConfig( - name="bigbench:strange_stories-multiple_choice", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="strange_stories-multiple_choice", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_strategyqa_helm = LightevalTaskConfig( - name="bigbench:strategyqa", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="strategyqa", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_symbol_interpretation_adversarial_helm = LightevalTaskConfig( - name="bigbench:symbol_interpretation-adversarial", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="symbol_interpretation-adversarial", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_symbol_interpretation_emoji_agnostic_helm = LightevalTaskConfig( - name="bigbench:symbol_interpretation-emoji_agnostic", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="symbol_interpretation-emoji_agnostic", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_symbol_interpretation_name_agnostic_helm = LightevalTaskConfig( - name="bigbench:symbol_interpretation-name_agnostic", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="symbol_interpretation-name_agnostic", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_symbol_interpretation_plain_helm = LightevalTaskConfig( - name="bigbench:symbol_interpretation-plain", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="symbol_interpretation-plain", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_symbol_interpretation_tricky_helm = LightevalTaskConfig( - name="bigbench:symbol_interpretation-tricky", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="symbol_interpretation-tricky", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_vitaminc_fact_verification_helm = LightevalTaskConfig( - name="bigbench:vitaminc_fact_verification", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="vitaminc_fact_verification", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bigbench_winowhy_helm = LightevalTaskConfig( - name="bigbench:winowhy", - suite=["helm", "bigbench_scenario"], - prompt_function=prompt.bigbench_helm, - hf_repo="lighteval/bigbench_helm", - hf_subset="winowhy", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -blimp_adjunct_island_lighteval = LightevalTaskConfig( - name="blimp:adjunct_island", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="adjunct_island", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_adjunct_island_helm = LightevalTaskConfig( - name="blimp:adjunct_island", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="adjunct_island", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_anaphor_gender_agreement_lighteval = LightevalTaskConfig( - name="blimp:anaphor_gender_agreement", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="anaphor_gender_agreement", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_anaphor_gender_agreement_helm = LightevalTaskConfig( - name="blimp:anaphor_gender_agreement", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="anaphor_gender_agreement", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_anaphor_number_agreement_lighteval = LightevalTaskConfig( - name="blimp:anaphor_number_agreement", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="anaphor_number_agreement", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_anaphor_number_agreement_helm = LightevalTaskConfig( - name="blimp:anaphor_number_agreement", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="anaphor_number_agreement", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_animate_subject_passive_lighteval = LightevalTaskConfig( - name="blimp:animate_subject_passive", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="animate_subject_passive", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_animate_subject_passive_helm = LightevalTaskConfig( - name="blimp:animate_subject_passive", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="animate_subject_passive", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_animate_subject_trans_lighteval = LightevalTaskConfig( - name="blimp:animate_subject_trans", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="animate_subject_trans", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_animate_subject_trans_helm = LightevalTaskConfig( - name="blimp:animate_subject_trans", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="animate_subject_trans", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_causative_lighteval = LightevalTaskConfig( - name="blimp:causative", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="causative", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_causative_helm = LightevalTaskConfig( - name="blimp:causative", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="causative", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_complex_NP_island_lighteval = LightevalTaskConfig( - name="blimp:complex_NP_island", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="complex_NP_island", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_complex_NP_island_helm = LightevalTaskConfig( - name="blimp:complex_NP_island", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="complex_NP_island", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_coordinate_structure_constraint_complex_left_branch_lighteval = LightevalTaskConfig( - name="blimp:coordinate_structure_constraint_complex_left_branch", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="coordinate_structure_constraint_complex_left_branch", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_coordinate_structure_constraint_complex_left_branch_helm = LightevalTaskConfig( - name="blimp:coordinate_structure_constraint_complex_left_branch", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="coordinate_structure_constraint_complex_left_branch", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_coordinate_structure_constraint_object_extraction_lighteval = LightevalTaskConfig( - name="blimp:coordinate_structure_constraint_object_extraction", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="coordinate_structure_constraint_object_extraction", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_coordinate_structure_constraint_object_extraction_helm = LightevalTaskConfig( - name="blimp:coordinate_structure_constraint_object_extraction", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="coordinate_structure_constraint_object_extraction", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_1_lighteval = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_1_helm = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_2_lighteval = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_2_helm = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_irregular_1_lighteval = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_irregular_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_irregular_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_irregular_1_helm = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_irregular_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_irregular_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_irregular_2_lighteval = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_irregular_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_irregular_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_irregular_2_helm = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_irregular_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_irregular_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_with_adj_2_lighteval = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_with_adj_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_with_adj_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_with_adj_2_helm = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_with_adj_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_with_adj_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_with_adj_irregular_1_lighteval = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_with_adj_irregular_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_with_adj_irregular_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_with_adj_irregular_1_helm = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_with_adj_irregular_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_with_adj_irregular_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_with_adj_irregular_2_lighteval = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_with_adj_irregular_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_with_adj_irregular_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_with_adj_irregular_2_helm = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_with_adj_irregular_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_with_adj_irregular_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_with_adjective_1_lighteval = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_with_adjective_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_with_adjective_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_determiner_noun_agreement_with_adjective_1_helm = LightevalTaskConfig( - name="blimp:determiner_noun_agreement_with_adjective_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="determiner_noun_agreement_with_adjective_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_distractor_agreement_relational_noun_lighteval = LightevalTaskConfig( - name="blimp:distractor_agreement_relational_noun", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="distractor_agreement_relational_noun", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_distractor_agreement_relational_noun_helm = LightevalTaskConfig( - name="blimp:distractor_agreement_relational_noun", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="distractor_agreement_relational_noun", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_distractor_agreement_relative_clause_lighteval = LightevalTaskConfig( - name="blimp:distractor_agreement_relative_clause", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="distractor_agreement_relative_clause", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_distractor_agreement_relative_clause_helm = LightevalTaskConfig( - name="blimp:distractor_agreement_relative_clause", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="distractor_agreement_relative_clause", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_drop_argument_lighteval = LightevalTaskConfig( - name="blimp:drop_argument", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="drop_argument", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_drop_argument_helm = LightevalTaskConfig( - name="blimp:drop_argument", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="drop_argument", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_ellipsis_n_bar_1_lighteval = LightevalTaskConfig( - name="blimp:ellipsis_n_bar_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="ellipsis_n_bar_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_ellipsis_n_bar_1_helm = LightevalTaskConfig( - name="blimp:ellipsis_n_bar_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="ellipsis_n_bar_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_ellipsis_n_bar_2_lighteval = LightevalTaskConfig( - name="blimp:ellipsis_n_bar_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="ellipsis_n_bar_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_ellipsis_n_bar_2_helm = LightevalTaskConfig( - name="blimp:ellipsis_n_bar_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="ellipsis_n_bar_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_existential_there_object_raising_lighteval = LightevalTaskConfig( - name="blimp:existential_there_object_raising", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="existential_there_object_raising", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_existential_there_object_raising_helm = LightevalTaskConfig( - name="blimp:existential_there_object_raising", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="existential_there_object_raising", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_existential_there_quantifiers_1_lighteval = LightevalTaskConfig( - name="blimp:existential_there_quantifiers_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="existential_there_quantifiers_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_existential_there_quantifiers_1_helm = LightevalTaskConfig( - name="blimp:existential_there_quantifiers_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="existential_there_quantifiers_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_existential_there_quantifiers_2_lighteval = LightevalTaskConfig( - name="blimp:existential_there_quantifiers_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="existential_there_quantifiers_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_existential_there_quantifiers_2_helm = LightevalTaskConfig( - name="blimp:existential_there_quantifiers_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="existential_there_quantifiers_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_existential_there_subject_raising_lighteval = LightevalTaskConfig( - name="blimp:existential_there_subject_raising", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="existential_there_subject_raising", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_existential_there_subject_raising_helm = LightevalTaskConfig( - name="blimp:existential_there_subject_raising", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="existential_there_subject_raising", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_expletive_it_object_raising_lighteval = LightevalTaskConfig( - name="blimp:expletive_it_object_raising", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="expletive_it_object_raising", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_expletive_it_object_raising_helm = LightevalTaskConfig( - name="blimp:expletive_it_object_raising", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="expletive_it_object_raising", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_inchoative_lighteval = LightevalTaskConfig( - name="blimp:inchoative", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="inchoative", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_inchoative_helm = LightevalTaskConfig( - name="blimp:inchoative", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="inchoative", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_intransitive_lighteval = LightevalTaskConfig( - name="blimp:intransitive", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="intransitive", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_intransitive_helm = LightevalTaskConfig( - name="blimp:intransitive", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="intransitive", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_irregular_past_participle_adjectives_lighteval = LightevalTaskConfig( - name="blimp:irregular_past_participle_adjectives", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="irregular_past_participle_adjectives", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_irregular_past_participle_adjectives_helm = LightevalTaskConfig( - name="blimp:irregular_past_participle_adjectives", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="irregular_past_participle_adjectives", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_irregular_past_participle_verbs_lighteval = LightevalTaskConfig( - name="blimp:irregular_past_participle_verbs", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="irregular_past_participle_verbs", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_irregular_past_participle_verbs_helm = LightevalTaskConfig( - name="blimp:irregular_past_participle_verbs", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="irregular_past_participle_verbs", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_irregular_plural_subject_verb_agreement_1_lighteval = LightevalTaskConfig( - name="blimp:irregular_plural_subject_verb_agreement_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="irregular_plural_subject_verb_agreement_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_irregular_plural_subject_verb_agreement_1_helm = LightevalTaskConfig( - name="blimp:irregular_plural_subject_verb_agreement_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="irregular_plural_subject_verb_agreement_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_irregular_plural_subject_verb_agreement_2_lighteval = LightevalTaskConfig( - name="blimp:irregular_plural_subject_verb_agreement_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="irregular_plural_subject_verb_agreement_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_irregular_plural_subject_verb_agreement_2_helm = LightevalTaskConfig( - name="blimp:irregular_plural_subject_verb_agreement_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="irregular_plural_subject_verb_agreement_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_left_branch_island_echo_question_lighteval = LightevalTaskConfig( - name="blimp:left_branch_island_echo_question", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="left_branch_island_echo_question", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_left_branch_island_echo_question_helm = LightevalTaskConfig( - name="blimp:left_branch_island_echo_question", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="left_branch_island_echo_question", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_left_branch_island_simple_question_lighteval = LightevalTaskConfig( - name="blimp:left_branch_island_simple_question", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="left_branch_island_simple_question", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_left_branch_island_simple_question_helm = LightevalTaskConfig( - name="blimp:left_branch_island_simple_question", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="left_branch_island_simple_question", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_matrix_question_npi_licensor_present_lighteval = LightevalTaskConfig( - name="blimp:matrix_question_npi_licensor_present", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="matrix_question_npi_licensor_present", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_matrix_question_npi_licensor_present_helm = LightevalTaskConfig( - name="blimp:matrix_question_npi_licensor_present", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="matrix_question_npi_licensor_present", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_npi_present_1_lighteval = LightevalTaskConfig( - name="blimp:npi_present_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="npi_present_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_npi_present_1_helm = LightevalTaskConfig( - name="blimp:npi_present_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="npi_present_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_npi_present_2_lighteval = LightevalTaskConfig( - name="blimp:npi_present_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="npi_present_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_npi_present_2_helm = LightevalTaskConfig( - name="blimp:npi_present_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="npi_present_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_only_npi_licensor_present_lighteval = LightevalTaskConfig( - name="blimp:only_npi_licensor_present", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="only_npi_licensor_present", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_only_npi_licensor_present_helm = LightevalTaskConfig( - name="blimp:only_npi_licensor_present", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="only_npi_licensor_present", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_only_npi_scope_lighteval = LightevalTaskConfig( - name="blimp:only_npi_scope", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="only_npi_scope", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_only_npi_scope_helm = LightevalTaskConfig( - name="blimp:only_npi_scope", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="only_npi_scope", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_passive_1_lighteval = LightevalTaskConfig( - name="blimp:passive_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="passive_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_passive_1_helm = LightevalTaskConfig( - name="blimp:passive_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="passive_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_passive_2_lighteval = LightevalTaskConfig( - name="blimp:passive_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="passive_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_passive_2_helm = LightevalTaskConfig( - name="blimp:passive_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="passive_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_c_command_lighteval = LightevalTaskConfig( - name="blimp:principle_A_c_command", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="principle_A_c_command", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_c_command_helm = LightevalTaskConfig( - name="blimp:principle_A_c_command", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="principle_A_c_command", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_case_1_lighteval = LightevalTaskConfig( - name="blimp:principle_A_case_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="principle_A_case_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_case_1_helm = LightevalTaskConfig( - name="blimp:principle_A_case_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="principle_A_case_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_case_2_lighteval = LightevalTaskConfig( - name="blimp:principle_A_case_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="principle_A_case_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_case_2_helm = LightevalTaskConfig( - name="blimp:principle_A_case_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="principle_A_case_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_domain_1_lighteval = LightevalTaskConfig( - name="blimp:principle_A_domain_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="principle_A_domain_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_domain_1_helm = LightevalTaskConfig( - name="blimp:principle_A_domain_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="principle_A_domain_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_domain_2_lighteval = LightevalTaskConfig( - name="blimp:principle_A_domain_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="principle_A_domain_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_domain_2_helm = LightevalTaskConfig( - name="blimp:principle_A_domain_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="principle_A_domain_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_domain_3_lighteval = LightevalTaskConfig( - name="blimp:principle_A_domain_3", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="principle_A_domain_3", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_domain_3_helm = LightevalTaskConfig( - name="blimp:principle_A_domain_3", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="principle_A_domain_3", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_reconstruction_lighteval = LightevalTaskConfig( - name="blimp:principle_A_reconstruction", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="principle_A_reconstruction", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_principle_A_reconstruction_helm = LightevalTaskConfig( - name="blimp:principle_A_reconstruction", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="principle_A_reconstruction", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_regular_plural_subject_verb_agreement_1_lighteval = LightevalTaskConfig( - name="blimp:regular_plural_subject_verb_agreement_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="regular_plural_subject_verb_agreement_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_regular_plural_subject_verb_agreement_1_helm = LightevalTaskConfig( - name="blimp:regular_plural_subject_verb_agreement_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="regular_plural_subject_verb_agreement_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_regular_plural_subject_verb_agreement_2_lighteval = LightevalTaskConfig( - name="blimp:regular_plural_subject_verb_agreement_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="regular_plural_subject_verb_agreement_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_regular_plural_subject_verb_agreement_2_helm = LightevalTaskConfig( - name="blimp:regular_plural_subject_verb_agreement_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="regular_plural_subject_verb_agreement_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_sentential_negation_npi_licensor_present_lighteval = LightevalTaskConfig( - name="blimp:sentential_negation_npi_licensor_present", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="sentential_negation_npi_licensor_present", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_sentential_negation_npi_licensor_present_helm = LightevalTaskConfig( - name="blimp:sentential_negation_npi_licensor_present", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="sentential_negation_npi_licensor_present", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_sentential_negation_npi_scope_lighteval = LightevalTaskConfig( - name="blimp:sentential_negation_npi_scope", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="sentential_negation_npi_scope", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_sentential_negation_npi_scope_helm = LightevalTaskConfig( - name="blimp:sentential_negation_npi_scope", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="sentential_negation_npi_scope", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_sentential_subject_island_lighteval = LightevalTaskConfig( - name="blimp:sentential_subject_island", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="sentential_subject_island", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_sentential_subject_island_helm = LightevalTaskConfig( - name="blimp:sentential_subject_island", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="sentential_subject_island", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_superlative_quantifiers_1_lighteval = LightevalTaskConfig( - name="blimp:superlative_quantifiers_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="superlative_quantifiers_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_superlative_quantifiers_1_helm = LightevalTaskConfig( - name="blimp:superlative_quantifiers_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="superlative_quantifiers_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_superlative_quantifiers_2_lighteval = LightevalTaskConfig( - name="blimp:superlative_quantifiers_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="superlative_quantifiers_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_superlative_quantifiers_2_helm = LightevalTaskConfig( - name="blimp:superlative_quantifiers_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="superlative_quantifiers_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_tough_vs_raising_1_lighteval = LightevalTaskConfig( - name="blimp:tough_vs_raising_1", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="tough_vs_raising_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_tough_vs_raising_1_helm = LightevalTaskConfig( - name="blimp:tough_vs_raising_1", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="tough_vs_raising_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_tough_vs_raising_2_lighteval = LightevalTaskConfig( - name="blimp:tough_vs_raising_2", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="tough_vs_raising_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_tough_vs_raising_2_helm = LightevalTaskConfig( - name="blimp:tough_vs_raising_2", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="tough_vs_raising_2", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_transitive_lighteval = LightevalTaskConfig( - name="blimp:transitive", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="transitive", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_transitive_helm = LightevalTaskConfig( - name="blimp:transitive", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="transitive", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_wh_island_lighteval = LightevalTaskConfig( - name="blimp:wh_island", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="wh_island", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_wh_island_helm = LightevalTaskConfig( - name="blimp:wh_island", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="wh_island", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_wh_questions_object_gap_lighteval = LightevalTaskConfig( - name="blimp:wh_questions_object_gap", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="wh_questions_object_gap", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_wh_questions_object_gap_helm = LightevalTaskConfig( - name="blimp:wh_questions_object_gap", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="wh_questions_object_gap", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_wh_questions_subject_gap_lighteval = LightevalTaskConfig( - name="blimp:wh_questions_subject_gap", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="wh_questions_subject_gap", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_wh_questions_subject_gap_helm = LightevalTaskConfig( - name="blimp:wh_questions_subject_gap", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="wh_questions_subject_gap", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_wh_questions_subject_gap_long_distance_lighteval = LightevalTaskConfig( - name="blimp:wh_questions_subject_gap_long_distance", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="wh_questions_subject_gap_long_distance", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_wh_questions_subject_gap_long_distance_helm = LightevalTaskConfig( - name="blimp:wh_questions_subject_gap_long_distance", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="wh_questions_subject_gap_long_distance", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_wh_vs_that_no_gap_lighteval = LightevalTaskConfig( - name="blimp:wh_vs_that_no_gap", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="wh_vs_that_no_gap", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_wh_vs_that_no_gap_helm = LightevalTaskConfig( - name="blimp:wh_vs_that_no_gap", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="wh_vs_that_no_gap", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_wh_vs_that_no_gap_long_distance_lighteval = LightevalTaskConfig( - name="blimp:wh_vs_that_no_gap_long_distance", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="wh_vs_that_no_gap_long_distance", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_wh_vs_that_no_gap_long_distance_helm = LightevalTaskConfig( - name="blimp:wh_vs_that_no_gap_long_distance", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="wh_vs_that_no_gap_long_distance", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_wh_vs_that_with_gap_lighteval = LightevalTaskConfig( - name="blimp:wh_vs_that_with_gap", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="wh_vs_that_with_gap", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_wh_vs_that_with_gap_helm = LightevalTaskConfig( - name="blimp:wh_vs_that_with_gap", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="wh_vs_that_with_gap", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -blimp_wh_vs_that_with_gap_long_distance_lighteval = LightevalTaskConfig( - name="blimp:wh_vs_that_with_gap_long_distance", - suite=["lighteval", "blimp"], - prompt_function=prompt.blimp, - hf_repo="blimp", - hf_subset="wh_vs_that_with_gap_long_distance", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -blimp_wh_vs_that_with_gap_long_distance_helm = LightevalTaskConfig( - name="blimp:wh_vs_that_with_gap_long_distance", - suite=["helm", "blimp"], - prompt_function=prompt.blimp_helm, - hf_repo="blimp", - hf_subset="wh_vs_that_with_gap_long_distance", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -bold_helm = LightevalTaskConfig( - name="bold", - suite=["helm"], - prompt_function=prompt.bold, - hf_repo="lighteval/bold_helm", - hf_subset="all", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.prediction_perplexity], - stop_sequence=["\n"], - version=0, -) -bold_gender_helm = LightevalTaskConfig( - name="bold:gender", - suite=["helm"], - prompt_function=prompt.bold, - hf_repo="lighteval/bold_helm", - hf_subset="gender", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.prediction_perplexity], - stop_sequence=["\n"], - version=0, -) -bold_political_ideology_helm = LightevalTaskConfig( - name="bold:political_ideology", - suite=["helm"], - prompt_function=prompt.bold, - hf_repo="lighteval/bold_helm", - hf_subset="political_ideology", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.prediction_perplexity], - stop_sequence=["\n"], - version=0, -) -bold_profession_helm = LightevalTaskConfig( - name="bold:profession", - suite=["helm"], - prompt_function=prompt.bold, - hf_repo="lighteval/bold_helm", - hf_subset="profession", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.prediction_perplexity], - stop_sequence=["\n"], - version=0, -) -bold_race_helm = LightevalTaskConfig( - name="bold:race", - suite=["helm"], - prompt_function=prompt.bold, - hf_repo="lighteval/bold_helm", - hf_subset="race", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.prediction_perplexity], - stop_sequence=["\n"], - version=0, -) -bold_religious_ideology_helm = LightevalTaskConfig( - name="bold:religious_ideology", - suite=["helm"], - prompt_function=prompt.bold, - hf_repo="lighteval/bold_helm", - hf_subset="religious_ideology", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.prediction_perplexity], - stop_sequence=["\n"], - version=0, -) -boolq_helm = LightevalTaskConfig( - name="boolq", - suite=["helm", "helm_general"], - prompt_function=prompt.boolq_helm, - hf_repo="lighteval/boolq_helm", - hf_subset="default", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -boolq_contrastset_helm = LightevalTaskConfig( - name="boolq:contrastset", - suite=["helm"], - prompt_function=prompt.boolq_helm_contrastset, - hf_repo="lighteval/boolq_helm", - hf_subset="default", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -bridging_anaphora_resolution_barqa_bigbench = LightevalTaskConfig( - name="bridging_anaphora_resolution_barqa", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="bridging_anaphora_resolution_barqa", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -causal_judgment_bigbench = LightevalTaskConfig( - name="causal_judgment", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="causal_judgment", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -cause_and_effect_bigbench = LightevalTaskConfig( - name="cause_and_effect", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="cause_and_effect", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -checkmate_in_one_bigbench = LightevalTaskConfig( - name="checkmate_in_one", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="checkmate_in_one", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -chess_state_tracking_bigbench = LightevalTaskConfig( - name="chess_state_tracking", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="chess_state_tracking", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -chinese_remainder_theorem_bigbench = LightevalTaskConfig( - name="chinese_remainder_theorem", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="chinese_remainder_theorem", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -cifar10_classification_bigbench = LightevalTaskConfig( - name="cifar10_classification", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="cifar10_classification", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -civil_comments_helm = LightevalTaskConfig( - name="civil_comments", - suite=["helm", "helm_general"], - prompt_function=prompt.civil_comments, - hf_repo="lighteval/civil_comments_helm", - hf_subset="all", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -civil_comments_LGBTQ_helm = LightevalTaskConfig( - name="civil_comments:LGBTQ", - suite=["helm"], - prompt_function=prompt.civil_comments, - hf_repo="lighteval/civil_comments_helm", - hf_subset="LGBTQ", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -civil_comments_black_helm = LightevalTaskConfig( - name="civil_comments:black", - suite=["helm"], - prompt_function=prompt.civil_comments, - hf_repo="lighteval/civil_comments_helm", - hf_subset="black", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -civil_comments_christian_helm = LightevalTaskConfig( - name="civil_comments:christian", - suite=["helm"], - prompt_function=prompt.civil_comments, - hf_repo="lighteval/civil_comments_helm", - hf_subset="christian", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -civil_comments_female_helm = LightevalTaskConfig( - name="civil_comments:female", - suite=["helm"], - prompt_function=prompt.civil_comments, - hf_repo="lighteval/civil_comments_helm", - hf_subset="female", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -civil_comments_male_helm = LightevalTaskConfig( - name="civil_comments:male", - suite=["helm"], - prompt_function=prompt.civil_comments, - hf_repo="lighteval/civil_comments_helm", - hf_subset="male", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -civil_comments_muslim_helm = LightevalTaskConfig( - name="civil_comments:muslim", - suite=["helm"], - prompt_function=prompt.civil_comments, - hf_repo="lighteval/civil_comments_helm", - hf_subset="muslim", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -civil_comments_other_religions_helm = LightevalTaskConfig( - name="civil_comments:other_religions", - suite=["helm"], - prompt_function=prompt.civil_comments, - hf_repo="lighteval/civil_comments_helm", - hf_subset="other_religions", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -civil_comments_white_helm = LightevalTaskConfig( - name="civil_comments:white", - suite=["helm"], - prompt_function=prompt.civil_comments, - hf_repo="lighteval/civil_comments_helm", - hf_subset="white", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -code_line_description_bigbench_lite = LightevalTaskConfig( - name="code_line_description", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_linefeed_before_and_after_query, - hf_repo="tasksource/bigbench", - hf_subset="code_line_description", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -codenames_bigbench = LightevalTaskConfig( - name="codenames", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="codenames", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.rouge_t5, Metrics.bleu], - stop_sequence=["\n"], - version=0, -) -color_bigbench = LightevalTaskConfig( - name="color", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="color", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.rouge_t5, - Metrics.bleu, - Metrics.loglikelihood_acc, - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -common_morpheme_bigbench = LightevalTaskConfig( - name="common_morpheme", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="common_morpheme", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -commonsenseqa_helm = LightevalTaskConfig( - name="commonsenseqa", - suite=["helm", "commonsense_scenario"], - prompt_function=prompt.commonsense_qa, - hf_repo="commonsense_qa", - hf_subset="default", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -conceptual_combinations_bigbench_lite = LightevalTaskConfig( - name="conceptual_combinations", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="conceptual_combinations", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -conlang_translation_bigbench_lite = LightevalTaskConfig( - name="conlang_translation", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="conlang_translation", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.rouge_t5, Metrics.bleu, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=[".", ";", "!", "?"], - version=0, -) -contextual_parametric_knowledge_conflicts_bigbench = LightevalTaskConfig( - name="contextual_parametric_knowledge_conflicts", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="contextual_parametric_knowledge_conflicts", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.rouge_t5, Metrics.loglikelihood_acc, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -copyright_n_books_1000_extractions_per_book_1_prefix_length_125_helm = LightevalTaskConfig( - name="copyright:n_books_1000-extractions_per_book_1-prefix_length_125", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="n_books_1000-extractions_per_book_1-prefix_length_125", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_n_books_1000_extractions_per_book_1_prefix_length_25_helm = LightevalTaskConfig( - name="copyright:n_books_1000-extractions_per_book_1-prefix_length_25", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="n_books_1000-extractions_per_book_1-prefix_length_25", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_n_books_1000_extractions_per_book_1_prefix_length_5_helm = LightevalTaskConfig( - name="copyright:n_books_1000-extractions_per_book_1-prefix_length_5", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="n_books_1000-extractions_per_book_1-prefix_length_5", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_n_books_1000_extractions_per_book_3_prefix_length_125_helm = LightevalTaskConfig( - name="copyright:n_books_1000-extractions_per_book_3-prefix_length_125", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="n_books_1000-extractions_per_book_3-prefix_length_125", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_n_books_1000_extractions_per_book_3_prefix_length_25_helm = LightevalTaskConfig( - name="copyright:n_books_1000-extractions_per_book_3-prefix_length_25", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="n_books_1000-extractions_per_book_3-prefix_length_25", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_n_books_1000_extractions_per_book_3_prefix_length_5_helm = LightevalTaskConfig( - name="copyright:n_books_1000-extractions_per_book_3-prefix_length_5", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="n_books_1000-extractions_per_book_3-prefix_length_5", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_oh_the_places_helm = LightevalTaskConfig( - name="copyright:oh_the_places", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="oh_the_places", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_pilot_helm = LightevalTaskConfig( - name="copyright:pilot", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="pilot", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_popular_books_prefix_length_10_helm = LightevalTaskConfig( - name="copyright:popular_books-prefix_length_10", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="popular_books-prefix_length_10", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_popular_books_prefix_length_125_helm = LightevalTaskConfig( - name="copyright:popular_books-prefix_length_125", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="popular_books-prefix_length_125", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_popular_books_prefix_length_25_helm = LightevalTaskConfig( - name="copyright:popular_books-prefix_length_25", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="popular_books-prefix_length_25", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_popular_books_prefix_length_250_helm = LightevalTaskConfig( - name="copyright:popular_books-prefix_length_250", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="popular_books-prefix_length_250", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_popular_books_prefix_length_5_helm = LightevalTaskConfig( - name="copyright:popular_books-prefix_length_5", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="popular_books-prefix_length_5", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_popular_books_prefix_length_50_helm = LightevalTaskConfig( - name="copyright:popular_books-prefix_length_50", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="popular_books-prefix_length_50", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_prompt_num_line_1_min_lines_20_helm = LightevalTaskConfig( - name="copyright:prompt_num_line_1-min_lines_20", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="prompt_num_line_1-min_lines_20", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_prompt_num_line_10_min_lines_20_helm = LightevalTaskConfig( - name="copyright:prompt_num_line_10-min_lines_20", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="prompt_num_line_10-min_lines_20", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -copyright_prompt_num_line_5_min_lines_20_helm = LightevalTaskConfig( - name="copyright:prompt_num_line_5-min_lines_20", - suite=["helm", "copyright_scenario"], - prompt_function=prompt.copyright, - hf_repo="lighteval/copyright_helm", - hf_subset="prompt_num_line_5-min_lines_20", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.copyright], - stop_sequence=["\n"], - version=0, -) -coqa_first_question = LightevalTaskConfig( - name="coqa", - prompt_function=get_qa_prompt_function( - Language.ENGLISH, - lambda line: { - "question": line["questions"][0], - "context": line["story"], - "choices": [line["answers"]["input_text"][0]], - }, - ), - suite=["lighteval"], - hf_repo="stanfordnlp/coqa", - hf_subset="default", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - stop_sequence=["\n", "Question:", "question:"], - generation_size=100, - version=1, - metrics=( - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ), -) -coqa_bb_lighteval = LightevalTaskConfig( - name="coqa_bb", - suite=["lighteval", "bigbench_programmatic", "bigbench"], - prompt_function=prompt.coqa, - hf_repo="coqa", - hf_subset="default", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False}), Metrics.f1_score], - stop_sequence=["\n"], - version=0, -) -covid_dialogue_helm = LightevalTaskConfig( - name="covid_dialogue", - suite=["helm"], - prompt_function=prompt.covid_dialogue, - hf_repo="lighteval/covid_dialogue", - hf_subset="default", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=128, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.rougeL, - Metrics.bleu_1, - Metrics.bleu_4, - ], - stop_sequence=["\n"], - version=0, -) -crash_blossom_bigbench = LightevalTaskConfig( - name="crash_blossom", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="crash_blossom", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -crass_ai_bigbench = LightevalTaskConfig( - name="crass_ai", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="crass_ai", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -cryobiology_spanish_bigbench = LightevalTaskConfig( - name="cryobiology_spanish", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="cryobiology_spanish", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -cryptonite_bigbench = LightevalTaskConfig( - name="cryptonite", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="cryptonite", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -cs_algorithms_bigbench = LightevalTaskConfig( - name="cs_algorithms", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="cs_algorithms", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -dark_humor_detection_bigbench = LightevalTaskConfig( - name="dark_humor_detection", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="dark_humor_detection", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -date_understanding_bigbench = LightevalTaskConfig( - name="date_understanding", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="date_understanding", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -disambiguation_qa_bigbench = LightevalTaskConfig( - name="disambiguation_qa", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="disambiguation_qa", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -discourse_marker_prediction_bigbench = LightevalTaskConfig( - name="discourse_marker_prediction", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="discourse_marker_prediction", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -disfl_qa_bigbench = LightevalTaskConfig( - name="disfl_qa", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="disfl_qa", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -drop_qa = LightevalTaskConfig( - name="drop", - prompt_function=get_qa_prompt_function( - Language.ENGLISH, - lambda line: { - "context": line["passage"], - "question": line["question"], - "choices": list( - filter( - lambda x: x, - [line["answer"].get("number")] - + line["answer"]["spans"] - + [prompt.get_drop_date(line["answer"].get("date"))], - ) - ), - }, - ), - suite=("lighteval",), - hf_repo="lighteval/drop_harness", - hf_subset="default", - hf_filter=lambda line: list( - filter( - lambda x: x, - [line["answer"].get("number")] - + line["answer"]["spans"] - + [prompt.get_drop_date(line["answer"].get("date"))], - ) - ), - evaluation_splits=("validation",), - few_shots_split="train", - generation_size=250, - stop_sequence=["Question:", "question:", "\n"], - metrics=( - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ), - version=1, -) -dyck_language_2_helm = LightevalTaskConfig( - name="dyck_language:2", - suite=["helm"], - prompt_function=prompt.dyck_language, - hf_repo="lighteval/DyckLanguage", - hf_subset="2", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[Metrics.exact_match], - stop_sequence=["\n"], - version=0, -) -dyck_language_3_helm = LightevalTaskConfig( - name="dyck_language:3", - suite=["helm"], - prompt_function=prompt.dyck_language, - hf_repo="lighteval/DyckLanguage", - hf_subset="3", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[Metrics.exact_match], - stop_sequence=["\n"], - version=0, -) -dyck_language_4_helm = LightevalTaskConfig( - name="dyck_language:4", - suite=["helm"], - prompt_function=prompt.dyck_language, - hf_repo="lighteval/DyckLanguage", - hf_subset="4", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[Metrics.exact_match], - stop_sequence=["\n"], - version=0, -) -dyck_languages_bigbench = LightevalTaskConfig( - name="dyck_languages", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="dyck_languages", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -elementary_math_qa_bigbench = LightevalTaskConfig( - name="elementary_math_qa", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="elementary_math_qa", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -emoji_movie_bigbench_lite = LightevalTaskConfig( - name="emoji_movie", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="emoji_movie", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.rouge_t5, - Metrics.bleu, - Metrics.loglikelihood_acc, - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -emojis_emotion_prediction_bigbench = LightevalTaskConfig( - name="emojis_emotion_prediction", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="emojis_emotion_prediction", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -empirical_judgments_bigbench = LightevalTaskConfig( - name="empirical_judgments", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="empirical_judgments", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -english_proverbs_bigbench = LightevalTaskConfig( - name="english_proverbs", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="english_proverbs", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -english_russian_proverbs_bigbench = LightevalTaskConfig( - name="english_russian_proverbs", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="english_russian_proverbs", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -entailed_polarity_bigbench = LightevalTaskConfig( - name="entailed_polarity", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="entailed_polarity", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -entailed_polarity_hindi_bigbench = LightevalTaskConfig( - name="entailed_polarity_hindi", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="entailed_polarity_hindi", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -entity_data_imputation_Buy_helm = LightevalTaskConfig( - name="entity_data_imputation:Buy", - suite=["helm"], - prompt_function=prompt.entity_data_imputation, - hf_repo="lighteval/Buy", - hf_subset="default", - hf_avail_splits=["train", "test", "valid"], - evaluation_splits=["valid", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_data_imputation_Restaurant_helm = LightevalTaskConfig( - name="entity_data_imputation:Restaurant", - suite=["helm"], - prompt_function=prompt.entity_data_imputation, - hf_repo="lighteval/Restaurant", - hf_subset="default", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_Abt_Buy_helm = LightevalTaskConfig( - name="entity_matching:Abt_Buy", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="Abt_Buy", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_Amazon_Google_helm = LightevalTaskConfig( - name="entity_matching:Amazon_Google", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="Amazon_Google", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_Beer_helm = LightevalTaskConfig( - name="entity_matching:Beer", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="Beer", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_Company_helm = LightevalTaskConfig( - name="entity_matching:Company", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="Company", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_DBLP_ACM_helm = LightevalTaskConfig( - name="entity_matching:DBLP_ACM", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="DBLP_ACM", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_DBLP_GoogleScholar_helm = LightevalTaskConfig( - name="entity_matching:DBLP_GoogleScholar", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="DBLP_GoogleScholar", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_Dirty_DBLP_ACM_helm = LightevalTaskConfig( - name="entity_matching:Dirty_DBLP_ACM", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="Dirty_DBLP_ACM", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_Dirty_DBLP_GoogleScholar_helm = LightevalTaskConfig( - name="entity_matching:Dirty_DBLP_GoogleScholar", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="Dirty_DBLP_GoogleScholar", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_Dirty_Walmart_Amazon_helm = LightevalTaskConfig( - name="entity_matching:Dirty_Walmart_Amazon", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="Dirty_Walmart_Amazon", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_Dirty_iTunes_Amazon_helm = LightevalTaskConfig( - name="entity_matching:Dirty_iTunes_Amazon", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="Dirty_iTunes_Amazon", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_Fodors_Zagats_helm = LightevalTaskConfig( - name="entity_matching=Fodors_Zagats", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="Fodors_Zagats", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_Walmart_Amazon_helm = LightevalTaskConfig( - name="entity_matching:Walmart_Amazon", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="Walmart_Amazon", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -entity_matching_iTunes_Amazon_helm = LightevalTaskConfig( - name="entity_matching:iTunes_Amazon", - suite=["helm"], - prompt_function=prompt.entity_matching, - hf_repo="lighteval/EntityMatching", - hf_subset="iTunes_Amazon", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -epistemic_reasoning_bigbench = LightevalTaskConfig( - name="epistemic_reasoning", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="epistemic_reasoning", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -ethics_commonsense_lighteval = LightevalTaskConfig( - name="ethics:commonsense", - suite=["lighteval", "ethics"], - prompt_function=prompt.ethics_commonsense, - hf_repo="lighteval/hendrycks_ethics", - hf_subset="commonsense", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -ethics_deontology_lighteval = LightevalTaskConfig( - name="ethics:deontology", - suite=["lighteval", "ethics"], - prompt_function=prompt.ethics_deontology, - hf_repo="lighteval/hendrycks_ethics", - hf_subset="deontology", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -ethics_justice_lighteval = LightevalTaskConfig( - name="ethics:justice", - suite=["lighteval", "ethics"], - prompt_function=prompt.ethics_justice, - hf_repo="lighteval/hendrycks_ethics", - hf_subset="justice", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -ethics_utilitarianism_lighteval = LightevalTaskConfig( - name="ethics:utilitarianism", - suite=["lighteval", "ethics"], - prompt_function=prompt.ethics_utilitarianism, - hf_repo="lighteval/hendrycks_ethics", - hf_subset="utilitarianism", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -ethics_virtue_lighteval = LightevalTaskConfig( - name="ethics:virtue", - suite=["lighteval", "ethics"], - prompt_function=prompt.ethics_virtue, - hf_repo="lighteval/hendrycks_ethics", - hf_subset="virtue", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -evaluating_information_essentiality_bigbench = LightevalTaskConfig( - name="evaluating_information_essentiality", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="evaluating_information_essentiality", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -fact_checker_bigbench = LightevalTaskConfig( - name="fact_checker", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="fact_checker", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -fantasy_reasoning_bigbench = LightevalTaskConfig( - name="fantasy_reasoning", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="fantasy_reasoning", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -few_shot_nlg_bigbench = LightevalTaskConfig( - name="few_shot_nlg", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="few_shot_nlg", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.bleurt], - stop_sequence=["\n"], - version=0, -) -figure_of_speech_detection_bigbench = LightevalTaskConfig( - name="figure_of_speech_detection", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="figure_of_speech_detection", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -formal_fallacies_syllogisms_negation_bigbench_lite = LightevalTaskConfig( - name="formal_fallacies_syllogisms_negation", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="formal_fallacies_syllogisms_negation", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -gem_bigbench = LightevalTaskConfig( - name="gem", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="gem", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.rouge_t5], - stop_sequence=["\n"], - version=0, -) -gender_inclusive_sentences_german_bigbench = LightevalTaskConfig( - name="gender_inclusive_sentences_german", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="gender_inclusive_sentences_german", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -general_knowledge_bigbench = LightevalTaskConfig( - name="general_knowledge", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="general_knowledge", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -geometric_shapes_bigbench = LightevalTaskConfig( - name="geometric_shapes", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="geometric_shapes", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.rouge_t5, - Metrics.bleu, - Metrics.loglikelihood_acc, - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -glue_cola_lighteval = LightevalTaskConfig( - name="glue:cola", - suite=["lighteval", "glue"], - prompt_function=prompt.cola, - hf_repo="glue", - hf_subset="cola", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc, Metrics.mcc], - stop_sequence=["\n"], - version=0, -) -glue_mnli_lighteval = LightevalTaskConfig( - name="glue:mnli", - suite=["lighteval", "glue"], - prompt_function=prompt.mnli, - hf_repo="glue", - hf_subset="mnli_matched", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -glue_mnli_mismatched_lighteval = LightevalTaskConfig( - name="glue:mnli_mismatched", - suite=["lighteval", "glue"], - prompt_function=prompt.mnli, - hf_repo="glue", - hf_subset="mnli_mismatched", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -glue_mrpc_lighteval = LightevalTaskConfig( - name="glue:mrpc", - suite=["lighteval", "glue"], - prompt_function=prompt.mrpc, - hf_repo="glue", - hf_subset="mrpc", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc, Metrics.loglikelihood_f1], - stop_sequence=["\n"], - version=0, -) -glue_qnli_lighteval = LightevalTaskConfig( - name="glue:qnli", - suite=["lighteval", "glue"], - prompt_function=prompt.qnli, - hf_repo="glue", - hf_subset="qnli", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -glue_qqp_lighteval = LightevalTaskConfig( - name="glue:qqp", - suite=["lighteval", "glue"], - prompt_function=prompt.qqp, - hf_repo="glue", - hf_subset="qqp", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc, Metrics.loglikelihood_f1], - stop_sequence=["\n"], - version=0, -) -glue_rte_lighteval = LightevalTaskConfig( - name="glue:rte", - suite=["lighteval", "glue"], - prompt_function=prompt.rte, - hf_repo="glue", - hf_subset="rte", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -glue_sst2_lighteval = LightevalTaskConfig( - name="glue:sst2", - suite=["lighteval", "glue"], - prompt_function=prompt.sst, - hf_repo="glue", - hf_subset="sst2", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -glue_stsb_lighteval = LightevalTaskConfig( - name="glue:stsb", - suite=["lighteval", "glue"], - prompt_function=prompt.stsb, - hf_repo="glue", - hf_subset="stsb", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -glue_wnli_lighteval = LightevalTaskConfig( - name="glue:wnli", - suite=["lighteval", "glue"], - prompt_function=prompt.wnli, - hf_repo="glue", - hf_subset="wnli", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -goal_step_wikihow_bigbench = LightevalTaskConfig( - name="goal_step_wikihow", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="goal_step_wikihow", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -gpqa_lighteval = LightevalTaskConfig( - name="gpqa:mc", - suite=["lighteval"], - prompt_function=prompt.gpqa, - hf_repo="Idavidrein/gpqa", - hf_subset="gpqa_main", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -gpqa_diamond = LightevalTaskConfig_inspect( - name="gpqa:diamond", - prompt_function=prompt.gpqa_instruct, - dataset_repo="Idavidrein/gpqa", - dataset_subset="gpqa_diamond", - dataset_split="train", - metrics=[multichoice_scorer(), choice()], - system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", -) -gpqa_extended_instruct_lighteval = LightevalTaskConfig( - name="gpqa:extended", - suite=["lighteval"], - prompt_function=prompt.gpqa_instruct, - hf_repo="Idavidrein/gpqa", - hf_subset="gpqa_extended", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=32768, # needed for reasoning models like R1 - metrics=[Metrics.gpqa_instruct_metric], - stop_sequence=[], # no stop sequence, will use eos token - version=0, -) -gpqa_main_instruct_lighteval = LightevalTaskConfig( - name="gpqa:main", - suite=["lighteval"], - prompt_function=prompt.gpqa_instruct, - hf_repo="Idavidrein/gpqa", - hf_subset="gpqa_main", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=32768, # needed for reasoning models like R1 - metrics=[Metrics.gpqa_instruct_metric], - stop_sequence=[], # no stop sequence, will use eos token - version=0, -) -gre_reading_comprehension_bigbench = LightevalTaskConfig( - name="gre_reading_comprehension", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="gre_reading_comprehension", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -gsm_plus = LightevalTaskConfig( - name="gsm_plus", - suite=["lighteval"], - prompt_function=prompt.gsm_plus, - hf_repo="qintongli/GSM-Plus", - hf_subset="default", - hf_avail_splits=["test", "testmini"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.expr_gold_metric], - stop_sequence=None, - version=0, -) -gsm8k_lighteval = LightevalTaskConfig_inspect( - name="gsm8k", - prompt_function=prompt.gsm8k, - dataset_repo="openai/gsm8k", - dataset_subset="main", - dataset_split="train", - dataset_revision="main", - metrics=[extractive_math_scorer()], - system_prompt="ANSWER USING THE FORMAT $ANSWER$", -) -headqa_en_lighteval = LightevalTaskConfig( - name="headqa:en", - suite=["lighteval", "headqa"], - prompt_function=prompt.headqa, - hf_repo="lighteval/headqa_harness", - hf_subset="en", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -headqa_es_lighteval = LightevalTaskConfig( - name="headqa:es", - suite=["lighteval", "headqa"], - prompt_function=prompt.headqa, - hf_repo="lighteval/headqa_harness", - hf_subset="es", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -hellaswag_leaderboard = LightevalTaskConfig( - name="hellaswag", - suite=["leaderboard"], - prompt_function=prompt.hellaswag_harness, - hf_repo="hellaswag", - hf_subset="default", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select="random_sampling_from_train", - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -hellaswag_generative = LightevalTaskConfig( - name="hellaswag", - suite=["helm", "helm_general"], - prompt_function=prompt.hellaswag_generative, - hf_repo="hellaswag", - hf_subset="default", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -hhh_alignment_bigbench = LightevalTaskConfig( - name="hhh_alignment", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="hhh_alignment", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -hindi_question_answering_bigbench = LightevalTaskConfig( - name="hindi_question_answering", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="hindi_question_answering", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -hindu_knowledge_bigbench_lite = LightevalTaskConfig( - name="hindu_knowledge", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="hindu_knowledge", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -hinglish_toxicity_bigbench = LightevalTaskConfig( - name="hinglish_toxicity", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="hinglish_toxicity", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -human_organs_senses_bigbench = LightevalTaskConfig( - name="human_organs_senses", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="human_organs_senses", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -hyperbaton_bigbench = LightevalTaskConfig( - name="hyperbaton", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="hyperbaton", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -identify_math_theorems_bigbench = LightevalTaskConfig( - name="identify_math_theorems", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="identify_math_theorems", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -identify_odd_metaphor_bigbench = LightevalTaskConfig( - name="identify_odd_metaphor", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="identify_odd_metaphor", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -imdb_helm = LightevalTaskConfig( - name="imdb", - suite=["helm", "helm_general"], - prompt_function=prompt.imdb, - hf_repo="lighteval/IMDB_helm", - hf_subset="default", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -imdb_contrastset_helm = LightevalTaskConfig( - name="imdb:contrastset", - suite=["helm"], - prompt_function=prompt.imdb_contrastset, - hf_repo="lighteval/IMDB_helm", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -implicatures_bigbench = LightevalTaskConfig( - name="implicatures", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="implicatures", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -implicit_relations_bigbench = LightevalTaskConfig( - name="implicit_relations", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="implicit_relations", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -intent_recognition_bigbench = LightevalTaskConfig( - name="intent_recognition", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="intent_recognition", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -interactive_qa_mmlu_abstract_algebra_helm = LightevalTaskConfig( - name="interactive_qa_mmlu:abstract_algebra", - suite=["helm", "interactive_qa_mmlu_scenario"], - prompt_function=prompt.mmlu_qa_abstract_algebra, - hf_repo="lighteval/mmlu", - hf_subset="abstract_algebra", - hf_avail_splits=["dev", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -interactive_qa_mmlu_college_chemistry_helm = LightevalTaskConfig( - name="interactive_qa_mmlu:college_chemistry", - suite=["helm", "interactive_qa_mmlu_scenario"], - prompt_function=prompt.mmlu_qa_college_chemistry, - hf_repo="lighteval/mmlu", - hf_subset="college_chemistry", - hf_avail_splits=["dev", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -interactive_qa_mmlu_global_facts_helm = LightevalTaskConfig( - name="interactive_qa_mmlu:global_facts", - suite=["helm", "interactive_qa_mmlu_scenario"], - prompt_function=prompt.mmlu_qa_global_facts, - hf_repo="lighteval/mmlu", - hf_subset="global_facts", - hf_avail_splits=["dev", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -interactive_qa_mmlu_miscellaneous_helm = LightevalTaskConfig( - name="interactive_qa_mmlu:miscellaneous", - suite=["helm", "interactive_qa_mmlu_scenario"], - prompt_function=prompt.mmlu_qa_miscellaneous, - hf_repo="lighteval/mmlu", - hf_subset="miscellaneous", - hf_avail_splits=["dev", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -interactive_qa_mmlu_nutrition_helm = LightevalTaskConfig( - name="interactive_qa_mmlu:nutrition", - suite=["helm", "interactive_qa_mmlu_scenario"], - prompt_function=prompt.mmlu_qa_nutrition, - hf_repo="lighteval/mmlu", - hf_subset="nutrition", - hf_avail_splits=["dev", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -interactive_qa_mmlu_us_foreign_policy_helm = LightevalTaskConfig( - name="interactive_qa_mmlu:us_foreign_policy", - suite=["helm", "interactive_qa_mmlu_scenario"], - prompt_function=prompt.mmlu_qa_us_foreign_policy, - hf_repo="lighteval/mmlu", - hf_subset="us_foreign_policy", - hf_avail_splits=["dev", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -international_phonetic_alphabet_nli_bigbench = LightevalTaskConfig( - name="international_phonetic_alphabet_nli", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="international_phonetic_alphabet_nli", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -international_phonetic_alphabet_transliterate_bigbench = LightevalTaskConfig( - name="international_phonetic_alphabet_transliterate", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="international_phonetic_alphabet_transliterate", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -intersect_geometry_bigbench = LightevalTaskConfig( - name="intersect_geometry", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="intersect_geometry", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -irony_identification_bigbench = LightevalTaskConfig( - name="irony_identification", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="irony_identification", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -iwslt17_ar_en_lighteval = LightevalTaskConfig( - name="iwslt17:ar-en", - suite=["lighteval", "harness_selection"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="iwslt17_ar-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -iwslt17_de_en_lighteval = LightevalTaskConfig( - name="iwslt17:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="iwslt17_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -iwslt17_en_ar_lighteval = LightevalTaskConfig( - name="iwslt17:en-ar", - suite=["lighteval", "harness_selection"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="iwslt17_ar-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -iwslt17_en_de_lighteval = LightevalTaskConfig( - name="iwslt17:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="iwslt17_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -iwslt17_en_fr_lighteval = LightevalTaskConfig( - name="iwslt17:en-fr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="iwslt17_en-fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -iwslt17_en_ja_lighteval = LightevalTaskConfig( - name="iwslt17:en-ja", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="iwslt17_en-ja", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -iwslt17_en_ko_lighteval = LightevalTaskConfig( - name="iwslt17:en-ko", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="iwslt17_en-ko", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -iwslt17_en_zh_lighteval = LightevalTaskConfig( - name="iwslt17:en-zh", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="iwslt17_en-zh", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -iwslt17_fr_en_lighteval = LightevalTaskConfig( - name="iwslt17:fr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="iwslt17_fr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -iwslt17_ja_en_lighteval = LightevalTaskConfig( - name="iwslt17:ja-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="iwslt17_ja-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -iwslt17_ko_en_lighteval = LightevalTaskConfig( - name="iwslt17:ko-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="iwslt17_ko-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -iwslt17_zh_en_lighteval = LightevalTaskConfig( - name="iwslt17:zh-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="iwslt17_zh-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -jeopardy = LightevalTaskConfig( - name="jeopardy", - prompt_function=get_qa_prompt_function( - Language.ENGLISH, - lambda line: { - "question": line["question"], - "choices": [line["answer"]], - }, - ), - suite=("lighteval",), - hf_repo="openaccess-ai-collective/jeopardy", - hf_subset="default", - evaluation_splits=("train",), - few_shots_split="train", - generation_size=250, - stop_sequence=["\n", "Question:", "question:"], - metrics=( - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ), -) -kanji_ascii_bigbench = LightevalTaskConfig( - name="kanji_ascii", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="kanji_ascii", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -kannada_bigbench = LightevalTaskConfig( - name="kannada", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="kannada", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -key_value_maps_bigbench = LightevalTaskConfig( - name="key_value_maps", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="key_value_maps", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -known_unknowns_bigbench_lite = LightevalTaskConfig( - name="known_unknowns", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="known_unknowns", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -lambada_standard_lighteval = LightevalTaskConfig( - name="lambada:standard", - suite=["lighteval", "lambada"], - prompt_function=prompt.lambada, - hf_repo="lambada", - hf_subset="plain_text", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[Metrics.target_perplexity], - stop_sequence=["\n"], - version=0, -) -lambada_standard_cloze_lighteval = LightevalTaskConfig( - name="lambada:standard_cloze", - suite=["lighteval", "lambada"], - prompt_function=prompt.lambada_cloze, - hf_repo="lambada", - hf_subset="plain_text", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[Metrics.target_perplexity], - stop_sequence=["\n"], - version=0, -) -lambada_openai_lighteval = LightevalTaskConfig( - name="lambada:openai", - suite=["lighteval", "lambada"], - prompt_function=prompt.lambada, - hf_repo="EleutherAI/lambada_openai", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[Metrics.target_perplexity], - stop_sequence=["\n"], - version=0, -) -lambada_openai_de_lighteval = LightevalTaskConfig( - name="lambada:openai:de", - suite=["lighteval", "lambada"], - prompt_function=prompt.lambada, - hf_repo="EleutherAI/lambada_openai", - hf_subset="de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[Metrics.target_perplexity], - stop_sequence=["\n"], - version=0, -) -lambada_openai_en_lighteval = LightevalTaskConfig( - name="lambada:openai:en", - suite=["lighteval", "lambada"], - prompt_function=prompt.lambada, - hf_repo="EleutherAI/lambada_openai", - hf_subset="en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[Metrics.target_perplexity], - stop_sequence=["\n"], - version=0, -) -lambada_openai_es_lighteval = LightevalTaskConfig( - name="lambada:openai:es", - suite=["lighteval", "lambada"], - prompt_function=prompt.lambada, - hf_repo="EleutherAI/lambada_openai", - hf_subset="es", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[Metrics.target_perplexity], - stop_sequence=["\n"], - version=0, -) -lambada_openai_fr_lighteval = LightevalTaskConfig( - name="lambada:openai:fr", - suite=["lighteval", "lambada"], - prompt_function=prompt.lambada, - hf_repo="EleutherAI/lambada_openai", - hf_subset="fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[Metrics.target_perplexity], - stop_sequence=["\n"], - version=0, -) -lambada_openai_it_lighteval = LightevalTaskConfig( - name="lambada:openai:it", - suite=["lighteval", "lambada"], - prompt_function=prompt.lambada, - hf_repo="EleutherAI/lambada_openai", - hf_subset="it", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[Metrics.target_perplexity], - stop_sequence=["\n"], - version=0, -) -lambada_openai_cloze_lighteval = LightevalTaskConfig( - name="lambada:openai_cloze", - suite=["lighteval", "lambada"], - prompt_function=prompt.lambada_cloze, - hf_repo="EleutherAI/lambada_openai", - hf_subset="en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[Metrics.target_perplexity], - stop_sequence=["\n"], - version=0, -) -language_games_bigbench = LightevalTaskConfig( - name="language_games", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="language_games", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -language_identification_bigbench_lite = LightevalTaskConfig( - name="language_identification", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="language_identification", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -legal_summarization_billsum_helm = LightevalTaskConfig( - name="legal_summarization:billsum", - suite=["helm"], - prompt_function=prompt.legal_summarization, - hf_repo="lighteval/legal_summarization", - hf_subset="BillSum", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=1024, - metrics=[ - Metrics.rouge1, - Metrics.rouge2, - Metrics.rougeL, - Metrics.faithfulness, - Metrics.extractiveness, - Metrics.bert_score, - ], - stop_sequence=["\n"], - version=0, -) -legal_summarization_eurlexsum_helm = LightevalTaskConfig( - name="legal_summarization:eurlexsum", - suite=["helm"], - prompt_function=prompt.legal_summarization, - hf_repo="lighteval/legal_summarization", - hf_subset="EurLexSum", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.rouge1, - Metrics.rouge2, - Metrics.rougeL, - Metrics.faithfulness, - Metrics.extractiveness, - Metrics.bert_score, - ], - stop_sequence=["\n"], - version=0, -) -legal_summarization_multilexsum_helm = LightevalTaskConfig( - name="legal_summarization:multilexsum", - suite=["helm"], - prompt_function=prompt.multilexsum, - hf_repo="lighteval/legal_summarization", - hf_subset="MultiLexSum", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=256, - metrics=[ - Metrics.rouge1, - Metrics.rouge2, - Metrics.rougeL, - Metrics.faithfulness, - Metrics.extractiveness, - Metrics.bert_score, - ], - stop_sequence=["\n"], - version=0, -) -legalsupport_helm = LightevalTaskConfig( - name="legalsupport", - suite=["helm"], - prompt_function=prompt.legal_support, - hf_repo="lighteval/LegalSupport", - hf_subset="default", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -lexglue_case_hold_helm = LightevalTaskConfig( - name="lexglue:case_hold", - suite=["helm", "lex_glue_scenario"], - prompt_function=prompt.lex_glue_case_hold, - hf_repo="lighteval/lexglue", - hf_subset="case_hold", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lexglue_ecthr_a_helm = LightevalTaskConfig( - name="lexglue:ecthr_a", - suite=["helm", "lex_glue_scenario"], - prompt_function=prompt.lex_glue_ecthr_a, - hf_repo="lighteval/lexglue", - hf_subset="ecthr_a", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lexglue_ecthr_b_helm = LightevalTaskConfig( - name="lexglue:ecthr_b", - suite=["helm", "lex_glue_scenario"], - prompt_function=prompt.lex_glue_ecthr_b, - hf_repo="lighteval/lexglue", - hf_subset="ecthr_b", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lexglue_eurlex_helm = LightevalTaskConfig( - name="lexglue:eurlex", - suite=["helm", "lex_glue_scenario"], - prompt_function=prompt.lex_glue_eurlex, - hf_repo="lighteval/lexglue", - hf_subset="eurlex", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lexglue_ledgar_helm = LightevalTaskConfig( - name="lexglue:ledgar", - suite=["helm", "lex_glue_scenario"], - prompt_function=prompt.lex_glue_ledgar, - hf_repo="lighteval/lexglue", - hf_subset="ledgar", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lexglue_scotus_helm = LightevalTaskConfig( - name="lexglue:scotus", - suite=["helm", "lex_glue_scenario"], - prompt_function=prompt.lex_glue_scotus, - hf_repo="lighteval/lexglue", - hf_subset="scotus", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lexglue_unfair_tos_helm = LightevalTaskConfig( - name="lexglue:unfair_tos", - suite=["helm", "lex_glue_scenario"], - prompt_function=prompt.lex_glue_unfair_tos, - hf_repo="lighteval/lexglue", - hf_subset="unfair_tos", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_brazilian_court_decisions_judgment_helm = LightevalTaskConfig( - name="lextreme:brazilian_court_decisions_judgment", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_brazilian_court_decisions_judgment, - hf_repo="lighteval/lextreme", - hf_subset="brazilian_court_decisions_judgment", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_brazilian_court_decisions_unanimity_helm = LightevalTaskConfig( - name="lextreme:brazilian_court_decisions_unanimity", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_brazilian_court_decisions_unanimity, - hf_repo="lighteval/lextreme", - hf_subset="brazilian_court_decisions_unanimity", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_covid19_emergency_event_helm = LightevalTaskConfig( - name="lextreme:covid19_emergency_event", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_covid19_emergency_event, - hf_repo="lighteval/lextreme", - hf_subset="covid19_emergency_event", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_german_argument_mining_helm = LightevalTaskConfig( - name="lextreme:german_argument_mining", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_german_argument_mining, - hf_repo="lighteval/lextreme", - hf_subset="german_argument_mining", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_greek_legal_code_chapter_helm = LightevalTaskConfig( - name="lextreme:greek_legal_code_chapter", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_greek_legal_code_chapter, - hf_repo="lighteval/lextreme", - hf_subset="greek_legal_code_chapter", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_greek_legal_code_subject_helm = LightevalTaskConfig( - name="lextreme:greek_legal_code_subject", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_greek_legal_code_subject, - hf_repo="lighteval/lextreme", - hf_subset="greek_legal_code_subject", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_greek_legal_code_volume_helm = LightevalTaskConfig( - name="lextreme:greek_legal_code_volume", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_greek_legal_code_volume, - hf_repo="lighteval/lextreme", - hf_subset="greek_legal_code_volume", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_greek_legal_ner_helm = LightevalTaskConfig( - name="lextreme:greek_legal_ner", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_greek_legal_ner, - hf_repo="lighteval/lextreme", - hf_subset="greek_legal_ner", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=430, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_legalnero_helm = LightevalTaskConfig( - name="lextreme:legalnero", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_legalnero, - hf_repo="lighteval/lextreme", - hf_subset="legalnero", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=788, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_lener_br_helm = LightevalTaskConfig( - name="lextreme:lener_br", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_lener_br, - hf_repo="lighteval/lextreme", - hf_subset="lener_br", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=338, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_mapa_coarse_helm = LightevalTaskConfig( - name="lextreme:mapa_coarse", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_mapa_coarse, - hf_repo="lighteval/lextreme", - hf_subset="mapa_coarse", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=274, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_mapa_fine_helm = LightevalTaskConfig( - name="lextreme:mapa_fine", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_mapa_fine, - hf_repo="lighteval/lextreme", - hf_subset="mapa_fine", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=274, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_multi_eurlex_level_1_helm = LightevalTaskConfig( - name="lextreme:multi_eurlex_level_1", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_multi_eurlex_level_1, - hf_repo="lighteval/lextreme", - hf_subset="multi_eurlex_level_1", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_multi_eurlex_level_2_helm = LightevalTaskConfig( - name="lextreme:multi_eurlex_level_2", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_multi_eurlex_level_2, - hf_repo="lighteval/lextreme", - hf_subset="multi_eurlex_level_2", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_multi_eurlex_level_3_helm = LightevalTaskConfig( - name="lextreme:multi_eurlex_level_3", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_multi_eurlex_level_3, - hf_repo="lighteval/lextreme", - hf_subset="multi_eurlex_level_3", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_online_terms_of_service_clause_topics_helm = LightevalTaskConfig( - name="lextreme:online_terms_of_service_clause_topics", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_online_terms_of_service_clause_topics, - hf_repo="lighteval/lextreme", - hf_subset="online_terms_of_service_clause_topics", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_online_terms_of_service_unfairness_levels_helm = LightevalTaskConfig( - name="lextreme:online_terms_of_service_unfairness_levels", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_online_terms_of_service_unfairness_levels, - hf_repo="lighteval/lextreme", - hf_subset="online_terms_of_service_unfairness_levels", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=10, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -lextreme_swiss_judgment_prediction_helm = LightevalTaskConfig( - name="lextreme:swiss_judgment_prediction", - suite=["helm", "lextreme_scenario"], - prompt_function=prompt.lextreme_swiss_judgment_prediction, - hf_repo="lighteval/lextreme", - hf_subset="swiss_judgment_prediction", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -linguistic_mappings_bigbench = LightevalTaskConfig( - name="linguistic_mappings", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="linguistic_mappings", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -linguistics_puzzles_bigbench_lite = LightevalTaskConfig( - name="linguistics_puzzles", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="linguistics_puzzles", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=None, - version=0, -) -logic_grid_puzzle_bigbench_lite = LightevalTaskConfig( - name="logic_grid_puzzle", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="logic_grid_puzzle", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -logical_args_bigbench = LightevalTaskConfig( - name="logical_args", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="logical_args", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -logical_deduction_bigbench_lite = LightevalTaskConfig( - name="logical_deduction", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="logical_deduction", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -logical_fallacy_detection_bigbench = LightevalTaskConfig( - name="logical_fallacy_detection", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="logical_fallacy_detection", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -logical_sequence_bigbench = LightevalTaskConfig( - name="logical_sequence", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="logical_sequence", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -logiqa_lighteval = LightevalTaskConfig( - name="logiqa", - suite=["lighteval"], - prompt_function=prompt.logiqa, - hf_repo="lighteval/logiqa_harness", - hf_subset="logiqa", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -lsat_qa_helm = LightevalTaskConfig( - name="lsat_qa", - suite=["helm", "lsat_qa_scenario"], - prompt_function=prompt.lsat_qa, - hf_repo="lighteval/lsat_qa", - hf_subset="all", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -lsat_qa_assignment_helm = LightevalTaskConfig( - name="lsat_qa:assignment", - suite=["helm", "lsat_qa_scenario"], - prompt_function=prompt.lsat_qa, - hf_repo="lighteval/lsat_qa", - hf_subset="assignment", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -lsat_qa_grouping_helm = LightevalTaskConfig( - name="lsat_qa:grouping", - suite=["helm", "lsat_qa_scenario"], - prompt_function=prompt.lsat_qa, - hf_repo="lighteval/lsat_qa", - hf_subset="grouping", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -lsat_qa_miscellaneous_helm = LightevalTaskConfig( - name="lsat_qa:miscellaneous", - suite=["helm", "lsat_qa_scenario"], - prompt_function=prompt.lsat_qa, - hf_repo="lighteval/lsat_qa", - hf_subset="miscellaneous", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -lsat_qa_ordering_helm = LightevalTaskConfig( - name="lsat_qa:ordering", - suite=["helm", "lsat_qa_scenario"], - prompt_function=prompt.lsat_qa, - hf_repo="lighteval/lsat_qa", - hf_subset="ordering", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -math_500 = LightevalTaskConfig( - name="math_500", - suite=["lighteval"], - prompt_function=prompt.math_500, - hf_repo="HuggingFaceH4/MATH-500", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=32768, - metrics=[ - Metrics.pass_at_k_math(sample_params={"k": 1, "n": 1}), - ], - version=2, -) -math_500_gpassk = LightevalTaskConfig( - name="math_500_gpassk", - suite=["lighteval"], - prompt_function=prompt.math_500, - hf_repo="HuggingFaceH4/MATH-500", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8192, - metrics=[Metrics.g_pass_at_k_latex(sample_params={"k": 16, "n": 48})], - version=1, -) -math_algebra_lighteval = LightevalTaskConfig( - name="math:algebra", - suite=["lighteval", "math"], - prompt_function=prompt.math, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="algebra", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=1, -) -math_counting_and_probability_lighteval = LightevalTaskConfig( - name="math:counting_and_probability", - suite=["lighteval", "math"], - prompt_function=prompt.math, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="counting_and_probability", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=1, -) -math_geometry_lighteval = LightevalTaskConfig( - name="math:geometry", - suite=["lighteval", "math"], - prompt_function=prompt.math, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="geometry", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=1, -) -math_intermediate_algebra_lighteval = LightevalTaskConfig( - name="math:intermediate_algebra", - suite=["lighteval", "math"], - prompt_function=prompt.math, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="intermediate_algebra", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=1, -) -math_number_theory_lighteval = LightevalTaskConfig( - name="math:number_theory", - suite=["lighteval", "math"], - prompt_function=prompt.math, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="number_theory", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=1, -) -math_prealgebra_lighteval = LightevalTaskConfig( - name="math:prealgebra", - suite=["lighteval", "math"], - prompt_function=prompt.math, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="prealgebra", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=1, -) -math_precalculus_lighteval = LightevalTaskConfig( - name="math:precalculus", - suite=["lighteval", "math"], - prompt_function=prompt.math, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="precalculus", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=1, -) -math_cot_algebra_lighteval = LightevalTaskConfig( - name="math_cot:algebra", - suite=["lighteval", "math"], - prompt_function=prompt.math_cot, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="algebra", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=0, -) -math_cot_counting_and_probability_lighteval = LightevalTaskConfig( - name="math_cot:counting_and_probability", - suite=["lighteval", "math"], - prompt_function=prompt.math_cot, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="counting_and_probability", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=0, -) -math_cot_geometry_lighteval = LightevalTaskConfig( - name="math_cot:geometry", - suite=["lighteval", "math"], - prompt_function=prompt.math_cot, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="geometry", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=0, -) -math_cot_intermediate_algebra_lighteval = LightevalTaskConfig( - name="math_cot:intermediate_algebra", - suite=["lighteval", "math"], - prompt_function=prompt.math_cot, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="intermediate_algebra", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=0, -) -math_cot_number_theory_lighteval = LightevalTaskConfig( - name="math_cot:number_theory", - suite=["lighteval", "math"], - prompt_function=prompt.math_cot, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="number_theory", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=0, -) -math_cot_prealgebra_lighteval = LightevalTaskConfig( - name="math_cot:prealgebra", - suite=["lighteval", "math"], - prompt_function=prompt.math_cot, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="prealgebra", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=0, -) -math_cot_precalculus_lighteval = LightevalTaskConfig( - name="math_cot:precalculus", - suite=["lighteval", "math"], - prompt_function=prompt.math_cot, - hf_repo="DigitalLearningGmbH/MATH-lighteval", - hf_subset="precalculus", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=2048, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.maj_at_k( - sample_params={ - "k": 4, - "strip_strings": True, - "normalize_pred": math_normalizer, - "normalize_gold": math_normalizer, - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mathematical_induction_bigbench = LightevalTaskConfig( - name="mathematical_induction", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="mathematical_induction", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mathqa_lighteval = LightevalTaskConfig( - name="mathqa", - suite=["lighteval"], - prompt_function=prompt.mathqa, - hf_repo="allenai/math_qa", - hf_subset="default", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -matrixshapes_bigbench = LightevalTaskConfig( - name="matrixshapes", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="matrixshapes", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -me_q_sum_helm = LightevalTaskConfig( - name="me_q_sum", - suite=["helm"], - prompt_function=prompt.me_q_sum, - hf_repo="lighteval/me_q_sum", - hf_subset="default", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=128, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.rougeL, - Metrics.bleu_1, - Metrics.bleu_4, - ], - stop_sequence=["\n"], - version=0, -) -med_dialog_healthcaremagic_helm = LightevalTaskConfig( - name="med_dialog:healthcaremagic", - suite=["helm"], - prompt_function=prompt.med_dialog, - hf_repo="lighteval/med_dialog", - hf_subset="healthcaremagic", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=128, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.rougeL, - Metrics.bleu_1, - Metrics.bleu_4, - ], - stop_sequence=["\n"], - version=0, -) -med_dialog_icliniq_helm = LightevalTaskConfig( - name="med_dialog:icliniq", - suite=["helm"], - prompt_function=prompt.med_dialog, - hf_repo="lighteval/med_dialog", - hf_subset="icliniq", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=128, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.rougeL, - Metrics.bleu_1, - Metrics.bleu_4, - ], - stop_sequence=["\n"], - version=0, -) -med_mcqa_helm = LightevalTaskConfig( - name="med_mcqa", - suite=["helm"], - prompt_function=prompt.med_mcqa, - hf_repo="lighteval/med_mcqa", - hf_subset="default", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -med_paragraph_simplification_helm = LightevalTaskConfig( - name="med_paragraph_simplification", - suite=["helm"], - prompt_function=prompt.med_paragraph_simplification, - hf_repo="lighteval/med_paragraph_simplification", - hf_subset="default", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=512, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.rougeL, - Metrics.bleu_1, - Metrics.bleu_4, - ], - stop_sequence=["\n"], - version=0, -) -med_qa_helm = LightevalTaskConfig( - name="med_qa", - suite=["helm"], - prompt_function=prompt.med_qa, - hf_repo="bigbio/med_qa", - hf_subset="med_qa_en_source", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -metaphor_boolean_bigbench = LightevalTaskConfig( - name="metaphor_boolean", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="metaphor_boolean", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -metaphor_understanding_bigbench = LightevalTaskConfig( - name="metaphor_understanding", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="metaphor_understanding", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mgsm_en_lighteval = LightevalTaskConfig( - name="mgsm:en", - suite=["lighteval"], - prompt_function=prompt.mgsm_en, - hf_repo="juletxara/mgsm", - hf_subset="en", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n", "=", "Question="], - version=0, -) -mgsm_es_lighteval = LightevalTaskConfig( - name="mgsm:es", - suite=["lighteval"], - prompt_function=prompt.mgsm_es, - hf_repo="juletxara/mgsm", - hf_subset="es", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n", "=", "Pregunta="], - version=0, -) -mgsm_fr_lighteval = LightevalTaskConfig( - name="mgsm:fr", - suite=["lighteval"], - prompt_function=prompt.mgsm_fr, - hf_repo="juletxara/mgsm", - hf_subset="fr", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n", "=", "Question="], - version=0, -) -mgsm_de_lighteval = LightevalTaskConfig( - name="mgsm:de", - suite=["lighteval"], - prompt_function=prompt.mgsm_de, - hf_repo="juletxara/mgsm", - hf_subset="de", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n", "=", "Frage="], - version=0, -) -mgsm_ru_lighteval = LightevalTaskConfig( - name="mgsm:ru", - suite=["lighteval"], - prompt_function=prompt.mgsm_ru, - hf_repo="juletxara/mgsm", - hf_subset="ru", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n", "=", "\u0417\u0430\u0434\u0430\u0447\u0430="], - version=0, -) -mgsm_zh_lighteval = LightevalTaskConfig( - name="mgsm:zh", - suite=["lighteval"], - prompt_function=prompt.mgsm_zh, - hf_repo="juletxara/mgsm", - hf_subset="zh", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n", "=", "\u95ee\u9898="], - version=0, -) -mgsm_ja_lighteval = LightevalTaskConfig( - name="mgsm:ja", - suite=["lighteval"], - prompt_function=prompt.mgsm_ja, - hf_repo="juletxara/mgsm", - hf_subset="ja", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n", "=", "\u554f\u984c="], - version=0, -) -mgsm_th_lighteval = LightevalTaskConfig( - name="mgsm:th", - suite=["lighteval"], - prompt_function=prompt.mgsm_th, - hf_repo="juletxara/mgsm", - hf_subset="th", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n", "=", "\u0e42\u0e08\u0e17\u0e22\u0e4c="], - version=0, -) -mgsm_sw_lighteval = LightevalTaskConfig( - name="mgsm:sw", - suite=["lighteval"], - prompt_function=prompt.mgsm_sw, - hf_repo="juletxara/mgsm", - hf_subset="sw", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n", "=", "Swali="], - version=0, -) -mgsm_bn_lighteval = LightevalTaskConfig( - name="mgsm:bn", - suite=["lighteval"], - prompt_function=prompt.mgsm_bn, - hf_repo="juletxara/mgsm", - hf_subset="bn", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n", "=", "\u09aa\u09cd\u09b0\u09b6\u09cd\u09a8="], - version=0, -) -mgsm_te_lighteval = LightevalTaskConfig( - name="mgsm:te", - suite=["lighteval"], - prompt_function=prompt.mgsm_te, - hf_repo="juletxara/mgsm", - hf_subset="te", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n", "=", "\u0c2a\u0c4d\u0c30\u0c36\u0c4d\u0c28="], - version=0, -) -minute_mysteries_qa_bigbench = LightevalTaskConfig( - name="minute_mysteries_qa", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="minute_mysteries_qa", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc, Metrics.rouge_t5], - stop_sequence=["\n"], - version=0, -) -misconceptions_bigbench = LightevalTaskConfig( - name="misconceptions", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="misconceptions", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -misconceptions_russian_bigbench_lite = LightevalTaskConfig( - name="misconceptions_russian", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="misconceptions_russian", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_abstract_algebra_original = LightevalTaskConfig( - name="mmlu:abstract_algebra", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_abstract_algebra, - hf_repo="cais/mmlu", - hf_subset="abstract_algebra", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_abstract_algebra_leaderboard = LightevalTaskConfig( - name="mmlu:abstract_algebra", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="abstract_algebra", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_abstract_algebra_helm = LightevalTaskConfig( - name="mmlu:abstract_algebra", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="abstract_algebra", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_anatomy_original = LightevalTaskConfig( - name="mmlu:anatomy", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_anatomy, - hf_repo="cais/mmlu", - hf_subset="anatomy", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_anatomy_leaderboard = LightevalTaskConfig( - name="mmlu:anatomy", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="anatomy", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_anatomy_helm = LightevalTaskConfig( - name="mmlu:anatomy", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="anatomy", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_astronomy_original = LightevalTaskConfig( - name="mmlu:astronomy", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_astronomy, - hf_repo="cais/mmlu", - hf_subset="astronomy", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_astronomy_leaderboard = LightevalTaskConfig( - name="mmlu:astronomy", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="astronomy", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_astronomy_helm = LightevalTaskConfig( - name="mmlu:astronomy", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="astronomy", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_business_ethics_original = LightevalTaskConfig( - name="mmlu:business_ethics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_business_ethics, - hf_repo="cais/mmlu", - hf_subset="business_ethics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_business_ethics_leaderboard = LightevalTaskConfig( - name="mmlu:business_ethics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="business_ethics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_business_ethics_helm = LightevalTaskConfig( - name="mmlu:business_ethics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="business_ethics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_clinical_knowledge_original = LightevalTaskConfig( - name="mmlu:clinical_knowledge", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_clinical_knowledge, - hf_repo="cais/mmlu", - hf_subset="clinical_knowledge", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_clinical_knowledge_leaderboard = LightevalTaskConfig( - name="mmlu:clinical_knowledge", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="clinical_knowledge", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_clinical_knowledge_helm = LightevalTaskConfig( - name="mmlu:clinical_knowledge", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="clinical_knowledge", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_college_biology_original = LightevalTaskConfig( - name="mmlu:college_biology", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_college_biology, - hf_repo="cais/mmlu", - hf_subset="college_biology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_college_biology_leaderboard = LightevalTaskConfig( - name="mmlu:college_biology", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="college_biology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_college_biology_helm = LightevalTaskConfig( - name="mmlu:college_biology", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="college_biology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_college_chemistry_original = LightevalTaskConfig( - name="mmlu:college_chemistry", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_college_chemistry, - hf_repo="cais/mmlu", - hf_subset="college_chemistry", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_college_chemistry_leaderboard = LightevalTaskConfig( - name="mmlu:college_chemistry", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="college_chemistry", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_college_chemistry_helm = LightevalTaskConfig( - name="mmlu:college_chemistry", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="college_chemistry", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_college_computer_science_original = LightevalTaskConfig( - name="mmlu:college_computer_science", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_college_computer_science, - hf_repo="cais/mmlu", - hf_subset="college_computer_science", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_college_computer_science_leaderboard = LightevalTaskConfig( - name="mmlu:college_computer_science", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="college_computer_science", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_college_computer_science_helm = LightevalTaskConfig( - name="mmlu:college_computer_science", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="college_computer_science", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_college_mathematics_original = LightevalTaskConfig( - name="mmlu:college_mathematics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_college_mathematics, - hf_repo="cais/mmlu", - hf_subset="college_mathematics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_college_mathematics_leaderboard = LightevalTaskConfig( - name="mmlu:college_mathematics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="college_mathematics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_college_mathematics_helm = LightevalTaskConfig( - name="mmlu:college_mathematics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="college_mathematics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_college_medicine_original = LightevalTaskConfig( - name="mmlu:college_medicine", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_college_medicine, - hf_repo="cais/mmlu", - hf_subset="college_medicine", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_college_medicine_leaderboard = LightevalTaskConfig( - name="mmlu:college_medicine", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="college_medicine", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_college_medicine_helm = LightevalTaskConfig( - name="mmlu:college_medicine", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="college_medicine", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_college_physics_original = LightevalTaskConfig( - name="mmlu:college_physics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_college_physics, - hf_repo="cais/mmlu", - hf_subset="college_physics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_college_physics_leaderboard = LightevalTaskConfig( - name="mmlu:college_physics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="college_physics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_college_physics_helm = LightevalTaskConfig( - name="mmlu:college_physics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="college_physics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_computer_security_original = LightevalTaskConfig( - name="mmlu:computer_security", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_computer_security, - hf_repo="cais/mmlu", - hf_subset="computer_security", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_computer_security_leaderboard = LightevalTaskConfig( - name="mmlu:computer_security", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="computer_security", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_computer_security_helm = LightevalTaskConfig( - name="mmlu:computer_security", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="computer_security", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_conceptual_physics_original = LightevalTaskConfig( - name="mmlu:conceptual_physics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_conceptual_physics, - hf_repo="cais/mmlu", - hf_subset="conceptual_physics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_conceptual_physics_leaderboard = LightevalTaskConfig( - name="mmlu:conceptual_physics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="conceptual_physics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_conceptual_physics_helm = LightevalTaskConfig( - name="mmlu:conceptual_physics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="conceptual_physics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_econometrics_original = LightevalTaskConfig( - name="mmlu:econometrics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_econometrics, - hf_repo="cais/mmlu", - hf_subset="econometrics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_econometrics_leaderboard = LightevalTaskConfig( - name="mmlu:econometrics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="econometrics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_econometrics_helm = LightevalTaskConfig( - name="mmlu:econometrics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="econometrics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_electrical_engineering_original = LightevalTaskConfig( - name="mmlu:electrical_engineering", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_electrical_engineering, - hf_repo="cais/mmlu", - hf_subset="electrical_engineering", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_electrical_engineering_leaderboard = LightevalTaskConfig( - name="mmlu:electrical_engineering", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="electrical_engineering", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_electrical_engineering_helm = LightevalTaskConfig( - name="mmlu:electrical_engineering", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="electrical_engineering", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_elementary_mathematics_original = LightevalTaskConfig( - name="mmlu:elementary_mathematics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_elementary_mathematics, - hf_repo="cais/mmlu", - hf_subset="elementary_mathematics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_elementary_mathematics_leaderboard = LightevalTaskConfig( - name="mmlu:elementary_mathematics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="elementary_mathematics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_elementary_mathematics_helm = LightevalTaskConfig( - name="mmlu:elementary_mathematics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="elementary_mathematics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_formal_logic_original = LightevalTaskConfig( - name="mmlu:formal_logic", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_formal_logic, - hf_repo="cais/mmlu", - hf_subset="formal_logic", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_formal_logic_leaderboard = LightevalTaskConfig( - name="mmlu:formal_logic", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="formal_logic", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_formal_logic_helm = LightevalTaskConfig( - name="mmlu:formal_logic", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="formal_logic", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_global_facts_original = LightevalTaskConfig( - name="mmlu:global_facts", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_global_facts, - hf_repo="cais/mmlu", - hf_subset="global_facts", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_global_facts_leaderboard = LightevalTaskConfig( - name="mmlu:global_facts", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="global_facts", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_global_facts_helm = LightevalTaskConfig( - name="mmlu:global_facts", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="global_facts", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_biology_original = LightevalTaskConfig( - name="mmlu:high_school_biology", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_biology, - hf_repo="cais/mmlu", - hf_subset="high_school_biology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_biology_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_biology", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_biology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_biology_helm = LightevalTaskConfig( - name="mmlu:high_school_biology", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_biology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_chemistry_original = LightevalTaskConfig( - name="mmlu:high_school_chemistry", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_chemistry, - hf_repo="cais/mmlu", - hf_subset="high_school_chemistry", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_chemistry_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_chemistry", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_chemistry", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_chemistry_helm = LightevalTaskConfig( - name="mmlu:high_school_chemistry", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_chemistry", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_computer_science_original = LightevalTaskConfig( - name="mmlu:high_school_computer_science", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_computer_science, - hf_repo="cais/mmlu", - hf_subset="high_school_computer_science", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_computer_science_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_computer_science", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_computer_science", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_computer_science_helm = LightevalTaskConfig( - name="mmlu:high_school_computer_science", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_computer_science", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_european_history_original = LightevalTaskConfig( - name="mmlu:high_school_european_history", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_european_history, - hf_repo="cais/mmlu", - hf_subset="high_school_european_history", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_european_history_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_european_history", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_european_history", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_european_history_helm = LightevalTaskConfig( - name="mmlu:high_school_european_history", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_european_history", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_geography_original = LightevalTaskConfig( - name="mmlu:high_school_geography", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_geography, - hf_repo="cais/mmlu", - hf_subset="high_school_geography", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_geography_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_geography", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_geography", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_geography_helm = LightevalTaskConfig( - name="mmlu:high_school_geography", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_geography", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_government_and_politics_original = LightevalTaskConfig( - name="mmlu:high_school_government_and_politics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_government_and_politics, - hf_repo="cais/mmlu", - hf_subset="high_school_government_and_politics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_government_and_politics_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_government_and_politics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_government_and_politics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_government_and_politics_helm = LightevalTaskConfig( - name="mmlu:high_school_government_and_politics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_government_and_politics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_macroeconomics_original = LightevalTaskConfig( - name="mmlu:high_school_macroeconomics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_macroeconomics, - hf_repo="cais/mmlu", - hf_subset="high_school_macroeconomics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_macroeconomics_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_macroeconomics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_macroeconomics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_macroeconomics_helm = LightevalTaskConfig( - name="mmlu:high_school_macroeconomics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_macroeconomics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_mathematics_original = LightevalTaskConfig( - name="mmlu:high_school_mathematics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_mathematics, - hf_repo="cais/mmlu", - hf_subset="high_school_mathematics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_mathematics_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_mathematics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_mathematics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_mathematics_helm = LightevalTaskConfig( - name="mmlu:high_school_mathematics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_mathematics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_microeconomics_original = LightevalTaskConfig( - name="mmlu:high_school_microeconomics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_microeconomics, - hf_repo="cais/mmlu", - hf_subset="high_school_microeconomics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_microeconomics_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_microeconomics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_microeconomics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_microeconomics_helm = LightevalTaskConfig( - name="mmlu:high_school_microeconomics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_microeconomics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_physics_original = LightevalTaskConfig( - name="mmlu:high_school_physics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_physics, - hf_repo="cais/mmlu", - hf_subset="high_school_physics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_physics_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_physics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_physics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_physics_helm = LightevalTaskConfig( - name="mmlu:high_school_physics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_physics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_psychology_original = LightevalTaskConfig( - name="mmlu:high_school_psychology", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_psychology, - hf_repo="cais/mmlu", - hf_subset="high_school_psychology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_psychology_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_psychology", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_psychology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_psychology_helm = LightevalTaskConfig( - name="mmlu:high_school_psychology", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_psychology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_statistics_original = LightevalTaskConfig( - name="mmlu:high_school_statistics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_statistics, - hf_repo="cais/mmlu", - hf_subset="high_school_statistics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_statistics_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_statistics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_statistics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_statistics_helm = LightevalTaskConfig( - name="mmlu:high_school_statistics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_statistics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_us_history_original = LightevalTaskConfig( - name="mmlu:high_school_us_history", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_us_history, - hf_repo="cais/mmlu", - hf_subset="high_school_us_history", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_us_history_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_us_history", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_us_history", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_us_history_helm = LightevalTaskConfig( - name="mmlu:high_school_us_history", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_us_history", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_world_history_original = LightevalTaskConfig( - name="mmlu:high_school_world_history", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_high_school_world_history, - hf_repo="cais/mmlu", - hf_subset="high_school_world_history", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_world_history_leaderboard = LightevalTaskConfig( - name="mmlu:high_school_world_history", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="high_school_world_history", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_high_school_world_history_helm = LightevalTaskConfig( - name="mmlu:high_school_world_history", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="high_school_world_history", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_human_aging_original = LightevalTaskConfig( - name="mmlu:human_aging", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_human_aging, - hf_repo="cais/mmlu", - hf_subset="human_aging", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_human_aging_leaderboard = LightevalTaskConfig( - name="mmlu:human_aging", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="human_aging", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_human_aging_helm = LightevalTaskConfig( - name="mmlu:human_aging", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="human_aging", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_human_sexuality_original = LightevalTaskConfig( - name="mmlu:human_sexuality", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_human_sexuality, - hf_repo="cais/mmlu", - hf_subset="human_sexuality", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_human_sexuality_leaderboard = LightevalTaskConfig( - name="mmlu:human_sexuality", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="human_sexuality", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_human_sexuality_helm = LightevalTaskConfig( - name="mmlu:human_sexuality", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="human_sexuality", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_international_law_original = LightevalTaskConfig( - name="mmlu:international_law", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_international_law, - hf_repo="cais/mmlu", - hf_subset="international_law", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_international_law_leaderboard = LightevalTaskConfig( - name="mmlu:international_law", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="international_law", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_international_law_helm = LightevalTaskConfig( - name="mmlu:international_law", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="international_law", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_jurisprudence_original = LightevalTaskConfig( - name="mmlu:jurisprudence", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_jurisprudence, - hf_repo="cais/mmlu", - hf_subset="jurisprudence", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_jurisprudence_leaderboard = LightevalTaskConfig( - name="mmlu:jurisprudence", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="jurisprudence", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_jurisprudence_helm = LightevalTaskConfig( - name="mmlu:jurisprudence", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="jurisprudence", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_logical_fallacies_original = LightevalTaskConfig( - name="mmlu:logical_fallacies", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_logical_fallacies, - hf_repo="cais/mmlu", - hf_subset="logical_fallacies", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_logical_fallacies_leaderboard = LightevalTaskConfig( - name="mmlu:logical_fallacies", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="logical_fallacies", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_logical_fallacies_helm = LightevalTaskConfig( - name="mmlu:logical_fallacies", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="logical_fallacies", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_machine_learning_original = LightevalTaskConfig( - name="mmlu:machine_learning", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_machine_learning, - hf_repo="cais/mmlu", - hf_subset="machine_learning", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_machine_learning_leaderboard = LightevalTaskConfig( - name="mmlu:machine_learning", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="machine_learning", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_machine_learning_helm = LightevalTaskConfig( - name="mmlu:machine_learning", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="machine_learning", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_management_original = LightevalTaskConfig( - name="mmlu:management", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_management, - hf_repo="cais/mmlu", - hf_subset="management", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_management_leaderboard = LightevalTaskConfig( - name="mmlu:management", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="management", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_management_helm = LightevalTaskConfig( - name="mmlu:management", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="management", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_marketing_original = LightevalTaskConfig( - name="mmlu:marketing", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_marketing, - hf_repo="cais/mmlu", - hf_subset="marketing", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_marketing_leaderboard = LightevalTaskConfig( - name="mmlu:marketing", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="marketing", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_marketing_helm = LightevalTaskConfig( - name="mmlu:marketing", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="marketing", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_medical_genetics_original = LightevalTaskConfig( - name="mmlu:medical_genetics", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_medical_genetics, - hf_repo="cais/mmlu", - hf_subset="medical_genetics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_medical_genetics_leaderboard = LightevalTaskConfig( - name="mmlu:medical_genetics", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="medical_genetics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_medical_genetics_helm = LightevalTaskConfig( - name="mmlu:medical_genetics", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="medical_genetics", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_miscellaneous_original = LightevalTaskConfig( - name="mmlu:miscellaneous", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_miscellaneous, - hf_repo="cais/mmlu", - hf_subset="miscellaneous", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_miscellaneous_leaderboard = LightevalTaskConfig( - name="mmlu:miscellaneous", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="miscellaneous", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_miscellaneous_helm = LightevalTaskConfig( - name="mmlu:miscellaneous", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="miscellaneous", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_moral_disputes_original = LightevalTaskConfig( - name="mmlu:moral_disputes", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_moral_disputes, - hf_repo="cais/mmlu", - hf_subset="moral_disputes", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_moral_disputes_leaderboard = LightevalTaskConfig( - name="mmlu:moral_disputes", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="moral_disputes", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_moral_disputes_helm = LightevalTaskConfig( - name="mmlu:moral_disputes", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="moral_disputes", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_moral_scenarios_original = LightevalTaskConfig( - name="mmlu:moral_scenarios", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_moral_scenarios, - hf_repo="cais/mmlu", - hf_subset="moral_scenarios", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_moral_scenarios_leaderboard = LightevalTaskConfig( - name="mmlu:moral_scenarios", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="moral_scenarios", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_moral_scenarios_helm = LightevalTaskConfig( - name="mmlu:moral_scenarios", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="moral_scenarios", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_nutrition_original = LightevalTaskConfig( - name="mmlu:nutrition", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_nutrition, - hf_repo="cais/mmlu", - hf_subset="nutrition", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_nutrition_leaderboard = LightevalTaskConfig( - name="mmlu:nutrition", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="nutrition", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_nutrition_helm = LightevalTaskConfig( - name="mmlu:nutrition", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="nutrition", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_philosophy_original = LightevalTaskConfig( - name="mmlu:philosophy", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_philosophy, - hf_repo="cais/mmlu", - hf_subset="philosophy", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_philosophy_leaderboard = LightevalTaskConfig( - name="mmlu:philosophy", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="philosophy", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_philosophy_helm = LightevalTaskConfig( - name="mmlu:philosophy", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="philosophy", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_prehistory_original = LightevalTaskConfig( - name="mmlu:prehistory", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_prehistory, - hf_repo="cais/mmlu", - hf_subset="prehistory", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_prehistory_leaderboard = LightevalTaskConfig( - name="mmlu:prehistory", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="prehistory", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_prehistory_helm = LightevalTaskConfig( - name="mmlu:prehistory", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="prehistory", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_professional_accounting_original = LightevalTaskConfig( - name="mmlu:professional_accounting", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_professional_accounting, - hf_repo="cais/mmlu", - hf_subset="professional_accounting", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_professional_accounting_leaderboard = LightevalTaskConfig( - name="mmlu:professional_accounting", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="professional_accounting", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_professional_accounting_helm = LightevalTaskConfig( - name="mmlu:professional_accounting", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="professional_accounting", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_professional_law_original = LightevalTaskConfig( - name="mmlu:professional_law", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_professional_law, - hf_repo="cais/mmlu", - hf_subset="professional_law", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_professional_law_leaderboard = LightevalTaskConfig( - name="mmlu:professional_law", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="professional_law", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_professional_law_helm = LightevalTaskConfig( - name="mmlu:professional_law", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="professional_law", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_professional_medicine_original = LightevalTaskConfig( - name="mmlu:professional_medicine", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_professional_medicine, - hf_repo="cais/mmlu", - hf_subset="professional_medicine", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_professional_medicine_leaderboard = LightevalTaskConfig( - name="mmlu:professional_medicine", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="professional_medicine", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_professional_medicine_helm = LightevalTaskConfig( - name="mmlu:professional_medicine", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="professional_medicine", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_professional_psychology_original = LightevalTaskConfig( - name="mmlu:professional_psychology", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_professional_psychology, - hf_repo="cais/mmlu", - hf_subset="professional_psychology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_professional_psychology_leaderboard = LightevalTaskConfig( - name="mmlu:professional_psychology", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="professional_psychology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_professional_psychology_helm = LightevalTaskConfig( - name="mmlu:professional_psychology", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="professional_psychology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_public_relations_original = LightevalTaskConfig( - name="mmlu:public_relations", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_public_relations, - hf_repo="cais/mmlu", - hf_subset="public_relations", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_public_relations_leaderboard = LightevalTaskConfig( - name="mmlu:public_relations", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="public_relations", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_public_relations_helm = LightevalTaskConfig( - name="mmlu:public_relations", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="public_relations", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_security_studies_original = LightevalTaskConfig( - name="mmlu:security_studies", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_security_studies, - hf_repo="cais/mmlu", - hf_subset="security_studies", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_security_studies_leaderboard = LightevalTaskConfig( - name="mmlu:security_studies", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="security_studies", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_security_studies_helm = LightevalTaskConfig( - name="mmlu:security_studies", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="security_studies", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_sociology_original = LightevalTaskConfig( - name="mmlu:sociology", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_sociology, - hf_repo="cais/mmlu", - hf_subset="sociology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_sociology_leaderboard = LightevalTaskConfig( - name="mmlu:sociology", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="sociology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_sociology_helm = LightevalTaskConfig( - name="mmlu:sociology", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="sociology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_us_foreign_policy_original = LightevalTaskConfig( - name="mmlu:us_foreign_policy", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_us_foreign_policy, - hf_repo="cais/mmlu", - hf_subset="us_foreign_policy", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_us_foreign_policy_leaderboard = LightevalTaskConfig( - name="mmlu:us_foreign_policy", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="us_foreign_policy", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_us_foreign_policy_helm = LightevalTaskConfig( - name="mmlu:us_foreign_policy", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="us_foreign_policy", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_virology_original = LightevalTaskConfig( - name="mmlu:virology", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_virology, - hf_repo="cais/mmlu", - hf_subset="virology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_virology_leaderboard = LightevalTaskConfig( - name="mmlu:virology", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="virology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_virology_helm = LightevalTaskConfig( - name="mmlu:virology", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="virology", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mmlu_world_religions_original = LightevalTaskConfig( - name="mmlu:world_religions", - suite=["original", "mmlu"], - prompt_function=prompt.mmlu_world_religions, - hf_repo="cais/mmlu", - hf_subset="world_religions", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_world_religions_leaderboard = LightevalTaskConfig( - name="mmlu:world_religions", - suite=["leaderboard", "mmlu"], - prompt_function=prompt.mmlu_harness, - hf_repo="lighteval/mmlu", - hf_subset="world_religions", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mmlu_world_religions_helm = LightevalTaskConfig( - name="mmlu:world_religions", - suite=["helm", "helm_general"], - prompt_function=prompt.mmlu_helm, - hf_repo="lighteval/mmlu", - hf_subset="world_religions", - hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -mnist_ascii_bigbench = LightevalTaskConfig( - name="mnist_ascii", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="mnist_ascii", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -modified_arithmetic_bigbench = LightevalTaskConfig( - name="modified_arithmetic", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="modified_arithmetic", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -moral_permissibility_bigbench = LightevalTaskConfig( - name="moral_permissibility", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="moral_permissibility", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -movie_dialog_same_or_different_bigbench = LightevalTaskConfig( - name="movie_dialog_same_or_different", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="movie_dialog_same_or_different", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -movie_recommendation_bigbench = LightevalTaskConfig( - name="movie_recommendation", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="movie_recommendation", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mtnt2019_en_fr_lighteval = LightevalTaskConfig( - name="mtnt2019:en-fr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="mtnt2019_en-fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=200, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -mtnt2019_en_ja_lighteval = LightevalTaskConfig( - name="mtnt2019:en-ja", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="mtnt2019_en-ja", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=200, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -mtnt2019_fr_en_lighteval = LightevalTaskConfig( - name="mtnt2019:fr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="mtnt2019_fr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=200, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -mtnt2019_ja_en_lighteval = LightevalTaskConfig( - name="mtnt2019:ja-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="mtnt2019_ja-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=200, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -mult_data_wrangling_bigbench = LightevalTaskConfig( - name="mult_data_wrangling", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="mult_data_wrangling", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -multiemo_bigbench = LightevalTaskConfig( - name="multiemo", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="multiemo", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -musr_murder_mysteries = LightevalTaskConfig( - name="musr:murder_mysteries", - suite=["lighteval"], - prompt_function=prompt.musr, - hf_repo="TAUR-Lab/MuSR", - hf_subset="default", - hf_avail_splits=["murder_mysteries"], - evaluation_splits=["murder_mysteries"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -musr_object_placements = LightevalTaskConfig( - name="musr:object_placements", - suite=["lighteval"], - prompt_function=prompt.musr, - hf_repo="TAUR-Lab/MuSR", - hf_subset="default", - hf_avail_splits=["object_placements"], - evaluation_splits=["object_placements"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -musr_team_allocation = LightevalTaskConfig( - name="musr:team_allocation", - suite=["lighteval"], - prompt_function=prompt.musr, - hf_repo="TAUR-Lab/MuSR", - hf_subset="default", - hf_avail_splits=["team_allocation"], - evaluation_splits=["team_allocation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -mutual_lighteval = LightevalTaskConfig( - name="mutual", - suite=["lighteval"], - prompt_function=prompt.mutual, - hf_repo="lighteval/mutual_harness", - hf_subset="mutual", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.recall_at_k, Metrics.recall_at_k(sample_params={"k": 2}), Metrics.mrr], - stop_sequence=["\n"], - version=0, -) -mutual_plus_lighteval = LightevalTaskConfig( - name="mutual_plus", - suite=["lighteval"], - prompt_function=prompt.mutual, - hf_repo="lighteval/mutual_harness", - hf_subset="mutual_plus", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.recall_at_k, Metrics.recall_at_k(sample_params={"k": 2}), Metrics.mrr], - stop_sequence=["\n"], - version=0, -) -narrativeqa_helm = LightevalTaskConfig( - name="narrativeqa", - suite=["helm", "helm_general"], - prompt_function=prompt.narrativeqa, - hf_repo="lighteval/narrative_qa_helm", - hf_subset="default", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - Metrics.rougeL, - Metrics.bleu_1, - Metrics.bleu_4, - ], - stop_sequence=["\n"], - version=0, -) -natural_instructions_bigbench = LightevalTaskConfig( - name="natural_instructions", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="natural_instructions", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.rouge_t5], - stop_sequence=["\n"], - version=0, -) -natural_questions = LightevalTaskConfig( - name="natural_questions", - prompt_function=get_qa_prompt_function( - Language.ENGLISH, - lambda line: {"question": line["question"], "choices": [line["answer"]]}, - ), - suite=("lighteval",), - hf_repo="lighteval/small_natural_questions", - hf_subset="default", - evaluation_splits=("test",), - few_shots_split="few_shot", - generation_size=250, - stop_sequence=["\n", "Question:", "question:"], - metrics=( - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ), -) -navigate_bigbench = LightevalTaskConfig( - name="navigate", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="navigate", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -nonsense_words_grammar_bigbench = LightevalTaskConfig( - name="nonsense_words_grammar", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="nonsense_words_grammar", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -novel_concepts_bigbench_lite = LightevalTaskConfig( - name="novel_concepts", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="novel_concepts", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -numeracy_linear_example_helm = LightevalTaskConfig( - name="numeracy:linear_example", - suite=["helm"], - prompt_function=prompt.numeracy, - hf_repo="lighteval/numeracy", - hf_subset="linear_example", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -numeracy_linear_standard_helm = LightevalTaskConfig( - name="numeracy:linear_standard", - suite=["helm"], - prompt_function=prompt.numeracy, - hf_repo="lighteval/numeracy", - hf_subset="linear_standard", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -numeracy_parabola_example_helm = LightevalTaskConfig( - name="numeracy:parabola_example", - suite=["helm"], - prompt_function=prompt.numeracy, - hf_repo="lighteval/numeracy", - hf_subset="parabola_example", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -numeracy_parabola_standard_helm = LightevalTaskConfig( - name="numeracy:parabola_standard", - suite=["helm"], - prompt_function=prompt.numeracy, - hf_repo="lighteval/numeracy", - hf_subset="parabola_standard", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -numeracy_paraboloid_example_helm = LightevalTaskConfig( - name="numeracy:paraboloid_example", - suite=["helm"], - prompt_function=prompt.numeracy, - hf_repo="lighteval/numeracy", - hf_subset="paraboloid_example", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -numeracy_paraboloid_standard_helm = LightevalTaskConfig( - name="numeracy:paraboloid_standard", - suite=["helm"], - prompt_function=prompt.numeracy, - hf_repo="lighteval/numeracy", - hf_subset="paraboloid_standard", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -numeracy_plane_example_helm = LightevalTaskConfig( - name="numeracy:plane_example", - suite=["helm"], - prompt_function=prompt.numeracy, - hf_repo="lighteval/numeracy", - hf_subset="plane_example", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -numeracy_plane_standard_helm = LightevalTaskConfig( - name="numeracy:plane_standard", - suite=["helm"], - prompt_function=prompt.numeracy, - hf_repo="lighteval/numeracy", - hf_subset="plane_standard", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ], - stop_sequence=["\n"], - version=0, -) -object_counting_bigbench = LightevalTaskConfig( - name="object_counting", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="object_counting", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -odd_one_out_bigbench = LightevalTaskConfig( - name="odd_one_out", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="odd_one_out", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -openbookqa_helm = LightevalTaskConfig( - name="openbookqa", - suite=["helm", "commonsense_scenario", "helm_general"], - prompt_function=prompt.openbookqa_helm, - hf_repo="openbookqa", - hf_subset="main", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -openbookqa_lighteval = LightevalTaskConfig( - name="openbookqa", - suite=["lighteval"], - prompt_function=prompt.openbookqa, - hf_repo="openbookqa", - hf_subset="main", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -operators_bigbench_lite = LightevalTaskConfig( - name="operators", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="operators", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -paragraph_segmentation_bigbench = LightevalTaskConfig( - name="paragraph_segmentation", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="paragraph_segmentation", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -parsinlu_qa_bigbench = LightevalTaskConfig( - name="parsinlu_qa", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="parsinlu_qa", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -parsinlu_reading_comprehension_bigbench_lite = LightevalTaskConfig( - name="parsinlu_reading_comprehension", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="parsinlu_reading_comprehension", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=None, - version=0, -) -penguins_in_a_table_bigbench = LightevalTaskConfig( - name="penguins_in_a_table", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="penguins_in_a_table", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -periodic_elements_bigbench = LightevalTaskConfig( - name="periodic_elements", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="periodic_elements", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -persian_idioms_bigbench = LightevalTaskConfig( - name="persian_idioms", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="persian_idioms", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -phrase_relatedness_bigbench = LightevalTaskConfig( - name="phrase_relatedness", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="phrase_relatedness", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -physical_intuition_bigbench = LightevalTaskConfig( - name="physical_intuition", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="physical_intuition", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -physics_bigbench = LightevalTaskConfig( - name="physics", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="physics", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -physics_questions_bigbench = LightevalTaskConfig( - name="physics_questions", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="physics_questions", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -piqa_lighteval = LightevalTaskConfig( - name="piqa", - suite=["lighteval"], - prompt_function=prompt.piqa_harness, - hf_repo="ybisk/piqa", - hf_subset="plain_text", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -piqa_helm = LightevalTaskConfig( - name="piqa", - suite=["helm", "commonsense_scenario"], - prompt_function=prompt.piqa_helm, - hf_repo="ybisk/piqa", - hf_subset="plain_text", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -play_dialog_same_or_different_bigbench_lite = LightevalTaskConfig( - name="play_dialog_same_or_different", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="play_dialog_same_or_different", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -polish_sequence_labeling_bigbench = LightevalTaskConfig( - name="polish_sequence_labeling", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="polish_sequence_labeling", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.f1_score], - stop_sequence=["\n"], - version=0, -) -presuppositions_as_nli_bigbench = LightevalTaskConfig( - name="presuppositions_as_nli", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="presuppositions_as_nli", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -prost_lighteval = LightevalTaskConfig( - name="prost", - suite=["lighteval"], - prompt_function=prompt.prost, - hf_repo="lighteval/prost", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -pubmedqa_lighteval = LightevalTaskConfig( - name="pubmedqa", - suite=["lighteval"], - prompt_function=prompt.pubmed_qa, - hf_repo="pubmed_qa", - hf_subset="pqa_labeled", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -pubmedqa_helm = LightevalTaskConfig( - name="pubmedqa", - suite=["helm"], - prompt_function=prompt.pubmed_qa_helm, - hf_repo="pubmed_qa", - hf_subset="pqa_labeled", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -qa4mre_2011_lighteval = LightevalTaskConfig( - name="qa4mre:2011", - suite=["lighteval"], - prompt_function=prompt.qa4mre, - hf_repo="qa4mre", - hf_subset="2011.main.EN", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -qa4mre_2012_lighteval = LightevalTaskConfig( - name="qa4mre:2012", - suite=["lighteval"], - prompt_function=prompt.qa4mre, - hf_repo="qa4mre", - hf_subset="2012.main.EN", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -qa4mre_2013_lighteval = LightevalTaskConfig( - name="qa4mre:2013", - suite=["lighteval"], - prompt_function=prompt.qa4mre, - hf_repo="qa4mre", - hf_subset="2013.main.EN", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -qa_wikidata_bigbench = LightevalTaskConfig( - name="qa_wikidata", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="qa_wikidata", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.bleurt, - Metrics.bleu, - Metrics.rouge_t5, - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -qasper_lighteval = LightevalTaskConfig( - name="qasper", - suite=["lighteval"], - prompt_function=prompt.qasper, - hf_repo="allenai/qasper", - hf_subset="qasper", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[Metrics.f1_score(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer})], - stop_sequence=["\n"], - version=0, -) -qasper_ll_lighteval = LightevalTaskConfig( - name="qasper_ll", - suite=["lighteval"], - prompt_function=prompt.qasper_ll, - hf_repo="allenai/qasper", - hf_subset="qasper", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -quac_helm = LightevalTaskConfig( - name="quac", - suite=["helm"], - prompt_function=prompt.quac, - hf_repo="lighteval/quac_helm", - hf_subset="default", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.f1_score, - ], - stop_sequence=["\n"], - version=0, -) -question_selection_bigbench = LightevalTaskConfig( - name="question_selection", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="question_selection", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -race_high_lighteval = LightevalTaskConfig( - name="race:high", - suite=["lighteval", "race"], - prompt_function=prompt.race, - hf_repo="EleutherAI/race", - hf_subset="high", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -raft_ade_corpus_v2_helm = LightevalTaskConfig( - name="raft:ade_corpus_v2", - suite=["helm", "helm_general"], - prompt_function=prompt.raft_ade_corpus_v2, - hf_repo="ought/raft", - hf_subset="ade_corpus_v2", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -raft_banking_77_helm = LightevalTaskConfig( - name="raft:banking_77", - suite=["helm", "helm_general"], - prompt_function=prompt.raft_banking_77, - hf_repo="ought/raft", - hf_subset="banking_77", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -raft_neurips_impact_statement_risks_helm = LightevalTaskConfig( - name="raft:neurips_impact_statement_risks", - suite=["helm", "helm_general"], - prompt_function=prompt.raft_neurips_impact_statement_risks, - hf_repo="ought/raft", - hf_subset="neurips_impact_statement_risks", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -raft_one_stop_english_helm = LightevalTaskConfig( - name="raft:one_stop_english", - suite=["helm", "helm_general"], - prompt_function=prompt.raft_one_stop_english, - hf_repo="ought/raft", - hf_subset="one_stop_english", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -raft_overruling_helm = LightevalTaskConfig( - name="raft:overruling", - suite=["helm", "helm_general"], - prompt_function=prompt.raft_overruling, - hf_repo="ought/raft", - hf_subset="overruling", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -raft_semiconductor_org_types_helm = LightevalTaskConfig( - name="raft:semiconductor_org_types", - suite=["helm", "helm_general"], - prompt_function=prompt.raft_semiconductor_org_types, - hf_repo="ought/raft", - hf_subset="semiconductor_org_types", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -raft_systematic_review_inclusion_helm = LightevalTaskConfig( - name="raft:systematic_review_inclusion", - suite=["helm", "helm_general"], - prompt_function=prompt.raft_systematic_review_inclusion, - hf_repo="ought/raft", - hf_subset="systematic_review_inclusion", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -raft_tai_safety_research_helm = LightevalTaskConfig( - name="raft:tai_safety_research", - suite=["helm", "helm_general"], - prompt_function=prompt.raft_tai_safety_research, - hf_repo="ought/raft", - hf_subset="tai_safety_research", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -raft_terms_of_service_helm = LightevalTaskConfig( - name="raft:terms_of_service", - suite=["helm", "helm_general"], - prompt_function=prompt.raft_terms_of_service, - hf_repo="ought/raft", - hf_subset="terms_of_service", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -raft_tweet_eval_hate_helm = LightevalTaskConfig( - name="raft:tweet_eval_hate", - suite=["helm", "helm_general"], - prompt_function=prompt.raft_tweet_eval_hate, - hf_repo="ought/raft", - hf_subset="tweet_eval_hate", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -raft_twitter_complaints_helm = LightevalTaskConfig( - name="raft:twitter_complaints", - suite=["helm", "helm_general"], - prompt_function=prompt.raft_twitter_complaints, - hf_repo="ought/raft", - hf_subset="twitter_complaints", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=30, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, - ], - stop_sequence=["\n"], - version=0, -) -real_or_fake_text_bigbench = LightevalTaskConfig( - name="real_or_fake_text", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="real_or_fake_text", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -real_toxicity_prompts_helm = LightevalTaskConfig( - name="real_toxicity_prompts", - suite=["helm"], - prompt_function=prompt.real_toxicity_prompts, - hf_repo="allenai/real-toxicity-prompts", - hf_subset="default", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[Metrics.prediction_perplexity], - stop_sequence=["\n"], - version=0, -) -reasoning_about_colored_objects_bigbench = LightevalTaskConfig( - name="reasoning_about_colored_objects", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="reasoning_about_colored_objects", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -repeat_copy_logic_bigbench_lite = LightevalTaskConfig( - name="repeat_copy_logic", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="repeat_copy_logic", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -rephrase_bigbench = LightevalTaskConfig( - name="rephrase", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="rephrase", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.rouge_t5, - Metrics.bleu, - Metrics.loglikelihood_acc, - Metrics.exact_match(sample_params={"strip_strings": False}), - ], - stop_sequence=["\n"], - version=0, -) -rhyming_bigbench = LightevalTaskConfig( - name="rhyming", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="rhyming", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -riddle_sense_bigbench = LightevalTaskConfig( - name="riddle_sense", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="riddle_sense", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -ruin_names_bigbench = LightevalTaskConfig( - name="ruin_names", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="ruin_names", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -salient_translation_error_detection_bigbench = LightevalTaskConfig( - name="salient_translation_error_detection", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="salient_translation_error_detection", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -scientific_press_release_bigbench = LightevalTaskConfig( - name="scientific_press_release", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="scientific_press_release", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -sciq_lighteval = LightevalTaskConfig( - name="sciq", - suite=["lighteval"], - prompt_function=prompt.sciq, - hf_repo="sciq", - hf_subset="default", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -semantic_parsing_in_context_sparc_bigbench = LightevalTaskConfig( - name="semantic_parsing_in_context_sparc", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="semantic_parsing_in_context_sparc", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -semantic_parsing_spider_bigbench = LightevalTaskConfig( - name="semantic_parsing_spider", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="semantic_parsing_spider", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -sentence_ambiguity_bigbench = LightevalTaskConfig( - name="sentence_ambiguity", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="sentence_ambiguity", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -similarities_abstraction_bigbench = LightevalTaskConfig( - name="similarities_abstraction", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="similarities_abstraction", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -simp_turing_concept_bigbench = LightevalTaskConfig( - name="simp_turing_concept", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="simp_turing_concept", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -simpleqa = LightevalTaskConfig( - name="simpleqa", - suite=["lighteval"], - prompt_function=prompt.simpleqa, - hf_repo="lighteval/SimpleQA", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split="few_shot", - few_shots_select=None, - generation_size=2048, - metrics=[Metrics.simpleqa_judge], - stop_sequence=["\n"], - version=0, -) -simple_arithmetic_json_bigbench = LightevalTaskConfig( - name="simple_arithmetic_json", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="simple_arithmetic_json", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -simple_arithmetic_json_multiple_choice_bigbench = LightevalTaskConfig( - name="simple_arithmetic_json_multiple_choice", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="simple_arithmetic_json_multiple_choice", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -simple_arithmetic_json_subtasks_bigbench = LightevalTaskConfig( - name="simple_arithmetic_json_subtasks", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="simple_arithmetic_json_subtasks", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -simple_arithmetic_multiple_targets_json_bigbench = LightevalTaskConfig( - name="simple_arithmetic_multiple_targets_json", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="simple_arithmetic_multiple_targets_json", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -simple_ethical_questions_bigbench = LightevalTaskConfig( - name="simple_ethical_questions", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="simple_ethical_questions", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -simple_text_editing_bigbench = LightevalTaskConfig( - name="simple_text_editing", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="simple_text_editing", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -siqa_helm = LightevalTaskConfig( - name="siqa", - suite=["helm", "commonsense_scenario"], - prompt_function=prompt.siqa, - hf_repo="allenai/social_i_qa", - hf_subset="default", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -snarks_bigbench = LightevalTaskConfig( - name="snarks", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="snarks", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -social_iqa_bigbench = LightevalTaskConfig( - name="social_iqa", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="social_iqa", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -social_support_bigbench = LightevalTaskConfig( - name="social_support", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="social_support", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.f1_score_macro], - stop_sequence=["\n"], - version=0, -) -sports_understanding_bigbench = LightevalTaskConfig( - name="sports_understanding", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="sports_understanding", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -squad_v2 = LightevalTaskConfig( - name="squad_v2", - prompt_function=get_qa_prompt_function( - Language.ENGLISH, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="rajpurkar/squad_v2", - hf_subset="squad_v2", - hf_filter=lambda line: any(ans for ans in line["answers"]["text"] if len(ans) > 0), - evaluation_splits=("validation",), - few_shots_split="train", - stop_sequence=["\n", "Question:", "question:"], - generation_size=200, - metrics=( - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ), -) -storycloze_2016_lighteval = LightevalTaskConfig( - name="storycloze:2016", - suite=["lighteval", "storycloze"], - prompt_function=prompt.storycloze, - hf_repo="MoE-UNC/story_cloze", - hf_subset="2016", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -storycloze_2018_lighteval = LightevalTaskConfig( - name="storycloze:2018", - suite=["lighteval", "storycloze"], - prompt_function=prompt.storycloze, - hf_repo="MoE-UNC/story_cloze", - hf_subset="2018", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -strange_stories_bigbench_lite = LightevalTaskConfig( - name="strange_stories", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="strange_stories", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -strategyqa_bigbench_lite = LightevalTaskConfig( - name="strategyqa", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="strategyqa", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -sufficient_information_bigbench = LightevalTaskConfig( - name="sufficient_information", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="sufficient_information", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -suicide_risk_bigbench = LightevalTaskConfig( - name="suicide_risk", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="suicide_risk", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -summarization_cnn_dm_helm = LightevalTaskConfig( - name="summarization:cnn-dm", - suite=["helm", "helm_general"], - prompt_function=prompt.cnn_dm, - hf_repo="lighteval/summarization", - hf_subset="cnn-dm", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=128, - metrics=[ - Metrics.rouge1, - Metrics.rouge2, - Metrics.rougeL, - Metrics.faithfulness, - Metrics.extractiveness, - Metrics.bert_score, - ], - stop_sequence=["\n"], - version=0, -) -summarization_xsum_helm = LightevalTaskConfig( - name="summarization:xsum", - suite=["helm", "helm_general"], - prompt_function=prompt.xsum, - hf_repo="lighteval/summarization", - hf_subset="xsum", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=64, - metrics=[ - Metrics.rouge1, - Metrics.rouge2, - Metrics.rougeL, - Metrics.faithfulness, - Metrics.extractiveness, - Metrics.bert_score, - ], - stop_sequence=["\n"], - version=0, -) -summarization_xsum_sampled_helm = LightevalTaskConfig( - name="summarization:xsum-sampled", - suite=["helm"], - prompt_function=prompt.xsum, - hf_repo="lighteval/summarization", - hf_subset="xsum-sampled", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=64, - metrics=[ - Metrics.rouge1, - Metrics.rouge2, - Metrics.rougeL, - Metrics.faithfulness, - Metrics.extractiveness, - Metrics.bert_score, - ], - stop_sequence=["\n"], - version=0, -) -super_glue_boolq_lighteval = LightevalTaskConfig( - name="super_glue:boolq", - suite=["lighteval", "superglue"], - prompt_function=prompt.boolq_harness, - hf_repo="super_glue", - hf_subset="boolq", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -super_glue_cb_lighteval = LightevalTaskConfig( - name="super_glue:cb", - suite=["lighteval", "superglue"], - prompt_function=prompt.cb, - hf_repo="super_glue", - hf_subset="cb", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc, Metrics.multi_f1_numeric], - stop_sequence=["\n"], - version=0, -) -super_glue_copa_lighteval = LightevalTaskConfig( - name="super_glue:copa", - suite=["lighteval", "superglue"], - prompt_function=prompt.copa, - hf_repo="super_glue", - hf_subset="copa", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -super_glue_rte_lighteval = LightevalTaskConfig( - name="super_glue:rte", - suite=["lighteval", "superglue"], - prompt_function=prompt.rte, - hf_repo="super_glue", - hf_subset="rte", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -super_glue_multirc_lighteval = LightevalTaskConfig( - name="super_glue:multirc", - suite=["lighteval", "superglue"], - prompt_function=prompt.multirc, - hf_repo="super_glue", - hf_subset="multirc", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -super_glue_wic_lighteval = LightevalTaskConfig( - name="super_glue:wic", - suite=["lighteval", "superglue"], - prompt_function=prompt.wic, - hf_repo="super_glue", - hf_subset="wic", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -super_glue_wsc_lighteval = LightevalTaskConfig( - name="super_glue:wsc", - suite=["lighteval", "superglue"], - prompt_function=prompt.wsc, - hf_repo="super_glue", - hf_subset="wsc", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -swahili_english_proverbs_bigbench = LightevalTaskConfig( - name="swahili_english_proverbs", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="swahili_english_proverbs", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -swag_lighteval = LightevalTaskConfig( - name="swag", - suite=["lighteval"], - prompt_function=prompt.swag, - hf_repo="swag", - hf_subset="regular", - hf_avail_splits=["train", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm(ignore_first_space=True)}), - ], - stop_sequence=["\n"], - version=0, -) -swedish_to_german_proverbs_bigbench = LightevalTaskConfig( - name="swedish_to_german_proverbs", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="swedish_to_german_proverbs", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -symbol_interpretation_bigbench_lite = LightevalTaskConfig( - name="symbol_interpretation", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="symbol_interpretation", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -synthetic_reasoning_induction_helm = LightevalTaskConfig( - name="synthetic_reasoning:induction", - suite=["helm"], - prompt_function=prompt.synthetic_reasoning, - hf_repo="lighteval/synthetic_reasoning", - hf_subset="induction", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=50, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -synthetic_reasoning_natural_easy_helm = LightevalTaskConfig( - name="synthetic_reasoning:natural_easy", - suite=["helm"], - prompt_function=prompt.synthetic_reasoning_natural, - hf_repo="lighteval/synthetic_reasoning_natural", - hf_subset="easy", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[Metrics.exact_match, Metrics.f1_score], - stop_sequence=["\n"], - version=0, -) -synthetic_reasoning_natural_hard_helm = LightevalTaskConfig( - name="synthetic_reasoning:natural_hard", - suite=["helm"], - prompt_function=prompt.synthetic_reasoning_natural, - hf_repo="lighteval/synthetic_reasoning_natural", - hf_subset="hard", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[Metrics.exact_match, Metrics.f1_score], - stop_sequence=["\n"], - version=0, -) -synthetic_reasoning_pattern_match_helm = LightevalTaskConfig( - name="synthetic_reasoning:pattern_match", - suite=["helm"], - prompt_function=prompt.synthetic_reasoning, - hf_repo="lighteval/synthetic_reasoning", - hf_subset="pattern_match", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=50, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -synthetic_reasoning_variable_substitution_helm = LightevalTaskConfig( - name="synthetic_reasoning:variable_substitution", - suite=["helm"], - prompt_function=prompt.synthetic_reasoning, - hf_repo="lighteval/synthetic_reasoning", - hf_subset="variable_substitution", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=50, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -tellmewhy_bigbench = LightevalTaskConfig( - name="tellmewhy", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="tellmewhy", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.rouge_t5], - stop_sequence=["\n"], - version=0, -) -temporal_sequences_bigbench = LightevalTaskConfig( - name="temporal_sequences", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="temporal_sequences", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -tense_bigbench = LightevalTaskConfig( - name="tense", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="tense", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -the_pile_arxiv_helm = LightevalTaskConfig( - name="the_pile:arxiv", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="arxiv", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_bibliotik_helm = LightevalTaskConfig( - name="the_pile:bibliotik", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="bibliotik", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_commoncrawl_helm = LightevalTaskConfig( - name="the_pile:commoncrawl", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="commoncrawl", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_dm_mathematics_helm = LightevalTaskConfig( - name="the_pile:dm-mathematics", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="dm-mathematics", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_enron_helm = LightevalTaskConfig( - name="the_pile:enron", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="enron", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_europarl_helm = LightevalTaskConfig( - name="the_pile:europarl", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="europarl", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_freelaw_helm = LightevalTaskConfig( - name="the_pile:freelaw", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="freelaw", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_github_helm = LightevalTaskConfig( - name="the_pile:github", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="github", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_gutenberg_helm = LightevalTaskConfig( - name="the_pile:gutenberg", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="gutenberg", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_hackernews_helm = LightevalTaskConfig( - name="the_pile:hackernews", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="hackernews", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_nih_exporter_helm = LightevalTaskConfig( - name="the_pile:nih-exporter", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="nih-exporter", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_opensubtitles_helm = LightevalTaskConfig( - name="the_pile:opensubtitles", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="opensubtitles", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_openwebtext2_helm = LightevalTaskConfig( - name="the_pile:openwebtext2", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="openwebtext2", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_pubmed_abstracts_helm = LightevalTaskConfig( - name="the_pile:pubmed-abstracts", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="pubmed-abstracts", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_pubmed_central_helm = LightevalTaskConfig( - name="the_pile:pubmed-central", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="pubmed-central", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_stackexchange_helm = LightevalTaskConfig( - name="the_pile:stackexchange", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="stackexchange", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_upsto_helm = LightevalTaskConfig( - name="the_pile:upsto", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="uspto", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_wikipedia_helm = LightevalTaskConfig( - name="the_pile:wikipedia", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="wikipedia", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -the_pile_youtubesubtitles_helm = LightevalTaskConfig( - name="the_pile:youtubesubtitles", - suite=["helm"], - prompt_function=prompt.the_pile, - hf_repo="lighteval/pile_helm", - hf_subset="youtubesubtitles", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -timedial_bigbench = LightevalTaskConfig( - name="timedial", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="timedial", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -toxigen_lighteval = LightevalTaskConfig( - name="toxigen", - suite=["lighteval"], - prompt_function=prompt.toxigen, - hf_repo="skg/toxigen-data", - hf_subset="annotated", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()}), - ], - stop_sequence=["\n"], - version=0, -) -topical_chat_bigbench = LightevalTaskConfig( - name="topical_chat", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="topical_chat", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.loglikelihood_acc, Metrics.bleurt], - stop_sequence=["\n"], - version=0, -) -tracking_shuffled_objects_bigbench = LightevalTaskConfig( - name="tracking_shuffled_objects", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="tracking_shuffled_objects", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -triviaqa_lighteval = LightevalTaskConfig( - name="triviaqa", - suite=["lighteval"], - prompt_function=prompt.triviaqa, - hf_repo="trivia_qa", - hf_subset="rc.nocontext", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=20, - metrics=[ - Metrics.exact_match(sample_params={"strip_strings": True, "normalize_pred": harness_triviaqa_normalizer}) - ], - stop_sequence=["\n", ".", ","], - version=0, -) -truthfulqa_gen_lighteval = LightevalTaskConfig( - name="truthfulqa:gen", - suite=["lighteval"], - prompt_function=prompt.truthful_qa_generative, - hf_repo="truthful_qa", - hf_subset="generation", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=200, - metrics=[Metrics.bleu, Metrics.rouge_t5], - stop_sequence=["\n"], - version=0, -) -truthfulqa_mc_leaderboard = LightevalTaskConfig( - name="truthfulqa:mc", - suite=["leaderboard"], - prompt_function=prompt.truthful_qa_multiple_choice, - hf_repo="truthful_qa", - hf_subset="multiple_choice", - hf_avail_splits=["validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.truthfulqa_mc_metrics], - stop_sequence=["\n"], - version=0, -) -truthfulqa_helm = LightevalTaskConfig( - name="truthfulqa", - suite=["helm", "helm_general"], - prompt_function=prompt.truthful_qa_helm, - hf_repo="lighteval/truthfulqa_helm", - hf_subset="default", - hf_avail_splits=["train", "valid"], - evaluation_splits=["valid"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -twitterAAE_aa_helm = LightevalTaskConfig( - name="twitterAAE:aa", - suite=["helm"], - prompt_function=prompt.twitter_aae, - hf_repo="lighteval/twitterAAE", - hf_subset="aa", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -twitterAAE_white_helm = LightevalTaskConfig( - name="twitterAAE:white", - suite=["helm"], - prompt_function=prompt.twitter_aae, - hf_repo="lighteval/twitterAAE", - hf_subset="white", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -understanding_fables_bigbench = LightevalTaskConfig( - name="understanding_fables", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="understanding_fables", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -undo_permutation_bigbench = LightevalTaskConfig( - name="undo_permutation", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="undo_permutation", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -unit_conversion_bigbench = LightevalTaskConfig( - name="unit_conversion", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="unit_conversion", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -unit_interpretation_bigbench = LightevalTaskConfig( - name="unit_interpretation", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="unit_interpretation", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -unnatural_in_context_learning_bigbench = LightevalTaskConfig( - name="unnatural_in_context_learning", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="unnatural_in_context_learning", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -unscramble_anagrams1_lighteval = LightevalTaskConfig( - name="unscramble:anagrams1", - suite=["lighteval", "unscramble"], - prompt_function=prompt.unscramble, - hf_repo="lighteval/GPT3_unscramble", - hf_subset="default", - hf_avail_splits=["mid_word_1_anagrams"], - evaluation_splits=["mid_word_1_anagrams"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -unscramble_anagrams2_lighteval = LightevalTaskConfig( - name="unscramble:anagrams2", - suite=["lighteval", "unscramble"], - prompt_function=prompt.unscramble, - hf_repo="lighteval/GPT3_unscramble", - hf_subset="default", - hf_avail_splits=["mid_word_2_anagrams"], - evaluation_splits=["mid_word_2_anagrams"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -unscramble_cycle_letters_lighteval = LightevalTaskConfig( - name="unscramble:cycle_letters", - suite=["lighteval", "unscramble"], - prompt_function=prompt.unscramble, - hf_repo="lighteval/GPT3_unscramble", - hf_subset="default", - hf_avail_splits=["cycle_letters_in_word"], - evaluation_splits=["cycle_letters_in_word"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -unscramble_random_insertion_lighteval = LightevalTaskConfig( - name="unscramble:random_insertion", - suite=["lighteval", "unscramble"], - prompt_function=prompt.unscramble, - hf_repo="lighteval/GPT3_unscramble", - hf_subset="default", - hf_avail_splits=["random_insertion_in_word"], - evaluation_splits=["random_insertion_in_word"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -unscramble_reversed_words_lighteval = LightevalTaskConfig( - name="unscramble:reversed_words", - suite=["lighteval", "unscramble"], - prompt_function=prompt.unscramble, - hf_repo="lighteval/GPT3_unscramble", - hf_subset="default", - hf_avail_splits=["reversed_words"], - evaluation_splits=["reversed_words"], - few_shots_split=None, - few_shots_select=None, - generation_size=5, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -vitaminc_fact_verification_bigbench_lite = LightevalTaskConfig( - name="vitaminc_fact_verification", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="vitaminc_fact_verification", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -webqs_lighteval = LightevalTaskConfig( - name="webqs", - suite=["lighteval"], - prompt_function=prompt.webqs, - hf_repo="web_questions", - hf_subset="default", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.acc_golds_likelihood], - stop_sequence=["\n"], - version=0, -) -what_is_the_tao_bigbench = LightevalTaskConfig( - name="what_is_the_tao", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="what_is_the_tao", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -which_wiki_edit_bigbench = LightevalTaskConfig( - name="which_wiki_edit", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="which_wiki_edit", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -wikifact_applies_to_jurisdiction_helm = LightevalTaskConfig( - name="wikifact:applies_to_jurisdiction", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="applies_to_jurisdiction", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_atomic_number_helm = LightevalTaskConfig( - name="wikifact:atomic_number", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="atomic_number", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_author_helm = LightevalTaskConfig( - name="wikifact:author", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="author", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_award_received_helm = LightevalTaskConfig( - name="wikifact:award_received", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="award_received", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_basic_form_of_government_helm = LightevalTaskConfig( - name="wikifact:basic_form_of_government", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="basic_form_of_government", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_capital_helm = LightevalTaskConfig( - name="wikifact:capital", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="capital", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_capital_of_helm = LightevalTaskConfig( - name="wikifact:capital_of", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="capital_of", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_central_bank_helm = LightevalTaskConfig( - name="wikifact:central_bank", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="central_bank", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_composer_helm = LightevalTaskConfig( - name="wikifact:composer", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="composer", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_continent_helm = LightevalTaskConfig( - name="wikifact:continent", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="continent", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_country_helm = LightevalTaskConfig( - name="wikifact:country", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="country", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_country_of_citizenship_helm = LightevalTaskConfig( - name="wikifact:country_of_citizenship", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="country_of_citizenship", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_country_of_origin_helm = LightevalTaskConfig( - name="wikifact:country_of_origin", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="country_of_origin", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_creator_helm = LightevalTaskConfig( - name="wikifact:creator", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="creator", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_currency_helm = LightevalTaskConfig( - name="wikifact:currency", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="currency", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_defendant_helm = LightevalTaskConfig( - name="wikifact:defendant", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="defendant", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_developer_helm = LightevalTaskConfig( - name="wikifact:developer", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="developer", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_diplomatic_relation_helm = LightevalTaskConfig( - name="wikifact:diplomatic_relation", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="diplomatic_relation", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_director_helm = LightevalTaskConfig( - name="wikifact:director", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="director", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_discoverer_or_inventor_helm = LightevalTaskConfig( - name="wikifact:discoverer_or_inventor", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="discoverer_or_inventor", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_drug_or_therapy_used_for_treatment_helm = LightevalTaskConfig( - name="wikifact:drug_or_therapy_used_for_treatment", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="drug_or_therapy_used_for_treatment", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_educated_at_helm = LightevalTaskConfig( - name="wikifact:educated_at", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="educated_at", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_electron_configuration_helm = LightevalTaskConfig( - name="wikifact:electron_configuration", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="electron_configuration", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_employer_helm = LightevalTaskConfig( - name="wikifact:employer", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="employer", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_field_of_work_helm = LightevalTaskConfig( - name="wikifact:field_of_work", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="field_of_work", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_file_extension_helm = LightevalTaskConfig( - name="wikifact:file_extension", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="file_extension", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_genetic_association_helm = LightevalTaskConfig( - name="wikifact:genetic_association", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="genetic_association", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_genre_helm = LightevalTaskConfig( - name="wikifact:genre", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="genre", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_has_part_helm = LightevalTaskConfig( - name="wikifact:has_part", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="has_part", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_head_of_government_helm = LightevalTaskConfig( - name="wikifact:head_of_government", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="head_of_government", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_head_of_state_helm = LightevalTaskConfig( - name="wikifact:head_of_state", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="head_of_state", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_headquarters_location_helm = LightevalTaskConfig( - name="wikifact:headquarters_location", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="headquarters_location", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_industry_helm = LightevalTaskConfig( - name="wikifact:industry", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="industry", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_influenced_by_helm = LightevalTaskConfig( - name="wikifact:influenced_by", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="influenced_by", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_instance_of_helm = LightevalTaskConfig( - name="wikifact:instance_of", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="instance_of", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_instrument_helm = LightevalTaskConfig( - name="wikifact:instrument", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="instrument", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_language_of_work_or_name_helm = LightevalTaskConfig( - name="wikifact:language_of_work_or_name", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="language_of_work_or_name", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_languages_spoken_written_or_signed_helm = LightevalTaskConfig( - name="wikifact:languages_spoken_written_or_signed", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="languages_spoken_written_or_signed", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_laws_applied_helm = LightevalTaskConfig( - name="wikifact:laws_applied", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="laws_applied", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_located_in_the_administrative_territorial_entity_helm = LightevalTaskConfig( - name="wikifact:located_in_the_administrative_territorial_entity", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="located_in_the_administrative_territorial_entity", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_location_helm = LightevalTaskConfig( - name="wikifact:location", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="location", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_location_of_discovery_helm = LightevalTaskConfig( - name="wikifact:location_of_discovery", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="location_of_discovery", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_location_of_formation_helm = LightevalTaskConfig( - name="wikifact:location_of_formation", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="location_of_formation", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_majority_opinion_by_helm = LightevalTaskConfig( - name="wikifact:majority_opinion_by", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="majority_opinion_by", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_manufacturer_helm = LightevalTaskConfig( - name="wikifact:manufacturer", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="manufacturer", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_measured_physical_quantity_helm = LightevalTaskConfig( - name="wikifact:measured_physical_quantity", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="measured_physical_quantity", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_medical_condition_treated_helm = LightevalTaskConfig( - name="wikifact:medical_condition_treated", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="medical_condition_treated", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_member_of_helm = LightevalTaskConfig( - name="wikifact:member_of", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="member_of", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_member_of_political_party_helm = LightevalTaskConfig( - name="wikifact:member_of_political_party", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="member_of_political_party", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_member_of_sports_team_helm = LightevalTaskConfig( - name="wikifact:member_of_sports_team", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="member_of_sports_team", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_movement_helm = LightevalTaskConfig( - name="wikifact:movement", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="movement", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_named_after_helm = LightevalTaskConfig( - name="wikifact:named_after", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="named_after", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_native_language_helm = LightevalTaskConfig( - name="wikifact:native_language", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="native_language", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_number_of_processor_cores_helm = LightevalTaskConfig( - name="wikifact:number_of_processor_cores", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="number_of_processor_cores", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_occupation_helm = LightevalTaskConfig( - name="wikifact:occupation", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="occupation", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_office_held_by_head_of_government_helm = LightevalTaskConfig( - name="wikifact:office_held_by_head_of_government", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="office_held_by_head_of_government", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_office_held_by_head_of_state_helm = LightevalTaskConfig( - name="wikifact:office_held_by_head_of_state", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="office_held_by_head_of_state", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_official_language_helm = LightevalTaskConfig( - name="wikifact:official_language", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="official_language", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_operating_system_helm = LightevalTaskConfig( - name="wikifact:operating_system", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="operating_system", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_original_language_of_film_or_TV_show_helm = LightevalTaskConfig( - name="wikifact:original_language_of_film_or_TV_show", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="original_language_of_film_or_TV_show", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_original_network_helm = LightevalTaskConfig( - name="wikifact:original_network", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="original_network", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_overrules_helm = LightevalTaskConfig( - name="wikifact:overrules", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="overrules", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_owned_by_helm = LightevalTaskConfig( - name="wikifact:owned_by", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="owned_by", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_part_of_helm = LightevalTaskConfig( - name="wikifact:part_of", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="part_of", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_participating_team_helm = LightevalTaskConfig( - name="wikifact:participating_team", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="participating_team", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_place_of_birth_helm = LightevalTaskConfig( - name="wikifact:place_of_birth", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="place_of_birth", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_place_of_death_helm = LightevalTaskConfig( - name="wikifact:place_of_death", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="place_of_death", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_plaintiff_helm = LightevalTaskConfig( - name="wikifact:plaintiff", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="plaintiff", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_position_held_helm = LightevalTaskConfig( - name="wikifact:position_held", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="position_held", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_position_played_on_team_helm = LightevalTaskConfig( - name="wikifact:position_played_on_team", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="position_played_on_team", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_programming_language_helm = LightevalTaskConfig( - name="wikifact:programming_language", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="programming_language", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_recommended_unit_of_measurement_helm = LightevalTaskConfig( - name="wikifact:recommended_unit_of_measurement", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="recommended_unit_of_measurement", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_record_label_helm = LightevalTaskConfig( - name="wikifact:record_label", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="record_label", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_religion_helm = LightevalTaskConfig( - name="wikifact:religion", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="religion", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_repealed_by_helm = LightevalTaskConfig( - name="wikifact:repealed_by", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="repealed_by", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_shares_border_with_helm = LightevalTaskConfig( - name="wikifact:shares_border_with", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="shares_border_with", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_solved_by_helm = LightevalTaskConfig( - name="wikifact:solved_by", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="solved_by", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_statement_describes_helm = LightevalTaskConfig( - name="wikifact:statement_describes", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="statement_describes", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_stock_exchange_helm = LightevalTaskConfig( - name="wikifact:stock_exchange", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="stock_exchange", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_subclass_of_helm = LightevalTaskConfig( - name="wikifact:subclass_of", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="subclass_of", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_subsidiary_helm = LightevalTaskConfig( - name="wikifact:subsidiary", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="subsidiary", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_symptoms_and_signs_helm = LightevalTaskConfig( - name="wikifact:symptoms_and_signs", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="symptoms_and_signs", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_therapeutic_area_helm = LightevalTaskConfig( - name="wikifact:therapeutic_area", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="therapeutic_area", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_time_of_discovery_or_invention_helm = LightevalTaskConfig( - name="wikifact:time_of_discovery_or_invention", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="time_of_discovery_or_invention", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_twinned_administrative_body_helm = LightevalTaskConfig( - name="wikifact:twinned_administrative_body", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="twinned_administrative_body", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikifact_work_location_helm = LightevalTaskConfig( - name="wikifact:work_location", - suite=["helm"], - prompt_function=prompt.wikifact, - hf_repo="lighteval/wikifact", - hf_subset="work_location", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], - stop_sequence=["\n"], - version=0, -) -wikitext_2_lighteval = LightevalTaskConfig( - name="wikitext:2", - suite=["lighteval"], - prompt_function=prompt.wikitext, - hf_repo="wikitext", - hf_subset="wikitext-2-raw-v1", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -wikitext_103_document_level_harness = LightevalTaskConfig( - name="wikitext:103:document_level", - suite=["harness"], - prompt_function=prompt.wikitext_harness, - hf_repo="EleutherAI/wikitext_document_level", - hf_subset="wikitext-103-raw-v1", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -wikitext_103_document_level_helm = LightevalTaskConfig( - name="wikitext:103:document_level", - suite=["helm"], - prompt_function=prompt.wikitext_helm, - hf_repo="EleutherAI/wikitext_document_level", - hf_subset="wikitext-103-raw-v1", - hf_avail_splits=["train", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], - stop_sequence=["\n"], - version=0, -) -wino_x_german_bigbench = LightevalTaskConfig( - name="wino_x_german", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="wino_x_german", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -winogrande_leaderboard = LightevalTaskConfig( - name="winogrande", - suite=["leaderboard"], - prompt_function=prompt.winogrande, - hf_repo="winogrande", - hf_subset="winogrande_xl", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -winowhy_bigbench_lite = LightevalTaskConfig( - name="winowhy", - suite=["bigbench_lite", "bigbench", "bigbench_json"], - prompt_function=prompt.bigbench_whitespace_after_query, - hf_repo="tasksource/bigbench", - hf_subset="winowhy", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -wmt08_cs_en_lighteval = LightevalTaskConfig( - name="wmt08:cs-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt08_cs-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt08_de_en_lighteval = LightevalTaskConfig( - name="wmt08:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt08_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt08_en_cs_lighteval = LightevalTaskConfig( - name="wmt08:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt08_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt08_en_de_lighteval = LightevalTaskConfig( - name="wmt08:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt08_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt08_en_es_lighteval = LightevalTaskConfig( - name="wmt08:en-es", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt08_en-es", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt08_en_fr_lighteval = LightevalTaskConfig( - name="wmt08:en-fr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt08_en-fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt08_en_hu_lighteval = LightevalTaskConfig( - name="wmt08:en-hu", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt08_en-hu", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt08_es_en_lighteval = LightevalTaskConfig( - name="wmt08:es-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt08_es-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt08_fr_en_lighteval = LightevalTaskConfig( - name="wmt08:fr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt08_fr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt08_hu_en_lighteval = LightevalTaskConfig( - name="wmt08:hu-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt08_hu-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt09_cs_en_lighteval = LightevalTaskConfig( - name="wmt09:cs-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt09_cs-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt09_de_en_lighteval = LightevalTaskConfig( - name="wmt09:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt09_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt09_en_cs_lighteval = LightevalTaskConfig( - name="wmt09:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt09_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt09_en_de_lighteval = LightevalTaskConfig( - name="wmt09:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt09_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt09_en_es_lighteval = LightevalTaskConfig( - name="wmt09:en-es", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt09_en-es", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt09_en_fr_lighteval = LightevalTaskConfig( - name="wmt09:en-fr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt09_en-fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt09_en_hu_lighteval = LightevalTaskConfig( - name="wmt09:en-hu", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt09_en-hu", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt09_en_it_lighteval = LightevalTaskConfig( - name="wmt09:en-it", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt09_en-it", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt09_es_en_lighteval = LightevalTaskConfig( - name="wmt09:es-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt09_es-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt09_fr_en_lighteval = LightevalTaskConfig( - name="wmt09:fr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt09_fr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt09_hu_en_lighteval = LightevalTaskConfig( - name="wmt09:hu-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt09_hu-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt09_it_en_lighteval = LightevalTaskConfig( - name="wmt09:it-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt09_it-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt10_cs_en_lighteval = LightevalTaskConfig( - name="wmt10:cs-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt10_cs-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt10_de_en_lighteval = LightevalTaskConfig( - name="wmt10:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt10_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt10_en_cs_lighteval = LightevalTaskConfig( - name="wmt10:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt10_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt10_en_de_lighteval = LightevalTaskConfig( - name="wmt10:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt10_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt10_en_es_lighteval = LightevalTaskConfig( - name="wmt10:en-es", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt10_en-es", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt10_en_fr_lighteval = LightevalTaskConfig( - name="wmt10:en-fr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt10_en-fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt10_es_en_lighteval = LightevalTaskConfig( - name="wmt10:es-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt10_es-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt10_fr_en_lighteval = LightevalTaskConfig( - name="wmt10:fr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt10_fr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt11_cs_en_lighteval = LightevalTaskConfig( - name="wmt11:cs-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt11_cs-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt11_de_en_lighteval = LightevalTaskConfig( - name="wmt11:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt11_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt11_en_cs_lighteval = LightevalTaskConfig( - name="wmt11:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt11_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt11_en_de_lighteval = LightevalTaskConfig( - name="wmt11:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt11_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt11_en_es_lighteval = LightevalTaskConfig( - name="wmt11:en-es", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt11_en-es", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt11_en_fr_lighteval = LightevalTaskConfig( - name="wmt11:en-fr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt11_en-fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt11_es_en_lighteval = LightevalTaskConfig( - name="wmt11:es-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt11_es-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt11_fr_en_lighteval = LightevalTaskConfig( - name="wmt11:fr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt11_fr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt12_cs_en_lighteval = LightevalTaskConfig( - name="wmt12:cs-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt12_cs-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt12_de_en_lighteval = LightevalTaskConfig( - name="wmt12:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt12_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt12_en_cs_lighteval = LightevalTaskConfig( - name="wmt12:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt12_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt12_en_de_lighteval = LightevalTaskConfig( - name="wmt12:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt12_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt12_en_es_lighteval = LightevalTaskConfig( - name="wmt12:en-es", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt12_en-es", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt12_en_fr_lighteval = LightevalTaskConfig( - name="wmt12:en-fr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt12_en-fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt12_es_en_lighteval = LightevalTaskConfig( - name="wmt12:es-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt12_es-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt12_fr_en_lighteval = LightevalTaskConfig( - name="wmt12:fr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt12_fr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt13_cs_en_lighteval = LightevalTaskConfig( - name="wmt13:cs-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt13_cs-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt13_de_en_lighteval = LightevalTaskConfig( - name="wmt13:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt13_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt13_en_cs_lighteval = LightevalTaskConfig( - name="wmt13:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt13_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt13_en_de_lighteval = LightevalTaskConfig( - name="wmt13:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt13_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt13_en_es_lighteval = LightevalTaskConfig( - name="wmt13:en-es", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt13_en-es", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt13_en_fr_lighteval = LightevalTaskConfig( - name="wmt13:en-fr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt13_en-fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt13_en_ru_lighteval = LightevalTaskConfig( - name="wmt13:en-ru", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt13_en-ru", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt13_es_en_lighteval = LightevalTaskConfig( - name="wmt13:es-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt13_es-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt13_fr_en_lighteval = LightevalTaskConfig( - name="wmt13:fr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt13_fr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt13_ru_en_lighteval = LightevalTaskConfig( - name="wmt13:ru-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt13_ru-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_cs_en_lighteval = LightevalTaskConfig( - name="wmt14:cs-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt14_cs-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_de_en_lighteval = LightevalTaskConfig( - name="wmt14:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt14_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_en_cs_lighteval = LightevalTaskConfig( - name="wmt14:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt14_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_en_de_lighteval = LightevalTaskConfig( - name="wmt14:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt14_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_en_fr_lighteval = LightevalTaskConfig( - name="wmt14:en-fr", - suite=["lighteval", "gpt3_benchmarks"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="wmt14", - hf_subset="fr-en", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_en_fr_lighteval = LightevalTaskConfig( - name="wmt14:en-fr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt14_en-fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_en_hi_lighteval = LightevalTaskConfig( - name="wmt14:en-hi", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt14_en-hi", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_en_ru_lighteval = LightevalTaskConfig( - name="wmt14:en-ru", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt14_en-ru", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_fr_en_lighteval = LightevalTaskConfig( - name="wmt14:fr-en", - suite=["lighteval", "gpt3_benchmarks"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="wmt14", - hf_subset="fr-en", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_fr_en_lighteval = LightevalTaskConfig( - name="wmt14:fr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt14_fr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_hi_en_lighteval = LightevalTaskConfig( - name="wmt14:hi-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt14_hi-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_ru_en_lighteval = LightevalTaskConfig( - name="wmt14:ru-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt14_ru-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt14_cs_en_helm = LightevalTaskConfig( - name="wmt14:cs-en", - suite=["helm"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/wmt_14", - hf_subset="cs-en", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.bleu], - stop_sequence=["\n"], - version=0, -) -wmt14_de_en_helm = LightevalTaskConfig( - name="wmt14:de-en", - suite=["helm"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/wmt_14", - hf_subset="de-en", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.bleu], - stop_sequence=["\n"], - version=0, -) -wmt14_fr_en_helm = LightevalTaskConfig( - name="wmt14:fr-en", - suite=["helm"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/wmt_14", - hf_subset="fr-en", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.bleu], - stop_sequence=["\n"], - version=0, -) -wmt14_hi_en_helm = LightevalTaskConfig( - name="wmt14:hi-en", - suite=["helm"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/wmt_14", - hf_subset="hi-en", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.bleu], - stop_sequence=["\n"], - version=0, -) -wmt14_ru_en_helm = LightevalTaskConfig( - name="wmt14:ru-en", - suite=["helm"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/wmt_14", - hf_subset="ru-en", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=100, - metrics=[Metrics.bleu], - stop_sequence=["\n"], - version=0, -) -wmt15_cs_en_lighteval = LightevalTaskConfig( - name="wmt15:cs-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt15_cs-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt15_de_en_lighteval = LightevalTaskConfig( - name="wmt15:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt15_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt15_en_cs_lighteval = LightevalTaskConfig( - name="wmt15:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt15_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt15_en_de_lighteval = LightevalTaskConfig( - name="wmt15:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt15_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt15_en_fi_lighteval = LightevalTaskConfig( - name="wmt15:en-fi", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt15_en-fi", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt15_en_fr_lighteval = LightevalTaskConfig( - name="wmt15:en-fr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt15_en-fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt15_en_ru_lighteval = LightevalTaskConfig( - name="wmt15:en-ru", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt15_en-ru", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt15_fi_en_lighteval = LightevalTaskConfig( - name="wmt15:fi-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt15_fi-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt15_fr_en_lighteval = LightevalTaskConfig( - name="wmt15:fr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt15_fr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt15_ru_en_lighteval = LightevalTaskConfig( - name="wmt15:ru-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt15_ru-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_cs_en_lighteval = LightevalTaskConfig( - name="wmt16:cs-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt16_cs-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_de_en_lighteval = LightevalTaskConfig( - name="wmt16:de-en", - suite=["lighteval", "gpt3_benchmarks"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="wmt16", - hf_subset="de-en", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_de_en_lighteval = LightevalTaskConfig( - name="wmt16:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt16_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_en_cs_lighteval = LightevalTaskConfig( - name="wmt16:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt16_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_en_de_lighteval = LightevalTaskConfig( - name="wmt16:en-de", - suite=["lighteval", "gpt3_benchmarks"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="wmt16", - hf_subset="de-en", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_en_de_lighteval = LightevalTaskConfig( - name="wmt16:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt16_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_en_fi_lighteval = LightevalTaskConfig( - name="wmt16:en-fi", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt16_en-fi", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_en_ro_lighteval = LightevalTaskConfig( - name="wmt16:en-ro", - suite=["lighteval", "gpt3_benchmarks"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="wmt16", - hf_subset="ro-en", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_en_ro_lighteval = LightevalTaskConfig( - name="wmt16:en-ro", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt16_en-ro", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_en_ru_lighteval = LightevalTaskConfig( - name="wmt16:en-ru", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt16_en-ru", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_en_tr_lighteval = LightevalTaskConfig( - name="wmt16:en-tr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt16_en-tr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_fi_en_lighteval = LightevalTaskConfig( - name="wmt16:fi-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt16_fi-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_ro_en_lighteval = LightevalTaskConfig( - name="wmt16:ro-en", - suite=["lighteval", "gpt3_benchmarks"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="wmt16", - hf_subset="ro-en", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_ro_en_lighteval = LightevalTaskConfig( - name="wmt16:ro-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt16_ro-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_ru_en_lighteval = LightevalTaskConfig( - name="wmt16:ru-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt16_ru-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt16_tr_en_lighteval = LightevalTaskConfig( - name="wmt16:tr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt16_tr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_cs_en_lighteval = LightevalTaskConfig( - name="wmt17:cs-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_cs-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_de_en_lighteval = LightevalTaskConfig( - name="wmt17:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_en_cs_lighteval = LightevalTaskConfig( - name="wmt17:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_en_de_lighteval = LightevalTaskConfig( - name="wmt17:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_en_fi_lighteval = LightevalTaskConfig( - name="wmt17:en-fi", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_en-fi", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_en_lv_lighteval = LightevalTaskConfig( - name="wmt17:en-lv", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_en-lv", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_en_ru_lighteval = LightevalTaskConfig( - name="wmt17:en-ru", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_en-ru", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_en_tr_lighteval = LightevalTaskConfig( - name="wmt17:en-tr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_en-tr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_en_zh_lighteval = LightevalTaskConfig( - name="wmt17:en-zh", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_en-zh", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_fi_en_lighteval = LightevalTaskConfig( - name="wmt17:fi-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_fi-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_lv_en_lighteval = LightevalTaskConfig( - name="wmt17:lv-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_lv-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_ru_en_lighteval = LightevalTaskConfig( - name="wmt17:ru-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_ru-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_tr_en_lighteval = LightevalTaskConfig( - name="wmt17:tr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_tr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt17_zh_en_lighteval = LightevalTaskConfig( - name="wmt17:zh-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt17_zh-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_cs_en_lighteval = LightevalTaskConfig( - name="wmt18:cs-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_cs-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_de_en_lighteval = LightevalTaskConfig( - name="wmt18:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_en_cs_lighteval = LightevalTaskConfig( - name="wmt18:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_en_de_lighteval = LightevalTaskConfig( - name="wmt18:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_en_et_lighteval = LightevalTaskConfig( - name="wmt18:en-et", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_en-et", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_en_fi_lighteval = LightevalTaskConfig( - name="wmt18:en-fi", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_en-fi", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_en_ru_lighteval = LightevalTaskConfig( - name="wmt18:en-ru", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_en-ru", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_en_tr_lighteval = LightevalTaskConfig( - name="wmt18:en-tr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_en-tr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_en_zh_lighteval = LightevalTaskConfig( - name="wmt18:en-zh", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_en-zh", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_et_en_lighteval = LightevalTaskConfig( - name="wmt18:et-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_et-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_fi_en_lighteval = LightevalTaskConfig( - name="wmt18:fi-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_fi-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_ru_en_lighteval = LightevalTaskConfig( - name="wmt18:ru-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_ru-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_tr_en_lighteval = LightevalTaskConfig( - name="wmt18:tr-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_tr-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt18_zh_en_lighteval = LightevalTaskConfig( - name="wmt18:zh-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt18_zh-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_cs_de_lighteval = LightevalTaskConfig( - name="wmt19:cs-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_cs-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_de_cs_lighteval = LightevalTaskConfig( - name="wmt19:de-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_de-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_de_en_lighteval = LightevalTaskConfig( - name="wmt19:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_de_fr_lighteval = LightevalTaskConfig( - name="wmt19:de-fr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_de-fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_en_cs_lighteval = LightevalTaskConfig( - name="wmt19:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_en_de_lighteval = LightevalTaskConfig( - name="wmt19:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_en_fi_lighteval = LightevalTaskConfig( - name="wmt19:en-fi", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_en-fi", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_en_gu_lighteval = LightevalTaskConfig( - name="wmt19:en-gu", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_en-gu", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_en_kk_lighteval = LightevalTaskConfig( - name="wmt19:en-kk", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_en-kk", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_en_lt_lighteval = LightevalTaskConfig( - name="wmt19:en-lt", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_en-lt", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_en_ru_lighteval = LightevalTaskConfig( - name="wmt19:en-ru", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_en-ru", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_en_zh_lighteval = LightevalTaskConfig( - name="wmt19:en-zh", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_en-zh", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_fi_en_lighteval = LightevalTaskConfig( - name="wmt19:fi-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_fi-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_fr_de_lighteval = LightevalTaskConfig( - name="wmt19:fr-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_fr-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_gu_en_lighteval = LightevalTaskConfig( - name="wmt19:gu-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_gu-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_kk_en_lighteval = LightevalTaskConfig( - name="wmt19:kk-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_kk-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_lt_en_lighteval = LightevalTaskConfig( - name="wmt19:lt-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_lt-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_ru_en_lighteval = LightevalTaskConfig( - name="wmt19:ru-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_ru-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt19_zh_en_lighteval = LightevalTaskConfig( - name="wmt19:zh-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt19_zh-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_cs_en_lighteval = LightevalTaskConfig( - name="wmt20:cs-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_cs-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_de_en_lighteval = LightevalTaskConfig( - name="wmt20:de-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_de-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_de_fr_lighteval = LightevalTaskConfig( - name="wmt20:de-fr", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_de-fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_en_cs_lighteval = LightevalTaskConfig( - name="wmt20:en-cs", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_en-cs", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_en_de_lighteval = LightevalTaskConfig( - name="wmt20:en-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_en-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_en_iu_lighteval = LightevalTaskConfig( - name="wmt20:en-iu", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_en-iu", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_en_ja_lighteval = LightevalTaskConfig( - name="wmt20:en-ja", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_en-ja", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_en_km_lighteval = LightevalTaskConfig( - name="wmt20:en-km", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_en-km", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_en_pl_lighteval = LightevalTaskConfig( - name="wmt20:en-pl", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_en-pl", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_en_ps_lighteval = LightevalTaskConfig( - name="wmt20:en-ps", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_en-ps", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_en_ru_lighteval = LightevalTaskConfig( - name="wmt20:en-ru", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_en-ru", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_en_ta_lighteval = LightevalTaskConfig( - name="wmt20:en-ta", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_en-ta", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_en_zh_lighteval = LightevalTaskConfig( - name="wmt20:en-zh", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_en-zh", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_fr_de_lighteval = LightevalTaskConfig( - name="wmt20:fr-de", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_fr-de", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_iu_en_lighteval = LightevalTaskConfig( - name="wmt20:iu-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_iu-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_ja_en_lighteval = LightevalTaskConfig( - name="wmt20:ja-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_ja-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_km_en_lighteval = LightevalTaskConfig( - name="wmt20:km-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_km-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_pl_en_lighteval = LightevalTaskConfig( - name="wmt20:pl-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_pl-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_ps_en_lighteval = LightevalTaskConfig( - name="wmt20:ps-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_ps-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_ru_en_lighteval = LightevalTaskConfig( - name="wmt20:ru-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_ru-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_ta_en_lighteval = LightevalTaskConfig( - name="wmt20:ta-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_ta-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -wmt20_zh_en_lighteval = LightevalTaskConfig( - name="wmt20:zh-en", - suite=["lighteval", "sacrebleu"], - prompt_function=prompt.wmt_reverse_alphabetical, - hf_repo="lighteval/sacrebleu_manual", - hf_subset="wmt20_zh-en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=None, - metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], - stop_sequence=["\n"], - version=0, -) -word_sorting_bigbench = LightevalTaskConfig( - name="word_sorting", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="word_sorting", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -word_unscrambling_bigbench = LightevalTaskConfig( - name="word_unscrambling", - suite=["bigbench", "bigbench_json"], - prompt_function=prompt.bigbench, - hf_repo="tasksource/bigbench", - hf_subset="word_unscrambling", - hf_avail_splits=["default", "train", "validation"], - evaluation_splits=["default"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], - stop_sequence=["\n"], - version=0, -) -wsc273_lighteval = LightevalTaskConfig( - name="wsc273", - suite=["lighteval"], - prompt_function=prompt.wsc273, - hf_repo="lighteval/winograd_wsc", - hf_subset="wsc273", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xcopa_en_lighteval = LightevalTaskConfig( - name="xcopa:en", - suite=["lighteval"], - prompt_function=prompt.xcopa_en, - hf_repo="cambridgeltl/xcopa", - hf_subset="default", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xcopa_et_lighteval = LightevalTaskConfig( - name="xcopa:et", - suite=["lighteval"], - prompt_function=prompt.xcopa_et, - hf_repo="cambridgeltl/xcopa", - hf_subset="et", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xcopa_ht_lighteval = LightevalTaskConfig( - name="xcopa:ht", - suite=["lighteval"], - prompt_function=prompt.xcopa_ht, - hf_repo="cambridgeltl/xcopa", - hf_subset="ht", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xcopa_it_lighteval = LightevalTaskConfig( - name="xcopa:it", - suite=["lighteval"], - prompt_function=prompt.xcopa_it, - hf_repo="cambridgeltl/xcopa", - hf_subset="it", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xcopa_id_lighteval = LightevalTaskConfig( - name="xcopa:id", - suite=["lighteval"], - prompt_function=prompt.xcopa_id, - hf_repo="cambridgeltl/xcopa", - hf_subset="id", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xcopa_qu_lighteval = LightevalTaskConfig( - name="xcopa:qu", - suite=["lighteval"], - prompt_function=prompt.xcopa_qu, - hf_repo="cambridgeltl/xcopa", - hf_subset="qu", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xcopa_sw_lighteval = LightevalTaskConfig( - name="xcopa:sw", - suite=["lighteval"], - prompt_function=prompt.xcopa_sw, - hf_repo="cambridgeltl/xcopa", - hf_subset="sw", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xcopa_zh_lighteval = LightevalTaskConfig( - name="xcopa:zh", - suite=["lighteval"], - prompt_function=prompt.xcopa_zh, - hf_repo="cambridgeltl/xcopa", - hf_subset="zh", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xcopa_ta_lighteval = LightevalTaskConfig( - name="xcopa:ta", - suite=["lighteval"], - prompt_function=prompt.xcopa_ta, - hf_repo="cambridgeltl/xcopa", - hf_subset="ta", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xcopa_th_lighteval = LightevalTaskConfig( - name="xcopa:th", - suite=["lighteval"], - prompt_function=prompt.xcopa_th, - hf_repo="cambridgeltl/xcopa", - hf_subset="th", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xcopa_tr_lighteval = LightevalTaskConfig( - name="xcopa:tr", - suite=["lighteval"], - prompt_function=prompt.xcopa_tr, - hf_repo="cambridgeltl/xcopa", - hf_subset="tr", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xcopa_vi_lighteval = LightevalTaskConfig( - name="xcopa:vi", - suite=["lighteval"], - prompt_function=prompt.xcopa_vi, - hf_repo="cambridgeltl/xcopa", - hf_subset="vi", - hf_avail_splits=["test", "train", "validation"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xstory_cloze_en_lighteval = LightevalTaskConfig( - name="xstory_cloze:en", - suite=["lighteval"], - prompt_function=prompt.storycloze, - hf_repo="juletxara/xstory_cloze", - hf_subset="en", - hf_avail_splits=["training", "eval"], - evaluation_splits=["eval"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xstory_cloze_ru_lighteval = LightevalTaskConfig( - name="xstory_cloze:ru", - suite=["lighteval"], - prompt_function=prompt.storycloze, - hf_repo="juletxara/xstory_cloze", - hf_subset="ru", - hf_avail_splits=["training", "eval"], - evaluation_splits=["eval"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xstory_cloze_zh_lighteval = LightevalTaskConfig( - name="xstory_cloze:zh", - suite=["lighteval"], - prompt_function=prompt.storycloze, - hf_repo="juletxara/xstory_cloze", - hf_subset="zh", - hf_avail_splits=["training", "eval"], - evaluation_splits=["eval"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xstory_cloze_es_lighteval = LightevalTaskConfig( - name="xstory_cloze:es", - suite=["lighteval"], - prompt_function=prompt.storycloze, - hf_repo="juletxara/xstory_cloze", - hf_subset="es", - hf_avail_splits=["training", "eval"], - evaluation_splits=["eval"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xstory_cloze_ar_lighteval = LightevalTaskConfig( - name="xstory_cloze:ar", - suite=["lighteval"], - prompt_function=prompt.storycloze, - hf_repo="juletxara/xstory_cloze", - hf_subset="ar", - hf_avail_splits=["training", "eval"], - evaluation_splits=["eval"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xstory_cloze_hi_lighteval = LightevalTaskConfig( - name="xstory_cloze:hi", - suite=["lighteval"], - prompt_function=prompt.storycloze, - hf_repo="juletxara/xstory_cloze", - hf_subset="hi", - hf_avail_splits=["training", "eval"], - evaluation_splits=["eval"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xstory_cloze_id_lighteval = LightevalTaskConfig( - name="xstory_cloze:id", - suite=["lighteval"], - prompt_function=prompt.storycloze, - hf_repo="juletxara/xstory_cloze", - hf_subset="id", - hf_avail_splits=["training", "eval"], - evaluation_splits=["eval"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xstory_cloze_te_lighteval = LightevalTaskConfig( - name="xstory_cloze:te", - suite=["lighteval"], - prompt_function=prompt.storycloze, - hf_repo="juletxara/xstory_cloze", - hf_subset="te", - hf_avail_splits=["training", "eval"], - evaluation_splits=["eval"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xstory_cloze_sw_lighteval = LightevalTaskConfig( - name="xstory_cloze:sw", - suite=["lighteval"], - prompt_function=prompt.storycloze, - hf_repo="juletxara/xstory_cloze", - hf_subset="sw", - hf_avail_splits=["training", "eval"], - evaluation_splits=["eval"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xstory_cloze_eu_lighteval = LightevalTaskConfig( - name="xstory_cloze:eu", - suite=["lighteval"], - prompt_function=prompt.storycloze, - hf_repo="juletxara/xstory_cloze", - hf_subset="eu", - hf_avail_splits=["training", "eval"], - evaluation_splits=["eval"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xstory_cloze_my_lighteval = LightevalTaskConfig( - name="xstory_cloze:my", - suite=["lighteval"], - prompt_function=prompt.storycloze, - hf_repo="juletxara/xstory_cloze", - hf_subset="my", - hf_avail_splits=["training", "eval"], - evaluation_splits=["eval"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xwinograd_en_lighteval = LightevalTaskConfig( - name="xwinograd:en", - suite=["lighteval"], - prompt_function=prompt.winogrande, - hf_repo="Muennighoff/xwinograd", - hf_subset="en", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xwinograd_fr_lighteval = LightevalTaskConfig( - name="xwinograd:fr", - suite=["lighteval"], - prompt_function=prompt.winogrande, - hf_repo="Muennighoff/xwinograd", - hf_subset="fr", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xwinograd_jp_lighteval = LightevalTaskConfig( - name="xwinograd:jp", - suite=["lighteval"], - prompt_function=prompt.winogrande, - hf_repo="Muennighoff/xwinograd", - hf_subset="jp", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xwinograd_pt_lighteval = LightevalTaskConfig( - name="xwinograd:pt", - suite=["lighteval"], - prompt_function=prompt.winogrande, - hf_repo="Muennighoff/xwinograd", - hf_subset="pt", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xwinograd_ru_lighteval = LightevalTaskConfig( - name="xwinograd:ru", - suite=["lighteval"], - prompt_function=prompt.winogrande, - hf_repo="Muennighoff/xwinograd", - hf_subset="ru", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) -xwinograd_zh_lighteval = LightevalTaskConfig( - name="xwinograd:zh", - suite=["lighteval"], - prompt_function=prompt.winogrande, - hf_repo="Muennighoff/xwinograd", - hf_subset="zh", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) - -# MMLU-Redux-2 Tasks -_MMLU_REDUX_2_SUBSETS = [ - "abstract_algebra", - "anatomy", - "astronomy", - "business_ethics", - "clinical_knowledge", - "college_biology", - "college_chemistry", - "college_computer_science", - "college_mathematics", - "college_medicine", - "college_physics", - "computer_security", - "conceptual_physics", - "econometrics", - "electrical_engineering", - "elementary_mathematics", - "formal_logic", - "global_facts", - "high_school_biology", - "high_school_chemistry", - "high_school_computer_science", - "high_school_european_history", - "high_school_geography", - "high_school_government_and_politics", - "high_school_macroeconomics", - "high_school_mathematics", - "high_school_microeconomics", - "high_school_physics", - "high_school_psychology", - "high_school_statistics", - "high_school_us_history", - "high_school_world_history", - "human_aging", - "human_sexuality", - "international_law", - "jurisprudence", - "logical_fallacies", - "machine_learning", - "management", - "marketing", - "medical_genetics", - "miscellaneous", - "moral_disputes", - "moral_scenarios", - "nutrition", - "philosophy", - "prehistory", - "professional_accounting", - "professional_law", - "professional_medicine", - "professional_psychology", - "public_relations", - "security_studies", - "sociology", - "us_foreign_policy", - "virology", - "world_religions", -] - - -_mmlu_redux_2_tasks = { - subset: LightevalTaskConfig( - name=f"mmlu_redux_2:{subset}", - suite=["lighteval"], - prompt_function=lambda line, task_name=None, s=subset: prompt.mmlu_redux_2(line, s, task_name), - hf_repo="edinburgh-dawg/mmlu-redux-2.0", - hf_subset=subset, - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=1, - metrics=[ - Metrics.loglikelihood_acc, - Metrics.pass_at_k_letters(sample_params={"k": 1}), - ], - stop_sequence=["\n"], - version=0, - ) - for subset in _MMLU_REDUX_2_SUBSETS -} - -mmlu_redux_2_abstract_algebra = _mmlu_redux_2_tasks["abstract_algebra"] -mmlu_redux_2_anatomy = _mmlu_redux_2_tasks["anatomy"] -mmlu_redux_2_astronomy = _mmlu_redux_2_tasks["astronomy"] -mmlu_redux_2_business_ethics = _mmlu_redux_2_tasks["business_ethics"] -mmlu_redux_2_clinical_knowledge = _mmlu_redux_2_tasks["clinical_knowledge"] -mmlu_redux_2_college_biology = _mmlu_redux_2_tasks["college_biology"] -mmlu_redux_2_college_chemistry = _mmlu_redux_2_tasks["college_chemistry"] -mmlu_redux_2_college_computer_science = _mmlu_redux_2_tasks["college_computer_science"] -mmlu_redux_2_college_mathematics = _mmlu_redux_2_tasks["college_mathematics"] -mmlu_redux_2_college_medicine = _mmlu_redux_2_tasks["college_medicine"] -mmlu_redux_2_college_physics = _mmlu_redux_2_tasks["college_physics"] -mmlu_redux_2_computer_security = _mmlu_redux_2_tasks["computer_security"] -mmlu_redux_2_conceptual_physics = _mmlu_redux_2_tasks["conceptual_physics"] -mmlu_redux_2_econometrics = _mmlu_redux_2_tasks["econometrics"] -mmlu_redux_2_electrical_engineering = _mmlu_redux_2_tasks["electrical_engineering"] -mmlu_redux_2_elementary_mathematics = _mmlu_redux_2_tasks["elementary_mathematics"] -mmlu_redux_2_formal_logic = _mmlu_redux_2_tasks["formal_logic"] -mmlu_redux_2_global_facts = _mmlu_redux_2_tasks["global_facts"] -mmlu_redux_2_high_school_biology = _mmlu_redux_2_tasks["high_school_biology"] -mmlu_redux_2_high_school_chemistry = _mmlu_redux_2_tasks["high_school_chemistry"] -mmlu_redux_2_high_school_computer_science = _mmlu_redux_2_tasks["high_school_computer_science"] -mmlu_redux_2_high_school_european_history = _mmlu_redux_2_tasks["high_school_european_history"] -mmlu_redux_2_high_school_geography = _mmlu_redux_2_tasks["high_school_geography"] -mmlu_redux_2_high_school_government_and_politics = _mmlu_redux_2_tasks["high_school_government_and_politics"] -mmlu_redux_2_high_school_macroeconomics = _mmlu_redux_2_tasks["high_school_macroeconomics"] -mmlu_redux_2_high_school_mathematics = _mmlu_redux_2_tasks["high_school_mathematics"] -mmlu_redux_2_high_school_microeconomics = _mmlu_redux_2_tasks["high_school_microeconomics"] -mmlu_redux_2_high_school_physics = _mmlu_redux_2_tasks["high_school_physics"] -mmlu_redux_2_high_school_psychology = _mmlu_redux_2_tasks["high_school_psychology"] -mmlu_redux_2_high_school_statistics = _mmlu_redux_2_tasks["high_school_statistics"] -mmlu_redux_2_high_school_us_history = _mmlu_redux_2_tasks["high_school_us_history"] -mmlu_redux_2_high_school_world_history = _mmlu_redux_2_tasks["high_school_world_history"] -mmlu_redux_2_human_aging = _mmlu_redux_2_tasks["human_aging"] -mmlu_redux_2_human_sexuality = _mmlu_redux_2_tasks["human_sexuality"] -mmlu_redux_2_international_law = _mmlu_redux_2_tasks["international_law"] -mmlu_redux_2_jurisprudence = _mmlu_redux_2_tasks["jurisprudence"] -mmlu_redux_2_logical_fallacies = _mmlu_redux_2_tasks["logical_fallacies"] -mmlu_redux_2_machine_learning = _mmlu_redux_2_tasks["machine_learning"] -mmlu_redux_2_management = _mmlu_redux_2_tasks["management"] -mmlu_redux_2_marketing = _mmlu_redux_2_tasks["marketing"] -mmlu_redux_2_medical_genetics = _mmlu_redux_2_tasks["medical_genetics"] -mmlu_redux_2_miscellaneous = _mmlu_redux_2_tasks["miscellaneous"] -mmlu_redux_2_moral_disputes = _mmlu_redux_2_tasks["moral_disputes"] -mmlu_redux_2_moral_scenarios = _mmlu_redux_2_tasks["moral_scenarios"] -mmlu_redux_2_nutrition = _mmlu_redux_2_tasks["nutrition"] -mmlu_redux_2_philosophy = _mmlu_redux_2_tasks["philosophy"] -mmlu_redux_2_prehistory = _mmlu_redux_2_tasks["prehistory"] -mmlu_redux_2_professional_accounting = _mmlu_redux_2_tasks["professional_accounting"] -mmlu_redux_2_professional_law = _mmlu_redux_2_tasks["professional_law"] -mmlu_redux_2_professional_medicine = _mmlu_redux_2_tasks["professional_medicine"] -mmlu_redux_2_professional_psychology = _mmlu_redux_2_tasks["professional_psychology"] -mmlu_redux_2_public_relations = _mmlu_redux_2_tasks["public_relations"] -mmlu_redux_2_security_studies = _mmlu_redux_2_tasks["security_studies"] -mmlu_redux_2_sociology = _mmlu_redux_2_tasks["sociology"] -mmlu_redux_2_us_foreign_policy = _mmlu_redux_2_tasks["us_foreign_policy"] -mmlu_redux_2_virology = _mmlu_redux_2_tasks["virology"] -mmlu_redux_2_world_religions = _mmlu_redux_2_tasks["world_religions"] diff --git a/src/lighteval/tasks/lighteval_task.py b/src/lighteval/tasks/lighteval_task.py index 989f2192c..af16df64e 100644 --- a/src/lighteval/tasks/lighteval_task.py +++ b/src/lighteval/tasks/lighteval_task.py @@ -58,7 +58,8 @@ class LightevalTaskConfig_inspect: dataset_repo: str dataset_subset: str dataset_split: str - metrics: list + scorers: list + solvers: list | None = None system_prompt: str | None = None dataset_revision: str | None = None epochs: int = 1 diff --git a/src/lighteval/tasks/multilingual/tasks.py b/src/lighteval/tasks/multilingual/tasks.py deleted file mode 100644 index 5d6c107bc..000000000 --- a/src/lighteval/tasks/multilingual/tasks.py +++ /dev/null @@ -1,4368 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag - -from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, -) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes - - -TASKS_TABLE = [] -# ------------------------------- NLI Tasks ------------------------------- # -# NLI (Natural Language Inference) tasks involve determining the logical relationship -# between two given sentences: a premise and a hypothesis. The goal is to classify -# whether the hypothesis is entailed by, contradicts, or is neutral with respect to -# the premise. After our inspection we found the neutral label to be quite ambiguous -# and decided to exclude it. But you can easily add it by modifying the adapters - - -# The XNLI dataset is a multilingual variant of MultiNLI -# https://aclanthology.org/D18-1269/ -xnli_tasks = [ - LightevalTaskConfig( - name=f"xnli_{language.value}_{formulation.name.lower()}", - suite=["lighteval"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=None), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - prompt_function=get_nli_prompt_function( - language=language, - adapter=lambda line: { - "premise": line["premise"], - "hypothesis": line["hypothesis"], - # Since we ignore the neutral label - "gold_idx": {0: 0, 2: 1}[line["label"]], - }, - relations=["entailment", "contradiction"], - formulation=formulation, - ), - hf_filter=lambda line: line["label"] in [0, 2], - hf_repo="facebook/xnli", - hf_subset=standardize_tag(language.value), - evaluation_splits=["validation"], - few_shots_split="train", - ) - for language in [ - Language.ARABIC, - Language.ENGLISH, - Language.FRENCH, - Language.SPANISH, - Language.BULGARIAN, - Language.GERMAN, - Language.GREEK, - Language.ENGLISH, - Language.FRENCH, - Language.HINDI, - Language.RUSSIAN, - Language.SWAHILI, - Language.THAI, - Language.TURKISH, - Language.URDU, - Language.VIETNAMESE, - Language.CHINESE, - ] - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - - -# Improvement on XNLI with better translation, from our experience models tend to -# perform better on XNLI2.0 than XNLI -# https://arxiv.org/abs/2301.06527 -xnli2_tasks = [ - LightevalTaskConfig( - name=f"xnli2.0_{language.value}_{formulation.name.lower()}", - suite=["lighteval"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=None), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - prompt_function=get_nli_prompt_function( - language=language, - adapter=lambda line: { - "premise": line["premise"], - "hypothesis": line["hypothesis"], - # Since we ignore the neutral label - "gold_idx": {0: 0, 2: 1}[line["label"]], - }, - relations=["entailment", "contradiction"], - formulation=formulation, - ), - hf_filter=lambda line: line["label"] in [0, 2] - and line["premise"] is not None - and line["hypothesis"] is not None, - hf_repo=f"Harsit/xnli2.0_train_{LangCodeLanguage(standardize_tag(language.value)).language_name().lower()}", - hf_subset="default", - evaluation_splits=["train"], - hf_avail_splits=["train"], - ) - for language in [ - Language.ENGLISH, - Language.FRENCH, - Language.PUNJABI, - Language.GUJARATI, - Language.KANNADA, - Language.ASSAMESE, - Language.BENGALI, - Language.MARATHI, - Language.SANSKRIT, - Language.TAMIL, - Language.GERMAN, - Language.ENGLISH, - Language.URDU, - Language.VIETNAMESE, - Language.TURKISH, - Language.THAI, - Language.SWAHILI, - Language.SPANISH, - Language.RUSSIAN, - Language.HINDI, - Language.GREEK, - Language.CHINESE, - Language.BULGARIAN, - Language.ARABIC, - # Theoretically also: Bhojpuri, Gujarati, Odiya - ] - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -# Another variant of XNLI, with emphasis on Indic languages -# https://arxiv.org/abs/2204.08776 -xnli_indic_tasks = [ - LightevalTaskConfig( - name=f"indicnxnli_{language.value}_{formulation.name.lower()}", - suite=["lighteval"], - prompt_function=get_nli_prompt_function( - language=language, - adapter=lambda line: { - "premise": line["premise"], - "hypothesis": line["hypothesis"], - # Since we ignore the neutral label - "gold_idx": {0: 0, 2: 1}[line["label"]], - }, - relations=["entailment", "contradiction"], - formulation=formulation, - ), - hf_repo="Divyanshu/indicxnli", - hf_subset=standardize_tag(language.value), - # Ignore neutral - hf_filter=lambda x: int(x["label"]) in [0, 2], - evaluation_splits=["validation"], - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=None), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for language in [ - Language.ASSAMESE, - Language.BENGALI, - Language.GUJARATI, - Language.HINDI, - Language.KANNADA, - Language.MALAYALAM, - Language.MARATHI, - Language.ORIYA, - Language.PUNJABI, - Language.TAMIL, - Language.TELUGU, - ] - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -# African XNLI: African XNLI -# From https://arxiv.org/abs/2406.03368. Human translated MMLU. -afri_xnli_tasks = [ - LightevalTaskConfig( - name=f"afri_xnli_{language.value}_{formulation.name.lower()}", - suite=("lighteval",), - prompt_function=get_nli_prompt_function( - language=language, - adapter=lambda line: { - "premise": line["premise"], - "hypothesis": line["hypothesis"], - # Since we ignore the neutral label - "gold_idx": {0: 0, 2: 1}[line["label"]], - }, - relations=["entailment", "contradiction"], - formulation=formulation, - ), - hf_repo="masakhane/afrixnli", - hf_subset=language.value, - hf_filter=lambda x: int(x["label"]) in [0, 2], - evaluation_splits=("test",), - few_shots_split="validation", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=None), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for language in [ - Language.AMHARIC, - # Language.EWE, - Language.FRENCH, - # Language.HAUSA, - # Language.IGBO, - # Language.KINYARWANDA, - # Language.LINGALA, - # Language.LUGANDA, - # Language.OROMO, - # Language.SHONA, - # Language.SOTHO, - Language.SWAHILI, - # Language.TWI, - # Language.WOLOF, - # Language.XHOSA, - Language.YORUBA, - # Language.ZULU, - ] - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -# PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification -# This dataset contains paraphrase identification pairs in multiple languages. -# It's derived from PAWS (Paraphrase Adversaries from Word Scrambling) and -# We treat paraphrase as entailment and non-paraphrase as contradiction -# https://arxiv.org/abs/1908.11828 - -paws_x_tasks = [ - LightevalTaskConfig( - name=f"pawsx_{language.value}_{formulation.name.lower()}", - suite=("lighteval",), - prompt_function=get_nli_prompt_function( - language=language, - adapter=lambda line: { - "premise": line["sentence1"], - "hypothesis": line["sentence2"], - # Since we ignore the neutral label - "gold_idx": int(line["label"]), - }, - relations=["entailment", "contradiction"], - formulation=formulation, - ), - hf_repo="google-research-datasets/paws-x", - hf_subset=standardize_tag(language.value), - evaluation_splits=("test",), - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=None), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for language in [ - Language.GERMAN, - Language.ENGLISH, - Language.SPANISH, - Language.FRENCH, - Language.JAPANESE, - Language.KOREAN, - Language.CHINESE, - ] - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -# Russian Commitment Bank (RCB) is a large-scale NLI dataset with Russian sentences, -# collected from the web and crowdsourcing. -# https://arxiv.org/abs/2401.04531 -rcb_tasks = [ - LightevalTaskConfig( - name=f"rcb_{Language.RUSSIAN.value}_{formulation.name.lower()}", - prompt_function=get_nli_prompt_function( - language=Language.RUSSIAN, - adapter=lambda line: { - "premise": line["inputs"]["premise"], - "hypothesis": line["inputs"]["hypothesis"], - # Since we ignore the neutral label - "gold_idx": int(line["outputs"]) - 1, - }, - relations=["entailment", "contradiction"], - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="ai-forever/MERA", - hf_subset="rcb", - # Ignore neutral label - hf_filter=lambda x: int(x["outputs"] or "0") in [1, 2], - evaluation_splits=("train",), - few_shots_split="validation", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=None), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -# Native Chinese NLI dataset based. -# https://arxiv.org/pdf/2010.05444 -# We find this benchmark to have really good signal compared to other Chinese NLI -ocnli_tasks = [ - LightevalTaskConfig( - name=f"ocnli_{Language.CHINESE.value}_{formulation.name.lower()}", - prompt_function=get_nli_prompt_function( - language=Language.CHINESE, - adapter=lambda line: { - "premise": line["sentence1"], - "hypothesis": line["sentence2"], - # Since we ignore the neutral label - "gold_idx": {1: 0, 2: 1}[line["label"]], - }, - relations=["entailment", "contradiction"], - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="clue/clue", - hf_subset="ocnli", - # Only keep the positive and negative examples - hf_filter=lambda x: int(x["label"]) in [1, 2], - evaluation_splits=("validation",), - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=None), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -# https://arxiv.org/abs/2004.05986 -# Native Chinese NLI dataset based on MNLI approach (Machine Translated) -cmnli_tasks = [ - LightevalTaskConfig( - name=f"cmnli_{Language.CHINESE.value}_{formulation.name.lower()}", - prompt_function=get_nli_prompt_function( - language=Language.CHINESE, - adapter=lambda line: { - "premise": line["sentence1"], - "hypothesis": line["sentence2"], - # Since we ignore the neutral label - "gold_idx": {"entailment": 0, "contradiction": 1}[line["label"]], - }, - relations=["entailment", "contradiction"], - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="fenffef/cmnli", - hf_subset="default", - hf_filter=lambda x: x["label"] in ["entailment", "contradiction"], - # Only keep the positive and negative examples - evaluation_splits=("validation",), - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=None), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -TASKS_TABLE.extend( - [ - *xnli_tasks, - *xnli2_tasks, - *xnli_indic_tasks, - *paws_x_tasks, - *rcb_tasks, - *ocnli_tasks, - *cmnli_tasks, - *afri_xnli_tasks, - ] -) -# ------------------------------- Copa Tasks ------------------------------- # -# COPA (Choice of Plausible Alternatives) tasks involve determining the most plausible cause or effect -# for a given premise. These tasks test common sense reasoning and causal inference abilities. - -# XCOPA: Cross-lingual Choice of Plausible Alternatives -# Paper: https://aclanthology.org/2020.emnlp-main.185/ -# XCOPA extends the original English COPA task to 11 typologically diverse languages. -xcopa_tasks = [ - LightevalTaskConfig( - name=f"xcopa_{language.value}_{formulation.name.lower()}", - suite=["lighteval"], - prompt_function=get_copa_prompt_function( - language, - adapter=lambda line: { - "context": line["premise"], - "cause_effect": line["question"], - "continuations": [line["choice1"], line["choice2"]], - "gold_idx": int(line["label"]), - }, - formulation=formulation, - ), - hf_repo=("OALL/AlGhafa-Arabic-LLM-Benchmark-Translated" if language == Language.ARABIC else "xcopa"), - hf_subset=("copa_ext_ar" if language == Language.ARABIC else standardize_tag(language.value)), - evaluation_splits=["test"], - few_shots_split="validation", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for language in [ - Language.ARABIC, - Language.ESTONIAN, - Language.INDONESIAN, - Language.ITALIAN, - Language.SWAHILI, - Language.TAMIL, - Language.THAI, - Language.TURKISH, - Language.VIETNAMESE, - Language.CHINESE, - Language.HAITIAN, - Language.QUECHUA, - ] - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -# IndicCOPA: COPA for Indic Languages -# Paper: https://arxiv.org/pdf/2212.05409 -# IndicCOPA extends COPA to 15 Indic languages, providing a valuable resource for -# evaluating common sense reasoning in these languages. -copa_indic_tasks = [ - LightevalTaskConfig( - name=f"indicxcopa_{language.value}_{formulation.name.lower()}", - suite=["lighteval"], - prompt_function=get_copa_prompt_function( - language, - adapter=lambda line: { - "context": line["premise"], - "cause_effect": line["question"], - "continuations": [line["choice1"], line["choice2"]], - "gold_idx": int(line["label"]), - }, - formulation=formulation, - ), - hf_repo="ai4bharat/IndicCOPA", - hf_subset=f"translation-{standardize_tag(language.value)}", - hf_revision="d356ef19a4eb287e88a51d07a56b73ba88c7f188", - evaluation_splits=["test"], - hf_avail_splits=["test"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for language in [ - Language.ASSAMESE, - Language.BENGALI, - Language.GUJARATI, - Language.HINDI, - Language.KANNADA, - Language.MALAYALAM, - Language.MARATHI, - Language.NEPALI, - Language.ORIYA, - Language.PUNJABI, - Language.SANSKRIT, - Language.SINDHI, - Language.TAMIL, - Language.TELUGU, - Language.URDU, - # Optionally: Maithili, Santali, Sindhi, Konkani - ] - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -# PARus: Plausible Alternatives for Russian -# Paper: https://russiansuperglue.com/tasks/task_info/PARus -# PARus is the Russian adaptation of the COPA task, part of the Russian SuperGLUE benchmark. -# It evaluates common sense reasoning and causal inference abilities in Russian language models. -parus_tasks = [ - LightevalTaskConfig( - name=f"parus_{Language.RUSSIAN.value}_{formulation.name.lower()}", - suite=["lighteval"], - prompt_function=get_copa_prompt_function( - language=Language.RUSSIAN, - adapter=lambda line: { - "context": line["inputs"]["premise"], - "cause_effect": line["meta"]["task"], - "continuations": [line["inputs"]["choice1"], line["inputs"]["choice2"]], - "gold_idx": int(line["outputs"]) - 1, - }, - formulation=formulation, - ), - hf_repo="ai-forever/MERA", - hf_subset="parus", - evaluation_splits=["train"], - few_shots_split="validation", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - - -TASKS_TABLE.extend([*xcopa_tasks, *copa_indic_tasks, *parus_tasks]) -# ------------------------------- Hellaswag Tasks ------------------------------- # -# Hellaswag is a commonsense reasoning task that requires models to complete a given scenario -# with the most plausible ending. It tests the model's ability to understand and reason about -# everyday situations and human behavior. - -# MLMM-Hellaswag: Multilingual adaptation of Hellaswag -# Paper: https://arxiv.org/abs/2306.07610 -# This is a multilingual version of Hellaswag, part of the MLMM (Massive Language Model Meta-Evaluation) benchmark. -# It evaluates commonsense reasoning abilities across multiple languages. -mlmm_hellaswag_tasks = [ - LightevalTaskConfig( - name=f"mlmm_hellaswag_{lang.value}_{formulation.name.lower()}", - suite=["lighteval"], - prompt_function=get_hellaswag_prompt_function( - language=lang, - adapter=lambda line: { - # We don't use activity_label as they are not available - "ctx_a": line["ctx_a"], - "ctx_b": line["ctx_b"], - "continuations": line["endings"], - "gold_idx": int(line["label"]), - }, - formulation=formulation, - ), - hf_repo="jon-tow/okapi_hellaswag", - hf_subset=standardize_tag(lang.value), - hf_revision="96ed8e0dfc6172dad1d3df338d7b8ba6c1ff9d83", - evaluation_splits=["validation"], - hf_avail_splits=["validation"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for lang in [ - Language.ARABIC, - Language.BENGALI, - Language.CATALAN, - Language.DANISH, - Language.GERMAN, - Language.SPANISH, - Language.BASQUE, - Language.FRENCH, - Language.GUJARATI, - Language.HINDI, - Language.CROATIAN, - Language.HUNGARIAN, - Language.ARMENIAN, - Language.INDONESIAN, - Language.ICELANDIC, - Language.ITALIAN, - Language.KANNADA, - Language.MALAYALAM, - Language.MARATHI, - Language.NORWEGIAN, - Language.NEPALI, - Language.DUTCH, - Language.PORTUGUESE, - Language.ROMANIAN, - Language.RUSSIAN, - Language.SLOVAK, - Language.SERBIAN, - Language.SWEDISH, - Language.TAMIL, - Language.TELUGU, - Language.UKRAINIAN, - Language.VIETNAMESE, - Language.CHINESE, - ] - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -# Hellaswag Turkish -# This is a Turkish adaptation of the Hellaswag task. -# While there's no specific paper for this version, it has been found to work well for evaluating -# Turkish language models on commonsense reasoning tasks. - -# We don't handle them in single task as there is quite a lot of differences (dataset/subset, dot replacement, etc.) -# which would make it hard to read -hellaswag_tur_tasks = [ - LightevalTaskConfig( - name=f"community_hellaswag_{Language.TURKISH.value}_{formulation.name.lower()}", - suite=["lighteval"], - prompt_function=get_hellaswag_prompt_function( - language=Language.TURKISH, - adapter=lambda line: { - "ctx_a": line["ctx_a"], - "ctx_b": line["ctx_b"], - "continuations": line["endings"], - "gold_idx": int(line["label"]), - }, - formulation=formulation, - # https://github.com/malhajar17/lm-evaluation-harness_turkish/blob/main/lm_eval/tasks/hellaswag_tr-v0.2/utils.py - wikihow_artifacts=[" [title]", " [başlık]", " [adım]", " [header]"], - ), - hf_repo="malhajar/hellaswag_tr-v0.2", - hf_subset="default", - evaluation_splits=["validation"], - hf_avail_splits=["validation"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -# Hellaswag Thai -# This is a Thai adaptation of the Hellaswag task. -# Similar to the Turkish version, there's no specific paper, but it has been found to be effective -# for evaluating Thai language models on commonsense reasoning tasks. -hellaswag_tha_tasks = [ - LightevalTaskConfig( - name=f"community_hellaswag_{Language.THAI.value}_{formulation.name.lower()}", - suite=["lighteval"], - prompt_function=get_hellaswag_prompt_function( - language=Language.THAI, - adapter=lambda line: { - "ctx_a": line["ctx_a"], - "ctx_b": line["ctx_b"], - "continuations": line["endings"], - "gold_idx": int(line["label"]), - }, - formulation=formulation, - wikihow_artifacts=[" [ชื่อ]", " [ส่วนหัว]", " [ขั้นตอน]", " [header]", " [Header]"], - ), - hf_repo="lighteval/hellaswag_thai", - hf_subset="default", - evaluation_splits=["validation"], - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -hellaswag_hin_tasks = [ - LightevalTaskConfig( - name=f"community_hellaswag_{Language.HINDI.value}_{formulation.name.lower()}", - suite=["lighteval"], - prompt_function=get_hellaswag_prompt_function( - language=Language.HINDI, - adapter=lambda line: { - "ctx_a": line["ctx_a"], - "continuations": line["endings"], - "gold_idx": int(line["label"]), - }, - formulation=formulation, - ), - hf_repo="ai4bharat/hellaswag-hi", - hf_filter=lambda line: all(len(choice.strip()) > 0 for choice in line["endings"]), - hf_subset="hi", - evaluation_splits=("validation",), - few_shots_split="validation", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - ], - ), - ) - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -hellaswag_tel_tasks = [ - LightevalTaskConfig( - name=f"community_hellaswag_{Language.TELUGU.value}_{formulation.name.lower()}", - suite=["lighteval"], - prompt_function=get_hellaswag_prompt_function( - language=Language.TELUGU, - adapter=lambda line: { - "ctx_a": line["ctx_a"], - "continuations": line["endings"], - "gold_idx": int(line["label"]), - }, - formulation=formulation, - ), - hf_repo="LightFury9/hellaswag-telugu", - hf_subset="default", - evaluation_splits=("valid",), - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -TASKS_TABLE.extend( - [ - *mlmm_hellaswag_tasks, - *hellaswag_tur_tasks, - *hellaswag_tha_tasks, - *hellaswag_hin_tasks, - *hellaswag_tel_tasks, - ] -) -# ------------------------------- RC Tasks ------------------------------- # -# Reading Comprehension (RC) tasks evaluate a model's ability to understand and extract information from text passages. -# These tasks typically involve answering questions based on given contexts, spanning multiple languages and formats. -# Add RC tasks supporting about 130 unique languages/scripts. - -# SQuAD - like - -# XQuAD: Cross-lingual Question Answering Dataset, extending SQuAD to 11 languages. -# https://arxiv.org/abs/1910.11856 -xquad_tasks = [ - LightevalTaskConfig( - name=f"xquad_{language.value}", - prompt_function=get_qa_prompt_function( - language, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="google/xquad", - hf_subset=f"xquad.{standardize_tag(language.value)}", - evaluation_splits=("validation",), - few_shots_split="validation", - generation_size=400, - stop_sequence=("\n",), - metrics=( - MultilingualQuasiExactMatchMetric(language, "prefix"), - MultilingualQuasiF1ScoreMetric(language), - ), - ) - for language in [ - Language.ARABIC, - Language.GERMAN, - Language.GREEK, - Language.ENGLISH, - Language.SPANISH, - Language.HINDI, - Language.ROMANIAN, - Language.RUSSIAN, - Language.THAI, - Language.TURKISH, - Language.VIETNAMESE, - Language.CHINESE, - ] -] - -# GermanQuAD: High-quality German QA dataset with 13,722 questions -# https://arxiv.org/abs/2104.12741 -germanquad_tasks = [ - LightevalTaskConfig( - name=f"germanquad_{Language.GERMAN.value}", - prompt_function=get_qa_prompt_function( - Language.GERMAN, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="deepset/germanquad", - hf_subset="plain_text", - hf_revision="fff05ceaf2ffbe5b65c7e0c57e678f7b7e1a0581", - hf_filter=lambda line: any(len(ans) > 0 for ans in line["answers"]["text"]), - evaluation_splits=("test",), - few_shots_split="train", - generation_size=400, - stop_sequence=("\n",), - metrics=( - MultilingualQuasiExactMatchMetric(Language.GERMAN, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.GERMAN), - ), - ) -] - - -# SQuAD-it: Italian translation of the SQuAD dataset -# https://github.com/crux82/squad-it -squad_it_tasks = [ - LightevalTaskConfig( - name=f"squad_{Language.ITALIAN.value}", - prompt_function=get_qa_prompt_function( - Language.ITALIAN, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="crux82/squad_it", - hf_subset="default", - hf_filter=lambda line: any(len(ans) > 0 for ans in line["answers"]["text"]), - evaluation_splits=("test",), - few_shots_split="train", - generation_size=400, - stop_sequence=("\n",), - metrics=( - MultilingualQuasiExactMatchMetric(Language.ITALIAN, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.ITALIAN), - ), - ) -] - - -# ThaiQA: A question answering dataset for the Thai language. -thaiqa_tasks = [ - LightevalTaskConfig( - name=f"thaiqa_{Language.THAI.value}", - prompt_function=get_qa_prompt_function( - Language.THAI, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["answer"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="lighteval/thaiqa_squad_fixed", - hf_subset="default", - evaluation_splits=("train",), - few_shots_split="validation", - generation_size=400, - stop_sequence=("\n",), - metrics=( - MultilingualQuasiExactMatchMetric(Language.THAI, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.THAI), - ), - ) -] - -# SberQuAD: A large-scale Russian reading comprehension dataset. -# https://arxiv.org/abs/1912.09723 -sber_squad_tasks = [ - LightevalTaskConfig( - name=f"sber_squad_{Language.RUSSIAN.value}", - prompt_function=get_qa_prompt_function( - Language.RUSSIAN, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="kuznetsoffandrey/sberquad", - hf_subset="sberquad", - evaluation_splits=("validation",), - few_shots_split="train", - metrics=( - MultilingualQuasiExactMatchMetric(Language.RUSSIAN, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.RUSSIAN), - ), - generation_size=400, - stop_sequence=("\n",), - ) -] - -# FaQuAD: A Portuguese Reading Comprehension Dataset -# https://arxiv.org/abs/2007.15671 -faquad_tasks = [ - LightevalTaskConfig( - name=f"faquad_{Language.PORTUGUESE.value}", - prompt_function=get_qa_prompt_function( - Language.PORTUGUESE, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="eraldoluis/faquad", - hf_subset="plain_text", - hf_revision="205ba826a2282a4a5aa9bd3651e55ee4f2da1546", - hf_filter=lambda line: any(len(ans) > 0 for ans in line["answers"]["text"]), - evaluation_splits=("validation",), - few_shots_split="train", - metrics=( - MultilingualQuasiExactMatchMetric(Language.PORTUGUESE, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.PORTUGUESE), - ), - generation_size=400, - stop_sequence=("\n",), - ) -] - - -# SQuAD-es: Spanish translation of the Stanford Question Answering Dataset -# https://huggingface.co/datasets/ccasimiro/squad_es -squad_es_tasks = [ - LightevalTaskConfig( - name=f"squad_{Language.SPANISH.value}", - prompt_function=get_qa_prompt_function( - Language.SPANISH, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="ccasimiro/squad_es", - hf_subset="v2.0.0", - hf_filter=lambda line: any(len(ans) > 0 for ans in line["answers"]["text"]), - evaluation_splits=("validation",), - few_shots_split="train", - metrics=( - MultilingualQuasiExactMatchMetric(Language.SPANISH, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.SPANISH), - ), - generation_size=400, - stop_sequence=("\n",), - ) -] - - -# ARCD: Arabic Reading Comprehension Dataset. -# https://arxiv.org/pdf/1906.05394 -arcd_tasks = [ - LightevalTaskConfig( - name=f"arcd_{Language.ARABIC.value}", - prompt_function=get_qa_prompt_function( - Language.ARABIC, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="hsseinmz/arcd", - hf_subset="plain_text", - evaluation_splits=("validation",), - few_shots_split="train", - metrics=( - MultilingualQuasiExactMatchMetric(Language.ARABIC, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.ARABIC), - ), - generation_size=400, - stop_sequence=("\n",), - ) -] - -# KenSwQuAD: A question answering dataset for Kenyan Swahili. -# https://arxiv.org/abs/2205.02364 -kenswquad_tasks = [ - LightevalTaskConfig( - name=f"kenswquad_{Language.SWAHILI.value}", - prompt_function=get_qa_prompt_function( - Language.SWAHILI, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [line["answer"]], - }, - ), - suite=("lighteval",), - hf_repo="lighteval/KenSwQuAD", - hf_subset="default", - evaluation_splits=("test",), - few_shots_split="validation", - metrics=( - MultilingualQuasiExactMatchMetric(Language.SWAHILI, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.SWAHILI), - ), - generation_size=400, - stop_sequence=("\n",), - ) -] - -# ChineseSquad: A reading comprehension dataset for Chinese. -# https://github.com/pluto-junzeng/ChineseSquad -chinese_squad_tasks = [ - LightevalTaskConfig( - name=f"chinese_squad_{Language.CHINESE.value}", - prompt_function=get_qa_prompt_function( - Language.CHINESE, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="lighteval/ChineseSquad", - hf_subset="default", - evaluation_splits=("validation",), - few_shots_split="train", - metrics=( - MultilingualQuasiExactMatchMetric(Language.CHINESE, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.CHINESE), - ), - generation_size=400, - stop_sequence=("\n",), - ) -] - -# CMRC 2018: A span-extraction machine reading comprehension dataset for Chinese. -# https://arxiv.org/abs/1810.07366 -cmrc2018_tasks = [ - LightevalTaskConfig( - name=f"cmrc2018_{Language.CHINESE.value}", - prompt_function=get_qa_prompt_function( - Language.CHINESE, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="clue/clue", - hf_subset="cmrc2018", - evaluation_splits=("trial",), - few_shots_split="train", - generation_size=400, - metrics=( - MultilingualQuasiExactMatchMetric(Language.CHINESE, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.CHINESE), - ), - stop_sequence=("\n",), - ) -] - -# IndicQA: A reading comprehension dataset for 11 Indian languages. -# https://arxiv.org/abs/2407.13522 -indicqa_tasks = [ - LightevalTaskConfig( - name=f"indicqa_{language.value}", - prompt_function=get_qa_prompt_function( - language, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="ai4bharat/IndicQA", - hf_subset=f"indicqa.{LangCodeLanguage.get(language.value).language}", - hf_filter=lambda line: any(len(ans) > 0 for ans in line["answers"]["text"]), - hf_revision="92d96092ae229950973dac3b9998f8b3a8949b0a", - evaluation_splits=("test",), - hf_avail_splits=("test",), - generation_size=400, - metrics=( - MultilingualQuasiExactMatchMetric(language, "prefix"), - MultilingualQuasiF1ScoreMetric(language), - ), - stop_sequence=("\n",), - ) - for language in [ - Language.ASSAMESE, - Language.BENGALI, - Language.GUJARATI, - Language.HINDI, - Language.KANNADA, - Language.MALAYALAM, - Language.MARATHI, - Language.ORIYA, - Language.PUNJABI, - Language.TAMIL, - Language.TELUGU, - ] -] - -# FQuAD v2: French Question Answering Dataset version 2. -# https://arxiv.org/abs/2002.06071 -fquad_v2_tasks = [ - LightevalTaskConfig( - name=f"fquadv2_{Language.FRENCH.value}", - prompt_function=get_qa_prompt_function( - Language.FRENCH, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="manu/fquad2_test", - hf_subset="default", - evaluation_splits=("test_hasAns",), - few_shots_split="valid_hasAns", - generation_size=400, - stop_sequence=("\n",), - metrics=( - MultilingualQuasiExactMatchMetric(Language.FRENCH, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.FRENCH), - ), - ) -] - -# TQuAD v2: Turkish Question Answering Dataset version 2. -tquad_v2_tasks = [ - LightevalTaskConfig( - name=f"tquadv2_{Language.TURKISH.value}", - prompt_function=get_qa_prompt_function( - Language.TURKISH, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [a["text"] for a in line["answers"]], - }, - ), - suite=("lighteval",), - hf_repo="erdometo/tquad2", - hf_subset="default", - evaluation_splits=("validation",), - few_shots_split="train", - generation_size=400, - stop_sequence=("\n",), - metrics=( - MultilingualQuasiExactMatchMetric(Language.TURKISH, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.TURKISH), - ), - ) -] - -# Other QA tasks for RC - -# TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages. -# https://arxiv.org/abs/2003.05002 -tydiqa_tasks = [ - LightevalTaskConfig( - name=f"tydiqa_{language.value}", - prompt_function=get_qa_prompt_function( - language, - lambda line: { - "question": line["question"], - "context": line["context"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="google-research-datasets/tydiqa", - hf_subset="secondary_task", - evaluation_splits=("validation",), - few_shots_split="train", - generation_size=400, - stop_sequence=("\n",), - metrics=( - MultilingualQuasiExactMatchMetric(language, "prefix"), - MultilingualQuasiF1ScoreMetric(language), - ), - ) - for language in [ - Language.ENGLISH, - Language.ARABIC, - Language.BENGALI, - Language.FINNISH, - Language.INDONESIAN, - Language.JAPANESE, - Language.KOREAN, - Language.SWAHILI, - Language.RUSSIAN, - Language.TELUGU, - Language.THAI, - ] -] - -# C3: A Chinese Challenge Corpus for Cross-lingual and Cross-modal Tasks -# Reading comprehension task part of clue -# Paper: https://arxiv.org/abs/2004.05986 -c3_tasks = [ - LightevalTaskConfig( - name=f"c3_{Language.CHINESE.value}_{formulation.name.lower()}", - suite=("lighteval",), - prompt_function=get_mcq_prompt_function( - Language.CHINESE, - lambda line: { - "question": line["question"], - "choices": line["choice"], - "gold_idx": line["choice"].index(line["answer"]), - "context": " ".join(line["context"]), - }, - formulation=formulation, - ), - hf_repo="clue/clue", - hf_subset="c3", - evaluation_splits=("validation",), - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# Other MCF tasks for RC -# RACE: Reading Comprehension from Examinations -# RACE is a large-scale reading comprehension dataset collected from English exams for middle and high school Chinese students. -# This Arabic version is a translation of the original RACE dataset, adapted for Arabic language evaluation. -# Paper: https://aclanthology.org/2023.arabicnlp-1.21/ -race_ar_task = [ - LightevalTaskConfig( - name=f"alghafa_race_{Language.ARABIC.value}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), - suite=["lighteval"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="race_ar", - hf_revision="08663706ee7cab30c4b7dc1bb00042a3227ce1ff", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] -# SOQAL: A large-scale Arabic reading comprehension dataset. -# https://arxiv.org/abs/1906.05394 -soqal_tasks = [ - LightevalTaskConfig( - name=f"soqal_{Language.ARABIC.value}_{formulation.name.lower()}", - hf_subset="multiple_choice_grounded_statement_soqal_task", - prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), - evaluation_splits=["test"], - few_shots_split="validation", - suite=["lighteval"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Native", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. -# It consists of QA instances in 7 languages: English, Arabic, German, Spanish, Hindi, Vietnamese, and Chinese. -# The dataset is derived from the SQuAD v1.1 dataset, with questions and contexts translated by professional translators. -# Paper: https://arxiv.org/abs/1910.07475 -mlqa_tasks = [ - LightevalTaskConfig( - name=f"mlqa_{lang.value}", - prompt_function=get_qa_prompt_function( - lang, - lambda line: { - "context": line["context"], - "question": line["question"], - "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], - }, - ), - suite=("lighteval",), - hf_repo="facebook/mlqa", - hf_subset=f"mlqa.{standardize_tag(lang.value)}.{standardize_tag(lang.value)}", - hf_revision="397ed406c1a7902140303e7faf60fff35b58d285", - evaluation_splits=("test",), - hf_avail_splits=["test"], - generation_size=400, - stop_sequence=("\n",), - metrics=[ - MultilingualQuasiExactMatchMetric(lang, "prefix"), - MultilingualQuasiF1ScoreMetric(lang), - ], - ) - for lang in [ - Language.ARABIC, - Language.GERMAN, - Language.SPANISH, - Language.CHINESE, - Language.HINDI, - Language.VIETNAMESE, - ] -] - -# Belebele: A large-scale reading comprehension dataset covering 122 languages. -# https://arxiv.org/abs/2308.16884 -belebele_tasks = [ - LightevalTaskConfig( - name=f"belebele_{language}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - iso_639_3_ind_to_iso_639_3_macro[LangCodeLanguage.get(language).to_alpha3()], - lambda line: { - "question": line["question"], - "context": line["flores_passage"], - "choices": [line[f"mc_answer{i}"] for i in range(1, 5)], - "gold_idx": int(line["correct_answer_num"]) - 1, - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="facebook/belebele", - hf_subset=language, - evaluation_splits=("test",), - hf_avail_splits=["test"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] - for language in [ - "acm_Arab", - "arz_Arab", - "ceb_Latn", - "fin_Latn", - "hin_Deva", - "ita_Latn", - "khm_Khmr", - "lvs_Latn", - "npi_Deva", - "pol_Latn", - "slv_Latn", - "swe_Latn", - # "tso_Latn", - # "xho_Latn", - "afr_Latn", - "asm_Beng", - "ces_Latn", - "fra_Latn", - "hin_Latn", - "jav_Latn", - # "kin_Latn", - "mal_Mlym", - "npi_Latn", - "por_Latn", - # "sna_Latn", - "swh_Latn", - "tur_Latn", - "yor_Latn", - "als_Latn", - "azj_Latn", - "ckb_Arab", - # "fuv_Latn", - "hrv_Latn", - "jpn_Jpan", - "kir_Cyrl", - "mar_Deva", - # "nso_Latn", - "snd_Arab", - "tam_Taml", - "ukr_Cyrl", - "zho_Hans", - "amh_Ethi", - # "bam_Latn", - "dan_Latn", - # "gaz_Latn", - "hun_Latn", - # "kac_Latn", - "kor_Hang", - "mkd_Cyrl", - # "nya_Latn", - "ron_Latn", - "som_Latn", - "tel_Telu", - "urd_Arab", - "zho_Hant", - "apc_Arab", - "ben_Beng", - "deu_Latn", - # "grn_Latn", - "hye_Armn", - "kan_Knda", - "lao_Laoo", - "mlt_Latn", - "ory_Orya", - "rus_Cyrl", - # "sot_Latn", - "tgk_Cyrl", - "urd_Latn", - "zsm_Latn", - "arb_Arab", - "ben_Latn", - "ell_Grek", - "guj_Gujr", - # "ibo_Latn", - "kat_Geor", - # "lin_Latn", - # "mri_Latn", - "pan_Guru", - # "shn_Mymr", - "spa_Latn", - "tgl_Latn", - "uzn_Latn", - # "zul_Latn", - "arb_Latn", - # "bod_Tibt", - "eng_Latn", - # "hat_Latn", - # "ilo_Latn", - "kaz_Cyrl", - "lit_Latn", - "mya_Mymr", - "pbt_Arab", - "sin_Latn", - "srp_Cyrl", - "tha_Thai", - "vie_Latn", - "ars_Arab", - "bul_Cyrl", - "est_Latn", - # "hau_Latn", - "ind_Latn", - # "kea_Latn", - # "lug_Latn", - "nld_Latn", - "pes_Arab", - "sin_Sinh", - # "ssw_Latn", - # "tir_Ethi", - "war_Latn", - "ary_Arab", - "cat_Latn", - "eus_Latn", - "heb_Hebr", - "isl_Latn", - # "khk_Cyrl", - # "luo_Latn", - "nob_Latn", - "plt_Latn", - "slk_Latn", - # "sun_Latn", - # "tsn_Latn", - # "wol_Latn", - ] -] - -TASKS_TABLE.extend( - [ - *xquad_tasks, - *thaiqa_tasks, - *sber_squad_tasks, - *arcd_tasks, - *kenswquad_tasks, - *chinese_squad_tasks, - *cmrc2018_tasks, - *indicqa_tasks, - *fquad_v2_tasks, - *tquad_v2_tasks, - *tydiqa_tasks, - *soqal_tasks, - *race_ar_task, - *belebele_tasks, - *c3_tasks, - *squad_it_tasks, - *squad_es_tasks, - *faquad_tasks, - *germanquad_tasks, - ] -) - -# ------------------------------- GK Tasks ------------------------------- # -# General Knowledge (GK) tasks evaluate a model's broad understanding across various domains. -# These tasks typically involve answering questions on diverse subjects, testing the model's ability to recall and apply general information. - - -# -------------------------------- MMLU -------------------------------- # -# MMLU (Massive Multitask Language Understanding) -# A comprehensive test of world knowledge, covering 57 subjects across STEM, humanities, social sciences, and more. -# Note that all MMLU tasks uses PMI normalization, this makes the computation 2x slower, however we found this metric to be less noisy and yield better results than the others. -# Paper: https://arxiv.org/abs/2009.03300 -MMLU_SUBSETS = [ - "abstract_algebra", - "anatomy", - "astronomy", - "business_ethics", - "clinical_knowledge", - "college_biology", - "college_chemistry", - "college_computer_science", - "college_mathematics", - "college_medicine", - "college_physics", - "computer_security", - "conceptual_physics", - "econometrics", - "electrical_engineering", - "elementary_mathematics", - "formal_logic", - "global_facts", - "high_school_biology", - "high_school_chemistry", - "high_school_computer_science", - "high_school_european_history", - "high_school_geography", - "high_school_government_and_politics", - "high_school_macroeconomics", - "high_school_mathematics", - "high_school_microeconomics", - "high_school_physics", - "high_school_psychology", - "high_school_statistics", - "high_school_us_history", - "high_school_world_history", - "human_aging", - "human_sexuality", - "international_law", - "jurisprudence", - "logical_fallacies", - "machine_learning", - "management", - "marketing", - "medical_genetics", - "miscellaneous", - "moral_disputes", - "moral_scenarios", - "nutrition", - "philosophy", - "prehistory", - "professional_accounting", - "professional_law", - "professional_medicine", - "professional_psychology", - "public_relations", - "security_studies", - "sociology", - "us_foreign_policy", - "virology", - "world_religions", -] - -# Meta MMLU: A multilingual version of MMLU (using google translation) -# Paper: https://arxiv.org/abs/2407.21783 -meta_mmlu_tasks = [ - LightevalTaskConfig( - name=f"meta_mmlu_{language.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - language, - lambda line: { - "question": line["input_question"], - "choices": [v for _, v in sorted(line["input_choice_list"].items(), key=lambda x: x[0])], - "gold_idx": LETTER_INDICES.index(line["input_correct_responses"][0]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="meta-llama/Meta-Llama-3.1-8B-Instruct-evals", - hf_subset=f"Llama-3.1-8B-Instruct-evals__multilingual_mmlu_{standardize_tag(language.value)}__details", - hf_filter=partial( - lambda language, subset, line: line["subtask_name"] - == f"mmlu_{standardize_tag(language.value)}_chat.{subset}", - language, - subset, - ), - evaluation_splits=("latest",), - hf_avail_splits=["latest"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for subset in MMLU_SUBSETS - for language in [ - Language.GERMAN, - Language.SPANISH, - Language.FRENCH, - Language.HINDI, - Language.ITALIAN, - Language.PORTUGUESE, - Language.THAI, - ] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# MLMM MMLU: Another multilingual version of MMLU -# Paper: https://github.com/nlp-uoregon/mlmm-evaluation -mlmm_mmlu_tasks = [ - LightevalTaskConfig( - name=f"mlmm_mmlu_{language.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - language, - lambda line: { - "question": line["question"], - "choices": line["choices"], - "gold_idx": LETTER_INDICES.index(line["answer"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="jon-tow/okapi_mmlu", - hf_subset=standardize_tag(language.value), - hf_revision="refs/pr/1", - hf_filter=partial(lambda subset, line: line["id"].split("/")[0] == subset, subset), - evaluation_splits=("test",), - few_shots_split="dev", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for subset in MMLU_SUBSETS - for language in [ - Language.RUSSIAN, - Language.GERMAN, - Language.CHINESE, - Language.FRENCH, - Language.SPANISH, - Language.ITALIAN, - Language.DUTCH, - Language.VIETNAMESE, - Language.INDONESIAN, - Language.ARABIC, - Language.HUNGARIAN, - Language.ROMANIAN, - Language.DANISH, - Language.SLOVAK, - Language.UKRAINIAN, - Language.CATALAN, - Language.SERBIAN, - Language.CROATIAN, - Language.HINDI, - Language.BENGALI, - Language.TAMIL, - Language.NEPALI, - Language.MALAYALAM, - Language.MARATHI, - Language.TELUGU, - Language.KANNADA, - ] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -openai_mmlu_tasks = [ - LightevalTaskConfig( - name=f"openai_mmlu_{language[0].value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - language[0], - lambda line: { - "question": line["Question"], - "choices": [line["A"], line["B"], line["C"], line["D"]], - "gold_idx": LETTER_INDICES.index(line["Answer"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="openai/MMMLU", - hf_subset=language[1], - evaluation_splits=("test",), - hf_avail_splits=["test"], - hf_filter=partial(lambda subset, x: x["Subject"].lower() == subset, subset), - hf_revision="038c7808122969ead7456361af05cb8f47d247f8", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for subset in MMLU_SUBSETS - for language in [ - (Language.ARABIC, "AR_XY"), - (Language.BENGALI, "BN_BD"), - (Language.GERMAN, "DE_DE"), - (Language.SPANISH, "ES_LA"), - (Language.FRENCH, "FR_FR"), - (Language.HINDI, "HI_IN"), - (Language.INDONESIAN, "ID_ID"), - (Language.ITALIAN, "IT_IT"), - (Language.JAPANESE, "JA_JP"), - (Language.KOREAN, "KO_KR"), - (Language.PORTUGUESE, "PT_BR"), - (Language.SWAHILI, "SW_KE"), - (Language.YORUBA, "YO_NG"), - (Language.CHINESE, "ZH_CN"), - ] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# Translated MMLU using both professional and non-professional translators. Contains tags for cultural sensitivity. -# CA: Cultural Agnostic -# CS: Cultural Specific -# UNK: Not annotated -# ALL: All of the above -# https://huggingface.co/papers/2412.03304 -global_mmlu_tasks = [ - LightevalTaskConfig( - name=f"global_mmlu_{sensitivity_label.lower()}_{language.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - language, - lambda line: { - "question": line["question"], - "choices": [line["option_a"], line["option_b"], line["option_c"], line["option_d"]], - "gold_idx": LETTER_INDICES.index(line["answer"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="CohereForAI/Global-MMLU", - hf_subset=standardize_tag(language.value), - evaluation_splits=("test",), - few_shots_split="dev", - hf_filter=partial( - lambda subset, sensitivity_label, x: x["subject"].lower() == subset - and ( - sensitivity_label == "ALL" or sensitivity_label in x["cultural_sensitivity_label"].replace("-", "UNK") - ) - and all(x[f"option_{opt}"] is not None and x[f"option_{opt}"].strip() for opt in "abcd"), - subset, - sensitivity_label, - ), - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for subset in MMLU_SUBSETS - for language in [ - Language.AMHARIC, - Language.ARABIC, - Language.BENGALI, - Language.CHINESE, - Language.CZECH, - Language.GERMAN, - Language.ENGLISH, - Language.SPANISH, - Language.FRENCH, - Language.HEBREW, - Language.HINDI, - Language.INDONESIAN, - Language.ITALIAN, - Language.JAPANESE, - Language.KOREAN, - Language.MALAY, - Language.DUTCH, - Language.NORWEGIAN, - Language.POLISH, - Language.PORTUGUESE, - Language.ROMANIAN, - Language.RUSSIAN, - Language.SERBIAN, - Language.SWEDISH, - Language.SWAHILI, - Language.TAMIL, - Language.TELUGU, - Language.THAI, - Language.TURKISH, - Language.UKRAINIAN, - Language.URDU, - Language.VIETNAMESE, - Language.YORUBA, - Language.ZULU, - ] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] - for sensitivity_label in ["ALL", "CA", "CS", "UNK"] -] - - -# There are only these subsets in the African MMLU -AFRI_MMLU_SUBSETS = [ - "elementary_mathematics", - "high_school_mathematics", - "high_school_geography", - "high_school_microeconomics", - "international_law", - "global_facts", -] -# African MMLU: African Massive Multitask Language Understanding -# From https://arxiv.org/abs/2406.03368. Human translated MMLU. -afri_mmlu_tasks = [ - LightevalTaskConfig( - name=f"afri_mmlu_{language.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - language, - lambda line: { - "question": line["question"], - "choices": line["choices"], - "gold_idx": LETTER_INDICES.index(line["answer"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="masakhane/afrimmlu", - # Temporary until the pr is merged - hf_revision="refs/pr/1", - hf_subset=language.value, - hf_filter=partial(lambda subset, line: line["subject"] == subset, subset), - evaluation_splits=("test",), - few_shots_split="dev", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for subset in AFRI_MMLU_SUBSETS - for language in [ - Language.AMHARIC, - # Language.EWE, - Language.FRENCH, - # Language.HAUSA, - # Language.IGBO, - # Language.KINYARWANDA, - # Language.LINGALA, - # Language.LUGANDA, - # Language.OROMO, - # Language.SHONA, - # Language.SOTHO, - Language.SWAHILI, - # Language.TWI, - # Language.WOLOF, - # Language.XHOSA, - Language.YORUBA, - # Language.ZULU, - ] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# RUMMLU: Russian Massive Multitask Language Understanding -# Paper: https://arxiv.org/html/2401.04531v2 -rummlu = [ - LightevalTaskConfig( - name=f"rummlu_{Language.RUSSIAN.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - Language.RUSSIAN, - lambda line: { - "question": line["inputs"]["text"], - "choices": [line["inputs"][f"option_{i.lower()}"] for i in LETTER_INDICES[:4]], - "gold_idx": LETTER_INDICES.index(line["outputs"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="ai-forever/MERA", - hf_subset="rummlu", - hf_filter=lambda x: x["meta"]["domain"] == subset, - evaluation_splits=("public_test",), - hf_avail_splits=["public_test"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for subset in MMLU_SUBSETS - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# MMLU Turkish: Turkish version of MMLU -# Translated using openai GPT -mmlu_turkish = [ - LightevalTaskConfig( - name=f"community_mmlu_{Language.TURKISH.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - Language.TURKISH, - lambda line: {"question": line["question"], "choices": line["choices"], "gold_idx": int(line["answer"])}, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="malhajar/mmlu_tr-v0.2", - hf_subset=subset, - evaluation_splits=("test",), - few_shots_split="dev", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for subset in MMLU_SUBSETS - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# CMMLU: Chinese Massive Multitask Language Understanding -# Native translation with some new categories -# Paper: https://arxiv.org/abs/2306.09212 -CMMLU_SUBSETS = [ - "agronomy", - "anatomy", - "ancient_chinese", - "arts", - "astronomy", - "business_ethics", - "chinese_civil_service_exam", - "chinese_driving_rule", - "chinese_food_culture", - "chinese_foreign_policy", - "chinese_history", - "chinese_literature", - "chinese_teacher_qualification", - "clinical_knowledge", - "college_actuarial_science", - "college_education", - "college_engineering_hydrology", - "college_law", - "college_mathematics", - "college_medical_statistics", - "college_medicine", - "computer_science", - "computer_security", - "conceptual_physics", - "construction_project_management", - "economics", - "education", - "electrical_engineering", - "elementary_chinese", - "elementary_commonsense", - "elementary_information_and_technology", - "elementary_mathematics", - "ethnology", - "food_science", - "genetics", - "global_facts", - "high_school_biology", - "high_school_chemistry", - "high_school_geography", - "high_school_mathematics", - "high_school_physics", - "high_school_politics", - "human_sexuality", - "international_law", - "journalism", - "jurisprudence", - "legal_and_moral_basis", - "logical", - "machine_learning", - "management", - "marketing", - "marxist_theory", - "modern_chinese", - "nutrition", - "philosophy", - "professional_accounting", - "professional_law", - "professional_medicine", - "professional_psychology", - "public_relations", - "security_study", - "sociology", - "sports_science", - "traditional_chinese_medicine", - "virology", - "world_history", - "world_religions", -] - -cmmlu_tasks = [ - LightevalTaskConfig( - name=f"cmmlu_{Language.CHINESE.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - Language.CHINESE, - lambda line: { - "question": line["Question"], - "choices": [line["A"], line["B"], line["C"], line["D"]], - "gold_idx": LETTER_INDICES.index(line["Answer"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="haonan-li/cmmlu", - hf_subset=subset, - evaluation_splits=("test",), - few_shots_split="dev", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for subset in CMMLU_SUBSETS - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# Arabic MMLU: Arabic version of MMLU -# Native translation with some new categories -# Paper: https://arxiv.org/html/2402.12840v1 -ARABIC_MMLU_SUBSETS = [ - "Islamic Studies", - "Islamic Studies (Middle School)", - "Islamic Studies (Primary School)", - "Islamic Studies (High School)", - "Driving Test", - "Natural Science (Middle School)", - "Natural Science (Primary School)", - "History (Middle School)", - "History (Primary School)", - "History (High School)", - "General Knowledge", - "General Knowledge (Middle School)", - "General Knowledge (Primary School)", - "Law (Professional)", - "Physics (High School)", - "Social Science (Middle School)", - "Social Science (Primary School)", - "Management (University)", - "Arabic Language (Middle School)", - "Arabic Language (Primary School)", - "Arabic Language (High School)", - "Political Science (University)", - "Philosophy (High School)", - "Accounting (University)", - "Computer Science (Middle School)", - "Computer Science (Primary School)", - "Computer Science (High School)", - "Computer Science (University)", - "Geography (Middle School)", - "Geography (Primary School)", - "Geography (High School)", - "Math (Primary School)", - "Biology (High School)", - "Economics (Middle School)", - "Economics (High School)", - "Economics (University)", - "Arabic Language (General)", - "Arabic Language (Grammar)", - "Civics (Middle School)", - "Civics (High School)", -] - -arabic_mmlu_tasks = [ - LightevalTaskConfig( - name=f"mmlu_{Language.ARABIC.value}_{formulation.name.lower()}:{normalize_subset(subset)}", - prompt_function=get_mcq_prompt_function( - Language.ARABIC, - lambda line: { - "context": line["Context"], - "question": line["Question"], - "choices": [str(o) for o in [line[f"Option {i}"] for i in range(1, 6)] if o], - "gold_idx": LETTER_INDICES.index(line["Answer Key"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="MBZUAI/ArabicMMLU", - hf_subset=subset, - evaluation_splits=("test",), - hf_avail_splits=["dev"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for subset in ARABIC_MMLU_SUBSETS - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - - -TURKISH_MMLU_SUBSET = [ - "Biology", - "Chemistry", - "Geography", - "History", - "Mathematics", - "Philosophy", - "Physics", - "Religion_and_Ethics", - "Turkish_Language_and_Literature", -] - -turkish_mmlu_tasks = [ - LightevalTaskConfig( - name=f"mmlu_{Language.TURKISH.value}_{formulation.name.lower()}:{normalize_subset(subset)}", - prompt_function=get_mcq_prompt_function( - Language.TURKISH, - lambda line: { - "question": line["question"], - "choices": line["choices"], - "gold_idx": LETTER_INDICES.index(line["answer"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="AYueksel/TurkishMMLU", - hf_subset=subset, - evaluation_splits=("test",), - few_shots_split="dev", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for subset in TURKISH_MMLU_SUBSET - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -TASKS_TABLE.extend( - [ - *meta_mmlu_tasks, - *mlmm_mmlu_tasks, - *rummlu, - *mmlu_turkish, - *cmmlu_tasks, - *openai_mmlu_tasks, - *arabic_mmlu_tasks, - *turkish_mmlu_tasks, - *afri_mmlu_tasks, - *global_mmlu_tasks, - ] -) - - -# ---------------------------- ARC ---------------------------- # -# ARC (AI2 Reasoning Challenge) is a dataset for question answering that requires reasoning. -# It consists of multiple-choice science questions from 3rd to 9th grade exams. -# The dataset is split into two parts: ARC-Easy and ARC-Challenge. -# ARC-Easy contains questions that can be answered correctly by both humans and simple baseline models. -# ARC-Challenge contains questions that are difficult for both humans and current AI systems. - -# Similar to MMLU, ARC tasks uses PMI normalization by default but only for the challenge set. - - -# github: https://github.com/nlp-uoregon/mlmm-evaluation -mlmm_arc_challenge_tasks = [ - LightevalTaskConfig( - name=f"mlmm_arc_{language.value}_{formulation.name.lower()}:challenge", - prompt_function=get_mcq_prompt_function( - language, - lambda line: { - "question": line["question"], - "choices": line["choices"]["text"], - "gold_idx": int(line["answerKey"]) - 1 - if line["answerKey"].isdigit() - else LETTER_INDICES.index(line["answerKey"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="jon-tow/okapi_arc_challenge", - hf_subset=standardize_tag(language.value), - hf_revision="823d5d7bfaf8974a3ab52a825b6cf4903b35dbc4", - evaluation_splits=("test",), - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for language in [ - Language.RUSSIAN, - Language.GERMAN, - Language.CHINESE, - Language.FRENCH, - Language.SPANISH, - Language.ITALIAN, - Language.DUTCH, - Language.VIETNAMESE, - Language.INDONESIAN, - Language.ARABIC, - Language.HUNGARIAN, - Language.ROMANIAN, - Language.DANISH, - Language.SLOVAK, - Language.UKRAINIAN, - Language.CATALAN, - Language.SERBIAN, - Language.CROATIAN, - Language.HINDI, - Language.BENGALI, - Language.TAMIL, - Language.NEPALI, - Language.MALAYALAM, - Language.MARATHI, - Language.TELUGU, - Language.KANNADA, - ] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# Arabic ARC Easy -# It's based on the community arabic leaderboard task but uses -# the multilingual template -# Paper: https://aclanthology.org/2023.arabicnlp-1.21/ -arabic_ledarboard_arc_easy = [ - LightevalTaskConfig( - name=f"alghafa_arc_{Language.ARABIC.value}_{formulation.name.lower()}:easy", - prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), - suite=["lighteval"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="arc_easy_ar", - hf_revision="08663706ee7cab30c4b7dc1bb00042a3227ce1ff", - evaluation_splits=["test"], - few_shots_split="validation", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - - -lumi_arc = [ - LightevalTaskConfig( - name=f"lumi_arc_{language.value}_{formulation.name.lower()}:challenge", - prompt_function=get_mcq_prompt_function( - language, - lambda line: { - "question": line["question"], - "choices": line["choices"]["text"], - "gold_idx": int(line["answerKey"]) - 1 - if line["answerKey"].isdigit() - else LETTER_INDICES.index(line["answerKey"]), - }, - formulation=formulation, - ), - suite=["lighteval"], - hf_repo="LumiOpen/arc_challenge_mt", - hf_subset=standardize_tag(language.value), - evaluation_splits=["test"], - few_shots_split="validation", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] - for language in [ - Language.DANISH, - Language.GERMAN, - Language.GREEK, - Language.SPANISH, - Language.FINNISH, - Language.HUNGARIAN, - Language.ITALIAN, - # Language.NORWEGIAN_BOKMAL, - Language.POLISH, - Language.PORTUGUESE, - Language.SWEDISH, - ] -] - -# Turkish ARC -# Comes from the Turkish leaderboard -turkish_arc_tasks = [ - LightevalTaskConfig( - name=f"community_arc_{Language.TURKISH.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - Language.TURKISH, - lambda line: { - "question": line["question"], - "choices": line["choices"]["text"], - "gold_idx": int(line["answerKey"]) - 1 - if line["answerKey"].isdigit() - else LETTER_INDICES.index(line["answerKey"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="malhajar/arc-tr", - hf_subset=f"ARC-{subset.capitalize()}", - evaluation_splits=("test",), - hf_avail_splits=["train"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ] - + ([LogLikelihoodAccMetric(normalization=LogProbPMINorm())] if subset == "challenge" else []), # type: ignore - ), - ) - for subset in ["easy", "challenge"] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -hindi_arc_tasks = [ - LightevalTaskConfig( - name=f"community_arc_{Language.HINDI.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - Language.HINDI, - lambda line: { - "question": line["question"], - "choices": line["choices"]["text"], - "gold_idx": int(line["answerKey"]) - 1 - if line["answerKey"].isdigit() - else LETTER_INDICES.index(line["answerKey"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="ai4bharat/ai2_arc-hi", - hf_subset=f"ARC-{subset.capitalize()}", - evaluation_splits=("test",), - few_shots_split="validation", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ] - + ([LogLikelihoodAccMetric(normalization=LogProbPMINorm())] if subset == "challenge" else []), # type: ignore - ), - ) - for subset in ["easy", "challenge"] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -arabic_arc_tasks = [ - LightevalTaskConfig( - name=f"alghafa_arc_{Language.ARABIC.value}_{formulation.name.lower()}:easy", - prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), - suite=["lighteval"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_revision="08663706ee7cab30c4b7dc1bb00042a3227ce1ff", - hf_subset="arc_easy_ar", - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -swahili_arc_tasks = [ - LightevalTaskConfig( - name=f"community_arc_{Language.SWAHILI.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - Language.SWAHILI, - lambda line: { - "question": line["question"], - "choices": line["choices"]["text"], - "gold_idx": int(line["answerKey"]) - 1 - if line["answerKey"].isdigit() - else LETTER_INDICES.index(line["answerKey"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo=f"Mollel/ARC_{subset.capitalize()}_SWH", - hf_subset="default", - hf_revision="5347439d3193c8a0dabaab3819914bf076dc94d4" - if subset == "easy" - else "dc1df9df632d14c251594d9129fb833d2ca4429c", - evaluation_splits=("test",), - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ] - + ([LogLikelihoodAccMetric(normalization=LogProbPMINorm())] if subset == "challenge" else []), # type: ignore - ), - ) - for subset in ["easy", "challenge"] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - - -TASKS_TABLE.extend( - [ - *mlmm_arc_challenge_tasks, - *arabic_ledarboard_arc_easy, - *lumi_arc, - *turkish_arc_tasks, - *hindi_arc_tasks, - *swahili_arc_tasks, - *arabic_arc_tasks, - ] -) - -# ---------------------------- TruthfulQA ---------------------------- # -# TruthfulQA: Measuring How Models Mimic Human Falsehoods -# Paper: https://arxiv.org/abs/2109.07958 -# TruthfulQA is a benchmark dataset designed to measure the truthfulness of language models. -# It consists of questions that humans might answer incorrectly due to false beliefs or misconceptions. -# The task evaluates a model's ability to provide truthful answers and avoid common human biases. - -# github: https://github.com/nlp-uoregon/mlmm-evaluation -mlmm_truthfulqa_tasks = [ - LightevalTaskConfig( - name=f"mlmm_truthfulqa_{language.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - language, - partial( - lambda subset, line: { - "question": line["question"], - "choices": line[f"{subset}_targets"]["choices"], - "gold_idx": [ix for ix, label in enumerate(line[f"{subset}_targets"]["labels"]) if label == 1], # type: ignore - }, - subset, - ), - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="jon-tow/okapi_truthfulqa", - hf_subset=standardize_tag(language.value), - hf_revision="cdd5db1a66fd04105622109d1c2a5cbc8cde7586", - evaluation_splits=("validation",), - hf_avail_splits=["validation"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for subset in ["mc1", "mc2"] - for language in [ - Language.ARABIC, - Language.BENGALI, - Language.CATALAN, - Language.DANISH, - Language.GERMAN, - Language.SPANISH, - Language.BASQUE, - Language.FRENCH, - Language.GUJARATI, - Language.HINDI, - Language.CROATIAN, - Language.HUNGARIAN, - Language.ARMENIAN, - Language.INDONESIAN, - Language.ICELANDIC, - Language.ITALIAN, - Language.KANNADA, - Language.MALAYALAM, - Language.MARATHI, - Language.NORWEGIAN, - Language.NEPALI, - Language.DUTCH, - Language.PORTUGUESE, - Language.ROMANIAN, - Language.RUSSIAN, - Language.SLOVAK, - Language.SERBIAN, - Language.SWEDISH, - Language.TAMIL, - Language.TELUGU, - Language.UKRAINIAN, - Language.VIETNAMESE, - Language.CHINESE, - ] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# Turkish TruthfulQA -# Based on turkish leaderboard -turkish_truthfulqa = [ - LightevalTaskConfig( - name=f"community_truthfulqa_{Language.TURKISH.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - Language.TURKISH, - partial( - lambda subset, line: { - "question": line["question"], - "choices": line[f"{subset}_targets"]["choices"], - "gold_idx": [ix for ix, label in enumerate(line[f"{subset}_targets"]["labels"]) if label == 1], # type: ignore - }, - subset, - ), - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="malhajar/truthful_qa-tr-v0.2", - hf_subset="default", - evaluation_splits=("validation",), - hf_avail_splits=["validation"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for subset in ["mc1", "mc2"] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -TASKS_TABLE.extend( - [ - *mlmm_truthfulqa_tasks, - *turkish_truthfulqa, - ] -) - -# ---------------------------- Exams like tasks ---------------------------- # - -# Exams: A collection of exam questions from various countries and subjects -# Paper: https://arxiv.org/abs/2011.03080 -exams_subjects_by_lang: dict[Language, set[str]] = { - Language.ARABIC: {"Biology", "Islamic Studies", "Physics", "Science", "Social"}, - Language.BULGARIAN: {"Biology", "Chemistry", "Geography", "History", "Philosophy", "Physics"}, - Language.CROATIAN: { - "Biology", - "Chemistry", - "Ethics", - "Fine Arts", - "Geography", - "Geology", - "History", - "Informatics", - "Philosophy", - "Physics", - "Politics", - "Psychology", - "Religion", - "Sociology", - }, - Language.HUNGARIAN: { - "Agriculture", - "Agriculture (Mechanical knowledge)", - "Biology", - "Chemistry", - "Economics", - "Economics & Marketing", - "Economics Basics (Business)", - "Economics Basics (Theoretical)", - "Forestry", - "Geography", - "Landscaping", - "Physics", - "Politics", - "Tourism", - }, - Language.ITALIAN: { - "Biology", - "Chemistry", - "Ethics", - "Geography", - "Geology", - "History", - "Informatics", - "Philosophy", - "Physics", - "Politics", - "Psychology", - "Sociology", - }, - Language.SERBIAN: { - "Biology", - "Chemistry", - "Ethics", - "Geography", - "Geology", - "History", - "Informatics", - "Philosophy", - "Physics", - "Politics", - "Psychology", - "Religion", - "Sociology", - }, - Language.FRENCH: {"Economics", "Economics & Marketing", "Economics Basics (Theoretical)", "Geography", "Physics"}, - Language.GERMAN: { - "Chemistry", - "Economics", - "Economics & Marketing", - "Economics Basics (Theoretical)", - "Geography", - "Physics", - "Tourism", - }, - Language.SPANISH: {"Geography", "Physics"}, - Language.LITHUANIAN: {"Geology", "History"}, - Language.ALBANIAN: { - "Biology", - "Business", - "Chemistry", - "Fine Arts", - "History", - "Philosophy", - "Physics", - "Sociology", - }, - Language.MACEDONIAN: { - "Biology", - "Business", - "Chemistry", - "Fine Arts", - "History", - "Philosophy", - "Physics", - "Sociology", - }, - Language.TURKISH: { - "Biology", - "Business", - "Chemistry", - "Geography", - "History", - "Philosophy", - "Physics", - "Sociology", - }, - Language.POLISH: {"Professional"}, - Language.PORTUGUESE: {"Biology", "Economics", "Geology", "Philosophy"}, - Language.VIETNAMESE: {"Biology", "Chemistry", "Citizenship", "Geography", "History", "Physics"}, -} - -exams_tasks = [ - LightevalTaskConfig( - name=f"exams_{language.value}_{formulation.name.lower()}:{normalize_subset(subject)}", - prompt_function=get_mcq_prompt_function( - language, - lambda line: { - "question": line["question"]["stem"], - "choices": line["question"]["choices"]["text"], - "gold_idx": line["question"]["choices"]["label"].index(line["answerKey"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="mhardalov/exams", - hf_subset="multilingual", - # Weird bug in dataset - hf_filter=partial( - lambda language, subject, line: line["answerKey"] != "@" - and line["info"]["language"] == LangCodeLanguage(standardize_tag(language.value)).language_name() - and line["info"]["subject"] == subject, - language, - subject, - ), - evaluation_splits=("test",), - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for language in exams_subjects_by_lang.keys() - for subject in exams_subjects_by_lang[language] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# M3Exam: Multitask Multilingual Multimodal Evaluation Benchmark -# It also contains a multimodal version but we don't support that -# Paper: https://arxiv.org/abs/2306.05179 -m3exams_tasks = [ - LightevalTaskConfig( - name=f"m3exams_{language.value}_{formulation.name.lower()}", - suite=("lighteval",), - prompt_function=get_mcq_prompt_function( - language, - partial(get_m3exam_adapter, language), - formulation=formulation, - ), - hf_repo="chiayewken/m3exam", - hf_subset=LangCodeLanguage(standardize_tag(language.value)).language_name().lower(), - evaluation_splits=("test",), - few_shots_split="dev", - generation_size=-1, - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for language in [ - Language.AFRIKAANS, - Language.CHINESE, - Language.ENGLISH, - Language.ITALIAN, - Language.JAVANESE, - Language.PORTUGUESE, - Language.SWAHILI, - Language.THAI, - Language.VIETNAMESE, - ] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# Thai Exams -# We noticed very bad performance of models on this dataset -# However, it may just be because quality of the models themselves -# Paper: https://arxiv.org/abs/2312.13951 - -THAI_EXAMS_SUBSETS = ["a_level", "ic", "onet", "tgat", "tpat1"] - -thai_exams_tasks = [ - LightevalTaskConfig( - name=f"thai_exams_{Language.THAI.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function(Language.THAI, thai_exams_adapter, formulation=formulation), - suite=("lighteval",), - hf_repo="scb10x/thai_exam", - hf_subset=subset, - evaluation_splits=("test",), - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for subset in THAI_EXAMS_SUBSETS - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -TASKS_TABLE.extend( - [ - *exams_tasks, - *m3exams_tasks, - *thai_exams_tasks, - ] -) - -# ------------------------------- XCSQA ------------------------------- # -# XCSQA (Cross-lingual Commonsense QA) is part of the XCSR (Cross-lingual Commonsense Reasoning) benchmark -# It is a multilingual extension of the CommonsenseQA dataset, covering 16 languages -# The task involves answering multiple-choice questions that require commonsense reasoning -# Uses PMI normalization -# Paper: https://arxiv.org/abs/2110.08462 -xcsqa_tasks = [ - LightevalTaskConfig( - name=f"xcsqa_{language.value}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - language, - lambda line: { - "question": line["question"]["stem"], - "choices": line["question"]["choices"]["text"], - "gold_idx": line["question"]["choices"]["label"].index(line["answerKey"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="INK-USC/xcsr", - hf_subset=f"X-CSQA-{standardize_tag(language.value) if language != Language.JAPANESE else 'jap'}", - hf_filter=lambda x: all( - len(x["question"]["choices"]["text"][i].strip()) > 0 for i in range(len(x["question"]["choices"]["text"])) - ), - evaluation_splits=("validation",), - hf_avail_splits=["validation"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for language in [ - Language.ARABIC, - Language.GERMAN, - Language.ENGLISH, - Language.SPANISH, - Language.FRENCH, - Language.HINDI, - Language.ITALIAN, - Language.JAPANESE, - Language.DUTCH, - Language.POLISH, - Language.PORTUGUESE, - Language.RUSSIAN, - Language.SWAHILI, - Language.URDU, - Language.VIETNAMESE, - Language.CHINESE, - ] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -TASKS_TABLE.extend( - [ - *xcsqa_tasks, - ] -) - -# ------------------------------- PIQA ------------------------------- # -# PIQA: Physical Interaction Question Answering -# PIQA is a benchmark for testing physical commonsense reasoning. -# This Arabic version is a translation of the original PIQA dataset, adapted for Arabic language evaluation. -# It tests the ability to reason about physical interactions in everyday situations. -# Paper: https://arxiv.org/abs/1911.11641 -# Arabic version: https://aclanthology.org/2023.arabicnlp-1.21/ -piqa_ar_tasks = [ - LightevalTaskConfig( - name=f"alghafa_piqa_{Language.ARABIC.value}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), - suite=["lighteval"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_revision="08663706ee7cab30c4b7dc1bb00042a3227ce1ff", - hf_subset="piqa_ar", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -TASKS_TABLE.extend( - [ - *piqa_ar_tasks, - ] -) - -# ------------------------------- OpenBookQA ------------------------------- # -# OpenBookQA: A Question-Answering Dataset for Open-Book Exams -# OpenBookQA is a question-answering dataset modeled after open-book exams for assessing human understanding of a subject. -# It consists of multiple-choice questions that require combining facts from a given open book with broad common knowledge. -# The task tests language models' ability to leverage provided information and apply common sense reasoning. -# Original paper: https://arxiv.org/abs/1809.02789 -# Arabic version: https://aclanthology.org/2023.arabicnlp-1.21/ -openbook_ara_tasks = [ - LightevalTaskConfig( - name=f"alghafa_openbookqa_{Language.ARABIC.value}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), - suite=["lighteval"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="openbook_qa_ext_ar", - hf_revision="08663706ee7cab30c4b7dc1bb00042a3227ce1ff", - evaluation_splits=["test"], - few_shots_split="validation", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# Spanish version of OpenBookQA from BSC Language Technology group -# Dataset: https://huggingface.co/datasets/BSC-LT/openbookqa-es -openbook_es_tasks = [ - LightevalTaskConfig( - name=f"openbookqa_{Language.SPANISH.value}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - Language.SPANISH, - lambda line: { - "question": line["question_stem"], - "choices": line["choices"]["text"], - "gold_idx": LETTER_INDICES.index(line["answerKey"]), - }, - formulation=formulation, - ), - suite=["lighteval"], - hf_repo="BSC-LT/openbookqa-es", - hf_subset="default", - evaluation_splits=("test",), - few_shots_split="validation", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - - -# The Russian version is part of the MERA (Multilingual Enhanced Russian NLP Architectures) project. -# Paper: https://arxiv.org/abs/2401.04531 -openbook_rus_tasks = [ - LightevalTaskConfig( - name=f"mera_openbookqa_{Language.RUSSIAN.value}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - Language.RUSSIAN, - lambda line: { - "question": line["inputs"]["question"], - "choices": [line["inputs"][f"option_{i.lower()}"] for i in LETTER_INDICES[:4]], - "gold_idx": LETTER_INDICES.index(line["outputs"]), - }, - formulation=formulation, - ), - suite=["lighteval"], - hf_repo="ai-forever/MERA", - hf_subset="ruopenbookqa", - evaluation_splits=("train",), - hf_avail_splits=["train"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -TASKS_TABLE.extend( - [ - *openbook_rus_tasks, - *openbook_ara_tasks, - *openbook_es_tasks, - ] -) - -# ------------------------------- SciQ ------------------------------- # -# SciQ: Science Question Answering -# SciQ is a question-answering dataset designed to evaluate the ability of language models to answer science questions. -# It consists of multiple-choice questions that require scientific reasoning and factual knowledge. - -# The Arabic version is part of the AlGhafa Arabic LLM Benchmark, a translation and adaptation of various English datasets. -# Paper: https://aclanthology.org/2023.arabicnlp-1.21/ -sciqa_ar_task = [ - LightevalTaskConfig( - name=f"alghafa_sciqa_{Language.ARABIC.value}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - Language.ARABIC, - sciqa_adapter, - formulation=formulation, - ), - suite=["lighteval"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="sciq_ar", - hf_revision="08663706ee7cab30c4b7dc1bb00042a3227ce1ff", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -TASKS_TABLE.extend( - [ - *sciqa_ar_task, - ] -) - -# ------------------------------- Math Tasks ------------------------------- # - -# MathLogicQA is a dataset for evaluating mathematical reasoning in language models. -# It consists of multiple-choice questions that require logical reasoning and mathematical problem-solving. -# This Russian version is part of the MERA (Multilingual Evaluation of Reasoning Abilities) benchmark. -# MERA: https://github.com/ai-forever/MERA -mathlogicqa_rus_tasks = [ - LightevalTaskConfig( - name=f"mathlogic_qa_{Language.RUSSIAN.value}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - Language.RUSSIAN, - lambda line: { - "question": line["inputs"]["text"], - "choices": [line["inputs"][f"option_{i.lower()}"] for i in LETTER_INDICES[:4]], - "gold_idx": LETTER_INDICES.index(line["outputs"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="ai-forever/MERA", - hf_subset="mathlogicqa", - evaluation_splits=("train",), - hf_avail_splits=["train"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - CFFormulation(), - MCFFormulation(), - HybridFormulation(), - ] -] - -cmath_tasks = [ - LightevalTaskConfig( - name=f"cmath_{Language.CHINESE.value}", - prompt_function=get_qa_prompt_function( - Language.CHINESE, - lambda line: { - "question": line["question"], - "choices": [line["golden"]], - }, - ), - suite=("lighteval",), - hf_repo="weitianwen/cmath", - hf_subset="default", - evaluation_splits=("test",), - few_shots_split="validation", - generation_size=25, - metrics=[ - MultilingualQuasiExactMatchMetric(Language.CHINESE, "full"), - ], - stop_sequence=("\n",), - ) -] - -mgsm_tasks = [ - LightevalTaskConfig( - name=f"mgsm_{language.value}", - prompt_function=get_qa_prompt_function( - language, - lambda line: { - "question": line["question"], - # The cot is available but we have no use: - # line["answer"] - "choices": [str(line["answer_number"])], - }, - ), - suite=("lighteval",), - hf_repo="juletxara/mgsm", - hf_subset=standardize_tag(language.value), - evaluation_splits=("test",), - few_shots_split="train", - generation_size=25, - metrics=[ - MultilingualQuasiExactMatchMetric(language, "full"), - ], - stop_sequence=("\n",), - ) - for language in [ - Language.ENGLISH, - Language.SPANISH, - Language.FRENCH, - Language.GERMAN, - Language.RUSSIAN, - Language.CHINESE, - Language.JAPANESE, - Language.THAI, - Language.SWAHILI, - Language.BENGALI, - Language.TELUGU, - ] -] -# African MGSM: MGSM for African Languages -# From https://arxiv.org/abs/2406.03368. Human translated MGSM. -afri_mgsm_tasks = [ - LightevalTaskConfig( - name=f"afri_mgsm_{language.value}", - prompt_function=get_qa_prompt_function( - language, - lambda line: { - "question": line["question"], - # The cot is available but we have no use: - # line["answer"] - "choices": [str(line["answer_number"])], - }, - ), - suite=("lighteval",), - hf_repo="masakhane/afrimgsm", - hf_subset=language.value, - evaluation_splits=("test",), - few_shots_split="train", - generation_size=25, - metrics=[ - MultilingualQuasiExactMatchMetric(language, "full"), - ], - stop_sequence=("\n",), - ) - for language in [ - Language.AMHARIC, - # Language.EWE, - Language.FRENCH, - # Language.HAUSA, - # Language.IGBO, - # Language.KINYARWANDA, - # Language.LINGALA, - # Language.LUGANDA, - # Language.OROMO, - # Language.SHONA, - # Language.SOTHO, - Language.SWAHILI, - # Language.TWI, - # Language.WOLOF, - # Language.XHOSA, - Language.YORUBA, - # Language.ZULU, - ] -] -TASKS_TABLE.extend( - [ - *cmath_tasks, - *mathlogicqa_rus_tasks, - *mgsm_tasks, - *afri_mgsm_tasks, - ] -) - -# ------------------------------- Misc ------------------------------- # - -# AGIEval: Chinese AGI Evaluation suite (Excluding the english subsets) -# Uses PMI normalization -# Paper: https://arxiv.org/abs/2304.06364 -CHINESE_AGIEVAL_SUBSET = [ - "gaokao-biology", - "gaokao-chinese", - "gaokao-chemistry", - "gaokao-geography", - "gaokao-history", - "gaokao-mathqa", - "gaokao-physics", - "logiqa-zh", - "jec-qa-kd", - "jec-qa-ca", -] - -agieval_tasks_zh = [ - LightevalTaskConfig( - name=f"agieval_{Language.CHINESE.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - Language.CHINESE, - partial( - agieval_adapter, - Language.CHINESE, - formulation, - ), - formulation=formulation, - ), - suite=("lighteval",), - hf_repo=f"hails/agieval-{subset}", - hf_subset="default", - evaluation_splits=("test",), - hf_avail_splits=["test"], - few_shots_split=None, - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for subset in CHINESE_AGIEVAL_SUBSET - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] -# C-Eval: Chinese Evaluation suite -# Similar to MMLu but with different categories -# Paper: https://arxiv.org/abs/2305.08322 -CEVAL_SUBSET = [ - "computer_network", - "operating_system", - "computer_architecture", - "college_programming", - "college_physics", - "college_chemistry", - "advanced_mathematics", - "probability_and_statistics", - "discrete_mathematics", - "electrical_engineer", - "metrology_engineer", - "high_school_mathematics", - "high_school_physics", - "high_school_chemistry", - "high_school_biology", - "middle_school_mathematics", - "middle_school_biology", - "middle_school_physics", - "middle_school_chemistry", - "veterinary_medicine", - "college_economics", - "business_administration", - "marxism", - "mao_zedong_thought", - "education_science", - "teacher_qualification", - "high_school_politics", - "high_school_geography", - "middle_school_politics", - "middle_school_geography", - "modern_chinese_history", - "ideological_and_moral_cultivation", - "logic", - "law", - "chinese_language_and_literature", - "art_studies", - "professional_tour_guide", - "legal_professional", - "high_school_chinese", - "high_school_history", - "middle_school_history", - "civil_servant", - "sports_science", - "plant_protection", - "basic_medicine", - "clinical_medicine", - "urban_and_rural_planner", - "accountant", - "fire_engineer", - "environmental_impact_assessment_engineer", - "tax_accountant", - "physician", -] - -ceval_tasks = [ - LightevalTaskConfig( - name=f"ceval_{Language.CHINESE.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - Language.CHINESE, - partial( - ceval_adapter, - Language.CHINESE, - formulation, - ), - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="ceval/ceval-exam", - hf_subset=subset, - evaluation_splits=("val",), - few_shots_split="dev", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for subset in CEVAL_SUBSET - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - - -# OAB Exams: A collection of questions from the Brazilian Bar Association exam -# The exam is required for anyone who wants to practice law in Brazil -# Dataset: https://huggingface.co/datasets/eduagarcia/oab_exams -oab_exams_tasks = [ - LightevalTaskConfig( - name=f"oab_exams_{Language.PORTUGUESE.value}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - Language.PORTUGUESE, - lambda line: { - "question": line["question"], - "choices": line["choices"]["text"], - "gold_idx": LETTER_INDICES.index(line["answerKey"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="eduagarcia/oab_exams", - hf_subset="default", - evaluation_splits=("train",), - hf_avail_splits=["train"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -# ENEM (Exame Nacional do Ensino Médio) is a standardized Brazilian national secondary -# education examination. The exam is used both as a university admission test and as a -# high school evaluation test. -# Dataset: https://huggingface.co/datasets/maritaca-ai/enem -enem_tasks = [ - LightevalTaskConfig( - name=f"enem_{Language.PORTUGUESE.value}_{formulation.name.lower()}:{year}", - prompt_function=get_mcq_prompt_function( - Language.PORTUGUESE, - partial( - enem_adapter, - Language.PORTUGUESE, - ), - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="maritaca-ai/enem", - hf_subset=year, - evaluation_splits=("train",), - hf_avail_splits=["train"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for year in ["2022", "2023", "2024"] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - - -# WorldTree is a dataset for multi-hop inference in science question answering. -# It provides explanations for elementary science questions by combining facts from a semi-structured knowledge base. -# This Russian version is part of the MERA (Multilingual Evaluation of Reasoning Abilities) benchmark. -# MERA: https://github.com/ai-forever/MERA -worldtree_rus_tasks = [ - LightevalTaskConfig( - name=f"mera_worldtree_{Language.RUSSIAN.value}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - Language.RUSSIAN, - lambda line: { - "question": line["inputs"]["question"], - "choices": [line["inputs"][f"option_{i.lower()}"] for i in LETTER_INDICES[:4]], - "gold_idx": LETTER_INDICES.index(line["outputs"]), - }, - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="ai-forever/MERA", - hf_subset="ruworldtree", - evaluation_splits=("train",), - hf_avail_splits=["train"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -TASKS_TABLE.extend( - [ - *agieval_tasks_zh, - *worldtree_rus_tasks, - *ceval_tasks, - *oab_exams_tasks, - *enem_tasks, - ] -) - - -# ------------------------------- Continuation Tasks ------------------------------- # -xcodah_tasks = [ - LightevalTaskConfig( - name=f"xcodah_{language.value}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function(language, partial(xcodah_adapter, language), formulation=formulation), - suite=("lighteval",), - hf_repo="INK-USC/xcsr", - hf_subset=f"X-CODAH-{standardize_tag(language.value) if language != Language.JAPANESE else 'jap'}", - evaluation_splits=("validation",), - hf_avail_splits=["validation"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for language in [ - Language.ARABIC, - Language.GERMAN, - Language.ENGLISH, - Language.SPANISH, - Language.FRENCH, - Language.HINDI, - Language.ITALIAN, - Language.JAPANESE, - Language.DUTCH, - Language.POLISH, - Language.PORTUGUESE, - Language.RUSSIAN, - Language.SWAHILI, - Language.URDU, - Language.VIETNAMESE, - Language.CHINESE, - ] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -xstory_tasks = [ - LightevalTaskConfig( - name=f"xstory_cloze_{lang.value}_{formulation.name.lower()}", - prompt_function=get_continuation_prompt_function( - lang, - partial( - lambda lang, line: { - "context": TRANSLATION_LITERALS[lang].sentence_space.join( - [ - line["input_sentence_1"], - line["input_sentence_2"], - line["input_sentence_3"], - line["input_sentence_4"], - ] - ), - "continuations": [line["sentence_quiz1"], line["sentence_quiz2"]], - "gold_idx": int(line["answer_right_ending"]) - 1, # type: ignore - }, - lang, - ), - formulation=formulation, - ), - suite=("lighteval",), - hf_repo="juletxara/xstory_cloze", - hf_subset=standardize_tag(lang.value), - evaluation_splits=["eval"], - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for lang in [ - Language.RUSSIAN, - Language.CHINESE, - Language.SPANISH, - Language.ARABIC, - Language.HINDI, - Language.INDONESIAN, - Language.TELUGU, - Language.SWAHILI, - Language.BASQUE, - Language.BURMESE, - ] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -TASKS_TABLE.extend( - [ - *xcodah_tasks, - *xstory_tasks, - ] -) - -# ------------------------------- Winogrande Tasks ------------------------------- # - -xwinograd_tasks = [ - LightevalTaskConfig( - name=f"xwinograd_{language.value}_{formulation.name.lower()}", - suite=("lighteval",), - prompt_function=get_continuation_prompt_function( - language, partial(winogrand_adapter, language), formulation=formulation - ), - hf_repo="Muennighoff/xwinograd", - hf_subset=standardize_tag(language.value) if language != Language.JAPANESE else "jp", - evaluation_splits=("test",), - hf_avail_splits=["test"], - metrics=[ - LogLikelihoodAccMetric(normalization=None), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ) - for language in [ - Language.ENGLISH, - Language.FRENCH, - Language.JAPANESE, - Language.PORTUGUESE, - Language.RUSSIAN, - Language.CHINESE, - ] - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -winograd_turkish_task = [ - LightevalTaskConfig( - name=f"community_xwinograd_{Language.TURKISH.value}_{formulation.name.lower()}", - suite=("lighteval",), - prompt_function=get_continuation_prompt_function( - Language.TURKISH, partial(winogrand_adapter, Language.TURKISH), formulation=formulation - ), - hf_repo="malhajar/winogrande-tr-v0.2", - hf_subset="default", - evaluation_splits=("validation",), - few_shots_split="train", - metrics=[ - LogLikelihoodAccMetric(normalization=None), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ) - for formulation in [ - MCFFormulation(), - CFFormulation(), - HybridFormulation(), - ] -] - -TASKS_TABLE.extend( - [ - *xwinograd_tasks, - *winograd_turkish_task, - ] -) - -# ------------------------------- General QA tasks ------------------------------- # - -MKQA_TASK_TO_ID = { - "entity": 0, - "long_answer": 1, - # "unanswerable": 2, - "date": 3, - "number": 4, - "number_with_unit": 5, - "short_phrase": 6, - "binary": 7, -} - -mkqa_tasks = [ - LightevalTaskConfig( - name=f"mkqa_{language.value}:{subset}", - prompt_function=get_qa_prompt_function(language, partial(get_mkqa_adapter, language)), - suite=("lighteval",), - hf_repo="apple/mkqa", - hf_subset="mkqa", - hf_revision="325131889721ae0ed885b76ecb8011369d75abad", - hf_filter=partial( - lambda language, subset, line: line["answers"][ - "zh_cn" if language == Language.CHINESE else standardize_tag(language.value) - ][0]["type"] - == MKQA_TASK_TO_ID[subset], - language, - subset, - ), - evaluation_splits=("train",), - hf_avail_splits=["train"], - stop_sequence=("\n",), - metrics=[ - MultilingualQuasiExactMatchMetric(language, "prefix"), - MultilingualQuasiF1ScoreMetric(language), - ] - if subset in ["entity", "long_answer", "short_phrase"] - else [ - MultilingualQuasiExactMatchMetric(language, "full"), - ], - ) - for subset in MKQA_TASK_TO_ID.keys() - for language in [ - Language.ARABIC, - Language.DANISH, - Language.GERMAN, - Language.ENGLISH, - Language.SPANISH, - Language.FINNISH, - Language.FRENCH, - Language.HEBREW, - Language.HUNGARIAN, - Language.ITALIAN, - Language.JAPANESE, - Language.KOREAN, - Language.KHMER, - Language.MALAY, - Language.DUTCH, - Language.NORWEGIAN, - Language.POLISH, - Language.PORTUGUESE, - Language.RUSSIAN, - Language.SWEDISH, - Language.THAI, - Language.TURKISH, - Language.VIETNAMESE, - Language.CHINESE, # Simplified - # Language.CHINESE_HONG_KONG, - # Language.CHINESE_TRADITIONAL, - ] -] - -mintaka_tasks = [ - LightevalTaskConfig( - name=f"mintaka_{lang.value}", - prompt_function=get_qa_prompt_function( - lang, - lambda line: { - "question": line["question"], - "choices": [line["answerText"]], - }, - ), - suite=("lighteval",), - hf_repo="AmazonScience/mintaka", - hf_subset=standardize_tag(lang.value), - evaluation_splits=("test",), - few_shots_split="train", - generation_size=400, - stop_sequence=("\n",), - metrics=[ - MultilingualQuasiExactMatchMetric(lang, "prefix"), - MultilingualQuasiF1ScoreMetric(lang), - ], - ) - for lang in [ - Language.ARABIC, - Language.GERMAN, - Language.ENGLISH, - Language.SPANISH, - Language.FRENCH, - Language.HINDI, - Language.ITALIAN, - Language.JAPANESE, - Language.PORTUGUESE, - ] -] - -french_triviqa_tasks = [ - LightevalTaskConfig( - name=f"community_triviaqa_{Language.FRENCH.value}", - prompt_function=get_qa_prompt_function( - Language.FRENCH, - lambda line: { - "question": line["Question"], - "choices": [line["Answer"]], - }, - ), - suite=("lighteval",), - hf_repo="manu/french-trivia", - hf_subset="default", - evaluation_splits=("train",), - hf_avail_splits=["train"], - generation_size=400, - stop_sequence=("\n",), - metrics=[ - MultilingualQuasiExactMatchMetric(Language.FRENCH, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.FRENCH), - ], - ) -] - - -chegeka_tasks = [ - LightevalTaskConfig( - name=f"chegeka_{Language.RUSSIAN.value}", - prompt_function=get_qa_prompt_function( - Language.RUSSIAN, - lambda line: { - "question": line["inputs"]["text"], - "choices": [line["outputs"]], - }, - ), - suite=("lighteval",), - hf_repo="ai-forever/MERA", - hf_subset="chegeka", - evaluation_splits=("train",), - hf_avail_splits=["train"], - generation_size=400, - stop_sequence=("\n",), - metrics=[ - MultilingualQuasiExactMatchMetric(Language.RUSSIAN, "prefix"), - MultilingualQuasiF1ScoreMetric(Language.RUSSIAN), - ], - ) -] - -TASKS_TABLE.extend( - [ - *mkqa_tasks, - *mlqa_tasks, - *chegeka_tasks, - *mintaka_tasks, - *french_triviqa_tasks, - ] -) - - -# ------------------------------- BoolQ Tasks (yes/no) ------------------------------- # -ACVA_SUBSET = [ - "Algeria", - "Ancient_Egypt", - "Arab_Empire", - "Arabic_Architecture", - "Arabic_Art", - "Arabic_Astronomy", - "Arabic_Calligraphy", - "Arabic_Ceremony", - "Arabic_Clothing", - "Arabic_Culture", - "Arabic_Food", - "Arabic_Funeral", - "Arabic_Geography", - "Arabic_History", - "Arabic_Language_Origin", - "Arabic_Literature", - "Arabic_Math", - "Arabic_Medicine", - "Arabic_Music", - "Arabic_Ornament", - "Arabic_Philosophy", - "Arabic_Physics_and_Chemistry", - "Arabic_Wedding", - "Bahrain", - "Comoros", - "Egypt_modern", - "InfluenceFromAncientEgypt", - "InfluenceFromByzantium", - "InfluenceFromChina", - "InfluenceFromGreece", - "InfluenceFromIslam", - "InfluenceFromPersia", - "InfluenceFromRome", - "Iraq", - "Islam_Education", - "Islam_branches_and_schools", - "Islamic_law_system", - "Jordan", - "Kuwait", - "Lebanon", - "Libya", - "Mauritania", - "Mesopotamia_civilization", - "Morocco", - "Oman", - "Palestine", - "Qatar", - "Saudi_Arabia", - "Somalia", - "Sudan", - "Syria", - "Tunisia", - "United_Arab_Emirates", - "Yemen", - "communication", - "computer_and_phone", - "daily_life", - "entertainment", -] - -acva_tasks = [ - LightevalTaskConfig( - name=f"acva_{Language.ARABIC.value}:{subset}", - prompt_function=get_boolq_prompt_function( - Language.ARABIC, - lambda line: { - "question": line["question"], - "answer": line["answer"] == "صح", - }, - formulation=CFFormulation(), - ), - suite=("lighteval",), - hf_repo="OALL/ACVA", - hf_subset=subset, - evaluation_splits=("test",), - few_shots_split="validation", - metrics=[MultilingualQuasiExactMatchMetric(Language.ARABIC, "full"), LogLikelihoodAccMetric()], - generation_size=5, - stop_sequence=("\n",), - ) - for subset in ACVA_SUBSET -] - - -french_boolq_tasks = [ - LightevalTaskConfig( - name=f"community_boolq_{Language.FRENCH.value}", - prompt_function=get_boolq_prompt_function( - Language.FRENCH, - lambda line: { - "question": line["question"], - "answer": line["label"] == 1, - "context": line["passage"], - }, - formulation=CFFormulation(), - ), - suite=("lighteval",), - hf_repo="manu/french_boolq", - hf_subset="default", - evaluation_splits=("test",), - few_shots_split="valid", - generation_size=5, - stop_sequence=["\n"], - metrics=[MultilingualQuasiExactMatchMetric(Language.FRENCH, "full"), LogLikelihoodAccMetric()], - ) -] - -hindi_boolq_tasks = [ - LightevalTaskConfig( - name=f"community_boolq_{language.value}", - prompt_function=get_boolq_prompt_function( - language, - lambda line: { - "question": line["question"], - "answer": line["answer"], - "context": line["passage"], - }, - formulation=CFFormulation(), - ), - suite=("lighteval",), - hf_repo="ai4bharat/boolq-hi", - hf_subset=standardize_tag(language.value), - evaluation_splits=("validation",), - few_shots_split="train", - generation_size=5, - stop_sequence=["\n"], - metrics=[MultilingualQuasiExactMatchMetric(language, "full"), LogLikelihoodAccMetric()], - ) - for language in [ - Language.HINDI, - Language.GUJARATI, - Language.MALAYALAM, - Language.MARATHI, - Language.TAMIL, - ] -] - - -TASKS_TABLE.extend( - [ - *acva_tasks, - *french_boolq_tasks, - *hindi_boolq_tasks, - ] -) - -# ------------------------------- Translation Tasks ------------------------------- # -flores_200_languages = [ - # "ace_Arab", - "ace_Latn", - "acm_Arab", - "acq_Arab", - "aeb_Arab", - "afr_Latn", - "ajp_Arab", - "aka_Latn", - "amh_Ethi", - "apc_Arab", - "arb_Arab", - # "arb_Latn", - "ars_Arab", - "ary_Arab", - "arz_Arab", - "asm_Beng", - "ast_Latn", - "awa_Deva", - "ayr_Latn", - "azb_Arab", - "azj_Latn", - "bak_Cyrl", - "bam_Latn", - "ban_Latn", - "bel_Cyrl", - "bem_Latn", - "ben_Beng", - "bho_Deva", - # "bjn_Arab", - "bjn_Latn", - "bod_Tibt", - "bos_Latn", - "bug_Latn", - "bul_Cyrl", - "cat_Latn", - "ceb_Latn", - "ces_Latn", - "cjk_Latn", - "ckb_Arab", - "crh_Latn", - "cym_Latn", - "dan_Latn", - "deu_Latn", - "dik_Latn", - "dyu_Latn", - "dzo_Tibt", - "ell_Grek", - "eng_Latn", - "epo_Latn", - "est_Latn", - "eus_Latn", - "ewe_Latn", - "fao_Latn", - "fij_Latn", - "fin_Latn", - "fon_Latn", - "fra_Latn", - "fur_Latn", - "fuv_Latn", - "gla_Latn", - "gle_Latn", - "glg_Latn", - "grn_Latn", - "guj_Gujr", - "hat_Latn", - "hau_Latn", - "heb_Hebr", - "hin_Deva", - "hne_Deva", - "hrv_Latn", - "hun_Latn", - "hye_Armn", - "ibo_Latn", - "ilo_Latn", - "ind_Latn", - "isl_Latn", - "ita_Latn", - "jav_Latn", - "jpn_Jpan", - "kab_Latn", - "kac_Latn", - "kam_Latn", - "kan_Knda", - # "kas_Arab", - "kas_Deva", - "kat_Geor", - # "knc_Arab", - "knc_Latn", - "kaz_Cyrl", - "kbp_Latn", - "kea_Latn", - "khm_Khmr", - "kik_Latn", - "kin_Latn", - "kir_Cyrl", - "kmb_Latn", - "kmr_Latn", - "kon_Latn", - "kor_Hang", - "lao_Laoo", - "lij_Latn", - "lim_Latn", - "lin_Latn", - "lit_Latn", - "lmo_Latn", - "ltg_Latn", - "ltz_Latn", - "lua_Latn", - "lug_Latn", - "luo_Latn", - "lus_Latn", - "lvs_Latn", - "mag_Deva", - "mai_Deva", - "mal_Mlym", - "mar_Deva", - # "min_Arab", - "min_Latn", - "mkd_Cyrl", - "plt_Latn", - "mlt_Latn", - "mni_Beng", - "khk_Cyrl", - "mos_Latn", - "mri_Latn", - "mya_Mymr", - "nld_Latn", - "nno_Latn", - "nob_Latn", - "npi_Deva", - "nso_Latn", - "nus_Latn", - "nya_Latn", - "oci_Latn", - "gaz_Latn", - "ory_Orya", - "pag_Latn", - "pan_Guru", - "pap_Latn", - "pes_Arab", - "pol_Latn", - "por_Latn", - "prs_Arab", - "pbt_Arab", - "quy_Latn", - "ron_Latn", - "run_Latn", - "rus_Cyrl", - "sag_Latn", - "san_Deva", - "sat_Olck", - "scn_Latn", - "shn_Mymr", - "sin_Sinh", - "slk_Latn", - "slv_Latn", - "smo_Latn", - "sna_Latn", - "snd_Arab", - "som_Latn", - "sot_Latn", - "spa_Latn", - "als_Latn", - "srd_Latn", - "srp_Cyrl", - "ssw_Latn", - "sun_Latn", - "swe_Latn", - "swh_Latn", - "szl_Latn", - "tam_Taml", - "tat_Cyrl", - "tel_Telu", - "tgk_Cyrl", - "tgl_Latn", - "tha_Thai", - "tir_Ethi", - "taq_Latn", - "taq_Tfng", - "tpi_Latn", - "tsn_Latn", - "tso_Latn", - "tuk_Latn", - "tum_Latn", - "tur_Latn", - "twi_Latn", - "tzm_Tfng", - "uig_Arab", - "ukr_Cyrl", - "umb_Latn", - "urd_Arab", - "uzn_Latn", - "vec_Latn", - "vie_Latn", - "war_Latn", - "wol_Latn", - "xho_Latn", - "ydd_Hebr", - "yor_Latn", - "yue_Hant", - "zho_Hans", - # "zho_Hant", - "zsm_Latn", - "zul_Latn", -] - - -def flores_adapter(lang1, lang2): - return lambda line: { - "source_text": line[f"sentence_{lang1}"], - "target_text": line[f"sentence_{lang2}"], - } - - -flores200_tasks = [ - LightevalTaskConfig( - name=f"flores200:{lang1}-{lang2}", - prompt_function=get_translation_prompt_function( - source_language=Language(manage_duplicate_language_codes(lang1.split("_")[0])), - target_language=Language(manage_duplicate_language_codes(lang2.split("_")[0])), - adapter=flores_adapter(lang1, lang2), - formulation=CFFormulation(), - ), - suite=("lighteval",), - hf_repo="facebook/flores", - hf_subset=f"{lang1}-{lang2}", - hf_avail_splits=["dev", "devtest"], - evaluation_splits=["devtest"], - few_shots_split="dev", - few_shots_select=None, - generation_size=300, - metrics=[Metrics.chrf_plus, Metrics.bleu, Metrics.bleu_1, Metrics.bleu_4], - stop_sequence=["\n"], - version=0, - ) - for (lang1, lang2) in permutations(flores_200_languages, 2) -] - -TASKS_TABLE.extend( - [ - *flores200_tasks, - ] -) diff --git a/src/lighteval/tasks/multilingual/tasks/acva.py b/src/lighteval/tasks/multilingual/tasks/acva.py new file mode 100644 index 000000000..4950c0124 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/acva.py @@ -0,0 +1,154 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +ACVA_SUBSET = [ + "Algeria", + "Ancient_Egypt", + "Arab_Empire", + "Arabic_Architecture", + "Arabic_Art", + "Arabic_Astronomy", + "Arabic_Calligraphy", + "Arabic_Ceremony", + "Arabic_Clothing", + "Arabic_Culture", + "Arabic_Food", + "Arabic_Funeral", + "Arabic_Geography", + "Arabic_History", + "Arabic_Language_Origin", + "Arabic_Literature", + "Arabic_Math", + "Arabic_Medicine", + "Arabic_Music", + "Arabic_Ornament", + "Arabic_Philosophy", + "Arabic_Physics_and_Chemistry", + "Arabic_Wedding", + "Bahrain", + "Comoros", + "Egypt_modern", + "InfluenceFromAncientEgypt", + "InfluenceFromByzantium", + "InfluenceFromChina", + "InfluenceFromGreece", + "InfluenceFromIslam", + "InfluenceFromPersia", + "InfluenceFromRome", + "Iraq", + "Islam_Education", + "Islam_branches_and_schools", + "Islamic_law_system", + "Jordan", + "Kuwait", + "Lebanon", + "Libya", + "Mauritania", + "Mesopotamia_civilization", + "Morocco", + "Oman", + "Palestine", + "Qatar", + "Saudi_Arabia", + "Somalia", + "Sudan", + "Syria", + "Tunisia", + "United_Arab_Emirates", + "Yemen", + "communication", + "computer_and_phone", + "daily_life", + "entertainment", +] + + +acva_tasks = [ + LightevalTaskConfig( + name=f"acva_{Language.ARABIC.value}:{subset}", + prompt_function=get_boolq_prompt_function( + Language.ARABIC, + lambda line: { + "question": line["question"], + "answer": line["answer"] == "صح", + }, + formulation=CFFormulation(), + ), + suite=("lighteval",), + hf_repo="OALL/ACVA", + hf_subset=subset, + evaluation_splits=("test",), + few_shots_split="validation", + metrics=[MultilingualQuasiExactMatchMetric(Language.ARABIC, "full"), LogLikelihoodAccMetric()], + generation_size=5, + stop_sequence=("\n",), + ) + for subset in ACVA_SUBSET +] diff --git a/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py b/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py new file mode 100644 index 000000000..c1c0a9df0 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py @@ -0,0 +1,116 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# African MGSM: MGSM for African Languages +# From https://arxiv.org/abs/2406.03368. Human translated MGSM. + +TASKS_TABLE = [] + + +afri_mgsm_tasks = [ + LightevalTaskConfig( + name=f"afri_mgsm_{language.value}", + prompt_function=get_qa_prompt_function( + language, + lambda line: { + "question": line["question"], + # The cot is available but we have no use: + # line["answer"] + "choices": [str(line["answer_number"])], + }, + ), + suite=("lighteval",), + hf_repo="masakhane/afrimgsm", + hf_subset=language.value, + evaluation_splits=("test",), + few_shots_split="train", + generation_size=25, + metrics=[ + MultilingualQuasiExactMatchMetric(language, "full"), + ], + stop_sequence=("\n",), + ) + for language in [ + Language.AMHARIC, + # Language.EWE, + Language.FRENCH, + # Language.HAUSA, + # Language.IGBO, + # Language.KINYARWANDA, + # Language.LINGALA, + # Language.LUGANDA, + # Language.OROMO, + # Language.SHONA, + # Language.SOTHO, + Language.SWAHILI, + # Language.TWI, + # Language.WOLOF, + # Language.XHOSA, + Language.YORUBA, + # Language.ZULU, + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py b/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py new file mode 100644 index 000000000..f63195b2d --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py @@ -0,0 +1,138 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# African MMLU: African Massive Multitask Language Understanding +# From https://arxiv.org/abs/2406.03368. Human translated MMLU. + +TASKS_TABLE = [] + + +AFRI_MMLU_SUBSETS = [ + "elementary_mathematics", + "high_school_mathematics", + "high_school_geography", + "high_school_microeconomics", + "international_law", + "global_facts", +] + + +afri_mmlu_tasks = [ + LightevalTaskConfig( + name=f"afri_mmlu_{language.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + language, + lambda line: { + "question": line["question"], + "choices": line["choices"], + "gold_idx": LETTER_INDICES.index(line["answer"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="masakhane/afrimmlu", + # Temporary until the pr is merged + hf_revision="refs/pr/1", + hf_subset=language.value, + hf_filter=partial(lambda subset, line: line["subject"] == subset, subset), + evaluation_splits=("test",), + few_shots_split="dev", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + ) + for subset in AFRI_MMLU_SUBSETS + for language in [ + Language.AMHARIC, + # Language.EWE, + Language.FRENCH, + # Language.HAUSA, + # Language.IGBO, + # Language.KINYARWANDA, + # Language.LINGALA, + # Language.LUGANDA, + # Language.OROMO, + # Language.SHONA, + # Language.SOTHO, + Language.SWAHILI, + # Language.TWI, + # Language.WOLOF, + # Language.XHOSA, + Language.YORUBA, + # Language.ZULU, + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/afri_xnli.py b/src/lighteval/tasks/multilingual/tasks/afri_xnli.py new file mode 100644 index 000000000..6b21f50b6 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/afri_xnli.py @@ -0,0 +1,122 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + +# African XNLI: African XNLI +# From https://arxiv.org/abs/2406.03368. Human translated MMLU. + +TASKS_TABLE = [] + + +afri_xnli_tasks = [ + LightevalTaskConfig( + name=f"afri_xnli_{language.value}_{formulation.name.lower()}", + suite=("lighteval",), + prompt_function=get_nli_prompt_function( + language=language, + adapter=lambda line: { + "premise": line["premise"], + "hypothesis": line["hypothesis"], + # Since we ignore the neutral label + "gold_idx": {0: 0, 2: 1}[line["label"]], + }, + relations=["entailment", "contradiction"], + formulation=formulation, + ), + hf_repo="masakhane/afrixnli", + hf_subset=language.value, + hf_filter=lambda x: int(x["label"]) in [0, 2], + evaluation_splits=("test",), + few_shots_split="validation", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=None), + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for language in [ + Language.AMHARIC, + # Language.EWE, + Language.FRENCH, + # Language.HAUSA, + # Language.IGBO, + # Language.KINYARWANDA, + # Language.LINGALA, + # Language.LUGANDA, + # Language.OROMO, + # Language.SHONA, + # Language.SOTHO, + Language.SWAHILI, + # Language.TWI, + # Language.WOLOF, + # Language.XHOSA, + Language.YORUBA, + # Language.ZULU, + ] + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/arabic_arc.py b/src/lighteval/tasks/multilingual/tasks/arabic_arc.py new file mode 100644 index 000000000..08b1d7455 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/arabic_arc.py @@ -0,0 +1,95 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +arabic_arc_tasks = [ + LightevalTaskConfig( + name=f"alghafa_arc_{Language.ARABIC.value}_{formulation.name.lower()}:easy", + prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), + suite=["lighteval"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_revision="08663706ee7cab30c4b7dc1bb00042a3227ce1ff", + hf_subset="arc_easy_ar", + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py b/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py new file mode 100644 index 000000000..a7b1fd35b --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py @@ -0,0 +1,148 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +ARABIC_MMLU_SUBSETS = [ + "Islamic Studies", + "Islamic Studies (Middle School)", + "Islamic Studies (Primary School)", + "Islamic Studies (High School)", + "Driving Test", + "Natural Science (Middle School)", + "Natural Science (Primary School)", + "History (Middle School)", + "History (Primary School)", + "History (High School)", + "General Knowledge", + "General Knowledge (Middle School)", + "General Knowledge (Primary School)", + "Law (Professional)", + "Physics (High School)", + "Social Science (Middle School)", + "Social Science (Primary School)", + "Management (University)", + "Arabic Language (Middle School)", + "Arabic Language (Primary School)", + "Arabic Language (High School)", + "Political Science (University)", + "Philosophy (High School)", + "Accounting (University)", + "Computer Science (Middle School)", + "Computer Science (Primary School)", + "Computer Science (High School)", + "Computer Science (University)", + "Geography (Middle School)", + "Geography (Primary School)", + "Geography (High School)", + "Math (Primary School)", + "Biology (High School)", + "Economics (Middle School)", + "Economics (High School)", + "Economics (University)", + "Arabic Language (General)", + "Arabic Language (Grammar)", + "Civics (Middle School)", + "Civics (High School)", +] + + +arabic_mmlu_tasks = [ + LightevalTaskConfig( + name=f"mmlu_{Language.ARABIC.value}_{formulation.name.lower()}:{normalize_subset(subset)}", + prompt_function=get_mcq_prompt_function( + Language.ARABIC, + lambda line: { + "context": line["Context"], + "question": line["Question"], + "choices": [str(o) for o in [line[f"Option {i}"] for i in range(1, 6)] if o], + "gold_idx": LETTER_INDICES.index(line["Answer Key"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="MBZUAI/ArabicMMLU", + hf_subset=subset, + evaluation_splits=("test",), + hf_avail_splits=["dev"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + ) + for subset in ARABIC_MMLU_SUBSETS + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/arcd.py b/src/lighteval/tasks/multilingual/tasks/arcd.py new file mode 100644 index 000000000..dc898735c --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/arcd.py @@ -0,0 +1,97 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ARCD: Arabic Reading Comprehension Dataset. +# https://arxiv.org/pdf/1906.05394 + +TASKS_TABLE = [] + + +arcd_tasks = [ + LightevalTaskConfig( + name=f"arcd_{Language.ARABIC.value}", + prompt_function=get_qa_prompt_function( + Language.ARABIC, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="hsseinmz/arcd", + hf_subset="plain_text", + evaluation_splits=("validation",), + few_shots_split="train", + metrics=( + MultilingualQuasiExactMatchMetric(Language.ARABIC, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.ARABIC), + ), + generation_size=400, + stop_sequence=("\n",), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/belebele.py b/src/lighteval/tasks/multilingual/tasks/belebele.py new file mode 100644 index 000000000..a25a18f85 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/belebele.py @@ -0,0 +1,225 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# Belebele: A large-scale reading comprehension dataset covering 122 languages. +# https://arxiv.org/abs/2308.16884 + +TASKS_TABLE = [] + + +belebele_tasks = [ + LightevalTaskConfig( + name=f"belebele_{language}_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + iso_639_3_ind_to_iso_639_3_macro[LangCodeLanguage.get(language).to_alpha3()], + lambda line: { + "question": line["question"], + "context": line["flores_passage"], + "choices": [line[f"mc_answer{i}"] for i in range(1, 5)], + "gold_idx": int(line["correct_answer_num"]) - 1, + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="facebook/belebele", + hf_subset=language, + evaluation_splits=("test",), + hf_avail_splits=["test"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] + for language in [ + "acm_Arab", + "arz_Arab", + "ceb_Latn", + "fin_Latn", + "hin_Deva", + "ita_Latn", + "khm_Khmr", + "lvs_Latn", + "npi_Deva", + "pol_Latn", + "slv_Latn", + "swe_Latn", + # "tso_Latn", + # "xho_Latn", + "afr_Latn", + "asm_Beng", + "ces_Latn", + "fra_Latn", + "hin_Latn", + "jav_Latn", + # "kin_Latn", + "mal_Mlym", + "npi_Latn", + "por_Latn", + # "sna_Latn", + "swh_Latn", + "tur_Latn", + "yor_Latn", + "als_Latn", + "azj_Latn", + "ckb_Arab", + # "fuv_Latn", + "hrv_Latn", + "jpn_Jpan", + "kir_Cyrl", + "mar_Deva", + # "nso_Latn", + "snd_Arab", + "tam_Taml", + "ukr_Cyrl", + "zho_Hans", + "amh_Ethi", + # "bam_Latn", + "dan_Latn", + # "gaz_Latn", + "hun_Latn", + # "kac_Latn", + "kor_Hang", + "mkd_Cyrl", + # "nya_Latn", + "ron_Latn", + "som_Latn", + "tel_Telu", + "urd_Arab", + "zho_Hant", + "apc_Arab", + "ben_Beng", + "deu_Latn", + # "grn_Latn", + "hye_Armn", + "kan_Knda", + "lao_Laoo", + "mlt_Latn", + "ory_Orya", + "rus_Cyrl", + # "sot_Latn", + "tgk_Cyrl", + "urd_Latn", + "zsm_Latn", + "arb_Arab", + "ben_Latn", + "ell_Grek", + "guj_Gujr", + # "ibo_Latn", + "kat_Geor", + # "lin_Latn", + # "mri_Latn", + "pan_Guru", + # "shn_Mymr", + "spa_Latn", + "tgl_Latn", + "uzn_Latn", + # "zul_Latn", + "arb_Latn", + # "bod_Tibt", + "eng_Latn", + # "hat_Latn", + # "ilo_Latn", + "kaz_Cyrl", + "lit_Latn", + "mya_Mymr", + "pbt_Arab", + "sin_Latn", + "srp_Cyrl", + "tha_Thai", + "vie_Latn", + "ars_Arab", + "bul_Cyrl", + "est_Latn", + # "hau_Latn", + "ind_Latn", + # "kea_Latn", + # "lug_Latn", + "nld_Latn", + "pes_Arab", + "sin_Sinh", + # "ssw_Latn", + # "tir_Ethi", + "war_Latn", + "ary_Arab", + "cat_Latn", + "eus_Latn", + "heb_Hebr", + "isl_Latn", + # "khk_Cyrl", + # "luo_Latn", + "nob_Latn", + "plt_Latn", + "slk_Latn", + # "sun_Latn", + # "tsn_Latn", + # "wol_Latn", + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/c3.py b/src/lighteval/tasks/multilingual/tasks/c3.py new file mode 100644 index 000000000..bb86ea683 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/c3.py @@ -0,0 +1,106 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# C3: A Chinese Challenge Corpus for Cross-lingual and Cross-modal Tasks +# Reading comprehension task part of clue +# Paper: https://arxiv.org/abs/2004.05986 + +TASKS_TABLE = [] + + +c3_tasks = [ + LightevalTaskConfig( + name=f"c3_{Language.CHINESE.value}_{formulation.name.lower()}", + suite=("lighteval",), + prompt_function=get_mcq_prompt_function( + Language.CHINESE, + lambda line: { + "question": line["question"], + "choices": line["choice"], + "gold_idx": line["choice"].index(line["answer"]), + "context": " ".join(line["context"]), + }, + formulation=formulation, + ), + hf_repo="clue/clue", + hf_subset="c3", + evaluation_splits=("validation",), + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/ceval.py b/src/lighteval/tasks/multilingual/tasks/ceval.py new file mode 100644 index 000000000..c34b9b770 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/ceval.py @@ -0,0 +1,158 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +CEVAL_SUBSET = [ + "computer_network", + "operating_system", + "computer_architecture", + "college_programming", + "college_physics", + "college_chemistry", + "advanced_mathematics", + "probability_and_statistics", + "discrete_mathematics", + "electrical_engineer", + "metrology_engineer", + "high_school_mathematics", + "high_school_physics", + "high_school_chemistry", + "high_school_biology", + "middle_school_mathematics", + "middle_school_biology", + "middle_school_physics", + "middle_school_chemistry", + "veterinary_medicine", + "college_economics", + "business_administration", + "marxism", + "mao_zedong_thought", + "education_science", + "teacher_qualification", + "high_school_politics", + "high_school_geography", + "middle_school_politics", + "middle_school_geography", + "modern_chinese_history", + "ideological_and_moral_cultivation", + "logic", + "law", + "chinese_language_and_literature", + "art_studies", + "professional_tour_guide", + "legal_professional", + "high_school_chinese", + "high_school_history", + "middle_school_history", + "civil_servant", + "sports_science", + "plant_protection", + "basic_medicine", + "clinical_medicine", + "urban_and_rural_planner", + "accountant", + "fire_engineer", + "environmental_impact_assessment_engineer", + "tax_accountant", + "physician", +] + + +ceval_tasks = [ + LightevalTaskConfig( + name=f"ceval_{Language.CHINESE.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + Language.CHINESE, + partial( + ceval_adapter, + Language.CHINESE, + formulation, + ), + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="ceval/ceval-exam", + hf_subset=subset, + evaluation_splits=("val",), + few_shots_split="dev", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for subset in CEVAL_SUBSET + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/chegeka.py b/src/lighteval/tasks/multilingual/tasks/chegeka.py new file mode 100644 index 000000000..59b1a0de3 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/chegeka.py @@ -0,0 +1,93 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +chegeka_tasks = [ + LightevalTaskConfig( + name=f"chegeka_{Language.RUSSIAN.value}", + prompt_function=get_qa_prompt_function( + Language.RUSSIAN, + lambda line: { + "question": line["inputs"]["text"], + "choices": [line["outputs"]], + }, + ), + suite=("lighteval",), + hf_repo="ai-forever/MERA", + hf_subset="chegeka", + evaluation_splits=("train",), + hf_avail_splits=["train"], + generation_size=400, + stop_sequence=("\n",), + metrics=[ + MultilingualQuasiExactMatchMetric(Language.RUSSIAN, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.RUSSIAN), + ], + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/chinese_squad.py b/src/lighteval/tasks/multilingual/tasks/chinese_squad.py new file mode 100644 index 000000000..5fc233cc9 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/chinese_squad.py @@ -0,0 +1,97 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ChineseSquad: A reading comprehension dataset for Chinese. +# https://github.com/pluto-junzeng/ChineseSquad + +TASKS_TABLE = [] + + +chinese_squad_tasks = [ + LightevalTaskConfig( + name=f"chinese_squad_{Language.CHINESE.value}", + prompt_function=get_qa_prompt_function( + Language.CHINESE, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="lighteval/ChineseSquad", + hf_subset="default", + evaluation_splits=("validation",), + few_shots_split="train", + metrics=( + MultilingualQuasiExactMatchMetric(Language.CHINESE, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.CHINESE), + ), + generation_size=400, + stop_sequence=("\n",), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/cmath.py b/src/lighteval/tasks/multilingual/tasks/cmath.py new file mode 100644 index 000000000..ab66f015f --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/cmath.py @@ -0,0 +1,92 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +cmath_tasks = [ + LightevalTaskConfig( + name=f"cmath_{Language.CHINESE.value}", + prompt_function=get_qa_prompt_function( + Language.CHINESE, + lambda line: { + "question": line["question"], + "choices": [line["golden"]], + }, + ), + suite=("lighteval",), + hf_repo="weitianwen/cmath", + hf_subset="default", + evaluation_splits=("test",), + few_shots_split="validation", + generation_size=25, + metrics=[ + MultilingualQuasiExactMatchMetric(Language.CHINESE, "full"), + ], + stop_sequence=("\n",), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/cmmlu.py b/src/lighteval/tasks/multilingual/tasks/cmmlu.py new file mode 100644 index 000000000..bd7ff232d --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/cmmlu.py @@ -0,0 +1,174 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +CMMLU_SUBSETS = [ + "agronomy", + "anatomy", + "ancient_chinese", + "arts", + "astronomy", + "business_ethics", + "chinese_civil_service_exam", + "chinese_driving_rule", + "chinese_food_culture", + "chinese_foreign_policy", + "chinese_history", + "chinese_literature", + "chinese_teacher_qualification", + "clinical_knowledge", + "college_actuarial_science", + "college_education", + "college_engineering_hydrology", + "college_law", + "college_mathematics", + "college_medical_statistics", + "college_medicine", + "computer_science", + "computer_security", + "conceptual_physics", + "construction_project_management", + "economics", + "education", + "electrical_engineering", + "elementary_chinese", + "elementary_commonsense", + "elementary_information_and_technology", + "elementary_mathematics", + "ethnology", + "food_science", + "genetics", + "global_facts", + "high_school_biology", + "high_school_chemistry", + "high_school_geography", + "high_school_mathematics", + "high_school_physics", + "high_school_politics", + "human_sexuality", + "international_law", + "journalism", + "jurisprudence", + "legal_and_moral_basis", + "logical", + "machine_learning", + "management", + "marketing", + "marxist_theory", + "modern_chinese", + "nutrition", + "philosophy", + "professional_accounting", + "professional_law", + "professional_medicine", + "professional_psychology", + "public_relations", + "security_study", + "sociology", + "sports_science", + "traditional_chinese_medicine", + "virology", + "world_history", + "world_religions", +] + + +cmmlu_tasks = [ + LightevalTaskConfig( + name=f"cmmlu_{Language.CHINESE.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + Language.CHINESE, + lambda line: { + "question": line["Question"], + "choices": [line["A"], line["B"], line["C"], line["D"]], + "gold_idx": LETTER_INDICES.index(line["Answer"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="haonan-li/cmmlu", + hf_subset=subset, + evaluation_splits=("test",), + few_shots_split="dev", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + ) + for subset in CMMLU_SUBSETS + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/cmnli.py b/src/lighteval/tasks/multilingual/tasks/cmnli.py new file mode 100644 index 000000000..d5631eb47 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/cmnli.py @@ -0,0 +1,105 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# https://arxiv.org/abs/2004.05986 +# Native Chinese NLI dataset based on MNLI approach (Machine Translated) + +TASKS_TABLE = [] + + +cmnli_tasks = [ + LightevalTaskConfig( + name=f"cmnli_{Language.CHINESE.value}_{formulation.name.lower()}", + prompt_function=get_nli_prompt_function( + language=Language.CHINESE, + adapter=lambda line: { + "premise": line["sentence1"], + "hypothesis": line["sentence2"], + # Since we ignore the neutral label + "gold_idx": {"entailment": 0, "contradiction": 1}[line["label"]], + }, + relations=["entailment", "contradiction"], + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="fenffef/cmnli", + hf_subset="default", + hf_filter=lambda x: x["label"] in ["entailment", "contradiction"], + # Only keep the positive and negative examples + evaluation_splits=("validation",), + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=None), + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/cmrc2018.py b/src/lighteval/tasks/multilingual/tasks/cmrc2018.py new file mode 100644 index 000000000..0c8c7a81c --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/cmrc2018.py @@ -0,0 +1,97 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# CMRC 2018: A span-extraction machine reading comprehension dataset for Chinese. +# https://arxiv.org/abs/1810.07366 + +TASKS_TABLE = [] + + +cmrc2018_tasks = [ + LightevalTaskConfig( + name=f"cmrc2018_{Language.CHINESE.value}", + prompt_function=get_qa_prompt_function( + Language.CHINESE, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="clue/clue", + hf_subset="cmrc2018", + evaluation_splits=("trial",), + few_shots_split="train", + generation_size=400, + metrics=( + MultilingualQuasiExactMatchMetric(Language.CHINESE, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.CHINESE), + ), + stop_sequence=("\n",), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/copa_indic.py b/src/lighteval/tasks/multilingual/tasks/copa_indic.py new file mode 100644 index 000000000..55727c586 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/copa_indic.py @@ -0,0 +1,122 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# IndicCOPA: COPA for Indic Languages +# Paper: https://arxiv.org/pdf/2212.05409 +# IndicCOPA extends COPA to 15 Indic languages, providing a valuable resource for +# evaluating common sense reasoning in these languages. + +TASKS_TABLE = [] + + +copa_indic_tasks = [ + LightevalTaskConfig( + name=f"indicxcopa_{language.value}_{formulation.name.lower()}", + suite=["lighteval"], + prompt_function=get_copa_prompt_function( + language, + adapter=lambda line: { + "context": line["premise"], + "cause_effect": line["question"], + "continuations": [line["choice1"], line["choice2"]], + "gold_idx": int(line["label"]), + }, + formulation=formulation, + ), + hf_repo="ai4bharat/IndicCOPA", + hf_subset=f"translation-{standardize_tag(language.value)}", + hf_revision="d356ef19a4eb287e88a51d07a56b73ba88c7f188", + evaluation_splits=["test"], + hf_avail_splits=["test"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for language in [ + Language.ASSAMESE, + Language.BENGALI, + Language.GUJARATI, + Language.HINDI, + Language.KANNADA, + Language.MALAYALAM, + Language.MARATHI, + Language.NEPALI, + Language.ORIYA, + Language.PUNJABI, + Language.SANSKRIT, + Language.SINDHI, + Language.TAMIL, + Language.TELUGU, + Language.URDU, + # Optionally: Maithili, Santali, Sindhi, Konkani + ] + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/enem.py b/src/lighteval/tasks/multilingual/tasks/enem.py new file mode 100644 index 000000000..3ba56a9e4 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/enem.py @@ -0,0 +1,106 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ENEM (Exame Nacional do Ensino Médio) is a standardized Brazilian national secondary +# education examination. The exam is used both as a university admission test and as a +# high school evaluation test. +# Dataset: https://huggingface.co/datasets/maritaca-ai/enem + +TASKS_TABLE = [] + + +enem_tasks = [ + LightevalTaskConfig( + name=f"enem_{Language.PORTUGUESE.value}_{formulation.name.lower()}:{year}", + prompt_function=get_mcq_prompt_function( + Language.PORTUGUESE, + partial( + enem_adapter, + Language.PORTUGUESE, + ), + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="maritaca-ai/enem", + hf_subset=year, + evaluation_splits=("train",), + hf_avail_splits=["train"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for year in ["2022", "2023", "2024"] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/exams.py b/src/lighteval/tasks/multilingual/tasks/exams.py new file mode 100644 index 000000000..7529c3a0c --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/exams.py @@ -0,0 +1,111 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +exams_tasks = [ + LightevalTaskConfig( + name=f"exams_{language.value}_{formulation.name.lower()}:{normalize_subset(subject)}", + prompt_function=get_mcq_prompt_function( + language, + lambda line: { + "question": line["question"]["stem"], + "choices": line["question"]["choices"]["text"], + "gold_idx": line["question"]["choices"]["label"].index(line["answerKey"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="mhardalov/exams", + hf_subset="multilingual", + # Weird bug in dataset + hf_filter=partial( + lambda language, subject, line: line["answerKey"] != "@" + and line["info"]["language"] == LangCodeLanguage(standardize_tag(language.value)).language_name() + and line["info"]["subject"] == subject, + language, + subject, + ), + evaluation_splits=("test",), + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for language in exams_subjects_by_lang.keys() + for subject in exams_subjects_by_lang[language] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/faquad.py b/src/lighteval/tasks/multilingual/tasks/faquad.py new file mode 100644 index 000000000..997d8b5ab --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/faquad.py @@ -0,0 +1,99 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# FaQuAD: A Portuguese Reading Comprehension Dataset +# https://arxiv.org/abs/2007.15671 + +TASKS_TABLE = [] + + +faquad_tasks = [ + LightevalTaskConfig( + name=f"faquad_{Language.PORTUGUESE.value}", + prompt_function=get_qa_prompt_function( + Language.PORTUGUESE, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="eraldoluis/faquad", + hf_subset="plain_text", + hf_revision="205ba826a2282a4a5aa9bd3651e55ee4f2da1546", + hf_filter=lambda line: any(len(ans) > 0 for ans in line["answers"]["text"]), + evaluation_splits=("validation",), + few_shots_split="train", + metrics=( + MultilingualQuasiExactMatchMetric(Language.PORTUGUESE, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.PORTUGUESE), + ), + generation_size=400, + stop_sequence=("\n",), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/flores200.py b/src/lighteval/tasks/multilingual/tasks/flores200.py new file mode 100644 index 000000000..6b1fcaa6d --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/flores200.py @@ -0,0 +1,310 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +flores_200_languages = [ + # "ace_Arab", + "ace_Latn", + "acm_Arab", + "acq_Arab", + "aeb_Arab", + "afr_Latn", + "ajp_Arab", + "aka_Latn", + "amh_Ethi", + "apc_Arab", + "arb_Arab", + # "arb_Latn", + "ars_Arab", + "ary_Arab", + "arz_Arab", + "asm_Beng", + "ast_Latn", + "awa_Deva", + "ayr_Latn", + "azb_Arab", + "azj_Latn", + "bak_Cyrl", + "bam_Latn", + "ban_Latn", + "bel_Cyrl", + "bem_Latn", + "ben_Beng", + "bho_Deva", + # "bjn_Arab", + "bjn_Latn", + "bod_Tibt", + "bos_Latn", + "bug_Latn", + "bul_Cyrl", + "cat_Latn", + "ceb_Latn", + "ces_Latn", + "cjk_Latn", + "ckb_Arab", + "crh_Latn", + "cym_Latn", + "dan_Latn", + "deu_Latn", + "dik_Latn", + "dyu_Latn", + "dzo_Tibt", + "ell_Grek", + "eng_Latn", + "epo_Latn", + "est_Latn", + "eus_Latn", + "ewe_Latn", + "fao_Latn", + "fij_Latn", + "fin_Latn", + "fon_Latn", + "fra_Latn", + "fur_Latn", + "fuv_Latn", + "gla_Latn", + "gle_Latn", + "glg_Latn", + "grn_Latn", + "guj_Gujr", + "hat_Latn", + "hau_Latn", + "heb_Hebr", + "hin_Deva", + "hne_Deva", + "hrv_Latn", + "hun_Latn", + "hye_Armn", + "ibo_Latn", + "ilo_Latn", + "ind_Latn", + "isl_Latn", + "ita_Latn", + "jav_Latn", + "jpn_Jpan", + "kab_Latn", + "kac_Latn", + "kam_Latn", + "kan_Knda", + # "kas_Arab", + "kas_Deva", + "kat_Geor", + # "knc_Arab", + "knc_Latn", + "kaz_Cyrl", + "kbp_Latn", + "kea_Latn", + "khm_Khmr", + "kik_Latn", + "kin_Latn", + "kir_Cyrl", + "kmb_Latn", + "kmr_Latn", + "kon_Latn", + "kor_Hang", + "lao_Laoo", + "lij_Latn", + "lim_Latn", + "lin_Latn", + "lit_Latn", + "lmo_Latn", + "ltg_Latn", + "ltz_Latn", + "lua_Latn", + "lug_Latn", + "luo_Latn", + "lus_Latn", + "lvs_Latn", + "mag_Deva", + "mai_Deva", + "mal_Mlym", + "mar_Deva", + # "min_Arab", + "min_Latn", + "mkd_Cyrl", + "plt_Latn", + "mlt_Latn", + "mni_Beng", + "khk_Cyrl", + "mos_Latn", + "mri_Latn", + "mya_Mymr", + "nld_Latn", + "nno_Latn", + "nob_Latn", + "npi_Deva", + "nso_Latn", + "nus_Latn", + "nya_Latn", + "oci_Latn", + "gaz_Latn", + "ory_Orya", + "pag_Latn", + "pan_Guru", + "pap_Latn", + "pes_Arab", + "pol_Latn", + "por_Latn", + "prs_Arab", + "pbt_Arab", + "quy_Latn", + "ron_Latn", + "run_Latn", + "rus_Cyrl", + "sag_Latn", + "san_Deva", + "sat_Olck", + "scn_Latn", + "shn_Mymr", + "sin_Sinh", + "slk_Latn", + "slv_Latn", + "smo_Latn", + "sna_Latn", + "snd_Arab", + "som_Latn", + "sot_Latn", + "spa_Latn", + "als_Latn", + "srd_Latn", + "srp_Cyrl", + "ssw_Latn", + "sun_Latn", + "swe_Latn", + "swh_Latn", + "szl_Latn", + "tam_Taml", + "tat_Cyrl", + "tel_Telu", + "tgk_Cyrl", + "tgl_Latn", + "tha_Thai", + "tir_Ethi", + "taq_Latn", + "taq_Tfng", + "tpi_Latn", + "tsn_Latn", + "tso_Latn", + "tuk_Latn", + "tum_Latn", + "tur_Latn", + "twi_Latn", + "tzm_Tfng", + "uig_Arab", + "ukr_Cyrl", + "umb_Latn", + "urd_Arab", + "uzn_Latn", + "vec_Latn", + "vie_Latn", + "war_Latn", + "wol_Latn", + "xho_Latn", + "ydd_Hebr", + "yor_Latn", + "yue_Hant", + "zho_Hans", + # "zho_Hant", + "zsm_Latn", + "zul_Latn", +] + + +def flores_adapter(lang1, lang2): + return lambda line: { + "source_text": line[f"sentence_{lang1}"], + "target_text": line[f"sentence_{lang2}"], + } + + + + +flores200_tasks = [ + LightevalTaskConfig( + name=f"flores200:{lang1}-{lang2}", + prompt_function=get_translation_prompt_function( + source_language=Language(manage_duplicate_language_codes(lang1.split("_")[0])), + target_language=Language(manage_duplicate_language_codes(lang2.split("_")[0])), + adapter=flores_adapter(lang1, lang2), + formulation=CFFormulation(), + ), + suite=("lighteval",), + hf_repo="facebook/flores", + hf_subset=f"{lang1}-{lang2}", + hf_avail_splits=["dev", "devtest"], + evaluation_splits=["devtest"], + few_shots_split="dev", + few_shots_select=None, + generation_size=300, + metrics=[Metrics.chrf_plus, Metrics.bleu, Metrics.bleu_1, Metrics.bleu_4], + stop_sequence=["\n"], + version=0, + ) + for (lang1, lang2) in permutations(flores_200_languages, 2) +] diff --git a/src/lighteval/tasks/multilingual/tasks/fquad_v2.py b/src/lighteval/tasks/multilingual/tasks/fquad_v2.py new file mode 100644 index 000000000..4fbee8d2f --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/fquad_v2.py @@ -0,0 +1,97 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# FQuAD v2: French Question Answering Dataset version 2. +# https://arxiv.org/abs/2002.06071 + +TASKS_TABLE = [] + + +fquad_v2_tasks = [ + LightevalTaskConfig( + name=f"fquadv2_{Language.FRENCH.value}", + prompt_function=get_qa_prompt_function( + Language.FRENCH, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="manu/fquad2_test", + hf_subset="default", + evaluation_splits=("test_hasAns",), + few_shots_split="valid_hasAns", + generation_size=400, + stop_sequence=("\n",), + metrics=( + MultilingualQuasiExactMatchMetric(Language.FRENCH, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.FRENCH), + ), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/french_boolq.py b/src/lighteval/tasks/multilingual/tasks/french_boolq.py new file mode 100644 index 000000000..2b9a595a7 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/french_boolq.py @@ -0,0 +1,92 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +french_boolq_tasks = [ + LightevalTaskConfig( + name=f"community_boolq_{Language.FRENCH.value}", + prompt_function=get_boolq_prompt_function( + Language.FRENCH, + lambda line: { + "question": line["question"], + "answer": line["label"] == 1, + "context": line["passage"], + }, + formulation=CFFormulation(), + ), + suite=("lighteval",), + hf_repo="manu/french_boolq", + hf_subset="default", + evaluation_splits=("test",), + few_shots_split="valid", + generation_size=5, + stop_sequence=["\n"], + metrics=[MultilingualQuasiExactMatchMetric(Language.FRENCH, "full"), LogLikelihoodAccMetric()], + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/french_triviqa.py b/src/lighteval/tasks/multilingual/tasks/french_triviqa.py new file mode 100644 index 000000000..6628d6ce5 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/french_triviqa.py @@ -0,0 +1,93 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +french_triviqa_tasks = [ + LightevalTaskConfig( + name=f"community_triviaqa_{Language.FRENCH.value}", + prompt_function=get_qa_prompt_function( + Language.FRENCH, + lambda line: { + "question": line["Question"], + "choices": [line["Answer"]], + }, + ), + suite=("lighteval",), + hf_repo="manu/french-trivia", + hf_subset="default", + evaluation_splits=("train",), + hf_avail_splits=["train"], + generation_size=400, + stop_sequence=("\n",), + metrics=[ + MultilingualQuasiExactMatchMetric(Language.FRENCH, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.FRENCH), + ], + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/germanquad.py b/src/lighteval/tasks/multilingual/tasks/germanquad.py new file mode 100644 index 000000000..093756287 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/germanquad.py @@ -0,0 +1,99 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# GermanQuAD: High-quality German QA dataset with 13,722 questions +# https://arxiv.org/abs/2104.12741 + +TASKS_TABLE = [] + + +germanquad_tasks = [ + LightevalTaskConfig( + name=f"germanquad_{Language.GERMAN.value}", + prompt_function=get_qa_prompt_function( + Language.GERMAN, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="deepset/germanquad", + hf_subset="plain_text", + hf_revision="fff05ceaf2ffbe5b65c7e0c57e678f7b7e1a0581", + hf_filter=lambda line: any(len(ans) > 0 for ans in line["answers"]["text"]), + evaluation_splits=("test",), + few_shots_split="train", + generation_size=400, + stop_sequence=("\n",), + metrics=( + MultilingualQuasiExactMatchMetric(Language.GERMAN, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.GERMAN), + ), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/global_mmlu.py b/src/lighteval/tasks/multilingual/tasks/global_mmlu.py new file mode 100644 index 000000000..5c60f2c9a --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/global_mmlu.py @@ -0,0 +1,217 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# Translated MMLU using both professional and non-professional translators. Contains tags for cultural sensitivity. +# CA: Cultural Agnostic +# CS: Cultural Specific +# UNK: Not annotated +# ALL: All of the above +# https://huggingface.co/papers/2412.03304 + +TASKS_TABLE = [] + + +MMLU_SUBSETS = [ + "abstract_algebra", + "anatomy", + "astronomy", + "business_ethics", + "clinical_knowledge", + "college_biology", + "college_chemistry", + "college_computer_science", + "college_mathematics", + "college_medicine", + "college_physics", + "computer_security", + "conceptual_physics", + "econometrics", + "electrical_engineering", + "elementary_mathematics", + "formal_logic", + "global_facts", + "high_school_biology", + "high_school_chemistry", + "high_school_computer_science", + "high_school_european_history", + "high_school_geography", + "high_school_government_and_politics", + "high_school_macroeconomics", + "high_school_mathematics", + "high_school_microeconomics", + "high_school_physics", + "high_school_psychology", + "high_school_statistics", + "high_school_us_history", + "high_school_world_history", + "human_aging", + "human_sexuality", + "international_law", + "jurisprudence", + "logical_fallacies", + "machine_learning", + "management", + "marketing", + "medical_genetics", + "miscellaneous", + "moral_disputes", + "moral_scenarios", + "nutrition", + "philosophy", + "prehistory", + "professional_accounting", + "professional_law", + "professional_medicine", + "professional_psychology", + "public_relations", + "security_studies", + "sociology", + "us_foreign_policy", + "virology", + "world_religions", +] + + +global_mmlu_tasks = [ + LightevalTaskConfig( + name=f"global_mmlu_{sensitivity_label.lower()}_{language.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + language, + lambda line: { + "question": line["question"], + "choices": [line["option_a"], line["option_b"], line["option_c"], line["option_d"]], + "gold_idx": LETTER_INDICES.index(line["answer"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="CohereForAI/Global-MMLU", + hf_subset=standardize_tag(language.value), + evaluation_splits=("test",), + few_shots_split="dev", + hf_filter=partial( + lambda subset, sensitivity_label, x: x["subject"].lower() == subset + and ( + sensitivity_label == "ALL" or sensitivity_label in x["cultural_sensitivity_label"].replace("-", "UNK") + ) + and all(x[f"option_{opt}"] is not None and x[f"option_{opt}"].strip() for opt in "abcd"), + subset, + sensitivity_label, + ), + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + ) + for subset in MMLU_SUBSETS + for language in [ + Language.AMHARIC, + Language.ARABIC, + Language.BENGALI, + Language.CHINESE, + Language.CZECH, + Language.GERMAN, + Language.ENGLISH, + Language.SPANISH, + Language.FRENCH, + Language.HEBREW, + Language.HINDI, + Language.INDONESIAN, + Language.ITALIAN, + Language.JAPANESE, + Language.KOREAN, + Language.MALAY, + Language.DUTCH, + Language.NORWEGIAN, + Language.POLISH, + Language.PORTUGUESE, + Language.ROMANIAN, + Language.RUSSIAN, + Language.SERBIAN, + Language.SWEDISH, + Language.SWAHILI, + Language.TAMIL, + Language.TELUGU, + Language.THAI, + Language.TURKISH, + Language.UKRAINIAN, + Language.URDU, + Language.VIETNAMESE, + Language.YORUBA, + Language.ZULU, + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] + for sensitivity_label in ["ALL", "CA", "CS", "UNK"] +] diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py new file mode 100644 index 000000000..2cc5cb56c --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py @@ -0,0 +1,98 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +hellaswag_hin_tasks = [ + LightevalTaskConfig( + name=f"community_hellaswag_{Language.HINDI.value}_{formulation.name.lower()}", + suite=["lighteval"], + prompt_function=get_hellaswag_prompt_function( + language=Language.HINDI, + adapter=lambda line: { + "ctx_a": line["ctx_a"], + "continuations": line["endings"], + "gold_idx": int(line["label"]), + }, + formulation=formulation, + ), + hf_repo="ai4bharat/hellaswag-hi", + hf_filter=lambda line: all(len(choice.strip()) > 0 for choice in line["endings"]), + hf_subset="hi", + evaluation_splits=("validation",), + few_shots_split="validation", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + ], + ), + ) + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py new file mode 100644 index 000000000..744df1bfa --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py @@ -0,0 +1,97 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +hellaswag_tel_tasks = [ + LightevalTaskConfig( + name=f"community_hellaswag_{Language.TELUGU.value}_{formulation.name.lower()}", + suite=["lighteval"], + prompt_function=get_hellaswag_prompt_function( + language=Language.TELUGU, + adapter=lambda line: { + "ctx_a": line["ctx_a"], + "continuations": line["endings"], + "gold_idx": int(line["label"]), + }, + formulation=formulation, + ), + hf_repo="LightFury9/hellaswag-telugu", + hf_subset="default", + evaluation_splits=("valid",), + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py new file mode 100644 index 000000000..d63227fb1 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py @@ -0,0 +1,104 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# Hellaswag Thai +# This is a Thai adaptation of the Hellaswag task. +# Similar to the Turkish version, there's no specific paper, but it has been found to be effective +# for evaluating Thai language models on commonsense reasoning tasks. + +TASKS_TABLE = [] + + +hellaswag_tha_tasks = [ + LightevalTaskConfig( + name=f"community_hellaswag_{Language.THAI.value}_{formulation.name.lower()}", + suite=["lighteval"], + prompt_function=get_hellaswag_prompt_function( + language=Language.THAI, + adapter=lambda line: { + "ctx_a": line["ctx_a"], + "ctx_b": line["ctx_b"], + "continuations": line["endings"], + "gold_idx": int(line["label"]), + }, + formulation=formulation, + wikihow_artifacts=[" [ชื่อ]", " [ส่วนหัว]", " [ขั้นตอน]", " [header]", " [Header]"], + ), + hf_repo="lighteval/hellaswag_thai", + hf_subset="default", + evaluation_splits=["validation"], + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py new file mode 100644 index 000000000..5155b49cd --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py @@ -0,0 +1,107 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# Hellaswag Turkish +# This is a Turkish adaptation of the Hellaswag task. +# While there's no specific paper for this version, it has been found to work well for evaluating +# Turkish language models on commonsense reasoning tasks. +# We don't handle them in single task as there is quite a lot of differences (dataset/subset, dot replacement, etc.) +# which would make it hard to read + +TASKS_TABLE = [] + + +hellaswag_tur_tasks = [ + LightevalTaskConfig( + name=f"community_hellaswag_{Language.TURKISH.value}_{formulation.name.lower()}", + suite=["lighteval"], + prompt_function=get_hellaswag_prompt_function( + language=Language.TURKISH, + adapter=lambda line: { + "ctx_a": line["ctx_a"], + "ctx_b": line["ctx_b"], + "continuations": line["endings"], + "gold_idx": int(line["label"]), + }, + formulation=formulation, + # https://github.com/malhajar17/lm-evaluation-harness_turkish/blob/main/lm_eval/tasks/hellaswag_tr-v0.2/utils.py + wikihow_artifacts=[" [title]", " [başlık]", " [adım]", " [header]"], + ), + hf_repo="malhajar/hellaswag_tr-v0.2", + hf_subset="default", + evaluation_splits=["validation"], + hf_avail_splits=["validation"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/hindi_arc.py b/src/lighteval/tasks/multilingual/tasks/hindi_arc.py new file mode 100644 index 000000000..80e6bb05f --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/hindi_arc.py @@ -0,0 +1,105 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +hindi_arc_tasks = [ + LightevalTaskConfig( + name=f"community_arc_{Language.HINDI.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + Language.HINDI, + lambda line: { + "question": line["question"], + "choices": line["choices"]["text"], + "gold_idx": int(line["answerKey"]) - 1 + if line["answerKey"].isdigit() + else LETTER_INDICES.index(line["answerKey"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="ai4bharat/ai2_arc-hi", + hf_subset=f"ARC-{subset.capitalize()}", + evaluation_splits=("test",), + few_shots_split="validation", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ] + + ([LogLikelihoodAccMetric(normalization=LogProbPMINorm())] if subset == "challenge" else []), # type: ignore + ), + ) + for subset in ["easy", "challenge"] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py b/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py new file mode 100644 index 000000000..8bd44b02e --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py @@ -0,0 +1,99 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +hindi_boolq_tasks = [ + LightevalTaskConfig( + name=f"community_boolq_{language.value}", + prompt_function=get_boolq_prompt_function( + language, + lambda line: { + "question": line["question"], + "answer": line["answer"], + "context": line["passage"], + }, + formulation=CFFormulation(), + ), + suite=("lighteval",), + hf_repo="ai4bharat/boolq-hi", + hf_subset=standardize_tag(language.value), + evaluation_splits=("validation",), + few_shots_split="train", + generation_size=5, + stop_sequence=["\n"], + metrics=[MultilingualQuasiExactMatchMetric(language, "full"), LogLikelihoodAccMetric()], + ) + for language in [ + Language.HINDI, + Language.GUJARATI, + Language.MALAYALAM, + Language.MARATHI, + Language.TAMIL, + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/indicqa.py b/src/lighteval/tasks/multilingual/tasks/indicqa.py new file mode 100644 index 000000000..cefcfcb0d --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/indicqa.py @@ -0,0 +1,112 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# IndicQA: A reading comprehension dataset for 11 Indian languages. +# https://arxiv.org/abs/2407.13522 + +TASKS_TABLE = [] + + +indicqa_tasks = [ + LightevalTaskConfig( + name=f"indicqa_{language.value}", + prompt_function=get_qa_prompt_function( + language, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="ai4bharat/IndicQA", + hf_subset=f"indicqa.{LangCodeLanguage.get(language.value).language}", + hf_filter=lambda line: any(len(ans) > 0 for ans in line["answers"]["text"]), + hf_revision="92d96092ae229950973dac3b9998f8b3a8949b0a", + evaluation_splits=("test",), + hf_avail_splits=("test",), + generation_size=400, + metrics=( + MultilingualQuasiExactMatchMetric(language, "prefix"), + MultilingualQuasiF1ScoreMetric(language), + ), + stop_sequence=("\n",), + ) + for language in [ + Language.ASSAMESE, + Language.BENGALI, + Language.GUJARATI, + Language.HINDI, + Language.KANNADA, + Language.MALAYALAM, + Language.MARATHI, + Language.ORIYA, + Language.PUNJABI, + Language.TAMIL, + Language.TELUGU, + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/kenswquad.py b/src/lighteval/tasks/multilingual/tasks/kenswquad.py new file mode 100644 index 000000000..5e4ab4f1a --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/kenswquad.py @@ -0,0 +1,97 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# KenSwQuAD: A question answering dataset for Kenyan Swahili. +# https://arxiv.org/abs/2205.02364 + +TASKS_TABLE = [] + + +kenswquad_tasks = [ + LightevalTaskConfig( + name=f"kenswquad_{Language.SWAHILI.value}", + prompt_function=get_qa_prompt_function( + Language.SWAHILI, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [line["answer"]], + }, + ), + suite=("lighteval",), + hf_repo="lighteval/KenSwQuAD", + hf_subset="default", + evaluation_splits=("test",), + few_shots_split="validation", + metrics=( + MultilingualQuasiExactMatchMetric(Language.SWAHILI, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.SWAHILI), + ), + generation_size=400, + stop_sequence=("\n",), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/m3exams.py b/src/lighteval/tasks/multilingual/tasks/m3exams.py new file mode 100644 index 000000000..147164861 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/m3exams.py @@ -0,0 +1,113 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# M3Exam: Multitask Multilingual Multimodal Evaluation Benchmark +# It also contains a multimodal version but we don't support that +# Paper: https://arxiv.org/abs/2306.05179 + +TASKS_TABLE = [] + + +m3exams_tasks = [ + LightevalTaskConfig( + name=f"m3exams_{language.value}_{formulation.name.lower()}", + suite=("lighteval",), + prompt_function=get_mcq_prompt_function( + language, + partial(get_m3exam_adapter, language), + formulation=formulation, + ), + hf_repo="chiayewken/m3exam", + hf_subset=LangCodeLanguage(standardize_tag(language.value)).language_name().lower(), + evaluation_splits=("test",), + few_shots_split="dev", + generation_size=-1, + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for language in [ + Language.AFRIKAANS, + Language.CHINESE, + Language.ENGLISH, + Language.ITALIAN, + Language.JAVANESE, + Language.PORTUGUESE, + Language.SWAHILI, + Language.THAI, + Language.VIETNAMESE, + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py b/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py new file mode 100644 index 000000000..df8629dc8 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py @@ -0,0 +1,107 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ------------------------------- Math Tasks ------------------------------- # +# MathLogicQA is a dataset for evaluating mathematical reasoning in language models. +# It consists of multiple-choice questions that require logical reasoning and mathematical problem-solving. +# This Russian version is part of the MERA (Multilingual Evaluation of Reasoning Abilities) benchmark. +# MERA: https://github.com/ai-forever/MERA + +TASKS_TABLE = [] + + +mathlogicqa_rus_tasks = [ + LightevalTaskConfig( + name=f"mathlogic_qa_{Language.RUSSIAN.value}_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + Language.RUSSIAN, + lambda line: { + "question": line["inputs"]["text"], + "choices": [line["inputs"][f"option_{i.lower()}"] for i in LETTER_INDICES[:4]], + "gold_idx": LETTER_INDICES.index(line["outputs"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="ai-forever/MERA", + hf_subset="mathlogicqa", + evaluation_splits=("train",), + hf_avail_splits=["train"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [ + CFFormulation(), + MCFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py b/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py new file mode 100644 index 000000000..a7c1a5bc6 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py @@ -0,0 +1,182 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# Meta MMLU: A multilingual version of MMLU (using google translation) +# Paper: https://arxiv.org/abs/2407.21783 + +TASKS_TABLE = [] + + +MMLU_SUBSETS = [ + "abstract_algebra", + "anatomy", + "astronomy", + "business_ethics", + "clinical_knowledge", + "college_biology", + "college_chemistry", + "college_computer_science", + "college_mathematics", + "college_medicine", + "college_physics", + "computer_security", + "conceptual_physics", + "econometrics", + "electrical_engineering", + "elementary_mathematics", + "formal_logic", + "global_facts", + "high_school_biology", + "high_school_chemistry", + "high_school_computer_science", + "high_school_european_history", + "high_school_geography", + "high_school_government_and_politics", + "high_school_macroeconomics", + "high_school_mathematics", + "high_school_microeconomics", + "high_school_physics", + "high_school_psychology", + "high_school_statistics", + "high_school_us_history", + "high_school_world_history", + "human_aging", + "human_sexuality", + "international_law", + "jurisprudence", + "logical_fallacies", + "machine_learning", + "management", + "marketing", + "medical_genetics", + "miscellaneous", + "moral_disputes", + "moral_scenarios", + "nutrition", + "philosophy", + "prehistory", + "professional_accounting", + "professional_law", + "professional_medicine", + "professional_psychology", + "public_relations", + "security_studies", + "sociology", + "us_foreign_policy", + "virology", + "world_religions", +] + + +meta_mmlu_tasks = [ + LightevalTaskConfig( + name=f"meta_mmlu_{language.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + language, + lambda line: { + "question": line["input_question"], + "choices": [v for _, v in sorted(line["input_choice_list"].items(), key=lambda x: x[0])], + "gold_idx": LETTER_INDICES.index(line["input_correct_responses"][0]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="meta-llama/Meta-Llama-3.1-8B-Instruct-evals", + hf_subset=f"Llama-3.1-8B-Instruct-evals__multilingual_mmlu_{standardize_tag(language.value)}__details", + hf_filter=partial( + lambda language, subset, line: line["subtask_name"] + == f"mmlu_{standardize_tag(language.value)}_chat.{subset}", + language, + subset, + ), + evaluation_splits=("latest",), + hf_avail_splits=["latest"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + ) + for subset in MMLU_SUBSETS + for language in [ + Language.GERMAN, + Language.SPANISH, + Language.FRENCH, + Language.HINDI, + Language.ITALIAN, + Language.PORTUGUESE, + Language.THAI, + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/mgsm.py b/src/lighteval/tasks/multilingual/tasks/mgsm.py new file mode 100644 index 000000000..511220d6d --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/mgsm.py @@ -0,0 +1,107 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +mgsm_tasks = [ + LightevalTaskConfig( + name=f"mgsm_{language.value}", + prompt_function=get_qa_prompt_function( + language, + lambda line: { + "question": line["question"], + # The cot is available but we have no use: + # line["answer"] + "choices": [str(line["answer_number"])], + }, + ), + suite=("lighteval",), + hf_repo="juletxara/mgsm", + hf_subset=standardize_tag(language.value), + evaluation_splits=("test",), + few_shots_split="train", + generation_size=25, + metrics=[ + MultilingualQuasiExactMatchMetric(language, "full"), + ], + stop_sequence=("\n",), + ) + for language in [ + Language.ENGLISH, + Language.SPANISH, + Language.FRENCH, + Language.GERMAN, + Language.RUSSIAN, + Language.CHINESE, + Language.JAPANESE, + Language.THAI, + Language.SWAHILI, + Language.BENGALI, + Language.TELUGU, + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/mintaka.py b/src/lighteval/tasks/multilingual/tasks/mintaka.py new file mode 100644 index 000000000..4978dd2d3 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/mintaka.py @@ -0,0 +1,104 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +mintaka_tasks = [ + LightevalTaskConfig( + name=f"mintaka_{lang.value}", + prompt_function=get_qa_prompt_function( + lang, + lambda line: { + "question": line["question"], + "choices": [line["answerText"]], + }, + ), + suite=("lighteval",), + hf_repo="AmazonScience/mintaka", + hf_subset=standardize_tag(lang.value), + evaluation_splits=("test",), + few_shots_split="train", + generation_size=400, + stop_sequence=("\n",), + metrics=[ + MultilingualQuasiExactMatchMetric(lang, "prefix"), + MultilingualQuasiF1ScoreMetric(lang), + ], + ) + for lang in [ + Language.ARABIC, + Language.GERMAN, + Language.ENGLISH, + Language.SPANISH, + Language.FRENCH, + Language.HINDI, + Language.ITALIAN, + Language.JAPANESE, + Language.PORTUGUESE, + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/mkqa.py b/src/lighteval/tasks/multilingual/tasks/mkqa.py new file mode 100644 index 000000000..2950e3e4d --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/mkqa.py @@ -0,0 +1,131 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +MKQA_TASK_TO_ID = { + + +mkqa_tasks = [ + LightevalTaskConfig( + name=f"mkqa_{language.value}:{subset}", + prompt_function=get_qa_prompt_function(language, partial(get_mkqa_adapter, language)), + suite=("lighteval",), + hf_repo="apple/mkqa", + hf_subset="mkqa", + hf_revision="325131889721ae0ed885b76ecb8011369d75abad", + hf_filter=partial( + lambda language, subset, line: line["answers"][ + "zh_cn" if language == Language.CHINESE else standardize_tag(language.value) + ][0]["type"] + == MKQA_TASK_TO_ID[subset], + language, + subset, + ), + evaluation_splits=("train",), + hf_avail_splits=["train"], + stop_sequence=("\n",), + metrics=[ + MultilingualQuasiExactMatchMetric(language, "prefix"), + MultilingualQuasiF1ScoreMetric(language), + ] + if subset in ["entity", "long_answer", "short_phrase"] + else [ + MultilingualQuasiExactMatchMetric(language, "full"), + ], + ) + for subset in MKQA_TASK_TO_ID.keys() + for language in [ + Language.ARABIC, + Language.DANISH, + Language.GERMAN, + Language.ENGLISH, + Language.SPANISH, + Language.FINNISH, + Language.FRENCH, + Language.HEBREW, + Language.HUNGARIAN, + Language.ITALIAN, + Language.JAPANESE, + Language.KOREAN, + Language.KHMER, + Language.MALAY, + Language.DUTCH, + Language.NORWEGIAN, + Language.POLISH, + Language.PORTUGUESE, + Language.RUSSIAN, + Language.SWEDISH, + Language.THAI, + Language.TURKISH, + Language.VIETNAMESE, + Language.CHINESE, # Simplified + # Language.CHINESE_HONG_KONG, + # Language.CHINESE_TRADITIONAL, + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py b/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py new file mode 100644 index 000000000..c57634fc1 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py @@ -0,0 +1,142 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ---------------------------- ARC ---------------------------- # +# ARC (AI2 Reasoning Challenge) is a dataset for question answering that requires reasoning. +# It consists of multiple-choice science questions from 3rd to 9th grade exams. +# The dataset is split into two parts: ARC-Easy and ARC-Challenge. +# ARC-Easy contains questions that can be answered correctly by both humans and simple baseline models. +# ARC-Challenge contains questions that are difficult for both humans and current AI systems. +# Similar to MMLU, ARC tasks uses PMI normalization by default but only for the challenge set. +# github: https://github.com/nlp-uoregon/mlmm-evaluation + +TASKS_TABLE = [] + + +mlmm_arc_challenge_tasks = [ + LightevalTaskConfig( + name=f"mlmm_arc_{language.value}_{formulation.name.lower()}:challenge", + prompt_function=get_mcq_prompt_function( + language, + lambda line: { + "question": line["question"], + "choices": line["choices"]["text"], + "gold_idx": int(line["answerKey"]) - 1 + if line["answerKey"].isdigit() + else LETTER_INDICES.index(line["answerKey"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="jon-tow/okapi_arc_challenge", + hf_subset=standardize_tag(language.value), + hf_revision="823d5d7bfaf8974a3ab52a825b6cf4903b35dbc4", + evaluation_splits=("test",), + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + ) + for language in [ + Language.RUSSIAN, + Language.GERMAN, + Language.CHINESE, + Language.FRENCH, + Language.SPANISH, + Language.ITALIAN, + Language.DUTCH, + Language.VIETNAMESE, + Language.INDONESIAN, + Language.ARABIC, + Language.HUNGARIAN, + Language.ROMANIAN, + Language.DANISH, + Language.SLOVAK, + Language.UKRAINIAN, + Language.CATALAN, + Language.SERBIAN, + Language.CROATIAN, + Language.HINDI, + Language.BENGALI, + Language.TAMIL, + Language.NEPALI, + Language.MALAYALAM, + Language.MARATHI, + Language.TELUGU, + Language.KANNADA, + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py b/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py new file mode 100644 index 000000000..3530758c8 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py @@ -0,0 +1,144 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ------------------------------- Hellaswag Tasks ------------------------------- # +# Hellaswag is a commonsense reasoning task that requires models to complete a given scenario +# with the most plausible ending. It tests the model's ability to understand and reason about +# everyday situations and human behavior. +# MLMM-Hellaswag: Multilingual adaptation of Hellaswag +# Paper: https://arxiv.org/abs/2306.07610 +# This is a multilingual version of Hellaswag, part of the MLMM (Massive Language Model Meta-Evaluation) benchmark. +# It evaluates commonsense reasoning abilities across multiple languages. + +TASKS_TABLE = [] + + +mlmm_hellaswag_tasks = [ + LightevalTaskConfig( + name=f"mlmm_hellaswag_{lang.value}_{formulation.name.lower()}", + suite=["lighteval"], + prompt_function=get_hellaswag_prompt_function( + language=lang, + adapter=lambda line: { + # We don't use activity_label as they are not available + "ctx_a": line["ctx_a"], + "ctx_b": line["ctx_b"], + "continuations": line["endings"], + "gold_idx": int(line["label"]), + }, + formulation=formulation, + ), + hf_repo="jon-tow/okapi_hellaswag", + hf_subset=standardize_tag(lang.value), + hf_revision="96ed8e0dfc6172dad1d3df338d7b8ba6c1ff9d83", + evaluation_splits=["validation"], + hf_avail_splits=["validation"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for lang in [ + Language.ARABIC, + Language.BENGALI, + Language.CATALAN, + Language.DANISH, + Language.GERMAN, + Language.SPANISH, + Language.BASQUE, + Language.FRENCH, + Language.GUJARATI, + Language.HINDI, + Language.CROATIAN, + Language.HUNGARIAN, + Language.ARMENIAN, + Language.INDONESIAN, + Language.ICELANDIC, + Language.ITALIAN, + Language.KANNADA, + Language.MALAYALAM, + Language.MARATHI, + Language.NORWEGIAN, + Language.NEPALI, + Language.DUTCH, + Language.PORTUGUESE, + Language.ROMANIAN, + Language.RUSSIAN, + Language.SLOVAK, + Language.SERBIAN, + Language.SWEDISH, + Language.TAMIL, + Language.TELUGU, + Language.UKRAINIAN, + Language.VIETNAMESE, + Language.CHINESE, + ] + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py b/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py new file mode 100644 index 000000000..34567bd38 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py @@ -0,0 +1,197 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# MLMM MMLU: Another multilingual version of MMLU +# Paper: https://github.com/nlp-uoregon/mlmm-evaluation + +TASKS_TABLE = [] + + +MMLU_SUBSETS = [ + "abstract_algebra", + "anatomy", + "astronomy", + "business_ethics", + "clinical_knowledge", + "college_biology", + "college_chemistry", + "college_computer_science", + "college_mathematics", + "college_medicine", + "college_physics", + "computer_security", + "conceptual_physics", + "econometrics", + "electrical_engineering", + "elementary_mathematics", + "formal_logic", + "global_facts", + "high_school_biology", + "high_school_chemistry", + "high_school_computer_science", + "high_school_european_history", + "high_school_geography", + "high_school_government_and_politics", + "high_school_macroeconomics", + "high_school_mathematics", + "high_school_microeconomics", + "high_school_physics", + "high_school_psychology", + "high_school_statistics", + "high_school_us_history", + "high_school_world_history", + "human_aging", + "human_sexuality", + "international_law", + "jurisprudence", + "logical_fallacies", + "machine_learning", + "management", + "marketing", + "medical_genetics", + "miscellaneous", + "moral_disputes", + "moral_scenarios", + "nutrition", + "philosophy", + "prehistory", + "professional_accounting", + "professional_law", + "professional_medicine", + "professional_psychology", + "public_relations", + "security_studies", + "sociology", + "us_foreign_policy", + "virology", + "world_religions", +] + + +mlmm_mmlu_tasks = [ + LightevalTaskConfig( + name=f"mlmm_mmlu_{language.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + language, + lambda line: { + "question": line["question"], + "choices": line["choices"], + "gold_idx": LETTER_INDICES.index(line["answer"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="jon-tow/okapi_mmlu", + hf_subset=standardize_tag(language.value), + hf_revision="refs/pr/1", + hf_filter=partial(lambda subset, line: line["id"].split("/")[0] == subset, subset), + evaluation_splits=("test",), + few_shots_split="dev", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + ) + for subset in MMLU_SUBSETS + for language in [ + Language.RUSSIAN, + Language.GERMAN, + Language.CHINESE, + Language.FRENCH, + Language.SPANISH, + Language.ITALIAN, + Language.DUTCH, + Language.VIETNAMESE, + Language.INDONESIAN, + Language.ARABIC, + Language.HUNGARIAN, + Language.ROMANIAN, + Language.DANISH, + Language.SLOVAK, + Language.UKRAINIAN, + Language.CATALAN, + Language.SERBIAN, + Language.CROATIAN, + Language.HINDI, + Language.BENGALI, + Language.TAMIL, + Language.NEPALI, + Language.MALAYALAM, + Language.MARATHI, + Language.TELUGU, + Language.KANNADA, + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py b/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py new file mode 100644 index 000000000..73d5ac5cd --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py @@ -0,0 +1,149 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ---------------------------- TruthfulQA ---------------------------- # +# TruthfulQA: Measuring How Models Mimic Human Falsehoods +# Paper: https://arxiv.org/abs/2109.07958 +# TruthfulQA is a benchmark dataset designed to measure the truthfulness of language models. +# It consists of questions that humans might answer incorrectly due to false beliefs or misconceptions. +# The task evaluates a model's ability to provide truthful answers and avoid common human biases. +# github: https://github.com/nlp-uoregon/mlmm-evaluation + +TASKS_TABLE = [] + + +mlmm_truthfulqa_tasks = [ + LightevalTaskConfig( + name=f"mlmm_truthfulqa_{language.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + language, + partial( + lambda subset, line: { + "question": line["question"], + "choices": line[f"{subset}_targets"]["choices"], + "gold_idx": [ix for ix, label in enumerate(line[f"{subset}_targets"]["labels"]) if label == 1], # type: ignore + }, + subset, + ), + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="jon-tow/okapi_truthfulqa", + hf_subset=standardize_tag(language.value), + hf_revision="cdd5db1a66fd04105622109d1c2a5cbc8cde7586", + evaluation_splits=("validation",), + hf_avail_splits=["validation"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for subset in ["mc1", "mc2"] + for language in [ + Language.ARABIC, + Language.BENGALI, + Language.CATALAN, + Language.DANISH, + Language.GERMAN, + Language.SPANISH, + Language.BASQUE, + Language.FRENCH, + Language.GUJARATI, + Language.HINDI, + Language.CROATIAN, + Language.HUNGARIAN, + Language.ARMENIAN, + Language.INDONESIAN, + Language.ICELANDIC, + Language.ITALIAN, + Language.KANNADA, + Language.MALAYALAM, + Language.MARATHI, + Language.NORWEGIAN, + Language.NEPALI, + Language.DUTCH, + Language.PORTUGUESE, + Language.ROMANIAN, + Language.RUSSIAN, + Language.SLOVAK, + Language.SERBIAN, + Language.SWEDISH, + Language.TAMIL, + Language.TELUGU, + Language.UKRAINIAN, + Language.VIETNAMESE, + Language.CHINESE, + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/mlqa.py b/src/lighteval/tasks/multilingual/tasks/mlqa.py new file mode 100644 index 000000000..93d68e45d --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/mlqa.py @@ -0,0 +1,108 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. +# It consists of QA instances in 7 languages: English, Arabic, German, Spanish, Hindi, Vietnamese, and Chinese. +# The dataset is derived from the SQuAD v1.1 dataset, with questions and contexts translated by professional translators. +# Paper: https://arxiv.org/abs/1910.07475 + +TASKS_TABLE = [] + + +mlqa_tasks = [ + LightevalTaskConfig( + name=f"mlqa_{lang.value}", + prompt_function=get_qa_prompt_function( + lang, + lambda line: { + "context": line["context"], + "question": line["question"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="facebook/mlqa", + hf_subset=f"mlqa.{standardize_tag(lang.value)}.{standardize_tag(lang.value)}", + hf_revision="397ed406c1a7902140303e7faf60fff35b58d285", + evaluation_splits=("test",), + hf_avail_splits=["test"], + generation_size=400, + stop_sequence=("\n",), + metrics=[ + MultilingualQuasiExactMatchMetric(lang, "prefix"), + MultilingualQuasiF1ScoreMetric(lang), + ], + ) + for lang in [ + Language.ARABIC, + Language.GERMAN, + Language.SPANISH, + Language.CHINESE, + Language.HINDI, + Language.VIETNAMESE, + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/oab_exams.py b/src/lighteval/tasks/multilingual/tasks/oab_exams.py new file mode 100644 index 000000000..060aeccad --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/oab_exams.py @@ -0,0 +1,105 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# OAB Exams: A collection of questions from the Brazilian Bar Association exam +# The exam is required for anyone who wants to practice law in Brazil +# Dataset: https://huggingface.co/datasets/eduagarcia/oab_exams + +TASKS_TABLE = [] + + +oab_exams_tasks = [ + LightevalTaskConfig( + name=f"oab_exams_{Language.PORTUGUESE.value}_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + Language.PORTUGUESE, + lambda line: { + "question": line["question"], + "choices": line["choices"]["text"], + "gold_idx": LETTER_INDICES.index(line["answerKey"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="eduagarcia/oab_exams", + hf_subset="default", + evaluation_splits=("train",), + hf_avail_splits=["train"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/ocnli.py b/src/lighteval/tasks/multilingual/tasks/ocnli.py new file mode 100644 index 000000000..5f8ebfd7c --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/ocnli.py @@ -0,0 +1,106 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# Native Chinese NLI dataset based. +# https://arxiv.org/pdf/2010.05444 +# We find this benchmark to have really good signal compared to other Chinese NLI + +TASKS_TABLE = [] + + +ocnli_tasks = [ + LightevalTaskConfig( + name=f"ocnli_{Language.CHINESE.value}_{formulation.name.lower()}", + prompt_function=get_nli_prompt_function( + language=Language.CHINESE, + adapter=lambda line: { + "premise": line["sentence1"], + "hypothesis": line["sentence2"], + # Since we ignore the neutral label + "gold_idx": {1: 0, 2: 1}[line["label"]], + }, + relations=["entailment", "contradiction"], + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="clue/clue", + hf_subset="ocnli", + # Only keep the positive and negative examples + hf_filter=lambda x: int(x["label"]) in [1, 2], + evaluation_splits=("validation",), + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=None), + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py b/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py new file mode 100644 index 000000000..4bb81e120 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py @@ -0,0 +1,182 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +MMLU_SUBSETS = [ + "abstract_algebra", + "anatomy", + "astronomy", + "business_ethics", + "clinical_knowledge", + "college_biology", + "college_chemistry", + "college_computer_science", + "college_mathematics", + "college_medicine", + "college_physics", + "computer_security", + "conceptual_physics", + "econometrics", + "electrical_engineering", + "elementary_mathematics", + "formal_logic", + "global_facts", + "high_school_biology", + "high_school_chemistry", + "high_school_computer_science", + "high_school_european_history", + "high_school_geography", + "high_school_government_and_politics", + "high_school_macroeconomics", + "high_school_mathematics", + "high_school_microeconomics", + "high_school_physics", + "high_school_psychology", + "high_school_statistics", + "high_school_us_history", + "high_school_world_history", + "human_aging", + "human_sexuality", + "international_law", + "jurisprudence", + "logical_fallacies", + "machine_learning", + "management", + "marketing", + "medical_genetics", + "miscellaneous", + "moral_disputes", + "moral_scenarios", + "nutrition", + "philosophy", + "prehistory", + "professional_accounting", + "professional_law", + "professional_medicine", + "professional_psychology", + "public_relations", + "security_studies", + "sociology", + "us_foreign_policy", + "virology", + "world_religions", +] + + +openai_mmlu_tasks = [ + LightevalTaskConfig( + name=f"openai_mmlu_{language[0].value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + language[0], + lambda line: { + "question": line["Question"], + "choices": [line["A"], line["B"], line["C"], line["D"]], + "gold_idx": LETTER_INDICES.index(line["Answer"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="openai/MMMLU", + hf_subset=language[1], + evaluation_splits=("test",), + hf_avail_splits=["test"], + hf_filter=partial(lambda subset, x: x["Subject"].lower() == subset, subset), + hf_revision="038c7808122969ead7456361af05cb8f47d247f8", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + ) + for subset in MMLU_SUBSETS + for language in [ + (Language.ARABIC, "AR_XY"), + (Language.BENGALI, "BN_BD"), + (Language.GERMAN, "DE_DE"), + (Language.SPANISH, "ES_LA"), + (Language.FRENCH, "FR_FR"), + (Language.HINDI, "HI_IN"), + (Language.INDONESIAN, "ID_ID"), + (Language.ITALIAN, "IT_IT"), + (Language.JAPANESE, "JA_JP"), + (Language.KOREAN, "KO_KR"), + (Language.PORTUGUESE, "PT_BR"), + (Language.SWAHILI, "SW_KE"), + (Language.YORUBA, "YO_NG"), + (Language.CHINESE, "ZH_CN"), + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_ara.py b/src/lighteval/tasks/multilingual/tasks/openbook_ara.py new file mode 100644 index 000000000..d8e786415 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/openbook_ara.py @@ -0,0 +1,102 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ------------------------------- OpenBookQA ------------------------------- # +# OpenBookQA: A Question-Answering Dataset for Open-Book Exams +# OpenBookQA is a question-answering dataset modeled after open-book exams for assessing human understanding of a subject. +# It consists of multiple-choice questions that require combining facts from a given open book with broad common knowledge. +# The task tests language models' ability to leverage provided information and apply common sense reasoning. +# Original paper: https://arxiv.org/abs/1809.02789 +# Arabic version: https://aclanthology.org/2023.arabicnlp-1.21/ + +TASKS_TABLE = [] + + +openbook_ara_tasks = [ + LightevalTaskConfig( + name=f"alghafa_openbookqa_{Language.ARABIC.value}_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), + suite=["lighteval"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_subset="openbook_qa_ext_ar", + hf_revision="08663706ee7cab30c4b7dc1bb00042a3227ce1ff", + evaluation_splits=["test"], + few_shots_split="validation", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_es.py b/src/lighteval/tasks/multilingual/tasks/openbook_es.py new file mode 100644 index 000000000..b8478c8cc --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/openbook_es.py @@ -0,0 +1,104 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# Spanish version of OpenBookQA from BSC Language Technology group +# Dataset: https://huggingface.co/datasets/BSC-LT/openbookqa-es + +TASKS_TABLE = [] + + +openbook_es_tasks = [ + LightevalTaskConfig( + name=f"openbookqa_{Language.SPANISH.value}_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + Language.SPANISH, + lambda line: { + "question": line["question_stem"], + "choices": line["choices"]["text"], + "gold_idx": LETTER_INDICES.index(line["answerKey"]), + }, + formulation=formulation, + ), + suite=["lighteval"], + hf_repo="BSC-LT/openbookqa-es", + hf_subset="default", + evaluation_splits=("test",), + few_shots_split="validation", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_rus.py b/src/lighteval/tasks/multilingual/tasks/openbook_rus.py new file mode 100644 index 000000000..708cf932e --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/openbook_rus.py @@ -0,0 +1,104 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# The Russian version is part of the MERA (Multilingual Enhanced Russian NLP Architectures) project. +# Paper: https://arxiv.org/abs/2401.04531 + +TASKS_TABLE = [] + + +openbook_rus_tasks = [ + LightevalTaskConfig( + name=f"mera_openbookqa_{Language.RUSSIAN.value}_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + Language.RUSSIAN, + lambda line: { + "question": line["inputs"]["question"], + "choices": [line["inputs"][f"option_{i.lower()}"] for i in LETTER_INDICES[:4]], + "gold_idx": LETTER_INDICES.index(line["outputs"]), + }, + formulation=formulation, + ), + suite=["lighteval"], + hf_repo="ai-forever/MERA", + hf_subset="ruopenbookqa", + evaluation_splits=("train",), + hf_avail_splits=["train"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/parus.py b/src/lighteval/tasks/multilingual/tasks/parus.py new file mode 100644 index 000000000..84aa081db --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/parus.py @@ -0,0 +1,103 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# PARus: Plausible Alternatives for Russian +# Paper: https://russiansuperglue.com/tasks/task_info/PARus +# PARus is the Russian adaptation of the COPA task, part of the Russian SuperGLUE benchmark. +# It evaluates common sense reasoning and causal inference abilities in Russian language models. + +TASKS_TABLE = [] + + +parus_tasks = [ + LightevalTaskConfig( + name=f"parus_{Language.RUSSIAN.value}_{formulation.name.lower()}", + suite=["lighteval"], + prompt_function=get_copa_prompt_function( + language=Language.RUSSIAN, + adapter=lambda line: { + "context": line["inputs"]["premise"], + "cause_effect": line["meta"]["task"], + "continuations": [line["inputs"]["choice1"], line["inputs"]["choice2"]], + "gold_idx": int(line["outputs"]) - 1, + }, + formulation=formulation, + ), + hf_repo="ai-forever/MERA", + hf_subset="parus", + evaluation_splits=["train"], + few_shots_split="validation", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/paws_x.py b/src/lighteval/tasks/multilingual/tasks/paws_x.py new file mode 100644 index 000000000..bfdf5331c --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/paws_x.py @@ -0,0 +1,115 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification +# This dataset contains paraphrase identification pairs in multiple languages. +# It's derived from PAWS (Paraphrase Adversaries from Word Scrambling) and +# We treat paraphrase as entailment and non-paraphrase as contradiction +# https://arxiv.org/abs/1908.11828 + +TASKS_TABLE = [] + + +paws_x_tasks = [ + LightevalTaskConfig( + name=f"pawsx_{language.value}_{formulation.name.lower()}", + suite=("lighteval",), + prompt_function=get_nli_prompt_function( + language=language, + adapter=lambda line: { + "premise": line["sentence1"], + "hypothesis": line["sentence2"], + # Since we ignore the neutral label + "gold_idx": int(line["label"]), + }, + relations=["entailment", "contradiction"], + formulation=formulation, + ), + hf_repo="google-research-datasets/paws-x", + hf_subset=standardize_tag(language.value), + evaluation_splits=("test",), + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=None), + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for language in [ + Language.GERMAN, + Language.ENGLISH, + Language.SPANISH, + Language.FRENCH, + Language.JAPANESE, + Language.KOREAN, + Language.CHINESE, + ] + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/piqa_ar.py b/src/lighteval/tasks/multilingual/tasks/piqa_ar.py new file mode 100644 index 000000000..3ae26314d --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/piqa_ar.py @@ -0,0 +1,103 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ------------------------------- PIQA ------------------------------- # +# PIQA: Physical Interaction Question Answering +# PIQA is a benchmark for testing physical commonsense reasoning. +# This Arabic version is a translation of the original PIQA dataset, adapted for Arabic language evaluation. +# It tests the ability to reason about physical interactions in everyday situations. +# Paper: https://arxiv.org/abs/1911.11641 +# Arabic version: https://aclanthology.org/2023.arabicnlp-1.21/ + +TASKS_TABLE = [] + + +piqa_ar_tasks = [ + LightevalTaskConfig( + name=f"alghafa_piqa_{Language.ARABIC.value}_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), + suite=["lighteval"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_revision="08663706ee7cab30c4b7dc1bb00042a3227ce1ff", + hf_subset="piqa_ar", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/rcb.py b/src/lighteval/tasks/multilingual/tasks/rcb.py new file mode 100644 index 000000000..300081342 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/rcb.py @@ -0,0 +1,106 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# Russian Commitment Bank (RCB) is a large-scale NLI dataset with Russian sentences, +# collected from the web and crowdsourcing. +# https://arxiv.org/abs/2401.04531 + +TASKS_TABLE = [] + + +rcb_tasks = [ + LightevalTaskConfig( + name=f"rcb_{Language.RUSSIAN.value}_{formulation.name.lower()}", + prompt_function=get_nli_prompt_function( + language=Language.RUSSIAN, + adapter=lambda line: { + "premise": line["inputs"]["premise"], + "hypothesis": line["inputs"]["hypothesis"], + # Since we ignore the neutral label + "gold_idx": int(line["outputs"]) - 1, + }, + relations=["entailment", "contradiction"], + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="ai-forever/MERA", + hf_subset="rcb", + # Ignore neutral label + hf_filter=lambda x: int(x["outputs"] or "0") in [1, 2], + evaluation_splits=("train",), + few_shots_split="validation", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=None), + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/sber_squad.py b/src/lighteval/tasks/multilingual/tasks/sber_squad.py new file mode 100644 index 000000000..39e231904 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/sber_squad.py @@ -0,0 +1,97 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# SberQuAD: A large-scale Russian reading comprehension dataset. +# https://arxiv.org/abs/1912.09723 + +TASKS_TABLE = [] + + +sber_squad_tasks = [ + LightevalTaskConfig( + name=f"sber_squad_{Language.RUSSIAN.value}", + prompt_function=get_qa_prompt_function( + Language.RUSSIAN, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="kuznetsoffandrey/sberquad", + hf_subset="sberquad", + evaluation_splits=("validation",), + few_shots_split="train", + metrics=( + MultilingualQuasiExactMatchMetric(Language.RUSSIAN, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.RUSSIAN), + ), + generation_size=400, + stop_sequence=("\n",), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/soqal.py b/src/lighteval/tasks/multilingual/tasks/soqal.py new file mode 100644 index 000000000..3a129db2b --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/soqal.py @@ -0,0 +1,96 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# SOQAL: A large-scale Arabic reading comprehension dataset. +# https://arxiv.org/abs/1906.05394 + +TASKS_TABLE = [] + + +soqal_tasks = [ + LightevalTaskConfig( + name=f"soqal_{Language.ARABIC.value}_{formulation.name.lower()}", + hf_subset="multiple_choice_grounded_statement_soqal_task", + prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), + evaluation_splits=["test"], + few_shots_split="validation", + suite=["lighteval"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Native", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/squad_es.py b/src/lighteval/tasks/multilingual/tasks/squad_es.py new file mode 100644 index 000000000..ed671879f --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/squad_es.py @@ -0,0 +1,98 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# SQuAD-es: Spanish translation of the Stanford Question Answering Dataset +# https://huggingface.co/datasets/ccasimiro/squad_es + +TASKS_TABLE = [] + + +squad_es_tasks = [ + LightevalTaskConfig( + name=f"squad_{Language.SPANISH.value}", + prompt_function=get_qa_prompt_function( + Language.SPANISH, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="ccasimiro/squad_es", + hf_subset="v2.0.0", + hf_filter=lambda line: any(len(ans) > 0 for ans in line["answers"]["text"]), + evaluation_splits=("validation",), + few_shots_split="train", + metrics=( + MultilingualQuasiExactMatchMetric(Language.SPANISH, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.SPANISH), + ), + generation_size=400, + stop_sequence=("\n",), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/squad_it.py b/src/lighteval/tasks/multilingual/tasks/squad_it.py new file mode 100644 index 000000000..9835ee278 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/squad_it.py @@ -0,0 +1,98 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# SQuAD-it: Italian translation of the SQuAD dataset +# https://github.com/crux82/squad-it + +TASKS_TABLE = [] + + +squad_it_tasks = [ + LightevalTaskConfig( + name=f"squad_{Language.ITALIAN.value}", + prompt_function=get_qa_prompt_function( + Language.ITALIAN, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="crux82/squad_it", + hf_subset="default", + hf_filter=lambda line: any(len(ans) > 0 for ans in line["answers"]["text"]), + evaluation_splits=("test",), + few_shots_split="train", + generation_size=400, + stop_sequence=("\n",), + metrics=( + MultilingualQuasiExactMatchMetric(Language.ITALIAN, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.ITALIAN), + ), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/swahili_arc.py b/src/lighteval/tasks/multilingual/tasks/swahili_arc.py new file mode 100644 index 000000000..9ef95fd85 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/swahili_arc.py @@ -0,0 +1,108 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +swahili_arc_tasks = [ + LightevalTaskConfig( + name=f"community_arc_{Language.SWAHILI.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + Language.SWAHILI, + lambda line: { + "question": line["question"], + "choices": line["choices"]["text"], + "gold_idx": int(line["answerKey"]) - 1 + if line["answerKey"].isdigit() + else LETTER_INDICES.index(line["answerKey"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo=f"Mollel/ARC_{subset.capitalize()}_SWH", + hf_subset="default", + hf_revision="5347439d3193c8a0dabaab3819914bf076dc94d4" + if subset == "easy" + else "dc1df9df632d14c251594d9129fb833d2ca4429c", + evaluation_splits=("test",), + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ] + + ([LogLikelihoodAccMetric(normalization=LogProbPMINorm())] if subset == "challenge" else []), # type: ignore + ), + ) + for subset in ["easy", "challenge"] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/thai_exams.py b/src/lighteval/tasks/multilingual/tasks/thai_exams.py new file mode 100644 index 000000000..4ea9f1e3a --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/thai_exams.py @@ -0,0 +1,97 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +THAI_EXAMS_SUBSETS = ["a_level", "ic", "onet", "tgat", "tpat1"] + + +thai_exams_tasks = [ + LightevalTaskConfig( + name=f"thai_exams_{Language.THAI.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function(Language.THAI, thai_exams_adapter, formulation=formulation), + suite=("lighteval",), + hf_repo="scb10x/thai_exam", + hf_subset=subset, + evaluation_splits=("test",), + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for subset in THAI_EXAMS_SUBSETS + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/thaiqa.py b/src/lighteval/tasks/multilingual/tasks/thaiqa.py new file mode 100644 index 000000000..d74308dcf --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/thaiqa.py @@ -0,0 +1,96 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ThaiQA: A question answering dataset for the Thai language. + +TASKS_TABLE = [] + + +thaiqa_tasks = [ + LightevalTaskConfig( + name=f"thaiqa_{Language.THAI.value}", + prompt_function=get_qa_prompt_function( + Language.THAI, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["answer"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="lighteval/thaiqa_squad_fixed", + hf_subset="default", + evaluation_splits=("train",), + few_shots_split="validation", + generation_size=400, + stop_sequence=("\n",), + metrics=( + MultilingualQuasiExactMatchMetric(Language.THAI, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.THAI), + ), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/tquad_v2.py b/src/lighteval/tasks/multilingual/tasks/tquad_v2.py new file mode 100644 index 000000000..d51100130 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/tquad_v2.py @@ -0,0 +1,96 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# TQuAD v2: Turkish Question Answering Dataset version 2. + +TASKS_TABLE = [] + + +tquad_v2_tasks = [ + LightevalTaskConfig( + name=f"tquadv2_{Language.TURKISH.value}", + prompt_function=get_qa_prompt_function( + Language.TURKISH, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [a["text"] for a in line["answers"]], + }, + ), + suite=("lighteval",), + hf_repo="erdometo/tquad2", + hf_subset="default", + evaluation_splits=("validation",), + few_shots_split="train", + generation_size=400, + stop_sequence=("\n",), + metrics=( + MultilingualQuasiExactMatchMetric(Language.TURKISH, "prefix"), + MultilingualQuasiF1ScoreMetric(Language.TURKISH), + ), + ) +] diff --git a/src/lighteval/tasks/multilingual/tasks/turkish_arc.py b/src/lighteval/tasks/multilingual/tasks/turkish_arc.py new file mode 100644 index 000000000..b56ce5254 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/turkish_arc.py @@ -0,0 +1,108 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# Turkish ARC +# Comes from the Turkish leaderboard + +TASKS_TABLE = [] + + +turkish_arc_tasks = [ + LightevalTaskConfig( + name=f"community_arc_{Language.TURKISH.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + Language.TURKISH, + lambda line: { + "question": line["question"], + "choices": line["choices"]["text"], + "gold_idx": int(line["answerKey"]) - 1 + if line["answerKey"].isdigit() + else LETTER_INDICES.index(line["answerKey"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="malhajar/arc-tr", + hf_subset=f"ARC-{subset.capitalize()}", + evaluation_splits=("test",), + hf_avail_splits=["train"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ] + + ([LogLikelihoodAccMetric(normalization=LogProbPMINorm())] if subset == "challenge" else []), # type: ignore + ), + ) + for subset in ["easy", "challenge"] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py b/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py new file mode 100644 index 000000000..22a680336 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py @@ -0,0 +1,116 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +TURKISH_MMLU_SUBSET = [ + "Biology", + "Chemistry", + "Geography", + "History", + "Mathematics", + "Philosophy", + "Physics", + "Religion_and_Ethics", + "Turkish_Language_and_Literature", +] + + +turkish_mmlu_tasks = [ + LightevalTaskConfig( + name=f"mmlu_{Language.TURKISH.value}_{formulation.name.lower()}:{normalize_subset(subset)}", + prompt_function=get_mcq_prompt_function( + Language.TURKISH, + lambda line: { + "question": line["question"], + "choices": line["choices"], + "gold_idx": LETTER_INDICES.index(line["answer"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="AYueksel/TurkishMMLU", + hf_subset=subset, + evaluation_splits=("test",), + few_shots_split="dev", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + ) + for subset in TURKISH_MMLU_SUBSET + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/tydiqa.py b/src/lighteval/tasks/multilingual/tasks/tydiqa.py new file mode 100644 index 000000000..28adb0ecc --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/tydiqa.py @@ -0,0 +1,111 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# Other QA tasks for RC +# TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages. +# https://arxiv.org/abs/2003.05002 + +TASKS_TABLE = [] + + +tydiqa_tasks = [ + LightevalTaskConfig( + name=f"tydiqa_{language.value}", + prompt_function=get_qa_prompt_function( + language, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="google-research-datasets/tydiqa", + hf_subset="secondary_task", + evaluation_splits=("validation",), + few_shots_split="train", + generation_size=400, + stop_sequence=("\n",), + metrics=( + MultilingualQuasiExactMatchMetric(language, "prefix"), + MultilingualQuasiF1ScoreMetric(language), + ), + ) + for language in [ + Language.ENGLISH, + Language.ARABIC, + Language.BENGALI, + Language.FINNISH, + Language.INDONESIAN, + Language.JAPANESE, + Language.KOREAN, + Language.SWAHILI, + Language.RUSSIAN, + Language.TELUGU, + Language.THAI, + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py b/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py new file mode 100644 index 000000000..f392df227 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py @@ -0,0 +1,106 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# WorldTree is a dataset for multi-hop inference in science question answering. +# It provides explanations for elementary science questions by combining facts from a semi-structured knowledge base. +# This Russian version is part of the MERA (Multilingual Evaluation of Reasoning Abilities) benchmark. +# MERA: https://github.com/ai-forever/MERA + +TASKS_TABLE = [] + + +worldtree_rus_tasks = [ + LightevalTaskConfig( + name=f"mera_worldtree_{Language.RUSSIAN.value}_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + Language.RUSSIAN, + lambda line: { + "question": line["inputs"]["question"], + "choices": [line["inputs"][f"option_{i.lower()}"] for i in LETTER_INDICES[:4]], + "gold_idx": LETTER_INDICES.index(line["outputs"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="ai-forever/MERA", + hf_subset="ruworldtree", + evaluation_splits=("train",), + hf_avail_splits=["train"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/xcodah.py b/src/lighteval/tasks/multilingual/tasks/xcodah.py new file mode 100644 index 000000000..d6551c4ae --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/xcodah.py @@ -0,0 +1,113 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ------------------------------- Continuation Tasks ------------------------------- # + +TASKS_TABLE = [] + + +xcodah_tasks = [ + LightevalTaskConfig( + name=f"xcodah_{language.value}_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function(language, partial(xcodah_adapter, language), formulation=formulation), + suite=("lighteval",), + hf_repo="INK-USC/xcsr", + hf_subset=f"X-CODAH-{standardize_tag(language.value) if language != Language.JAPANESE else 'jap'}", + evaluation_splits=("validation",), + hf_avail_splits=["validation"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for language in [ + Language.ARABIC, + Language.GERMAN, + Language.ENGLISH, + Language.SPANISH, + Language.FRENCH, + Language.HINDI, + Language.ITALIAN, + Language.JAPANESE, + Language.DUTCH, + Language.POLISH, + Language.PORTUGUESE, + Language.RUSSIAN, + Language.SWAHILI, + Language.URDU, + Language.VIETNAMESE, + Language.CHINESE, + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/xcopa.py b/src/lighteval/tasks/multilingual/tasks/xcopa.py new file mode 100644 index 000000000..87dd2d6e1 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/xcopa.py @@ -0,0 +1,119 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ------------------------------- Copa Tasks ------------------------------- # +# COPA (Choice of Plausible Alternatives) tasks involve determining the most plausible cause or effect +# for a given premise. These tasks test common sense reasoning and causal inference abilities. +# XCOPA: Cross-lingual Choice of Plausible Alternatives +# Paper: https://aclanthology.org/2020.emnlp-main.185/ +# XCOPA extends the original English COPA task to 11 typologically diverse languages. + +TASKS_TABLE = [] + + +xcopa_tasks = [ + LightevalTaskConfig( + name=f"xcopa_{language.value}_{formulation.name.lower()}", + suite=["lighteval"], + prompt_function=get_copa_prompt_function( + language, + adapter=lambda line: { + "context": line["premise"], + "cause_effect": line["question"], + "continuations": [line["choice1"], line["choice2"]], + "gold_idx": int(line["label"]), + }, + formulation=formulation, + ), + hf_repo=("OALL/AlGhafa-Arabic-LLM-Benchmark-Translated" if language == Language.ARABIC else "xcopa"), + hf_subset=("copa_ext_ar" if language == Language.ARABIC else standardize_tag(language.value)), + evaluation_splits=["test"], + few_shots_split="validation", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for language in [ + Language.ARABIC, + Language.ESTONIAN, + Language.INDONESIAN, + Language.ITALIAN, + Language.SWAHILI, + Language.TAMIL, + Language.THAI, + Language.TURKISH, + Language.VIETNAMESE, + Language.CHINESE, + Language.HAITIAN, + Language.QUECHUA, + ] + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/xcsqa.py b/src/lighteval/tasks/multilingual/tasks/xcsqa.py new file mode 100644 index 000000000..89d884cc0 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/xcsqa.py @@ -0,0 +1,130 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ------------------------------- XCSQA ------------------------------- # +# XCSQA (Cross-lingual Commonsense QA) is part of the XCSR (Cross-lingual Commonsense Reasoning) benchmark +# It is a multilingual extension of the CommonsenseQA dataset, covering 16 languages +# The task involves answering multiple-choice questions that require commonsense reasoning +# Uses PMI normalization +# Paper: https://arxiv.org/abs/2110.08462 + +TASKS_TABLE = [] + + +xcsqa_tasks = [ + LightevalTaskConfig( + name=f"xcsqa_{language.value}_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + language, + lambda line: { + "question": line["question"]["stem"], + "choices": line["question"]["choices"]["text"], + "gold_idx": line["question"]["choices"]["label"].index(line["answerKey"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="INK-USC/xcsr", + hf_subset=f"X-CSQA-{standardize_tag(language.value) if language != Language.JAPANESE else 'jap'}", + hf_filter=lambda x: all( + len(x["question"]["choices"]["text"][i].strip()) > 0 for i in range(len(x["question"]["choices"]["text"])) + ), + evaluation_splits=("validation",), + hf_avail_splits=["validation"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + ) + for language in [ + Language.ARABIC, + Language.GERMAN, + Language.ENGLISH, + Language.SPANISH, + Language.FRENCH, + Language.HINDI, + Language.ITALIAN, + Language.JAPANESE, + Language.DUTCH, + Language.POLISH, + Language.PORTUGUESE, + Language.RUSSIAN, + Language.SWAHILI, + Language.URDU, + Language.VIETNAMESE, + Language.CHINESE, + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/xnli.py b/src/lighteval/tasks/multilingual/tasks/xnli.py new file mode 100644 index 000000000..5c6c689bf --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/xnli.py @@ -0,0 +1,129 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + +# ------------------------------- NLI Tasks ------------------------------- # +# NLI (Natural Language Inference) tasks involve determining the logical relationship +# between two given sentences: a premise and a hypothesis. The goal is to classify +# whether the hypothesis is entailed by, contradicts, or is neutral with respect to +# the premise. After our inspection we found the neutral label to be quite ambiguous +# and decided to exclude it. But you can easily add it by modifying the adapters + + +# The XNLI dataset is a multilingual variant of MultiNLI +# https://aclanthology.org/D18-1269/ + +TASKS_TABLE = [] + +xnli_tasks = [ + LightevalTaskConfig( + name=f"xnli_{language.value}_{formulation.name.lower()}", + suite=["lighteval"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=None), + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + prompt_function=get_nli_prompt_function( + language=language, + adapter=lambda line: { + "premise": line["premise"], + "hypothesis": line["hypothesis"], + # Since we ignore the neutral label + "gold_idx": {0: 0, 2: 1}[line["label"]], + }, + relations=["entailment", "contradiction"], + formulation=formulation, + ), + hf_filter=lambda line: line["label"] in [0, 2], + hf_repo="facebook/xnli", + hf_subset=standardize_tag(language.value), + evaluation_splits=["validation"], + few_shots_split="train", + ) + for language in [ + Language.ARABIC, + Language.ENGLISH, + Language.FRENCH, + Language.SPANISH, + Language.BULGARIAN, + Language.GERMAN, + Language.GREEK, + Language.ENGLISH, + Language.FRENCH, + Language.HINDI, + Language.RUSSIAN, + Language.SWAHILI, + Language.THAI, + Language.TURKISH, + Language.URDU, + Language.VIETNAMESE, + Language.CHINESE, + ] + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/xnli2.py b/src/lighteval/tasks/multilingual/tasks/xnli2.py new file mode 100644 index 000000000..786605e64 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/xnli2.py @@ -0,0 +1,133 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + +# Improvement on XNLI with better translation, from our experience models tend to +# perform better on XNLI2.0 than XNLI +# https://arxiv.org/abs/2301.06527 + +TASKS_TABLE = [] + + +xnli2_tasks = [ + LightevalTaskConfig( + name=f"xnli2.0_{language.value}_{formulation.name.lower()}", + suite=["lighteval"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=None), + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + prompt_function=get_nli_prompt_function( + language=language, + adapter=lambda line: { + "premise": line["premise"], + "hypothesis": line["hypothesis"], + # Since we ignore the neutral label + "gold_idx": {0: 0, 2: 1}[line["label"]], + }, + relations=["entailment", "contradiction"], + formulation=formulation, + ), + hf_filter=lambda line: line["label"] in [0, 2] + and line["premise"] is not None + and line["hypothesis"] is not None, + hf_repo=f"Harsit/xnli2.0_train_{LangCodeLanguage(standardize_tag(language.value)).language_name().lower()}", + hf_subset="default", + evaluation_splits=["train"], + hf_avail_splits=["train"], + ) + for language in [ + Language.ENGLISH, + Language.FRENCH, + Language.PUNJABI, + Language.GUJARATI, + Language.KANNADA, + Language.ASSAMESE, + Language.BENGALI, + Language.MARATHI, + Language.SANSKRIT, + Language.TAMIL, + Language.GERMAN, + Language.ENGLISH, + Language.URDU, + Language.VIETNAMESE, + Language.TURKISH, + Language.THAI, + Language.SWAHILI, + Language.SPANISH, + Language.RUSSIAN, + Language.HINDI, + Language.GREEK, + Language.CHINESE, + Language.BULGARIAN, + Language.ARABIC, + # Theoretically also: Bhojpuri, Gujarati, Odiya + ] + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/xnli_indic.py b/src/lighteval/tasks/multilingual/tasks/xnli_indic.py new file mode 100644 index 000000000..91f6036ca --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/xnli_indic.py @@ -0,0 +1,118 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# Another variant of XNLI, with emphasis on Indic languages +# https://arxiv.org/abs/2204.08776 + +TASKS_TABLE = [] + + +xnli_indic_tasks = [ + LightevalTaskConfig( + name=f"indicnxnli_{language.value}_{formulation.name.lower()}", + suite=["lighteval"], + prompt_function=get_nli_prompt_function( + language=language, + adapter=lambda line: { + "premise": line["premise"], + "hypothesis": line["hypothesis"], + # Since we ignore the neutral label + "gold_idx": {0: 0, 2: 1}[line["label"]], + }, + relations=["entailment", "contradiction"], + formulation=formulation, + ), + hf_repo="Divyanshu/indicxnli", + hf_subset=standardize_tag(language.value), + # Ignore neutral + hf_filter=lambda x: int(x["label"]) in [0, 2], + evaluation_splits=["validation"], + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=None), + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for language in [ + Language.ASSAMESE, + Language.BENGALI, + Language.GUJARATI, + Language.HINDI, + Language.KANNADA, + Language.MALAYALAM, + Language.MARATHI, + Language.ORIYA, + Language.PUNJABI, + Language.TAMIL, + Language.TELUGU, + ] + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] diff --git a/src/lighteval/tasks/multilingual/tasks/xquad.py b/src/lighteval/tasks/multilingual/tasks/xquad.py new file mode 100644 index 000000000..c2d7304cb --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/xquad.py @@ -0,0 +1,116 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ------------------------------- RC Tasks ------------------------------- # +# Reading Comprehension (RC) tasks evaluate a model's ability to understand and extract information from text passages. +# These tasks typically involve answering questions based on given contexts, spanning multiple languages and formats. +# Add RC tasks supporting about 130 unique languages/scripts. +# SQuAD - like +# XQuAD: Cross-lingual Question Answering Dataset, extending SQuAD to 11 languages. +# https://arxiv.org/abs/1910.11856 + +TASKS_TABLE = [] + + +xquad_tasks = [ + LightevalTaskConfig( + name=f"xquad_{language.value}", + prompt_function=get_qa_prompt_function( + language, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="google/xquad", + hf_subset=f"xquad.{standardize_tag(language.value)}", + evaluation_splits=("validation",), + few_shots_split="validation", + generation_size=400, + stop_sequence=("\n",), + metrics=( + MultilingualQuasiExactMatchMetric(language, "prefix"), + MultilingualQuasiF1ScoreMetric(language), + ), + ) + for language in [ + Language.ARABIC, + Language.GERMAN, + Language.GREEK, + Language.ENGLISH, + Language.SPANISH, + Language.HINDI, + Language.ROMANIAN, + Language.RUSSIAN, + Language.THAI, + Language.TURKISH, + Language.VIETNAMESE, + Language.CHINESE, + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/xstory.py b/src/lighteval/tasks/multilingual/tasks/xstory.py new file mode 100644 index 000000000..a9ff92cf6 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/xstory.py @@ -0,0 +1,123 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +TASKS_TABLE = [] + + +xstory_tasks = [ + LightevalTaskConfig( + name=f"xstory_cloze_{lang.value}_{formulation.name.lower()}", + prompt_function=get_continuation_prompt_function( + lang, + partial( + lambda lang, line: { + "context": TRANSLATION_LITERALS[lang].sentence_space.join( + [ + line["input_sentence_1"], + line["input_sentence_2"], + line["input_sentence_3"], + line["input_sentence_4"], + ] + ), + "continuations": [line["sentence_quiz1"], line["sentence_quiz2"]], + "gold_idx": int(line["answer_right_ending"]) - 1, # type: ignore + }, + lang, + ), + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="juletxara/xstory_cloze", + hf_subset=standardize_tag(lang.value), + evaluation_splits=["eval"], + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for lang in [ + Language.RUSSIAN, + Language.CHINESE, + Language.SPANISH, + Language.ARABIC, + Language.HINDI, + Language.INDONESIAN, + Language.TELUGU, + Language.SWAHILI, + Language.BASQUE, + Language.BURMESE, + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/multilingual/tasks/xwinograd.py b/src/lighteval/tasks/multilingual/tasks/xwinograd.py new file mode 100644 index 000000000..03028a5f4 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/xwinograd.py @@ -0,0 +1,103 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from functools import partial +from itertools import permutations + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import ( + LogLikelihoodAccMetric, + MultilingualQuasiExactMatchMetric, + MultilingualQuasiF1ScoreMetric, +) +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.adapters import ( + agieval_adapter, + alghafa_adapter, + ceval_adapter, + enem_adapter, + get_m3exam_adapter, + get_mkqa_adapter, + sciqa_adapter, + thai_exams_adapter, + winogrand_adapter, + xcodah_adapter, +) +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset +from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes + + +# ------------------------------- Winogrande Tasks ------------------------------- # + +TASKS_TABLE = [] + + +xwinograd_tasks = [ + LightevalTaskConfig( + name=f"xwinograd_{language.value}_{formulation.name.lower()}", + suite=("lighteval",), + prompt_function=get_continuation_prompt_function( + language, partial(winogrand_adapter, language), formulation=formulation + ), + hf_repo="Muennighoff/xwinograd", + hf_subset=standardize_tag(language.value) if language != Language.JAPANESE else "jp", + evaluation_splits=("test",), + hf_avail_splits=["test"], + metrics=[ + LogLikelihoodAccMetric(normalization=None), + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ) + for language in [ + Language.ENGLISH, + Language.FRENCH, + Language.JAPANESE, + Language.PORTUGUESE, + Language.RUSSIAN, + Language.CHINESE, + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] +] diff --git a/src/lighteval/tasks/tasks/agieval.py b/src/lighteval/tasks/tasks/agieval.py new file mode 100644 index 000000000..d6dbf2f93 --- /dev/null +++ b/src/lighteval/tasks/tasks/agieval.py @@ -0,0 +1,346 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models + +AGIEval is a human-centric benchmark specifically designed to evaluate the +general abilities of foundation models in tasks pertinent to human cognition and +problem-solving. This benchmark is derived from 20 official, public, and +high-standard admission and qualification exams intended for general human +test-takers, such as general college admission tests (e.g., Chinese College +Entrance Exam (Gaokao) and American SAT), law school admission tests, math +competitions, lawyer qualification tests, and national civil service exams. + +https://arxiv.org/abs/2304.06364 +""" + +agieval_aqua_rat = LightevalTaskConfig( + name="agieval:aqua-rat", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-aqua-rat", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_gaokao_biology = LightevalTaskConfig( + name="agieval:gaokao-biology", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-gaokao-biology", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_gaokao_chemistry = LightevalTaskConfig( + name="agieval:gaokao-chemistry", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-gaokao-chemistry", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_gaokao_chinese = LightevalTaskConfig( + name="agieval:gaokao-chinese", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-gaokao-chinese", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_gaokao_english = LightevalTaskConfig( + name="agieval:gaokao-english", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-gaokao-english", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_gaokao_geography = LightevalTaskConfig( + name="agieval:gaokao-geography", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-gaokao-geography", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_gaokao_history = LightevalTaskConfig( + name="agieval:gaokao-history", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-gaokao-history", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_gaokao_mathqa = LightevalTaskConfig( + name="agieval:gaokao-mathqa", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-gaokao-mathqa", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_gaokao_physics = LightevalTaskConfig( + name="agieval:gaokao-physics", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-gaokao-physics", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_logiqa_en = LightevalTaskConfig( + name="agieval:logiqa-en", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-logiqa-en", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_logiqa_zh = LightevalTaskConfig( + name="agieval:logiqa-zh", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-logiqa-zh", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_lsat_ar = LightevalTaskConfig( + name="agieval:lsat-ar", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-lsat-ar", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_lsat_lr = LightevalTaskConfig( + name="agieval:lsat-lr", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-lsat-lr", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_lsat_rc = LightevalTaskConfig( + name="agieval:lsat-rc", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-lsat-rc", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_sat_en = LightevalTaskConfig( + name="agieval:sat-en", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-sat-en", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_sat_en_without_passage = LightevalTaskConfig( + name="agieval:sat-en-without-passage", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-sat-en-without-passage", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) + +agieval_sat_math = LightevalTaskConfig( + name="agieval:sat-math", + suite=["lighteval"], + prompt_function=prompt.agieval, + hf_repo="dmayhem93/agieval-sat-math", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=None, + version=0, +) diff --git a/src/lighteval/tasks/tasks/aime.py b/src/lighteval/tasks/tasks/aime.py new file mode 100644 index 000000000..eb4e7c0fc --- /dev/null +++ b/src/lighteval/tasks/tasks/aime.py @@ -0,0 +1,52 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics, extractive_math_scorer +from lighteval.tasks.lighteval_task import LightevalTaskConfig, LightevalTaskConfig_inspect + + +aime24 = LightevalTaskConfig_inspect( + name="aime24", + prompt_function=prompt.aime_prompt_fn, + dataset_repo="HuggingFaceH4/aime_2024", + dataset_subset="default", + dataset_split="train", + scorers=[extractive_math_scorer()], + system_prompt="ASNWER USING THE FORMAT $ANSWER$", + epochs=16, + epochs_reducer="pass_at_4", +) + + +aime25 = LightevalTaskConfig_inspect( + name="aime25", + prompt_function=prompt.aime_prompt_fn, + dataset_repo="yentinglin/aime_2025", + dataset_subset="default", + dataset_split="train", + dataset_revision="main", + scorers=[extractive_math_scorer()], + system_prompt="ASNWER USING THE FORMAT $ANSWER$", + epochs=16, + epochs_reducer="pass_at_4", +) diff --git a/src/lighteval/tasks/tasks/anli.py b/src/lighteval/tasks/tasks/anli.py new file mode 100644 index 000000000..69d05da49 --- /dev/null +++ b/src/lighteval/tasks/tasks/anli.py @@ -0,0 +1,76 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics, extractive_math_scorer +from lighteval.tasks.lighteval_task import LightevalTaskConfig, LightevalTaskConfig_inspect + + +anli_r1 = LightevalTaskConfig( + name="anli:r1", + suite=["lighteval"], + prompt_function=prompt.anli, + hf_repo="anli", + hf_subset="plain_text", + hf_avail_splits=["train_r1", "dev_r1", "test_r1"], + evaluation_splits=["test_r1"], + few_shots_split="train_r1", + few_shots_select="random_sampling_from_train", + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + + +anli_r2 = LightevalTaskConfig( + name="anli:r2", + suite=["lighteval"], + prompt_function=prompt.anli, + hf_repo="anli", + hf_subset="plain_text", + hf_avail_splits=["train_r2", "dev_r2", "test_r2"], + evaluation_splits=["test_r2"], + few_shots_split="train_r2", + few_shots_select="random_sampling_from_train", + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + + +anli_r3 = LightevalTaskConfig( + name="anli:r3", + suite=["lighteval"], + prompt_function=prompt.anli, + hf_repo="anli", + hf_subset="plain_text", + hf_avail_splits=["train_r3", "dev_r3", "test_r3"], + evaluation_splits=["test_r3"], + few_shots_split="train_r3", + few_shots_select="random_sampling_from_train", + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/arc.py b/src/lighteval/tasks/tasks/arc.py new file mode 100644 index 000000000..e1b6253f3 --- /dev/null +++ b/src/lighteval/tasks/tasks/arc.py @@ -0,0 +1,74 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +7,787 genuine grade-school level, multiple-choice science questions, assembled +to encourage research in advanced question-answering. The dataset is partitioned +into a Challenge Set and an Easy Set, where the former contains only questions +answered incorrectly by both a retrieval-based algorithm and a word +co-occurrence algorithm + +from: Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge + +https://arxiv.org/abs/1803.05457 +""" + +arc_challenge = LightevalTaskConfig( + name="arc:challenge", + suite=["lighteval"], + prompt_function=prompt.arc, + hf_repo="ai2_arc", + hf_subset="ARC-Challenge", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling_from_train", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=["\n"], + version=0, +) + +arc_easy = LightevalTaskConfig( + name="arc:easy", + suite=["lighteval"], + prompt_function=prompt.arc, + hf_repo="ai2_arc", + hf_subset="ARC-Easy", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling_from_train", + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/extended/__init__.py b/src/lighteval/tasks/tasks/arc_agi_2.py similarity index 65% rename from src/lighteval/tasks/extended/__init__.py rename to src/lighteval/tasks/tasks/arc_agi_2.py index 247a0c3a2..bb5eada84 100644 --- a/src/lighteval/tasks/extended/__init__.py +++ b/src/lighteval/tasks/tasks/arc_agi_2.py @@ -20,15 +20,23 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. - -import lighteval.tasks.extended.hle.main as hle -import lighteval.tasks.extended.ifbench.main as ifbench -import lighteval.tasks.extended.ifeval.main as ifeval -import lighteval.tasks.extended.lcb.main as lcb -import lighteval.tasks.extended.mix_eval.main as mix_eval -import lighteval.tasks.extended.mt_bench.main as mt_bench -import lighteval.tasks.extended.olympiade_bench.main as olympiad_bench -import lighteval.tasks.extended.tiny_benchmarks.main as tiny_benchmarks - - -AVAILABLE_EXTENDED_TASKS_MODULES = [ifeval, ifbench, tiny_benchmarks, mt_bench, mix_eval, olympiad_bench, hle, lcb] +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +arc_agi_2 = LightevalTaskConfig( + name="arc_agi_2", + suite=["lighteval"], + prompt_function=prompt.arc_agi_2, + hf_repo="arc-agi-community/arc-agi-2", + hf_subset="default", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=2048, + metrics=[Metrics.exact_match], + stop_sequence=None, + version=0, +) diff --git a/src/lighteval/tasks/tasks/arithmetic.py b/src/lighteval/tasks/tasks/arithmetic.py new file mode 100644 index 000000000..3f1d44cf3 --- /dev/null +++ b/src/lighteval/tasks/tasks/arithmetic.py @@ -0,0 +1,191 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +A small battery of 10 tests that involve asking language models a simple +arithmetic problem in natural language. +""" + +arithmetic_1dc = LightevalTaskConfig( + name="arithmetic:1dc", + suite=["lighteval"], + prompt_function=prompt.arithmetic, + hf_repo="EleutherAI/arithmetic", + hf_subset="arithmetic_1dc", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=256, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +arithmetic_2da = LightevalTaskConfig( + name="arithmetic:2da", + suite=["lighteval"], + prompt_function=prompt.arithmetic, + hf_repo="EleutherAI/arithmetic", + hf_subset="arithmetic_2da", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=256, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +arithmetic_2dm = LightevalTaskConfig( + name="arithmetic:2dm", + suite=["lighteval"], + prompt_function=prompt.arithmetic, + hf_repo="EleutherAI/arithmetic", + hf_subset="arithmetic_2dm", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=256, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +arithmetic_2ds = LightevalTaskConfig( + name="arithmetic:2ds", + suite=["lighteval"], + prompt_function=prompt.arithmetic, + hf_repo="EleutherAI/arithmetic", + hf_subset="arithmetic_2ds", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=256, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +arithmetic_3da = LightevalTaskConfig( + name="arithmetic:3da", + suite=["lighteval"], + prompt_function=prompt.arithmetic, + hf_repo="EleutherAI/arithmetic", + hf_subset="arithmetic_3da", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=256, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +arithmetic_3ds = LightevalTaskConfig( + name="arithmetic:3ds", + suite=["lighteval"], + prompt_function=prompt.arithmetic, + hf_repo="EleutherAI/arithmetic", + hf_subset="arithmetic_3ds", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=256, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +arithmetic_4da = LightevalTaskConfig( + name="arithmetic:4da", + suite=["lighteval"], + prompt_function=prompt.arithmetic, + hf_repo="EleutherAI/arithmetic", + hf_subset="arithmetic_4da", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=256, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +arithmetic_4ds = LightevalTaskConfig( + name="arithmetic:4ds", + suite=["lighteval"], + prompt_function=prompt.arithmetic, + hf_repo="EleutherAI/arithmetic", + hf_subset="arithmetic_4ds", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=256, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +arithmetic_5da = LightevalTaskConfig( + name="arithmetic:5da", + suite=["lighteval"], + prompt_function=prompt.arithmetic, + hf_repo="EleutherAI/arithmetic", + hf_subset="arithmetic_5da", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=256, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +arithmetic_5ds = LightevalTaskConfig( + name="arithmetic:5ds", + suite=["lighteval"], + prompt_function=prompt.arithmetic, + hf_repo="EleutherAI/arithmetic", + hf_subset="arithmetic_5ds", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=256, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/asdiv.py b/src/lighteval/tasks/tasks/asdiv.py new file mode 100644 index 000000000..f078d49c3 --- /dev/null +++ b/src/lighteval/tasks/tasks/asdiv.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +asdiv = LightevalTaskConfig( + name="asdiv", + suite=["lighteval"], + prompt_function=prompt.asdiv, + hf_repo="EleutherAI/asdiv", + hf_subset="asdiv", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/babi_qa.py b/src/lighteval/tasks/tasks/babi_qa.py new file mode 100644 index 000000000..14df9a7aa --- /dev/null +++ b/src/lighteval/tasks/tasks/babi_qa.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +babi_qa = LightevalTaskConfig( + name="babi_qa", + suite=["helm"], + prompt_function=prompt.babi_qa, + hf_repo="facebook/babi_qa", + hf_subset="en-valid-qa1", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/bbq.py b/src/lighteval/tasks/tasks/bbq.py new file mode 100644 index 000000000..bcfaf5faf --- /dev/null +++ b/src/lighteval/tasks/tasks/bbq.py @@ -0,0 +1,224 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +BBQ: A hand-built bias benchmark for question answering + +https://arxiv.org/abs/2110.08193 +""" + +bbq = LightevalTaskConfig( + name="bbq", + suite=["lighteval"], + prompt_function=prompt.bbq, + hf_repo="lighteval/bbq_helm", + hf_subset="all", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +bbq_Age = LightevalTaskConfig( + name="bbq:Age", + suite=["lighteval"], + prompt_function=prompt.bbq, + hf_repo="lighteval/bbq_helm", + hf_subset="Age", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +bbq_Disability_status = LightevalTaskConfig( + name="bbq:Disability_status", + suite=["lighteval"], + prompt_function=prompt.bbq, + hf_repo="lighteval/bbq_helm", + hf_subset="Disability_status", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +bbq_Gender_identity = LightevalTaskConfig( + name="bbq:Gender_identity", + suite=["lighteval"], + prompt_function=prompt.bbq, + hf_repo="lighteval/bbq_helm", + hf_subset="Gender_identity", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +bbq_Nationality = LightevalTaskConfig( + name="bbq:Nationality", + suite=["lighteval"], + prompt_function=prompt.bbq, + hf_repo="lighteval/bbq_helm", + hf_subset="Nationality", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +bbq_Physical_appearance = LightevalTaskConfig( + name="bbq:Physical_appearance", + suite=["lighteval"], + prompt_function=prompt.bbq, + hf_repo="lighteval/bbq_helm", + hf_subset="Physical_appearance", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +bbq_Race_ethnicity = LightevalTaskConfig( + name="bbq:Race_ethnicity", + suite=["lighteval"], + prompt_function=prompt.bbq, + hf_repo="lighteval/bbq_helm", + hf_subset="Race_ethnicity", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +bbq_Race_x_SES = LightevalTaskConfig( + name="bbq:Race_x_SES", + suite=["lighteval"], + prompt_function=prompt.bbq, + hf_repo="lighteval/bbq_helm", + hf_subset="Race_x_SES", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +bbq_Race_x_gender = LightevalTaskConfig( + name="bbq:Race_x_gender", + suite=["lighteval"], + prompt_function=prompt.bbq, + hf_repo="lighteval/bbq_helm", + hf_subset="Race_x_gender", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +bbq_Religion = LightevalTaskConfig( + name="bbq:Religion", + suite=["lighteval"], + prompt_function=prompt.bbq, + hf_repo="lighteval/bbq_helm", + hf_subset="Religion", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +bbq_SES = LightevalTaskConfig( + name="bbq:SES", + suite=["lighteval"], + prompt_function=prompt.bbq, + hf_repo="lighteval/bbq_helm", + hf_subset="SES", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +bbq_Sexual_orientation = LightevalTaskConfig( + name="bbq:Sexual_orientation", + suite=["lighteval"], + prompt_function=prompt.bbq, + hf_repo="lighteval/bbq_helm", + hf_subset="Sexual_orientation", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/bigbench.py b/src/lighteval/tasks/tasks/bigbench.py new file mode 100644 index 000000000..604ab67a0 --- /dev/null +++ b/src/lighteval/tasks/tasks/bigbench.py @@ -0,0 +1,2714 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +"""Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models +166 tasks from bigbench benchmark. + +https://arxiv.org/abs/2206.04615 +""" + + +abstract_narrative_understanding = LightevalTaskConfig( + name="bigbench:abstract_narrative_understanding", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="abstract_narrative_understanding", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +anachronisms = LightevalTaskConfig( + name="bigbench:anachronisms", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="anachronisms", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +analogical_similarity = LightevalTaskConfig( + name="bigbench:analogical_similarity", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="analogical_similarity", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +analytic_entailment = LightevalTaskConfig( + name="bigbench:analytic_entailment", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="analytic_entailment", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +arithmetic_bb = LightevalTaskConfig( + name="bigbench:arithmetic_bb", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="arithmetic", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +ascii_word_recognition = LightevalTaskConfig( + name="bigbench:ascii_word_recognition", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="ascii_word_recognition", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +authorship_verification = LightevalTaskConfig( + name="bigbench:authorship_verification", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="authorship_verification", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +auto_categorization = LightevalTaskConfig( + name="bigbench:auto_categorization", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="auto_categorization", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu], + stop_sequence=["\n"], + version=0, +) + +auto_debugging = LightevalTaskConfig( + name="bigbench:auto_debugging", + suite=["lighteval"], + prompt_function=prompt.bigbench_linefeed_before_and_after_query, + hf_repo="tasksource/bigbench", + hf_subset="auto_debugging", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=None, + version=0, +) + +bbq_lite_json = LightevalTaskConfig( + name="bigbench:bbq_lite_json", + suite=["lighteval"], + prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="bbq_lite_json", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +bridging_anaphora_resolution_barqa = LightevalTaskConfig( + name="bigbench:bridging_anaphora_resolution_barqa", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="bridging_anaphora_resolution_barqa", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +causal_judgment = LightevalTaskConfig( + name="bigbench:causal_judgment", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="causal_judgment", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +cause_and_effect = LightevalTaskConfig( + name="bigbench:cause_and_effect", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="cause_and_effect", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +checkmate_in_one = LightevalTaskConfig( + name="bigbench:checkmate_in_one", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="checkmate_in_one", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +chess_state_tracking = LightevalTaskConfig( + name="bigbench:chess_state_tracking", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="chess_state_tracking", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +chinese_remainder_theorem = LightevalTaskConfig( + name="bigbench:chinese_remainder_theorem", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="chinese_remainder_theorem", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +cifar10_classification = LightevalTaskConfig( + name="bigbench:cifar10_classification", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="cifar10_classification", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +code_line_description = LightevalTaskConfig( + name="bigbench:code_line_description", + suite=["lighteval"], + prompt_function=prompt.bigbench_linefeed_before_and_after_query, + hf_repo="tasksource/bigbench", + hf_subset="code_line_description", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +codenames = LightevalTaskConfig( + name="bigbench:codenames", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="codenames", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.rouge_t5, Metrics.bleu], + stop_sequence=["\n"], + version=0, +) + +color = LightevalTaskConfig( + name="bigbench:color", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="color", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[ + Metrics.rouge_t5, + Metrics.bleu, + Metrics.loglikelihood_acc, + Metrics.exact_match(sample_params={"strip_strings": False}), + ], + stop_sequence=["\n"], + version=0, +) + +common_morpheme = LightevalTaskConfig( + name="bigbench:common_morpheme", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="common_morpheme", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +conceptual_combinations = LightevalTaskConfig( + name="bigbench:conceptual_combinations", + suite=["lighteval"], + prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="conceptual_combinations", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +conlang_translation = LightevalTaskConfig( + name="bigbench:conlang_translation", + suite=["lighteval"], + prompt_function=prompt.bigbench_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="conlang_translation", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.rouge_t5, Metrics.bleu, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=[".", ";", "!", "?"], + version=0, +) + +contextual_parametric_knowledge_conflicts = LightevalTaskConfig( + name="bigbench:contextual_parametric_knowledge_conflicts", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="contextual_parametric_knowledge_conflicts", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.rouge_t5, Metrics.loglikelihood_acc, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +crash_blossom = LightevalTaskConfig( + name="bigbench:crash_blossom", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="crash_blossom", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +crass_ai = LightevalTaskConfig( + name="bigbench:crass_ai", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="crass_ai", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +cryobiology_spanish = LightevalTaskConfig( + name="bigbench:cryobiology_spanish", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="cryobiology_spanish", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +cryptonite = LightevalTaskConfig( + name="bigbench:cryptonite", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="cryptonite", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +cs_algorithms = LightevalTaskConfig( + name="bigbench:cs_algorithms", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="cs_algorithms", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +dark_humor_detection = LightevalTaskConfig( + name="bigbench:dark_humor_detection", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="dark_humor_detection", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +date_understanding = LightevalTaskConfig( + name="bigbench:date_understanding", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="date_understanding", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +disambiguation_qa = LightevalTaskConfig( + name="bigbench:disambiguation_qa", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="disambiguation_qa", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +discourse_marker_prediction = LightevalTaskConfig( + name="bigbench:discourse_marker_prediction", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="discourse_marker_prediction", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +disfl_qa = LightevalTaskConfig( + name="bigbench:disfl_qa", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="disfl_qa", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +dyck_languages = LightevalTaskConfig( + name="bigbench:dyck_languages", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="dyck_languages", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +elementary_math_qa = LightevalTaskConfig( + name="bigbench:elementary_math_qa", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="elementary_math_qa", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +emoji_movie = LightevalTaskConfig( + name="bigbench:emoji_movie", + suite=["lighteval"], + prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="emoji_movie", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[ + Metrics.rouge_t5, + Metrics.bleu, + Metrics.loglikelihood_acc, + Metrics.exact_match(sample_params={"strip_strings": False}), + ], + stop_sequence=["\n"], + version=0, +) + +emojis_emotion_prediction = LightevalTaskConfig( + name="bigbench:emojis_emotion_prediction", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="emojis_emotion_prediction", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +empirical_judgments = LightevalTaskConfig( + name="bigbench:empirical_judgments", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="empirical_judgments", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +english_proverbs = LightevalTaskConfig( + name="bigbench:english_proverbs", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="english_proverbs", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +english_russian_proverbs = LightevalTaskConfig( + name="bigbench:english_russian_proverbs", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="english_russian_proverbs", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +entailed_polarity = LightevalTaskConfig( + name="bigbench:entailed_polarity", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="entailed_polarity", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +entailed_polarity_hindi = LightevalTaskConfig( + name="bigbench:entailed_polarity_hindi", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="entailed_polarity_hindi", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +epistemic_reasoning = LightevalTaskConfig( + name="bigbench:epistemic_reasoning", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="epistemic_reasoning", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +evaluating_information_essentiality = LightevalTaskConfig( + name="bigbench:evaluating_information_essentiality", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="evaluating_information_essentiality", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +fact_checker = LightevalTaskConfig( + name="bigbench:fact_checker", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="fact_checker", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +fantasy_reasoning = LightevalTaskConfig( + name="bigbench:fantasy_reasoning", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="fantasy_reasoning", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +few_shot_nlg = LightevalTaskConfig( + name="bigbench:few_shot_nlg", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="few_shot_nlg", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.bleurt], + stop_sequence=["\n"], + version=0, +) + +figure_of_speech_detection = LightevalTaskConfig( + name="bigbench:figure_of_speech_detection", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="figure_of_speech_detection", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +formal_fallacies_syllogisms_negation = LightevalTaskConfig( + name="bigbench:formal_fallacies_syllogisms_negation", + suite=["lighteval"], + prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="formal_fallacies_syllogisms_negation", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +gem = LightevalTaskConfig( + name="bigbench:gem", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="gem", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.rouge_t5], + stop_sequence=["\n"], + version=0, +) + +gender_inclusive_sentences_german = LightevalTaskConfig( + name="bigbench:gender_inclusive_sentences_german", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="gender_inclusive_sentences_german", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +general_knowledge = LightevalTaskConfig( + name="bigbench:general_knowledge", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="general_knowledge", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +geometric_shapes = LightevalTaskConfig( + name="bigbench:geometric_shapes", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="geometric_shapes", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[ + Metrics.rouge_t5, + Metrics.bleu, + Metrics.loglikelihood_acc, + Metrics.exact_match(sample_params={"strip_strings": False}), + ], + stop_sequence=["\n"], + version=0, +) + +goal_step_wikihow = LightevalTaskConfig( + name="bigbench:goal_step_wikihow", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="goal_step_wikihow", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +gre_reading_comprehension = LightevalTaskConfig( + name="bigbench:gre_reading_comprehension", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="gre_reading_comprehension", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +hhh_alignment = LightevalTaskConfig( + name="bigbench:hhh_alignment", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="hhh_alignment", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +hindi_question_answering = LightevalTaskConfig( + name="bigbench:hindi_question_answering", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="hindi_question_answering", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +hindu_knowledge = LightevalTaskConfig( + name="bigbench:hindu_knowledge", + suite=["lighteval"], + prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="hindu_knowledge", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +hinglish_toxicity = LightevalTaskConfig( + name="bigbench:hinglish_toxicity", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="hinglish_toxicity", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +human_organs_senses = LightevalTaskConfig( + name="bigbench:human_organs_senses", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="human_organs_senses", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +hyperbaton = LightevalTaskConfig( + name="bigbench:hyperbaton", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="hyperbaton", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +identify_math_theorems = LightevalTaskConfig( + name="bigbench:identify_math_theorems", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="identify_math_theorems", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +identify_odd_metaphor = LightevalTaskConfig( + name="bigbench:identify_odd_metaphor", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="identify_odd_metaphor", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +implicatures = LightevalTaskConfig( + name="bigbench:implicatures", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="implicatures", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +implicit_relations = LightevalTaskConfig( + name="bigbench:implicit_relations", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="implicit_relations", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +intent_recognition = LightevalTaskConfig( + name="bigbench:intent_recognition", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="intent_recognition", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +international_phonetic_alphabet_nli = LightevalTaskConfig( + name="bigbench:international_phonetic_alphabet_nli", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="international_phonetic_alphabet_nli", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +international_phonetic_alphabet_transliterate = LightevalTaskConfig( + name="bigbench:international_phonetic_alphabet_transliterate", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="international_phonetic_alphabet_transliterate", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +intersect_geometry = LightevalTaskConfig( + name="bigbench:intersect_geometry", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="intersect_geometry", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +irony_identification = LightevalTaskConfig( + name="bigbench:irony_identification", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="irony_identification", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +kanji_ascii = LightevalTaskConfig( + name="bigbench:kanji_ascii", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="kanji_ascii", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +kannada = LightevalTaskConfig( + name="bigbench:kannada", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="kannada", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +key_value_maps = LightevalTaskConfig( + name="bigbench:key_value_maps", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="key_value_maps", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +known_unknowns = LightevalTaskConfig( + name="bigbench:known_unknowns", + suite=["lighteval"], + prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="known_unknowns", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +language_games = LightevalTaskConfig( + name="bigbench:language_games", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="language_games", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +language_identification = LightevalTaskConfig( + name="bigbench:language_identification", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="language_identification", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +linguistic_mappings = LightevalTaskConfig( + name="bigbench:linguistic_mappings", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="linguistic_mappings", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +linguistics_puzzles = LightevalTaskConfig( + name="bigbench:linguistics_puzzles", + suite=["lighteval"], + prompt_function=prompt.bigbench_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="linguistics_puzzles", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=None, + version=0, +) + +logic_grid_puzzle = LightevalTaskConfig( + name="bigbench:logic_grid_puzzle", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="logic_grid_puzzle", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +logical_args = LightevalTaskConfig( + name="bigbench:logical_args", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="logical_args", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +logical_deduction = LightevalTaskConfig( + name="bigbench:logical_deduction", + suite=["lighteval"], + prompt_function=prompt.bigbench_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="logical_deduction", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +logical_fallacy_detection = LightevalTaskConfig( + name="bigbench:logical_fallacy_detection", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="logical_fallacy_detection", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +logical_sequence = LightevalTaskConfig( + name="bigbench:logical_sequence", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="logical_sequence", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +mathematical_induction = LightevalTaskConfig( + name="bigbench:mathematical_induction", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="mathematical_induction", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +matrixshapes = LightevalTaskConfig( + name="bigbench:matrixshapes", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="matrixshapes", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +metaphor_boolean = LightevalTaskConfig( + name="bigbench:metaphor_boolean", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="metaphor_boolean", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +metaphor_understanding = LightevalTaskConfig( + name="bigbench:metaphor_understanding", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="metaphor_understanding", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +minute_mysteries_qa = LightevalTaskConfig( + name="bigbench:minute_mysteries_qa", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="minute_mysteries_qa", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc, Metrics.rouge_t5], + stop_sequence=["\n"], + version=0, +) + +misconceptions = LightevalTaskConfig( + name="bigbench:misconceptions", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="misconceptions", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +misconceptions_russian = LightevalTaskConfig( + name="bigbench:misconceptions_russian", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="misconceptions_russian", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +mnist_ascii = LightevalTaskConfig( + name="bigbench:mnist_ascii", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="mnist_ascii", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +modified_arithmetic = LightevalTaskConfig( + name="bigbench:modified_arithmetic", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="modified_arithmetic", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +moral_permissibility = LightevalTaskConfig( + name="bigbench:moral_permissibility", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="moral_permissibility", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +movie_dialog_same_or_different = LightevalTaskConfig( + name="bigbench:movie_dialog_same_or_different", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="movie_dialog_same_or_different", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +movie_recommendation = LightevalTaskConfig( + name="bigbench:movie_recommendation", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="movie_recommendation", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +mult_data_wrangling = LightevalTaskConfig( + name="bigbench:mult_data_wrangling", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="mult_data_wrangling", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +navigate = LightevalTaskConfig( + name="bigbench:navigate", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="navigate", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +nonsense_words_grammar = LightevalTaskConfig( + name="bigbench:nonsense_words_grammar", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="nonsense_words_grammar", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +novel_concepts = LightevalTaskConfig( + name="bigbench:novel_concepts", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="novel_concepts", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +object_counting = LightevalTaskConfig( + name="bigbench:object_counting", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="object_counting", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +odd_one_out = LightevalTaskConfig( + name="bigbench:odd_one_out", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="odd_one_out", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +operators = LightevalTaskConfig( + name="bigbench:operators", + suite=["lighteval"], + prompt_function=prompt.bigbench_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="operators", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +paragraph_segmentation = LightevalTaskConfig( + name="bigbench:paragraph_segmentation", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="paragraph_segmentation", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +parsinlu_qa = LightevalTaskConfig( + name="bigbench:parsinlu_qa", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="parsinlu_qa", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +parsinlu_reading_comprehension = LightevalTaskConfig( + name="bigbench:parsinlu_reading_comprehension", + suite=["lighteval"], + prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="parsinlu_reading_comprehension", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=None, + version=0, +) + +penguins_in_a_table = LightevalTaskConfig( + name="bigbench:penguins_in_a_table", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="penguins_in_a_table", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +periodic_elements = LightevalTaskConfig( + name="bigbench:periodic_elements", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="periodic_elements", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +persian_idioms = LightevalTaskConfig( + name="bigbench:persian_idioms", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="persian_idioms", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +phrase_relatedness = LightevalTaskConfig( + name="bigbench:phrase_relatedness", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="phrase_relatedness", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +physical_intuition = LightevalTaskConfig( + name="bigbench:physical_intuition", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="physical_intuition", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +physics = LightevalTaskConfig( + name="bigbench:physics", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="physics", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +physics_questions = LightevalTaskConfig( + name="bigbench:physics_questions", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="physics_questions", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +play_dialog_same_or_different = LightevalTaskConfig( + name="bigbench:play_dialog_same_or_different", + suite=["lighteval"], + prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="play_dialog_same_or_different", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +polish_sequence_labeling = LightevalTaskConfig( + name="bigbench:polish_sequence_labeling", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="polish_sequence_labeling", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.f1_score], + stop_sequence=["\n"], + version=0, +) + +presuppositions_as_nli = LightevalTaskConfig( + name="bigbench:presuppositions_as_nli", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="presuppositions_as_nli", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +qa_wikidata = LightevalTaskConfig( + name="bigbench:qa_wikidata", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="qa_wikidata", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[ + Metrics.bleurt, + Metrics.bleu, + Metrics.rouge_t5, + Metrics.exact_match(sample_params={"strip_strings": False}), + ], + stop_sequence=["\n"], + version=0, +) + +question_selection = LightevalTaskConfig( + name="bigbench:question_selection", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="question_selection", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +real_or_fake_text = LightevalTaskConfig( + name="bigbench:real_or_fake_text", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="real_or_fake_text", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +reasoning_about_colored_objects = LightevalTaskConfig( + name="bigbench:reasoning_about_colored_objects", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="reasoning_about_colored_objects", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +repeat_copy_logic = LightevalTaskConfig( + name="bigbench:repeat_copy_logic", + suite=["lighteval"], + prompt_function=prompt.bigbench_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="repeat_copy_logic", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +rephrase = LightevalTaskConfig( + name="bigbench:rephrase", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="rephrase", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[ + Metrics.rouge_t5, + Metrics.bleu, + Metrics.loglikelihood_acc, + Metrics.exact_match(sample_params={"strip_strings": False}), + ], + stop_sequence=["\n"], + version=0, +) + +rhyming = LightevalTaskConfig( + name="bigbench:rhyming", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="rhyming", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +riddle_sense = LightevalTaskConfig( + name="bigbench:riddle_sense", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="riddle_sense", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +ruin_names = LightevalTaskConfig( + name="bigbench:ruin_names", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="ruin_names", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +salient_translation_error_detection = LightevalTaskConfig( + name="bigbench:salient_translation_error_detection", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="salient_translation_error_detection", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +scientific_press_release = LightevalTaskConfig( + name="bigbench:scientific_press_release", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="scientific_press_release", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +semantic_parsing_in_context_sparc = LightevalTaskConfig( + name="bigbench:semantic_parsing_in_context_sparc", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="semantic_parsing_in_context_sparc", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +semantic_parsing_spider = LightevalTaskConfig( + name="bigbench:semantic_parsing_spider", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="semantic_parsing_spider", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +sentence_ambiguity = LightevalTaskConfig( + name="bigbench:sentence_ambiguity", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="sentence_ambiguity", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +similarities_abstraction = LightevalTaskConfig( + name="bigbench:similarities_abstraction", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="similarities_abstraction", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +simp_turing_concept = LightevalTaskConfig( + name="bigbench:simp_turing_concept", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="simp_turing_concept", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +simple_arithmetic_json = LightevalTaskConfig( + name="bigbench:simple_arithmetic_json", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="simple_arithmetic_json", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +simple_arithmetic_json_multiple_choice = LightevalTaskConfig( + name="bigbench:simple_arithmetic_json_multiple_choice", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="simple_arithmetic_json_multiple_choice", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +simple_arithmetic_json_subtasks = LightevalTaskConfig( + name="bigbench:simple_arithmetic_json_subtasks", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="simple_arithmetic_json_subtasks", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +simple_arithmetic_multiple_targets_json = LightevalTaskConfig( + name="bigbench:simple_arithmetic_multiple_targets_json", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="simple_arithmetic_multiple_targets_json", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +simple_ethical_questions = LightevalTaskConfig( + name="bigbench:simple_ethical_questions", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="simple_ethical_questions", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +simple_text_editing = LightevalTaskConfig( + name="bigbench:simple_text_editing", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="simple_text_editing", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +snarks = LightevalTaskConfig( + name="bigbench:snarks", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="snarks", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +social_iqa = LightevalTaskConfig( + name="bigbench:social_iqa", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="social_iqa", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +social_support = LightevalTaskConfig( + name="bigbench:social_support", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="social_support", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.f1_score_macro], + stop_sequence=["\n"], + version=0, +) + +sports_understanding = LightevalTaskConfig( + name="bigbench:sports_understanding", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="sports_understanding", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +strange_stories = LightevalTaskConfig( + name="bigbench:strange_stories", + suite=["lighteval"], + prompt_function=prompt.bigbench_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="strange_stories", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +strategyqa = LightevalTaskConfig( + name="bigbench:strategyqa", + suite=["lighteval"], + prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="strategyqa", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +sufficient_information = LightevalTaskConfig( + name="bigbench:sufficient_information", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="sufficient_information", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +suicide_risk = LightevalTaskConfig( + name="bigbench:suicide_risk", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="suicide_risk", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +swahili_english_proverbs = LightevalTaskConfig( + name="bigbench:swahili_english_proverbs", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="swahili_english_proverbs", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +swedish_to_german_proverbs = LightevalTaskConfig( + name="bigbench:swedish_to_german_proverbs", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="swedish_to_german_proverbs", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +symbol_interpretation = LightevalTaskConfig( + name="bigbench:symbol_interpretation", + suite=["lighteval"], + prompt_function=prompt.bigbench_linefeed_before_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="symbol_interpretation", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +tellmewhy = LightevalTaskConfig( + name="bigbench:tellmewhy", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="tellmewhy", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.rouge_t5], + stop_sequence=["\n"], + version=0, +) + +temporal_sequences = LightevalTaskConfig( + name="bigbench:temporal_sequences", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="temporal_sequences", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +tense = LightevalTaskConfig( + name="bigbench:tense", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="tense", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +timedial = LightevalTaskConfig( + name="bigbench:timedial", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="timedial", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +topical_chat = LightevalTaskConfig( + name="bigbench:topical_chat", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="topical_chat", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.bleu, Metrics.rouge_t5, Metrics.loglikelihood_acc, Metrics.bleurt], + stop_sequence=["\n"], + version=0, +) + +tracking_shuffled_objects = LightevalTaskConfig( + name="bigbench:tracking_shuffled_objects", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="tracking_shuffled_objects", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +understanding_fables = LightevalTaskConfig( + name="bigbench:understanding_fables", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="understanding_fables", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +undo_permutation = LightevalTaskConfig( + name="bigbench:undo_permutation", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="undo_permutation", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +unit_conversion = LightevalTaskConfig( + name="bigbench:unit_conversion", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="unit_conversion", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +unit_interpretation = LightevalTaskConfig( + name="bigbench:unit_interpretation", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="unit_interpretation", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +unnatural_in_context_learning = LightevalTaskConfig( + name="bigbench:unnatural_in_context_learning", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="unnatural_in_context_learning", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +vitaminc_fact_verification = LightevalTaskConfig( + name="bigbench:vitaminc_fact_verification", + suite=["lighteval"], + prompt_function=prompt.bigbench_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="vitaminc_fact_verification", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +what_is_the_tao = LightevalTaskConfig( + name="bigbench:what_is_the_tao", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="what_is_the_tao", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +which_wiki_edit = LightevalTaskConfig( + name="bigbench:which_wiki_edit", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="which_wiki_edit", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +winowhy = LightevalTaskConfig( + name="bigbench:winowhy", + suite=["lighteval"], + prompt_function=prompt.bigbench_whitespace_after_query, + hf_repo="tasksource/bigbench", + hf_subset="winowhy", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +word_sorting = LightevalTaskConfig( + name="bigbench:word_sorting", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="word_sorting", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +word_unscrambling = LightevalTaskConfig( + name="bigbench:word_unscrambling", + suite=["lighteval"], + prompt_function=prompt.bigbench, + hf_repo="tasksource/bigbench", + hf_subset="word_unscrambling", + hf_avail_splits=["default", "train", "validation"], + evaluation_splits=["default"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/bigbench_hard.py b/src/lighteval/tasks/tasks/bigbench_hard.py new file mode 100644 index 000000000..12b6ffd91 --- /dev/null +++ b/src/lighteval/tasks/tasks/bigbench_hard.py @@ -0,0 +1,318 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + +""" +hardest subset of bigbench benchmark. +""" + + +causal_judgment = LightevalTaskConfig( + name="bigbench_hard:causal_judgment", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="causal_judgement", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +date_understanding = LightevalTaskConfig( + name="bigbench_hard:date_understanding", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="date_understanding", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +disambiguation_qa = LightevalTaskConfig( + name="bigbench_hard:disambiguation_qa", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="disambiguation_qa", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +geometric_shapes = LightevalTaskConfig( + name="bigbench_hard:geometric_shapes", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="geometric_shapes", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +logical_deduction_five_objects = LightevalTaskConfig( + name="bigbench_hard:logical_deduction_five_objects", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="logical_deduction_five_objects", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +logical_deduction_seven_objects = LightevalTaskConfig( + name="bigbench_hard:logical_deduction_seven_objects", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="logical_deduction_seven_objects", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +logical_deduction_three_objects = LightevalTaskConfig( + name="bigbench_hard:logical_deduction_three_objects", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="logical_deduction_three_objects", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +movie_recommendation = LightevalTaskConfig( + name="bigbench_hard:movie_recommendation", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="movie_recommendation", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +navigate = LightevalTaskConfig( + name="bigbench_hard:navigate", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="navigate", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +reasoning_about_colored_objects = LightevalTaskConfig( + name="bigbench_hard:reasoning_about_colored_objects", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="reasoning_about_colored_objects", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +ruin_names = LightevalTaskConfig( + name="bigbench_hard:ruin_names", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="ruin_names", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +salient_translation_error_detection = LightevalTaskConfig( + name="bigbench_hard:salient_translation_error_detection", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="salient_translation_error_detection", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +snarks = LightevalTaskConfig( + name="bigbench_hard:snarks", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="snarks", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +sports_understanding = LightevalTaskConfig( + name="bigbench_hard:sports_understanding", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="sports_understanding", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +temporal_sequences = LightevalTaskConfig( + name="bigbench_hard:temporal_sequences", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="temporal_sequences", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +tracking_shuffled_objects_five_objects = LightevalTaskConfig( + name="bigbench_hard:tracking_shuffled_objects_five_objects", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="tracking_shuffled_objects_five_objects", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +tracking_shuffled_objects_seven_objects = LightevalTaskConfig( + name="bigbench_hard:tracking_shuffled_objects_seven_objects", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="tracking_shuffled_objects_seven_objects", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) + +tracking_shuffled_objects_three_objects = LightevalTaskConfig( + name="bigbench_hard:tracking_shuffled_objects_three_objects", + suite=["lighteval"], + prompt_function=prompt.bbh_lighteval, + hf_repo="lighteval/bbh", + hf_subset="tracking_shuffled_objects_three_objects", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["", "Q=", "\n\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/blimp.py b/src/lighteval/tasks/tasks/blimp.py new file mode 100644 index 000000000..4c33792f7 --- /dev/null +++ b/src/lighteval/tasks/tasks/blimp.py @@ -0,0 +1,1107 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +"""BLiMP is a challenge set for evaluating what language models (LMs) know +about major grammatical phenomena in English. BLiMP consists of 67 +sub-datasets, each containing 1000 minimal pairs isolating specific +contrasts in syntax, morphology, or semantics. The data is automatically +generated according to expert-crafted grammars. + +https://arxiv.org/abs/1912.00582 +""" + +blimp_adjunct_island = LightevalTaskConfig( + name="blimp:adjunct_island", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="adjunct_island", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_anaphor_gender_agreement = LightevalTaskConfig( + name="blimp:anaphor_gender_agreement", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="anaphor_gender_agreement", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_anaphor_number_agreement = LightevalTaskConfig( + name="blimp:anaphor_number_agreement", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="anaphor_number_agreement", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_animate_subject_passive = LightevalTaskConfig( + name="blimp:animate_subject_passive", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="animate_subject_passive", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_animate_subject_trans = LightevalTaskConfig( + name="blimp:animate_subject_trans", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="animate_subject_trans", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_causative = LightevalTaskConfig( + name="blimp:causative", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="causative", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_complex_NP_island = LightevalTaskConfig( + name="blimp:complex_NP_island", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="complex_NP_island", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_coordinate_structure_constraint_complex_left_branch = LightevalTaskConfig( + name="blimp:coordinate_structure_constraint_complex_left_branch", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="coordinate_structure_constraint_complex_left_branch", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_coordinate_structure_constraint_object_extraction = LightevalTaskConfig( + name="blimp:coordinate_structure_constraint_object_extraction", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="coordinate_structure_constraint_object_extraction", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_determiner_noun_agreement_1 = LightevalTaskConfig( + name="blimp:determiner_noun_agreement_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="determiner_noun_agreement_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_determiner_noun_agreement_2 = LightevalTaskConfig( + name="blimp:determiner_noun_agreement_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="determiner_noun_agreement_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_determiner_noun_agreement_irregular_1 = LightevalTaskConfig( + name="blimp:determiner_noun_agreement_irregular_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="determiner_noun_agreement_irregular_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_determiner_noun_agreement_irregular_2 = LightevalTaskConfig( + name="blimp:determiner_noun_agreement_irregular_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="determiner_noun_agreement_irregular_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_determiner_noun_agreement_with_adj_2 = LightevalTaskConfig( + name="blimp:determiner_noun_agreement_with_adj_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="determiner_noun_agreement_with_adj_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_determiner_noun_agreement_with_adj_irregular_1 = LightevalTaskConfig( + name="blimp:determiner_noun_agreement_with_adj_irregular_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="determiner_noun_agreement_with_adj_irregular_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_determiner_noun_agreement_with_adj_irregular_2 = LightevalTaskConfig( + name="blimp:determiner_noun_agreement_with_adj_irregular_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="determiner_noun_agreement_with_adj_irregular_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_determiner_noun_agreement_with_adjective_1 = LightevalTaskConfig( + name="blimp:determiner_noun_agreement_with_adjective_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="determiner_noun_agreement_with_adjective_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_distractor_agreement_relational_noun = LightevalTaskConfig( + name="blimp:distractor_agreement_relational_noun", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="distractor_agreement_relational_noun", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_distractor_agreement_relative_clause = LightevalTaskConfig( + name="blimp:distractor_agreement_relative_clause", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="distractor_agreement_relative_clause", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_drop_argument = LightevalTaskConfig( + name="blimp:drop_argument", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="drop_argument", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_ellipsis_n_bar_1 = LightevalTaskConfig( + name="blimp:ellipsis_n_bar_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="ellipsis_n_bar_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_ellipsis_n_bar_2 = LightevalTaskConfig( + name="blimp:ellipsis_n_bar_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="ellipsis_n_bar_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_existential_there_object_raising = LightevalTaskConfig( + name="blimp:existential_there_object_raising", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="existential_there_object_raising", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_existential_there_quantifiers_1 = LightevalTaskConfig( + name="blimp:existential_there_quantifiers_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="existential_there_quantifiers_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_existential_there_quantifiers_2 = LightevalTaskConfig( + name="blimp:existential_there_quantifiers_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="existential_there_quantifiers_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_existential_there_subject_raising = LightevalTaskConfig( + name="blimp:existential_there_subject_raising", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="existential_there_subject_raising", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_expletive_it_object_raising = LightevalTaskConfig( + name="blimp:expletive_it_object_raising", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="expletive_it_object_raising", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_inchoative = LightevalTaskConfig( + name="blimp:inchoative", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="inchoative", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_intransitive = LightevalTaskConfig( + name="blimp:intransitive", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="intransitive", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_irregular_past_participle_adjectives = LightevalTaskConfig( + name="blimp:irregular_past_participle_adjectives", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="irregular_past_participle_adjectives", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_irregular_past_participle_verbs = LightevalTaskConfig( + name="blimp:irregular_past_participle_verbs", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="irregular_past_participle_verbs", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_irregular_plural_subject_verb_agreement_1 = LightevalTaskConfig( + name="blimp:irregular_plural_subject_verb_agreement_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="irregular_plural_subject_verb_agreement_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_irregular_plural_subject_verb_agreement_2 = LightevalTaskConfig( + name="blimp:irregular_plural_subject_verb_agreement_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="irregular_plural_subject_verb_agreement_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_left_branch_island_echo_question = LightevalTaskConfig( + name="blimp:left_branch_island_echo_question", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="left_branch_island_echo_question", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_left_branch_island_simple_question = LightevalTaskConfig( + name="blimp:left_branch_island_simple_question", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="left_branch_island_simple_question", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_matrix_question_npi_licensor_present = LightevalTaskConfig( + name="blimp:matrix_question_npi_licensor_present", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="matrix_question_npi_licensor_present", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_npi_present_1 = LightevalTaskConfig( + name="blimp:npi_present_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="npi_present_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_npi_present_2 = LightevalTaskConfig( + name="blimp:npi_present_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="npi_present_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_only_npi_licensor_present = LightevalTaskConfig( + name="blimp:only_npi_licensor_present", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="only_npi_licensor_present", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_only_npi_scope = LightevalTaskConfig( + name="blimp:only_npi_scope", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="only_npi_scope", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_passive_1 = LightevalTaskConfig( + name="blimp:passive_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="passive_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_passive_2 = LightevalTaskConfig( + name="blimp:passive_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="passive_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_principle_A_c_command = LightevalTaskConfig( + name="blimp:principle_A_c_command", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="principle_A_c_command", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_principle_A_case_1 = LightevalTaskConfig( + name="blimp:principle_A_case_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="principle_A_case_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_principle_A_case_2 = LightevalTaskConfig( + name="blimp:principle_A_case_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="principle_A_case_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_principle_A_domain_1 = LightevalTaskConfig( + name="blimp:principle_A_domain_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="principle_A_domain_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_principle_A_domain_2 = LightevalTaskConfig( + name="blimp:principle_A_domain_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="principle_A_domain_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_principle_A_domain_3 = LightevalTaskConfig( + name="blimp:principle_A_domain_3", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="principle_A_domain_3", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_principle_A_reconstruction = LightevalTaskConfig( + name="blimp:principle_A_reconstruction", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="principle_A_reconstruction", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_regular_plural_subject_verb_agreement_1 = LightevalTaskConfig( + name="blimp:regular_plural_subject_verb_agreement_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="regular_plural_subject_verb_agreement_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_regular_plural_subject_verb_agreement_2 = LightevalTaskConfig( + name="blimp:regular_plural_subject_verb_agreement_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="regular_plural_subject_verb_agreement_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_sentential_negation_npi_licensor_present = LightevalTaskConfig( + name="blimp:sentential_negation_npi_licensor_present", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="sentential_negation_npi_licensor_present", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_sentential_negation_npi_scope = LightevalTaskConfig( + name="blimp:sentential_negation_npi_scope", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="sentential_negation_npi_scope", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_sentential_subject_island = LightevalTaskConfig( + name="blimp:sentential_subject_island", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="sentential_subject_island", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_superlative_quantifiers_1 = LightevalTaskConfig( + name="blimp:superlative_quantifiers_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="superlative_quantifiers_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_superlative_quantifiers_2 = LightevalTaskConfig( + name="blimp:superlative_quantifiers_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="superlative_quantifiers_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_tough_vs_raising_1 = LightevalTaskConfig( + name="blimp:tough_vs_raising_1", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="tough_vs_raising_1", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_tough_vs_raising_2 = LightevalTaskConfig( + name="blimp:tough_vs_raising_2", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="tough_vs_raising_2", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_transitive = LightevalTaskConfig( + name="blimp:transitive", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="transitive", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_wh_island = LightevalTaskConfig( + name="blimp:wh_island", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="wh_island", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_wh_questions_object_gap = LightevalTaskConfig( + name="blimp:wh_questions_object_gap", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="wh_questions_object_gap", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_wh_questions_subject_gap = LightevalTaskConfig( + name="blimp:wh_questions_subject_gap", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="wh_questions_subject_gap", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_wh_questions_subject_gap_long_distance = LightevalTaskConfig( + name="blimp:wh_questions_subject_gap_long_distance", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="wh_questions_subject_gap_long_distance", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_wh_vs_that_no_gap = LightevalTaskConfig( + name="blimp:wh_vs_that_no_gap", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="wh_vs_that_no_gap", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_wh_vs_that_no_gap_long_distance = LightevalTaskConfig( + name="blimp:wh_vs_that_no_gap_long_distance", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="wh_vs_that_no_gap_long_distance", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_wh_vs_that_with_gap = LightevalTaskConfig( + name="blimp:wh_vs_that_with_gap", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="wh_vs_that_with_gap", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +blimp_wh_vs_that_with_gap_long_distance = LightevalTaskConfig( + name="blimp:wh_vs_that_with_gap_long_distance", + suite=["lighteval"], + prompt_function=prompt.blimp, + hf_repo="nyu-mll/blimp", + hf_subset="wh_vs_that_with_gap_long_distance", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/bold.py b/src/lighteval/tasks/tasks/bold.py new file mode 100644 index 000000000..612faac86 --- /dev/null +++ b/src/lighteval/tasks/tasks/bold.py @@ -0,0 +1,128 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation + +https://dl.acm.org/doi/10.1145/3442188.3445924 +""" + +bold = LightevalTaskConfig( + name="bold", + suite=["lighteval"], + prompt_function=prompt.bold, + hf_repo="lighteval/bold_helm", + hf_subset="all", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.prediction_perplexity], + stop_sequence=["\n"], + version=0, +) + +bold_gender = LightevalTaskConfig( + name="bold:gender", + suite=["lighteval"], + prompt_function=prompt.bold, + hf_repo="lighteval/bold_helm", + hf_subset="gender", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.prediction_perplexity], + stop_sequence=["\n"], + version=0, +) + +bold_political_ideology = LightevalTaskConfig( + name="bold:political_ideology", + suite=["lighteval"], + prompt_function=prompt.bold, + hf_repo="lighteval/bold_helm", + hf_subset="political_ideology", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.prediction_perplexity], + stop_sequence=["\n"], + version=0, +) + +bold_profession = LightevalTaskConfig( + name="bold:profession", + suite=["lighteval"], + prompt_function=prompt.bold, + hf_repo="lighteval/bold_helm", + hf_subset="profession", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.prediction_perplexity], + stop_sequence=["\n"], + version=0, +) + +bold_race = LightevalTaskConfig( + name="bold:race", + suite=["lighteval"], + prompt_function=prompt.bold, + hf_repo="lighteval/bold_helm", + hf_subset="race", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.prediction_perplexity], + stop_sequence=["\n"], + version=0, +) + +bold_religious_ideology = LightevalTaskConfig( + name="bold:religious_ideology", + suite=["lighteval"], + prompt_function=prompt.bold, + hf_repo="lighteval/bold_helm", + hf_subset="religious_ideology", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.prediction_perplexity], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/boolq.py b/src/lighteval/tasks/tasks/boolq.py new file mode 100644 index 000000000..61874d734 --- /dev/null +++ b/src/lighteval/tasks/tasks/boolq.py @@ -0,0 +1,63 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +boolq = LightevalTaskConfig( + name="boolq", + suite=["lighteval"], + prompt_function=prompt.boolq_helm, + hf_repo="lighteval/boolq_helm", + hf_subset="default", + hf_avail_splits=["train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) + + +boolq_contrastset = LightevalTaskConfig( + name="boolq:contrastset", + suite=["lighteval"], + prompt_function=prompt.boolq_helm_contrastset, + hf_repo="lighteval/boolq_helm", + hf_subset="default", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/civil_comments.py b/src/lighteval/tasks/tasks/civil_comments.py new file mode 100644 index 000000000..e72a8ba16 --- /dev/null +++ b/src/lighteval/tasks/tasks/civil_comments.py @@ -0,0 +1,176 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification. + +https://arxiv.org/abs/1903.04561 +""" + +civil_comments = LightevalTaskConfig( + name="civil_comments", + suite=["lighteval"], + prompt_function=prompt.civil_comments, + hf_repo="lighteval/civil_comments_helm", + hf_subset="all", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +civil_comments_LGBTQ = LightevalTaskConfig( + name="civil_comments:LGBTQ", + suite=["lighteval"], + prompt_function=prompt.civil_comments, + hf_repo="lighteval/civil_comments_helm", + hf_subset="LGBTQ", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +civil_comments_black = LightevalTaskConfig( + name="civil_comments:black", + suite=["lighteval"], + prompt_function=prompt.civil_comments, + hf_repo="lighteval/civil_comments_helm", + hf_subset="black", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +civil_comments_christian = LightevalTaskConfig( + name="civil_comments:christian", + suite=["lighteval"], + prompt_function=prompt.civil_comments, + hf_repo="lighteval/civil_comments_helm", + hf_subset="christian", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +civil_comments_female = LightevalTaskConfig( + name="civil_comments:female", + suite=["lighteval"], + prompt_function=prompt.civil_comments, + hf_repo="lighteval/civil_comments_helm", + hf_subset="female", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +civil_comments_male = LightevalTaskConfig( + name="civil_comments:male", + suite=["lighteval"], + prompt_function=prompt.civil_comments, + hf_repo="lighteval/civil_comments_helm", + hf_subset="male", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +civil_comments_muslim = LightevalTaskConfig( + name="civil_comments:muslim", + suite=["lighteval"], + prompt_function=prompt.civil_comments, + hf_repo="lighteval/civil_comments_helm", + hf_subset="muslim", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +civil_comments_other_religions = LightevalTaskConfig( + name="civil_comments:other_religions", + suite=["lighteval"], + prompt_function=prompt.civil_comments, + hf_repo="lighteval/civil_comments_helm", + hf_subset="other_religions", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +civil_comments_white = LightevalTaskConfig( + name="civil_comments:white", + suite=["lighteval"], + prompt_function=prompt.civil_comments, + hf_repo="lighteval/civil_comments_helm", + hf_subset="white", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/commonsenseqa.py b/src/lighteval/tasks/tasks/commonsenseqa.py new file mode 100644 index 000000000..e97fba27d --- /dev/null +++ b/src/lighteval/tasks/tasks/commonsenseqa.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +commonsenseqa = LightevalTaskConfig( + name="commonsenseqa", + suite=["helm", "commonsense_scenario"], + prompt_function=prompt.commonsense_qa, + hf_repo="commonsense_qa", + hf_subset="default", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/coqa.py b/src/lighteval/tasks/tasks/coqa.py new file mode 100644 index 000000000..22146cf66 --- /dev/null +++ b/src/lighteval/tasks/tasks/coqa.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +coqa_first_question = LightevalTaskConfig( + name="coqa", + prompt_function=prompt.coqa, + suite=["lighteval"], + hf_repo="stanfordnlp/coqa", + hf_subset="default", + hf_avail_splits=["train", "validation"], + evaluation_splits=["validation"], + stop_sequence=["\n", "Question:", "question:"], + generation_size=100, + version=1, + metrics=( + Metrics.exact_match + ), +) diff --git a/src/lighteval/tasks/tasks/covid_dialogue.py b/src/lighteval/tasks/tasks/covid_dialogue.py new file mode 100644 index 000000000..ed54ae170 --- /dev/null +++ b/src/lighteval/tasks/tasks/covid_dialogue.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +covid_dialogue = LightevalTaskConfig( + name="covid_dialogue", + suite=["helm"], + prompt_function=prompt.covid_dialogue, + hf_repo="lighteval/covid_dialogue", + hf_subset="default", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=128, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/drop_qa.py b/src/lighteval/tasks/tasks/drop_qa.py new file mode 100644 index 000000000..49ea79eac --- /dev/null +++ b/src/lighteval/tasks/tasks/drop_qa.py @@ -0,0 +1,41 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +drop_qa = LightevalTaskConfig( + name="drop", + prompt_function=get_qa_prompt_function( + Language.ENGLISH, + lambda line: { + "context": line["passage"], + "question": line["question"], + "choices": list( + filter( + lambda x: x, + [line["answer"].get("number")] + + line["answer"]["spans"] + + [prompt.get_drop_date(line["answer"].get("date"))], + ) diff --git a/src/lighteval/tasks/tasks/dyck_language.py b/src/lighteval/tasks/tasks/dyck_language.py new file mode 100644 index 000000000..439cf0b1b --- /dev/null +++ b/src/lighteval/tasks/tasks/dyck_language.py @@ -0,0 +1,76 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +dyck_language_2 = LightevalTaskConfig( + name="dyck_language:2", + suite=["lighteval"], + prompt_function=prompt.dyck_language, + hf_repo="lighteval/DyckLanguage", + hf_subset="2", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + + +dyck_language_3 = LightevalTaskConfig( + name="dyck_language:3", + suite=["lighteval"], + prompt_function=prompt.dyck_language, + hf_repo="lighteval/DyckLanguage", + hf_subset="3", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + + +dyck_language_4 = LightevalTaskConfig( + name="dyck_language:4", + suite=["lighteval"], + prompt_function=prompt.dyck_language, + hf_repo="lighteval/DyckLanguage", + hf_subset="4", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/entity_data_imputation.py b/src/lighteval/tasks/tasks/entity_data_imputation.py new file mode 100644 index 000000000..807a839c2 --- /dev/null +++ b/src/lighteval/tasks/tasks/entity_data_imputation.py @@ -0,0 +1,68 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + +""" +Capturing Semantics for Imputation with Pre-trained Language Models + +https://ieeexplore.ieee.org/document/9458712 +""" + +entity_data_imputation_Buy = LightevalTaskConfig( + name="entity_data_imputation:Buy", + suite=["lighteval"], + prompt_function=prompt.entity_data_imputation, + hf_repo="lighteval/Buy", + hf_subset="default", + hf_avail_splits=["train", "test", "valid"], + evaluation_splits=["valid", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) + + +entity_data_imputation_Restaurant = LightevalTaskConfig( + name="entity_data_imputation:Restaurant", + suite=["lighteval"], + prompt_function=prompt.entity_data_imputation, + hf_repo="lighteval/Restaurant", + hf_subset="default", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/entitymatching.py b/src/lighteval/tasks/tasks/entitymatching.py new file mode 100644 index 000000000..6f68fa62f --- /dev/null +++ b/src/lighteval/tasks/tasks/entitymatching.py @@ -0,0 +1,240 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +Simple entity matching benchmark. + +https://dl.acm.org/doi/10.14778/3007263.3007314 +""" + +entity_matching_Abt_Buy = LightevalTaskConfig( + name="entity_matching:Abt_Buy", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="Abt_Buy", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +entity_matching_Amazon_Google = LightevalTaskConfig( + name="entity_matching:Amazon_Google", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="Amazon_Google", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +entity_matching_Beer = LightevalTaskConfig( + name="entity_matching:Beer", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="Beer", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +entity_matching_Company = LightevalTaskConfig( + name="entity_matching:Company", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="Company", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +entity_matching_DBLP_ACM = LightevalTaskConfig( + name="entity_matching:DBLP_ACM", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="DBLP_ACM", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +entity_matching_DBLP_GoogleScholar = LightevalTaskConfig( + name="entity_matching:DBLP_GoogleScholar", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="DBLP_GoogleScholar", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +entity_matching_Dirty_DBLP_ACM = LightevalTaskConfig( + name="entity_matching:Dirty_DBLP_ACM", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="Dirty_DBLP_ACM", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +entity_matching_Dirty_DBLP_GoogleScholar = LightevalTaskConfig( + name="entity_matching:Dirty_DBLP_GoogleScholar", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="Dirty_DBLP_GoogleScholar", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +entity_matching_Dirty_Walmart_Amazon = LightevalTaskConfig( + name="entity_matching:Dirty_Walmart_Amazon", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="Dirty_Walmart_Amazon", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +entity_matching_Dirty_iTunes_Amazon = LightevalTaskConfig( + name="entity_matching:Dirty_iTunes_Amazon", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="Dirty_iTunes_Amazon", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +entity_matching_Fodors_Zagats = LightevalTaskConfig( + name="entity_matching=Fodors_Zagats", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="Fodors_Zagats", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +entity_matching_Walmart_Amazon = LightevalTaskConfig( + name="entity_matching:Walmart_Amazon", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="Walmart_Amazon", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +entity_matching_iTunes_Amazon = LightevalTaskConfig( + name="entity_matching:iTunes_Amazon", + suite=["lighteval"], + prompt_function=prompt.entity_matching, + hf_repo="lighteval/EntityMatching", + hf_subset="iTunes_Amazon", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/ethics.py b/src/lighteval/tasks/tasks/ethics.py new file mode 100644 index 000000000..7663451f6 --- /dev/null +++ b/src/lighteval/tasks/tasks/ethics.py @@ -0,0 +1,112 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +from: Aligning AI With Shared Human Values + +https://arxiv.org/abs/2008.02275 +""" + +ethics_commonsense = LightevalTaskConfig( + name="ethics:commonsense", + suite=["lighteval"], + prompt_function=prompt.ethics_commonsense, + hf_repo="lighteval/hendrycks_ethics", + hf_subset="commonsense", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +ethics_deontology = LightevalTaskConfig( + name="ethics:deontology", + suite=["lighteval"], + prompt_function=prompt.ethics_deontology, + hf_repo="lighteval/hendrycks_ethics", + hf_subset="deontology", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +ethics_justice = LightevalTaskConfig( + name="ethics:justice", + suite=["lighteval"], + prompt_function=prompt.ethics_justice, + hf_repo="lighteval/hendrycks_ethics", + hf_subset="justice", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +ethics_utilitarianism = LightevalTaskConfig( + name="ethics:utilitarianism", + suite=["lighteval"], + prompt_function=prompt.ethics_utilitarianism, + hf_repo="lighteval/hendrycks_ethics", + hf_subset="utilitarianism", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +ethics_virtue = LightevalTaskConfig( + name="ethics:virtue", + suite=["lighteval"], + prompt_function=prompt.ethics_virtue, + hf_repo="lighteval/hendrycks_ethics", + hf_subset="virtue", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/glue.py b/src/lighteval/tasks/tasks/glue.py new file mode 100644 index 000000000..c75acb95e --- /dev/null +++ b/src/lighteval/tasks/tasks/glue.py @@ -0,0 +1,298 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +glue_cola_lighteval = LightevalTaskConfig( + name="glue:cola", + suite=["lighteval"], + prompt_function=prompt.cola, + hf_repo="glue", + hf_subset="cola", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc, Metrics.mcc], + stop_sequence=["\n"], + version=0, +) + +glue_mnli_lighteval = LightevalTaskConfig( + name="glue:mnli", + suite=["lighteval"], + prompt_function=prompt.mnli, + hf_repo="glue", + hf_subset="mnli_matched", + hf_avail_splits=["train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +glue_mnli_mismatched_lighteval = LightevalTaskConfig( + name="glue:mnli_mismatched", + suite=["lighteval"], + prompt_function=prompt.mnli, + hf_repo="glue", + hf_subset="mnli_mismatched", + hf_avail_splits=["train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +glue_mrpc_lighteval = LightevalTaskConfig( + name="glue:mrpc", + suite=["lighteval"], + prompt_function=prompt.mrpc, + hf_repo="glue", + hf_subset="mrpc", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc, Metrics.loglikelihood_f1], + stop_sequence=["\n"], + version=0, +) + +glue_qnli_lighteval = LightevalTaskConfig( + name="glue:qnli", + suite=["lighteval"], + prompt_function=prompt.qnli, + hf_repo="glue", + hf_subset="qnli", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +glue_qqp_lighteval = LightevalTaskConfig( + name="glue:qqp", + suite=["lighteval"], + prompt_function=prompt.qqp, + hf_repo="glue", + hf_subset="qqp", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc, Metrics.loglikelihood_f1], + stop_sequence=["\n"], + version=0, +) + +glue_rte_lighteval = LightevalTaskConfig( + name="glue:rte", + suite=["lighteval"], + prompt_function=prompt.rte, + hf_repo="glue", + hf_subset="rte", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +glue_sst2_lighteval = LightevalTaskConfig( + name="glue:sst2", + suite=["lighteval"], + prompt_function=prompt.sst, + hf_repo="glue", + hf_subset="sst2", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +glue_stsb_lighteval = LightevalTaskConfig( + name="glue:stsb", + suite=["lighteval"], + prompt_function=prompt.stsb, + hf_repo="glue", + hf_subset="stsb", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +glue_wnli_lighteval = LightevalTaskConfig( + name="glue:wnli", + suite=["lighteval"], + prompt_function=prompt.wnli, + hf_repo="glue", + hf_subset="wnli", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +super_glue_boolq_lighteval = LightevalTaskConfig( + name="super_glue:boolq", + suite=["lighteval"], + prompt_function=prompt.boolq_harness, + hf_repo="super_glue", + hf_subset="boolq", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +super_glue_cb_lighteval = LightevalTaskConfig( + name="super_glue:cb", + suite=["lighteval"], + prompt_function=prompt.cb, + hf_repo="super_glue", + hf_subset="cb", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc, Metrics.multi_f1_numeric], + stop_sequence=["\n"], + version=0, +) + +super_glue_copa_lighteval = LightevalTaskConfig( + name="super_glue:copa", + suite=["lighteval"], + prompt_function=prompt.copa, + hf_repo="super_glue", + hf_subset="copa", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +super_glue_rte_lighteval = LightevalTaskConfig( + name="super_glue:rte", + suite=["lighteval"], + prompt_function=prompt.rte, + hf_repo="super_glue", + hf_subset="rte", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +super_glue_multirc_lighteval = LightevalTaskConfig( + name="super_glue:multirc", + suite=["lighteval"], + prompt_function=prompt.multirc, + hf_repo="super_glue", + hf_subset="multirc", + hf_avail_splits=["train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +super_glue_wic_lighteval = LightevalTaskConfig( + name="super_glue:wic", + suite=["lighteval"], + prompt_function=prompt.wic, + hf_repo="super_glue", + hf_subset="wic", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +super_glue_wsc_lighteval = LightevalTaskConfig( + name="super_glue:wsc", + suite=["lighteval"], + prompt_function=prompt.wsc, + hf_repo="super_glue", + hf_subset="wsc", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/gpqa.py b/src/lighteval/tasks/tasks/gpqa.py new file mode 100644 index 000000000..e2adc6040 --- /dev/null +++ b/src/lighteval/tasks/tasks/gpqa.py @@ -0,0 +1,64 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +from inspect_ai.scorer import choice +from inspect_ai.solver import multiple_choice + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import multichoice_scorer +from lighteval.tasks.lighteval_task import LightevalTaskConfig_inspect + + +gpqa_diamond = LightevalTaskConfig_inspect( + name="gpqa:diamond", + prompt_function=prompt.gpqa_instruct, + dataset_repo="Idavidrein/gpqa", + dataset_subset="gpqa_diamond", + dataset_split="train", + scorers=[multichoice_scorer(), choice()], + solvers=[multiple_choice()], + system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", +) + + +gpqa_extended = LightevalTaskConfig_inspect( + name="gpqa:extended", + prompt_function=prompt.gpqa_instruct, + dataset_repo="Idavidrein/gpqa", + dataset_subset="gpqa_extended", + dataset_split="train", + scorers=[multichoice_scorer(), choice()], + solvers=[multiple_choice()], + system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", +) + + +gpqa_main = LightevalTaskConfig_inspect( + name="gpqa:main", + prompt_function=prompt.gpqa_instruct, + dataset_repo="Idavidrein/gpqa", + dataset_subset="gpqa_main", + dataset_split="train", + scorers=[multichoice_scorer(), choice()], + solvers=[multiple_choice()], + system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", +) diff --git a/src/lighteval/tasks/tasks/gsm8k.py b/src/lighteval/tasks/tasks/gsm8k.py new file mode 100644 index 000000000..31bed67fb --- /dev/null +++ b/src/lighteval/tasks/tasks/gsm8k.py @@ -0,0 +1,37 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +gsm8k = LightevalTaskConfig_inspect( + name="gsm8k", + prompt_function=prompt.gsm8k, + dataset_repo="openai/gsm8k", + dataset_subset="main", + dataset_split="train", + dataset_revision="main", + scorers=[extractive_math_scorer()], + system_prompt="ANSWER USING THE FORMAT $ANSWER$", +) diff --git a/src/lighteval/tasks/tasks/gsm_plus.py b/src/lighteval/tasks/tasks/gsm_plus.py new file mode 100644 index 000000000..c386324bd --- /dev/null +++ b/src/lighteval/tasks/tasks/gsm_plus.py @@ -0,0 +1,38 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics, extractive_math_scorer +from lighteval.tasks.lighteval_task import LightevalTaskConfig, LightevalTaskConfig_inspect + + +gsm_plus = LightevalTaskConfig_inspect( + name="gsm_plus", + prompt_function=prompt.gsm_plus, + dataset_repo="qintongli/GSM-Plus", + dataset_subset="default", + dataset_split="test", + system_prompt="ANSWER USING THE FORMAT $ANSWER$", + epochs=48, + epochs_reducer="pass_at_16", + scorers=[extractive_math_scorer(), model_graded_fact()] +) diff --git a/src/lighteval/tasks/tasks/headqa.py b/src/lighteval/tasks/tasks/headqa.py new file mode 100644 index 000000000..9ffda300c --- /dev/null +++ b/src/lighteval/tasks/tasks/headqa.py @@ -0,0 +1,74 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to +access a specialized position in the Spanish healthcare system, and are +challenging even for highly specialized humans. They are designed by the +Ministerio de Sanidad, Consumo y Bienestar Social, who also provides direct +access to the exams of the last 5 years. + +https://arxiv.org/abs/1906.04701 +""" + + +headqa_en = LightevalTaskConfig( + name="headqa:en", + suite=["lighteval"], + prompt_function=prompt.headqa, + hf_repo="lighteval/headqa_harness", + hf_subset="en", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=["\n"], + version=0, +) + + +headqa_es = LightevalTaskConfig( + name="headqa:es", + suite=["lighteval"], + prompt_function=prompt.headqa, + hf_repo="lighteval/headqa_harness", + hf_subset="es", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/hellaswag.py b/src/lighteval/tasks/tasks/hellaswag.py new file mode 100644 index 000000000..e05d1707a --- /dev/null +++ b/src/lighteval/tasks/tasks/hellaswag.py @@ -0,0 +1,50 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +HellaSwag: Can a Machine Really Finish Your Sentence? + +https://arxiv.org/abs/1905.07830 +""" + +hellaswag = LightevalTaskConfig( + name="hellaswag", + suite=["lighteval"], + prompt_function=prompt.hellaswag_generative, + hf_repo="hellaswag", + hf_subset="default", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/extended/hle/main.py b/src/lighteval/tasks/tasks/hle/main.py similarity index 100% rename from src/lighteval/tasks/extended/hle/main.py rename to src/lighteval/tasks/tasks/hle/main.py diff --git a/src/lighteval/tasks/extended/ifbench/evaluation_lib.py b/src/lighteval/tasks/tasks/ifbench/evaluation_lib.py similarity index 100% rename from src/lighteval/tasks/extended/ifbench/evaluation_lib.py rename to src/lighteval/tasks/tasks/ifbench/evaluation_lib.py diff --git a/src/lighteval/tasks/extended/ifbench/instructions.py b/src/lighteval/tasks/tasks/ifbench/instructions.py similarity index 100% rename from src/lighteval/tasks/extended/ifbench/instructions.py rename to src/lighteval/tasks/tasks/ifbench/instructions.py diff --git a/src/lighteval/tasks/extended/ifbench/instructions_registry.py b/src/lighteval/tasks/tasks/ifbench/instructions_registry.py similarity index 100% rename from src/lighteval/tasks/extended/ifbench/instructions_registry.py rename to src/lighteval/tasks/tasks/ifbench/instructions_registry.py diff --git a/src/lighteval/tasks/extended/ifbench/main.py b/src/lighteval/tasks/tasks/ifbench/main.py similarity index 100% rename from src/lighteval/tasks/extended/ifbench/main.py rename to src/lighteval/tasks/tasks/ifbench/main.py diff --git a/src/lighteval/tasks/extended/ifeval/instructions.py b/src/lighteval/tasks/tasks/ifeval/instructions.py similarity index 100% rename from src/lighteval/tasks/extended/ifeval/instructions.py rename to src/lighteval/tasks/tasks/ifeval/instructions.py diff --git a/src/lighteval/tasks/extended/ifeval/instructions_registry.py b/src/lighteval/tasks/tasks/ifeval/instructions_registry.py similarity index 100% rename from src/lighteval/tasks/extended/ifeval/instructions_registry.py rename to src/lighteval/tasks/tasks/ifeval/instructions_registry.py diff --git a/src/lighteval/tasks/extended/ifeval/instructions_utils.py b/src/lighteval/tasks/tasks/ifeval/instructions_utils.py similarity index 100% rename from src/lighteval/tasks/extended/ifeval/instructions_utils.py rename to src/lighteval/tasks/tasks/ifeval/instructions_utils.py diff --git a/src/lighteval/tasks/extended/ifeval/main.py b/src/lighteval/tasks/tasks/ifeval/main.py similarity index 100% rename from src/lighteval/tasks/extended/ifeval/main.py rename to src/lighteval/tasks/tasks/ifeval/main.py diff --git a/src/lighteval/tasks/tasks/imdb.py b/src/lighteval/tasks/tasks/imdb.py new file mode 100644 index 000000000..cf8ada89e --- /dev/null +++ b/src/lighteval/tasks/tasks/imdb.py @@ -0,0 +1,70 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + +""" +The IMDB benchmark for sentiment analysis in movie review, from: +Learning Word Vectors for Sentiment Analysis + +https://aclanthology.org/P11-1015/ +""" + + +imdb = LightevalTaskConfig( + name="imdb", + suite=["lighteval"], + prompt_function=prompt.imdb, + hf_repo="lighteval/IMDB_helm", + hf_subset="default", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) + + +imdb_contrastset = LightevalTaskConfig( + name="imdb:contrastset", + suite=["lighteval"], + prompt_function=prompt.imdb_contrastset, + hf_repo="lighteval/IMDB_helm", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/jeopardy.py b/src/lighteval/tasks/tasks/jeopardy.py new file mode 100644 index 000000000..331cf5671 --- /dev/null +++ b/src/lighteval/tasks/tasks/jeopardy.py @@ -0,0 +1,54 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +jeopardy = LightevalTaskConfig( + name="jeopardy", + prompt_function=get_qa_prompt_function( + Language.ENGLISH, + lambda line: { + "question": line["question"], + "choices": [line["answer"]], + }, + ), + suite=("lighteval",), + hf_repo="openaccess-ai-collective/jeopardy", + hf_subset="default", + evaluation_splits=("train",), + few_shots_split="train", + generation_size=250, + stop_sequence=["\n", "Question:", "question:"], + metrics=( + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + ), +) diff --git a/src/lighteval/tasks/tasks/lambada.py b/src/lighteval/tasks/tasks/lambada.py new file mode 100644 index 000000000..29e87b854 --- /dev/null +++ b/src/lighteval/tasks/tasks/lambada.py @@ -0,0 +1,64 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + +""" +The LAMBADA dataset: Word prediction requiring a broad discourse context + +https://arxiv.org/abs/1606.06031 +""" + +lambada_standard = LightevalTaskConfig( + name="lambada:standard", + suite=["lighteval"], + prompt_function=prompt.lambada, + hf_repo="lambada", + hf_subset="plain_text", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=10, + metrics=[Metrics.target_perplexity], + stop_sequence=["\n"], + version=0, +) + + +lambada_standard_cloze = LightevalTaskConfig( + name="lambada:standard_cloze", + suite=["lighteval"], + prompt_function=prompt.lambada_cloze, + hf_repo="lambada", + hf_subset="plain_text", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=10, + metrics=[Metrics.target_perplexity], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/extended/lcb/codegen_metrics.py b/src/lighteval/tasks/tasks/lcb/codegen_metrics.py similarity index 100% rename from src/lighteval/tasks/extended/lcb/codegen_metrics.py rename to src/lighteval/tasks/tasks/lcb/codegen_metrics.py diff --git a/src/lighteval/tasks/extended/lcb/main.py b/src/lighteval/tasks/tasks/lcb/main.py similarity index 100% rename from src/lighteval/tasks/extended/lcb/main.py rename to src/lighteval/tasks/tasks/lcb/main.py diff --git a/src/lighteval/tasks/tasks/legal_summarization.py b/src/lighteval/tasks/tasks/legal_summarization.py new file mode 100644 index 000000000..ae84e50c2 --- /dev/null +++ b/src/lighteval/tasks/tasks/legal_summarization.py @@ -0,0 +1,103 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +Cant find this one in HELM, just a paper. + +https://arxiv.org/abs/2210.13448 +""" + +legal_summarization_billsum = LightevalTaskConfig( + name="legal_summarization:billsum", + suite=["lighteval"], + prompt_function=prompt.legal_summarization, + hf_repo="lighteval/legal_summarization", + hf_subset="BillSum", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=1024, + metrics=[ + Metrics.rouge1, + Metrics.rouge2, + Metrics.rougeL, + Metrics.faithfulness, + Metrics.extractiveness, + Metrics.bert_score, + ], + stop_sequence=["\n"], + version=0, +) + + +legal_summarization_eurlexsum = LightevalTaskConfig( + name="legal_summarization:eurlexsum", + suite=["lighteval"], + prompt_function=prompt.legal_summarization, + hf_repo="lighteval/legal_summarization", + hf_subset="EurLexSum", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=2048, + metrics=[ + Metrics.rouge1, + Metrics.rouge2, + Metrics.rougeL, + Metrics.faithfulness, + Metrics.extractiveness, + Metrics.bert_score, + ], + stop_sequence=["\n"], + version=0, +) + + +legal_summarization_multilexsum = LightevalTaskConfig( + name="legal_summarization:multilexsum", + suite=["lighteval"], + prompt_function=prompt.multilexsum, + hf_repo="lighteval/legal_summarization", + hf_subset="MultiLexSum", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=256, + metrics=[ + Metrics.rouge1, + Metrics.rouge2, + Metrics.rougeL, + Metrics.faithfulness, + Metrics.extractiveness, + Metrics.bert_score, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/legalsupport.py b/src/lighteval/tasks/tasks/legalsupport.py new file mode 100644 index 000000000..1d6a753dc --- /dev/null +++ b/src/lighteval/tasks/tasks/legalsupport.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +legalsupport = LightevalTaskConfig( + name="legalsupport", + suite=["helm"], + prompt_function=prompt.legal_support, + hf_repo="lighteval/LegalSupport", + hf_subset="default", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/lexglue.py b/src/lighteval/tasks/tasks/lexglue.py new file mode 100644 index 000000000..c8abbce31 --- /dev/null +++ b/src/lighteval/tasks/tasks/lexglue.py @@ -0,0 +1,144 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +LexGLUE: A Benchmark Dataset for Legal Language Understanding in English + +https://arxiv.org/abs/2110.00976 +""" + +lexglue_case_hold = LightevalTaskConfig( + name="lexglue:case_hold", + suite=["lighteval"], + prompt_function=prompt.lex_glue_case_hold, + hf_repo="lighteval/lexglue", + hf_subset="case_hold", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lexglue_ecthr_a = LightevalTaskConfig( + name="lexglue:ecthr_a", + suite=["lighteval"], + prompt_function=prompt.lex_glue_ecthr_a, + hf_repo="lighteval/lexglue", + hf_subset="ecthr_a", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lexglue_ecthr_b = LightevalTaskConfig( + name="lexglue:ecthr_b", + suite=["lighteval"], + prompt_function=prompt.lex_glue_ecthr_b, + hf_repo="lighteval/lexglue", + hf_subset="ecthr_b", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lexglue_eurlex = LightevalTaskConfig( + name="lexglue:eurlex", + suite=["lighteval"], + prompt_function=prompt.lex_glue_eurlex, + hf_repo="lighteval/lexglue", + hf_subset="eurlex", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lexglue_ledgar = LightevalTaskConfig( + name="lexglue:ledgar", + suite=["lighteval"], + prompt_function=prompt.lex_glue_ledgar, + hf_repo="lighteval/lexglue", + hf_subset="ledgar", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lexglue_scotus = LightevalTaskConfig( + name="lexglue:scotus", + suite=["lighteval"], + prompt_function=prompt.lex_glue_scotus, + hf_repo="lighteval/lexglue", + hf_subset="scotus", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lexglue_unfair_tos = LightevalTaskConfig( + name="lexglue:unfair_tos", + suite=["lighteval"], + prompt_function=prompt.lex_glue_unfair_tos, + hf_repo="lighteval/lexglue", + hf_subset="unfair_tos", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/lextreme.py b/src/lighteval/tasks/tasks/lextreme.py new file mode 100644 index 000000000..336fff7c3 --- /dev/null +++ b/src/lighteval/tasks/tasks/lextreme.py @@ -0,0 +1,319 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +"""LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain + +https://arxiv.org/abs/2301.13126 +""" + +lextreme_brazilian_court_decisions_judgment = LightevalTaskConfig( + name="lextreme:brazilian_court_decisions_judgment", + suite=["lighteval"], + prompt_function=prompt.lextreme_brazilian_court_decisions_judgment, + hf_repo="lighteval/lextreme", + hf_subset="brazilian_court_decisions_judgment", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_brazilian_court_decisions_unanimity = LightevalTaskConfig( + name="lextreme:brazilian_court_decisions_unanimity", + suite=["lighteval"], + prompt_function=prompt.lextreme_brazilian_court_decisions_unanimity, + hf_repo="lighteval/lextreme", + hf_subset="brazilian_court_decisions_unanimity", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_covid19_emergency_event = LightevalTaskConfig( + name="lextreme:covid19_emergency_event", + suite=["lighteval"], + prompt_function=prompt.lextreme_covid19_emergency_event, + hf_repo="lighteval/lextreme", + hf_subset="covid19_emergency_event", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=10, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_german_argument_mining = LightevalTaskConfig( + name="lextreme:german_argument_mining", + suite=["lighteval"], + prompt_function=prompt.lextreme_german_argument_mining, + hf_repo="lighteval/lextreme", + hf_subset="german_argument_mining", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_greek_legal_code_chapter = LightevalTaskConfig( + name="lextreme:greek_legal_code_chapter", + suite=["lighteval"], + prompt_function=prompt.lextreme_greek_legal_code_chapter, + hf_repo="lighteval/lextreme", + hf_subset="greek_legal_code_chapter", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_greek_legal_code_subject = LightevalTaskConfig( + name="lextreme:greek_legal_code_subject", + suite=["lighteval"], + prompt_function=prompt.lextreme_greek_legal_code_subject, + hf_repo="lighteval/lextreme", + hf_subset="greek_legal_code_subject", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_greek_legal_code_volume = LightevalTaskConfig( + name="lextreme:greek_legal_code_volume", + suite=["lighteval"], + prompt_function=prompt.lextreme_greek_legal_code_volume, + hf_repo="lighteval/lextreme", + hf_subset="greek_legal_code_volume", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_greek_legal_ner = LightevalTaskConfig( + name="lextreme:greek_legal_ner", + suite=["lighteval"], + prompt_function=prompt.lextreme_greek_legal_ner, + hf_repo="lighteval/lextreme", + hf_subset="greek_legal_ner", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=430, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_legalnero = LightevalTaskConfig( + name="lextreme:legalnero", + suite=["lighteval"], + prompt_function=prompt.lextreme_legalnero, + hf_repo="lighteval/lextreme", + hf_subset="legalnero", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=788, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_lener_br = LightevalTaskConfig( + name="lextreme:lener_br", + suite=["lighteval"], + prompt_function=prompt.lextreme_lener_br, + hf_repo="lighteval/lextreme", + hf_subset="lener_br", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=338, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_mapa_coarse = LightevalTaskConfig( + name="lextreme:mapa_coarse", + suite=["lighteval"], + prompt_function=prompt.lextreme_mapa_coarse, + hf_repo="lighteval/lextreme", + hf_subset="mapa_coarse", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=274, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_mapa_fine = LightevalTaskConfig( + name="lextreme:mapa_fine", + suite=["lighteval"], + prompt_function=prompt.lextreme_mapa_fine, + hf_repo="lighteval/lextreme", + hf_subset="mapa_fine", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=274, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_multi_eurlex_level_1 = LightevalTaskConfig( + name="lextreme:multi_eurlex_level_1", + suite=["lighteval"], + prompt_function=prompt.lextreme_multi_eurlex_level_1, + hf_repo="lighteval/lextreme", + hf_subset="multi_eurlex_level_1", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=10, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_multi_eurlex_level_2 = LightevalTaskConfig( + name="lextreme:multi_eurlex_level_2", + suite=["lighteval"], + prompt_function=prompt.lextreme_multi_eurlex_level_2, + hf_repo="lighteval/lextreme", + hf_subset="multi_eurlex_level_2", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=10, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_multi_eurlex_level_3 = LightevalTaskConfig( + name="lextreme:multi_eurlex_level_3", + suite=["lighteval"], + prompt_function=prompt.lextreme_multi_eurlex_level_3, + hf_repo="lighteval/lextreme", + hf_subset="multi_eurlex_level_3", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=10, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_online_terms_of_service_clause_topics = LightevalTaskConfig( + name="lextreme:online_terms_of_service_clause_topics", + suite=["lighteval"], + prompt_function=prompt.lextreme_online_terms_of_service_clause_topics, + hf_repo="lighteval/lextreme", + hf_subset="online_terms_of_service_clause_topics", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=10, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_online_terms_of_service_unfairness_levels = LightevalTaskConfig( + name="lextreme:online_terms_of_service_unfairness_levels", + suite=["lighteval"], + prompt_function=prompt.lextreme_online_terms_of_service_unfairness_levels, + hf_repo="lighteval/lextreme", + hf_subset="online_terms_of_service_unfairness_levels", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=10, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lextreme_swiss_judgment_prediction = LightevalTaskConfig( + name="lextreme:swiss_judgment_prediction", + suite=["lighteval"], + prompt_function=prompt.lextreme_swiss_judgment_prediction, + hf_repo="lighteval/lextreme", + hf_subset="swiss_judgment_prediction", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/logiqa.py b/src/lighteval/tasks/tasks/logiqa.py new file mode 100644 index 000000000..2f96cbbe3 --- /dev/null +++ b/src/lighteval/tasks/tasks/logiqa.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +logiqa = LightevalTaskConfig( + name="logiqa", + suite=["lighteval"], + prompt_function=prompt.logiqa, + hf_repo="lighteval/logiqa_harness", + hf_subset="logiqa", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/lsat_qa.py b/src/lighteval/tasks/tasks/lsat_qa.py new file mode 100644 index 000000000..3ef5a31c0 --- /dev/null +++ b/src/lighteval/tasks/tasks/lsat_qa.py @@ -0,0 +1,106 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +lsat_qa = LightevalTaskConfig( + name="lsat_qa", + suite=["lighteval"], + prompt_function=prompt.lsat_qa, + hf_repo="lighteval/lsat_qa", + hf_subset="all", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lsat_qa_assignment = LightevalTaskConfig( + name="lsat_qa:assignment", + suite=["lighteval"], + prompt_function=prompt.lsat_qa, + hf_repo="lighteval/lsat_qa", + hf_subset="assignment", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lsat_qa_grouping = LightevalTaskConfig( + name="lsat_qa:grouping", + suite=["lighteval"], + prompt_function=prompt.lsat_qa, + hf_repo="lighteval/lsat_qa", + hf_subset="grouping", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lsat_qa_miscellaneous = LightevalTaskConfig( + name="lsat_qa:miscellaneous", + suite=["lighteval"], + prompt_function=prompt.lsat_qa, + hf_repo="lighteval/lsat_qa", + hf_subset="miscellaneous", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +lsat_qa_ordering = LightevalTaskConfig( + name="lsat_qa:ordering", + suite=["lighteval"], + prompt_function=prompt.lsat_qa, + hf_repo="lighteval/lsat_qa", + hf_subset="ordering", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/math.py b/src/lighteval/tasks/tasks/math.py new file mode 100644 index 000000000..870360057 --- /dev/null +++ b/src/lighteval/tasks/tasks/math.py @@ -0,0 +1,147 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems +from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. +Each problem in MATH has a full step-by-step solution, which can be used to +teach models to generate answer derivations and explanations. + +https://arxiv.org/abs/2103.03874 +""" + +math_algebra = LightevalTaskConfig( + name="math:algebra", + suite=["lighteval"], + prompt_function=prompt.math, + hf_repo="DigitalLearningGmbH/MATH-lighteval", + hf_subset="algebra", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=2048, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=1, +) + +math_counting_and_probability = LightevalTaskConfig( + name="math:counting_and_probability", + suite=["lighteval"], + prompt_function=prompt.math, + hf_repo="DigitalLearningGmbH/MATH-lighteval", + hf_subset="counting_and_probability", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=2048, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=1, +) + +math_geometry = LightevalTaskConfig( + name="math:geometry", + suite=["lighteval"], + prompt_function=prompt.math, + hf_repo="DigitalLearningGmbH/MATH-lighteval", + hf_subset="geometry", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=2048, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=1, +) + +math_intermediate_algebra = LightevalTaskConfig( + name="math:intermediate_algebra", + suite=["lighteval"], + prompt_function=prompt.math, + hf_repo="DigitalLearningGmbH/MATH-lighteval", + hf_subset="intermediate_algebra", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=2048, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=1, +) + +math_number_theory = LightevalTaskConfig( + name="math:number_theory", + suite=["lighteval"], + prompt_function=prompt.math, + hf_repo="DigitalLearningGmbH/MATH-lighteval", + hf_subset="number_theory", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=2048, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=1, +) + +math_prealgebra = LightevalTaskConfig( + name="math:prealgebra", + suite=["lighteval"], + prompt_function=prompt.math, + hf_repo="DigitalLearningGmbH/MATH-lighteval", + hf_subset="prealgebra", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=2048, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=1, +) + +math_precalculus = LightevalTaskConfig( + name="math:precalculus", + suite=["lighteval"], + prompt_function=prompt.math, + hf_repo="DigitalLearningGmbH/MATH-lighteval", + hf_subset="precalculus", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=2048, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=1, +) diff --git a/src/lighteval/tasks/tasks/math_500.py b/src/lighteval/tasks/tasks/math_500.py new file mode 100644 index 000000000..c5213dfc9 --- /dev/null +++ b/src/lighteval/tasks/tasks/math_500.py @@ -0,0 +1,38 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics, extractive_math_scorer +from lighteval.tasks.lighteval_task import LightevalTaskConfig, LightevalTaskConfig_inspect + + +math_500 = LightevalTaskConfig_inspect( + name="math_500", + prompt_function=prompt.math_500, + dataset_repo="HuggingFaceH4/MATH-500", + dataset_subset="default", + dataset_split="test", + scorers=[extractive_math_scorer()], + system_prompt="Solve the following math problem efficiently and clearly. The last line of your response should be of the following format: 'Therefore, the final answer is: $\\boxed{{ANSWER}}$. I hope it is correct' (without quotes) where ANSWER is just the final number or expression that solves the problem. Think step by step before answering.", + epochs=48, + epochs_reducer="pass_at_16", +) diff --git a/src/lighteval/tasks/tasks/mathqa.py b/src/lighteval/tasks/tasks/mathqa.py new file mode 100644 index 000000000..b1e3cf569 --- /dev/null +++ b/src/lighteval/tasks/tasks/mathqa.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +mathqa = LightevalTaskConfig( + name="mathqa", + suite=["lighteval"], + prompt_function=prompt.mathqa, + hf_repo="allenai/math_qa", + hf_subset="default", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/me_q_sum.py b/src/lighteval/tasks/tasks/me_q_sum.py new file mode 100644 index 000000000..15dc20df6 --- /dev/null +++ b/src/lighteval/tasks/tasks/me_q_sum.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +me_q_sum = LightevalTaskConfig( + name="me_q_sum", + suite=["helm"], + prompt_function=prompt.me_q_sum, + hf_repo="lighteval/me_q_sum", + hf_subset="default", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=128, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/med.py b/src/lighteval/tasks/tasks/med.py new file mode 100644 index 000000000..def107ef8 --- /dev/null +++ b/src/lighteval/tasks/tasks/med.py @@ -0,0 +1,88 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering + +https://medmcqa.github.io/ +""" + +med_mcqa = LightevalTaskConfig( + name="med_mcqa", + suite=["lighteval"], + prompt_function=prompt.med_mcqa, + hf_repo="lighteval/med_mcqa", + hf_subset="default", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) + + +med_paragraph_simplification = LightevalTaskConfig( + name="med_paragraph_simplification", + suite=["lighteval"], + prompt_function=prompt.med_paragraph_simplification, + hf_repo="lighteval/med_paragraph_simplification", + hf_subset="default", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=512, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) + + +med_qa = LightevalTaskConfig( + name="med_qa", + suite=["lighteval"], + prompt_function=prompt.med_qa, + hf_repo="bigbio/med_qa", + hf_subset="med_qa_en_source", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/med_dialog.py b/src/lighteval/tasks/tasks/med_dialog.py new file mode 100644 index 000000000..0e99730c9 --- /dev/null +++ b/src/lighteval/tasks/tasks/med_dialog.py @@ -0,0 +1,63 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +med_dialog_healthcaremagic = LightevalTaskConfig( + name="med_dialog:healthcaremagic", + suite=["lighteval"], + prompt_function=prompt.med_dialog, + hf_repo="lighteval/med_dialog", + hf_subset="healthcaremagic", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=128, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) + + +med_dialog_icliniq = LightevalTaskConfig( + name="med_dialog:icliniq", + suite=["lighteval"], + prompt_function=prompt.med_dialog, + hf_repo="lighteval/med_dialog", + hf_subset="icliniq", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=128, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/mgsm.py b/src/lighteval/tasks/tasks/mgsm.py new file mode 100644 index 000000000..95a979f81 --- /dev/null +++ b/src/lighteval/tasks/tasks/mgsm.py @@ -0,0 +1,212 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school +math problems. +The same 250 problems from GSM8K are each translated via human annotators in 10 +languages. +language list: en, es, fr, de, ru, zh, ja, th, sw, bn, te + +https://arxiv.org/abs/2210.03057 +""" + +mgsm_en = LightevalTaskConfig( + name="mgsm:en", + suite=["lighteval"], + prompt_function=prompt.mgsm_en, + hf_repo="juletxara/mgsm", + hf_subset="en", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.exact_match], + stop_sequence=["\n", "=", "Question="], + version=0, +) + +mgsm_es = LightevalTaskConfig( + name="mgsm:es", + suite=["lighteval"], + prompt_function=prompt.mgsm_es, + hf_repo="juletxara/mgsm", + hf_subset="es", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.exact_match], + stop_sequence=["\n", "=", "Pregunta="], + version=0, +) + +mgsm_fr = LightevalTaskConfig( + name="mgsm:fr", + suite=["lighteval"], + prompt_function=prompt.mgsm_fr, + hf_repo="juletxara/mgsm", + hf_subset="fr", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.exact_match], + stop_sequence=["\n", "=", "Question="], + version=0, +) + +mgsm_de = LightevalTaskConfig( + name="mgsm:de", + suite=["lighteval"], + prompt_function=prompt.mgsm_de, + hf_repo="juletxara/mgsm", + hf_subset="de", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.exact_match], + stop_sequence=["\n", "=", "Frage="], + version=0, +) + +mgsm_ru = LightevalTaskConfig( + name="mgsm:ru", + suite=["lighteval"], + prompt_function=prompt.mgsm_ru, + hf_repo="juletxara/mgsm", + hf_subset="ru", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.exact_match], + stop_sequence=["\n", "=", "\u0417\u0430\u0434\u0430\u0447\u0430="], + version=0, +) + +mgsm_zh = LightevalTaskConfig( + name="mgsm:zh", + suite=["lighteval"], + prompt_function=prompt.mgsm_zh, + hf_repo="juletxara/mgsm", + hf_subset="zh", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.exact_match], + stop_sequence=["\n", "=", "\u95ee\u9898="], + version=0, +) + +mgsm_ja = LightevalTaskConfig( + name="mgsm:ja", + suite=["lighteval"], + prompt_function=prompt.mgsm_ja, + hf_repo="juletxara/mgsm", + hf_subset="ja", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.exact_match], + stop_sequence=["\n", "=", "\u554f\u984c="], + version=0, +) + +mgsm_th = LightevalTaskConfig( + name="mgsm:th", + suite=["lighteval"], + prompt_function=prompt.mgsm_th, + hf_repo="juletxara/mgsm", + hf_subset="th", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.exact_match], + stop_sequence=["\n", "=", "\u0e42\u0e08\u0e17\u0e22\u0e4c="], + version=0, +) + +mgsm_sw = LightevalTaskConfig( + name="mgsm:sw", + suite=["lighteval"], + prompt_function=prompt.mgsm_sw, + hf_repo="juletxara/mgsm", + hf_subset="sw", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.exact_match], + stop_sequence=["\n", "=", "Swali="], + version=0, +) + +mgsm_bn = LightevalTaskConfig( + name="mgsm:bn", + suite=["lighteval"], + prompt_function=prompt.mgsm_bn, + hf_repo="juletxara/mgsm", + hf_subset="bn", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.exact_match], + stop_sequence=["\n", "=", "\u09aa\u09cd\u09b0\u09b6\u09cd\u09a8="], + version=0, +) + +mgsm_te = LightevalTaskConfig( + name="mgsm:te", + suite=["lighteval"], + prompt_function=prompt.mgsm_te, + hf_repo="juletxara/mgsm", + hf_subset="te", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.exact_match], + stop_sequence=["\n", "=", "\u0c2a\u0c4d\u0c30\u0c36\u0c4d\u0c28="], + version=0, +) diff --git a/src/lighteval/tasks/extended/mix_eval/judge_prompts.py b/src/lighteval/tasks/tasks/mix_eval/judge_prompts.py similarity index 100% rename from src/lighteval/tasks/extended/mix_eval/judge_prompts.py rename to src/lighteval/tasks/tasks/mix_eval/judge_prompts.py diff --git a/src/lighteval/tasks/extended/mix_eval/main.py b/src/lighteval/tasks/tasks/mix_eval/main.py similarity index 100% rename from src/lighteval/tasks/extended/mix_eval/main.py rename to src/lighteval/tasks/tasks/mix_eval/main.py diff --git a/src/lighteval/tasks/extended/mix_eval/prompts.py b/src/lighteval/tasks/tasks/mix_eval/prompts.py similarity index 100% rename from src/lighteval/tasks/extended/mix_eval/prompts.py rename to src/lighteval/tasks/tasks/mix_eval/prompts.py diff --git a/src/lighteval/tasks/tasks/mmlu.py b/src/lighteval/tasks/tasks/mmlu.py new file mode 100644 index 000000000..f1f6e4352 --- /dev/null +++ b/src/lighteval/tasks/tasks/mmlu.py @@ -0,0 +1,938 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +mmlu_abstract_algebra = LightevalTaskConfig( + name="mmlu:abstract_algebra", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="abstract_algebra", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_anatomy = LightevalTaskConfig( + name="mmlu:anatomy", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="anatomy", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_astronomy = LightevalTaskConfig( + name="mmlu:astronomy", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="astronomy", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_business_ethics = LightevalTaskConfig( + name="mmlu:business_ethics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="business_ethics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_clinical_knowledge = LightevalTaskConfig( + name="mmlu:clinical_knowledge", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="clinical_knowledge", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_college_biology = LightevalTaskConfig( + name="mmlu:college_biology", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="college_biology", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_college_chemistry = LightevalTaskConfig( + name="mmlu:college_chemistry", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="college_chemistry", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_college_computer_science = LightevalTaskConfig( + name="mmlu:college_computer_science", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="college_computer_science", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_college_mathematics = LightevalTaskConfig( + name="mmlu:college_mathematics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="college_mathematics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_college_medicine = LightevalTaskConfig( + name="mmlu:college_medicine", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="college_medicine", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_college_physics = LightevalTaskConfig( + name="mmlu:college_physics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="college_physics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_computer_security = LightevalTaskConfig( + name="mmlu:computer_security", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="computer_security", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_conceptual_physics = LightevalTaskConfig( + name="mmlu:conceptual_physics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="conceptual_physics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_econometrics = LightevalTaskConfig( + name="mmlu:econometrics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="econometrics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_electrical_engineering = LightevalTaskConfig( + name="mmlu:electrical_engineering", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="electrical_engineering", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_elementary_mathematics = LightevalTaskConfig( + name="mmlu:elementary_mathematics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="elementary_mathematics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_formal_logic = LightevalTaskConfig( + name="mmlu:formal_logic", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="formal_logic", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_global_facts = LightevalTaskConfig( + name="mmlu:global_facts", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="global_facts", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_biology = LightevalTaskConfig( + name="mmlu:high_school_biology", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_biology", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_chemistry = LightevalTaskConfig( + name="mmlu:high_school_chemistry", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_chemistry", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_computer_science = LightevalTaskConfig( + name="mmlu:high_school_computer_science", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_computer_science", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_european_history = LightevalTaskConfig( + name="mmlu:high_school_european_history", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_european_history", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_geography = LightevalTaskConfig( + name="mmlu:high_school_geography", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_geography", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_government_and_politics = LightevalTaskConfig( + name="mmlu:high_school_government_and_politics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_government_and_politics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_macroeconomics = LightevalTaskConfig( + name="mmlu:high_school_macroeconomics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_macroeconomics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_mathematics = LightevalTaskConfig( + name="mmlu:high_school_mathematics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_mathematics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_microeconomics = LightevalTaskConfig( + name="mmlu:high_school_microeconomics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_microeconomics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_physics = LightevalTaskConfig( + name="mmlu:high_school_physics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_physics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_psychology = LightevalTaskConfig( + name="mmlu:high_school_psychology", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_psychology", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_statistics = LightevalTaskConfig( + name="mmlu:high_school_statistics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_statistics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_us_history = LightevalTaskConfig( + name="mmlu:high_school_us_history", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_us_history", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_high_school_world_history = LightevalTaskConfig( + name="mmlu:high_school_world_history", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="high_school_world_history", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_human_aging = LightevalTaskConfig( + name="mmlu:human_aging", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="human_aging", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_human_sexuality = LightevalTaskConfig( + name="mmlu:human_sexuality", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="human_sexuality", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_international_law = LightevalTaskConfig( + name="mmlu:international_law", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="international_law", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_jurisprudence = LightevalTaskConfig( + name="mmlu:jurisprudence", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="jurisprudence", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_logical_fallacies = LightevalTaskConfig( + name="mmlu:logical_fallacies", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="logical_fallacies", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_machine_learning = LightevalTaskConfig( + name="mmlu:machine_learning", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="machine_learning", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_management = LightevalTaskConfig( + name="mmlu:management", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="management", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_marketing = LightevalTaskConfig( + name="mmlu:marketing", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="marketing", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_medical_genetics = LightevalTaskConfig( + name="mmlu:medical_genetics", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="medical_genetics", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_miscellaneous = LightevalTaskConfig( + name="mmlu:miscellaneous", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="miscellaneous", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_moral_disputes = LightevalTaskConfig( + name="mmlu:moral_disputes", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="moral_disputes", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_moral_scenarios = LightevalTaskConfig( + name="mmlu:moral_scenarios", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="moral_scenarios", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_nutrition = LightevalTaskConfig( + name="mmlu:nutrition", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="nutrition", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_philosophy = LightevalTaskConfig( + name="mmlu:philosophy", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="philosophy", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_prehistory = LightevalTaskConfig( + name="mmlu:prehistory", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="prehistory", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_professional_accounting = LightevalTaskConfig( + name="mmlu:professional_accounting", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="professional_accounting", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_professional_law = LightevalTaskConfig( + name="mmlu:professional_law", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="professional_law", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_professional_medicine = LightevalTaskConfig( + name="mmlu:professional_medicine", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="professional_medicine", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_professional_psychology = LightevalTaskConfig( + name="mmlu:professional_psychology", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="professional_psychology", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_public_relations = LightevalTaskConfig( + name="mmlu:public_relations", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="public_relations", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_security_studies = LightevalTaskConfig( + name="mmlu:security_studies", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="security_studies", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_sociology = LightevalTaskConfig( + name="mmlu:sociology", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="sociology", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_us_foreign_policy = LightevalTaskConfig( + name="mmlu:us_foreign_policy", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="us_foreign_policy", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_virology = LightevalTaskConfig( + name="mmlu:virology", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="virology", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +mmlu_world_religions = LightevalTaskConfig( + name="mmlu:world_religions", + suite=["lighteval"], + prompt_function=prompt.mmlu_helm, + hf_repo="lighteval/mmlu", + hf_subset="world_religions", + hf_avail_splits=["auxiliary_train", "test", "validation", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/mmlu_redux.py b/src/lighteval/tasks/tasks/mmlu_redux.py new file mode 100644 index 000000000..27ef9fab1 --- /dev/null +++ b/src/lighteval/tasks/tasks/mmlu_redux.py @@ -0,0 +1,167 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +_MMLU_REDUX_2_SUBSETS = [ + "abstract_algebra", + "anatomy", + "astronomy", + "business_ethics", + "clinical_knowledge", + "college_biology", + "college_chemistry", + "college_computer_science", + "college_mathematics", + "college_medicine", + "college_physics", + "computer_security", + "conceptual_physics", + "econometrics", + "electrical_engineering", + "elementary_mathematics", + "formal_logic", + "global_facts", + "high_school_biology", + "high_school_chemistry", + "high_school_computer_science", + "high_school_european_history", + "high_school_geography", + "high_school_government_and_politics", + "high_school_macroeconomics", + "high_school_mathematics", + "high_school_microeconomics", + "high_school_physics", + "high_school_psychology", + "high_school_statistics", + "high_school_us_history", + "high_school_world_history", + "human_aging", + "human_sexuality", + "international_law", + "jurisprudence", + "logical_fallacies", + "machine_learning", + "management", + "marketing", + "medical_genetics", + "miscellaneous", + "moral_disputes", + "moral_scenarios", + "nutrition", + "philosophy", + "prehistory", + "professional_accounting", + "professional_law", + "professional_medicine", + "professional_psychology", + "public_relations", + "security_studies", + "sociology", + "us_foreign_policy", + "virology", + "world_religions", +] + + +_mmlu_redux_2_tasks = { + subset: LightevalTaskConfig( + name=f"mmlu_redux_2:{subset}", + suite=["lighteval"], + prompt_function=lambda line, task_name=None, s=subset: prompt.mmlu_redux_2(line, s, task_name), + hf_repo="edinburgh-dawg/mmlu-redux-2.0", + hf_subset=subset, + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[ + Metrics.loglikelihood_acc, + Metrics.pass_at_k_letters(sample_params={"k": 1}), + ], + stop_sequence=["\n"], + version=0, + ) + for subset in _MMLU_REDUX_2_SUBSETS +} + +mmlu_redux_2_abstract_algebra = _mmlu_redux_2_tasks["abstract_algebra"] +mmlu_redux_2_anatomy = _mmlu_redux_2_tasks["anatomy"] +mmlu_redux_2_astronomy = _mmlu_redux_2_tasks["astronomy"] +mmlu_redux_2_business_ethics = _mmlu_redux_2_tasks["business_ethics"] +mmlu_redux_2_clinical_knowledge = _mmlu_redux_2_tasks["clinical_knowledge"] +mmlu_redux_2_college_biology = _mmlu_redux_2_tasks["college_biology"] +mmlu_redux_2_college_chemistry = _mmlu_redux_2_tasks["college_chemistry"] +mmlu_redux_2_college_computer_science = _mmlu_redux_2_tasks["college_computer_science"] +mmlu_redux_2_college_mathematics = _mmlu_redux_2_tasks["college_mathematics"] +mmlu_redux_2_college_medicine = _mmlu_redux_2_tasks["college_medicine"] +mmlu_redux_2_college_physics = _mmlu_redux_2_tasks["college_physics"] +mmlu_redux_2_computer_security = _mmlu_redux_2_tasks["computer_security"] +mmlu_redux_2_conceptual_physics = _mmlu_redux_2_tasks["conceptual_physics"] +mmlu_redux_2_econometrics = _mmlu_redux_2_tasks["econometrics"] +mmlu_redux_2_electrical_engineering = _mmlu_redux_2_tasks["electrical_engineering"] +mmlu_redux_2_elementary_mathematics = _mmlu_redux_2_tasks["elementary_mathematics"] +mmlu_redux_2_formal_logic = _mmlu_redux_2_tasks["formal_logic"] +mmlu_redux_2_global_facts = _mmlu_redux_2_tasks["global_facts"] +mmlu_redux_2_high_school_biology = _mmlu_redux_2_tasks["high_school_biology"] +mmlu_redux_2_high_school_chemistry = _mmlu_redux_2_tasks["high_school_chemistry"] +mmlu_redux_2_high_school_computer_science = _mmlu_redux_2_tasks["high_school_computer_science"] +mmlu_redux_2_high_school_european_history = _mmlu_redux_2_tasks["high_school_european_history"] +mmlu_redux_2_high_school_geography = _mmlu_redux_2_tasks["high_school_geography"] +mmlu_redux_2_high_school_government_and_politics = _mmlu_redux_2_tasks["high_school_government_and_politics"] +mmlu_redux_2_high_school_macroeconomics = _mmlu_redux_2_tasks["high_school_macroeconomics"] +mmlu_redux_2_high_school_mathematics = _mmlu_redux_2_tasks["high_school_mathematics"] +mmlu_redux_2_high_school_microeconomics = _mmlu_redux_2_tasks["high_school_microeconomics"] +mmlu_redux_2_high_school_physics = _mmlu_redux_2_tasks["high_school_physics"] +mmlu_redux_2_high_school_psychology = _mmlu_redux_2_tasks["high_school_psychology"] +mmlu_redux_2_high_school_statistics = _mmlu_redux_2_tasks["high_school_statistics"] +mmlu_redux_2_high_school_us_history = _mmlu_redux_2_tasks["high_school_us_history"] +mmlu_redux_2_high_school_world_history = _mmlu_redux_2_tasks["high_school_world_history"] +mmlu_redux_2_human_aging = _mmlu_redux_2_tasks["human_aging"] +mmlu_redux_2_human_sexuality = _mmlu_redux_2_tasks["human_sexuality"] +mmlu_redux_2_international_law = _mmlu_redux_2_tasks["international_law"] +mmlu_redux_2_jurisprudence = _mmlu_redux_2_tasks["jurisprudence"] +mmlu_redux_2_logical_fallacies = _mmlu_redux_2_tasks["logical_fallacies"] +mmlu_redux_2_machine_learning = _mmlu_redux_2_tasks["machine_learning"] +mmlu_redux_2_management = _mmlu_redux_2_tasks["management"] +mmlu_redux_2_marketing = _mmlu_redux_2_tasks["marketing"] +mmlu_redux_2_medical_genetics = _mmlu_redux_2_tasks["medical_genetics"] +mmlu_redux_2_miscellaneous = _mmlu_redux_2_tasks["miscellaneous"] +mmlu_redux_2_moral_disputes = _mmlu_redux_2_tasks["moral_disputes"] +mmlu_redux_2_moral_scenarios = _mmlu_redux_2_tasks["moral_scenarios"] +mmlu_redux_2_nutrition = _mmlu_redux_2_tasks["nutrition"] +mmlu_redux_2_philosophy = _mmlu_redux_2_tasks["philosophy"] +mmlu_redux_2_prehistory = _mmlu_redux_2_tasks["prehistory"] +mmlu_redux_2_professional_accounting = _mmlu_redux_2_tasks["professional_accounting"] +mmlu_redux_2_professional_law = _mmlu_redux_2_tasks["professional_law"] +mmlu_redux_2_professional_medicine = _mmlu_redux_2_tasks["professional_medicine"] +mmlu_redux_2_professional_psychology = _mmlu_redux_2_tasks["professional_psychology"] +mmlu_redux_2_public_relations = _mmlu_redux_2_tasks["public_relations"] +mmlu_redux_2_security_studies = _mmlu_redux_2_tasks["security_studies"] +mmlu_redux_2_sociology = _mmlu_redux_2_tasks["sociology"] +mmlu_redux_2_us_foreign_policy = _mmlu_redux_2_tasks["us_foreign_policy"] +mmlu_redux_2_virology = _mmlu_redux_2_tasks["virology"] +mmlu_redux_2_world_religions = _mmlu_redux_2_tasks["world_religions"] diff --git a/src/lighteval/tasks/tasks/mmmu_pro.py b/src/lighteval/tasks/tasks/mmmu_pro.py new file mode 100644 index 000000000..b83b2ea2c --- /dev/null +++ b/src/lighteval/tasks/tasks/mmmu_pro.py @@ -0,0 +1,84 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +MMMU-Pro is an enhanced multimodal benchmark designed to rigorously assess the +true understanding capabilities of advanced AI models across multiple +modalities. + +https://arxiv.org/abs/2409.02813 +""" + +mmmu_pro_standard_4_options = LightevalTaskConfig( + name="mmmu_pro:standard-4", + suite=["lighteval"], + prompt_function=prompt.mmmu_pro, + hf_repo="MMMU/MMMU_pro", + hf_subset="standard (4 options)", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, # expected an answer in a format 'Answer: B' + metrics=[Metrics.gpqa_instruct_metric], + stop_sequence=None, + version=0, +) + + +mmmu_pro_standard_10_options = LightevalTaskConfig( + name="mmmu_pro:standard-10", + suite=["lighteval"], + prompt_function=prompt.mmmu_pro, + hf_repo="MMMU/MMMU_pro", + hf_subset="standard (10 options)", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, # expected an answer in a format 'Answer: B' + metrics=[Metrics.gpqa_instruct_metric], + stop_sequence=None, + version=0, +) + + +mmmu_pro_vision = LightevalTaskConfig( + name="mmmu_pro:vision", + suite=["lighteval"], + prompt_function=prompt.mmmu_pro_vision, + hf_repo="MMMU/MMMU_pro", + hf_subset="vision", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, # expected an answer in a format 'Answer: B' + metrics=[Metrics.gpqa_instruct_metric], + stop_sequence=None, + version=0, +) diff --git a/src/lighteval/tasks/extended/mt_bench/judge_prompt_templates.py b/src/lighteval/tasks/tasks/mt_bench/judge_prompt_templates.py similarity index 100% rename from src/lighteval/tasks/extended/mt_bench/judge_prompt_templates.py rename to src/lighteval/tasks/tasks/mt_bench/judge_prompt_templates.py diff --git a/src/lighteval/tasks/extended/mt_bench/main.py b/src/lighteval/tasks/tasks/mt_bench/main.py similarity index 100% rename from src/lighteval/tasks/extended/mt_bench/main.py rename to src/lighteval/tasks/tasks/mt_bench/main.py diff --git a/src/lighteval/tasks/tasks/musr.py b/src/lighteval/tasks/tasks/musr.py new file mode 100644 index 000000000..950370fdf --- /dev/null +++ b/src/lighteval/tasks/tasks/musr.py @@ -0,0 +1,82 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning + +https://arxiv.org/abs/2310.16049 +""" + +musr_murder_mysteries = LightevalTaskConfig( + name="musr:murder_mysteries", + suite=["lighteval"], + prompt_function=prompt.musr, + hf_repo="TAUR-Lab/MuSR", + hf_subset="default", + hf_avail_splits=["murder_mysteries"], + evaluation_splits=["murder_mysteries"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + + +musr_object_placements = LightevalTaskConfig( + name="musr:object_placements", + suite=["lighteval"], + prompt_function=prompt.musr, + hf_repo="TAUR-Lab/MuSR", + hf_subset="default", + hf_avail_splits=["object_placements"], + evaluation_splits=["object_placements"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + + +musr_team_allocation = LightevalTaskConfig( + name="musr:team_allocation", + suite=["lighteval"], + prompt_function=prompt.musr, + hf_repo="TAUR-Lab/MuSR", + hf_subset="default", + hf_avail_splits=["team_allocation"], + evaluation_splits=["team_allocation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/narrativeqa.py b/src/lighteval/tasks/tasks/narrativeqa.py new file mode 100644 index 000000000..305bbfd26 --- /dev/null +++ b/src/lighteval/tasks/tasks/narrativeqa.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +narrativeqa = LightevalTaskConfig( + name="narrativeqa", + suite=["helm", "helm_general"], + prompt_function=prompt.narrativeqa, + hf_repo="lighteval/narrative_qa_helm", + hf_subset="default", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/natural_questions.py b/src/lighteval/tasks/tasks/natural_questions.py new file mode 100644 index 000000000..b2f0e4f0c --- /dev/null +++ b/src/lighteval/tasks/tasks/natural_questions.py @@ -0,0 +1,51 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +natural_questions = LightevalTaskConfig( + name="natural_questions", + prompt_function=get_qa_prompt_function( + Language.ENGLISH, + lambda line: {"question": line["question"], "choices": [line["answer"]]}, + ), + suite=("lighteval",), + hf_repo="lighteval/small_natural_questions", + hf_subset="default", + evaluation_splits=("test",), + few_shots_split="few_shot", + generation_size=250, + stop_sequence=["\n", "Question:", "question:"], + metrics=( + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + ), +) diff --git a/src/lighteval/tasks/tasks/numeracy.py b/src/lighteval/tasks/tasks/numeracy.py new file mode 100644 index 000000000..b5f535cc7 --- /dev/null +++ b/src/lighteval/tasks/tasks/numeracy.py @@ -0,0 +1,154 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +numeracy_linear_example = LightevalTaskConfig( + name="numeracy:linear_example", + suite=["lighteval"], + prompt_function=prompt.numeracy, + hf_repo="lighteval/numeracy", + hf_subset="linear_example", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +numeracy_linear_standard = LightevalTaskConfig( + name="numeracy:linear_standard", + suite=["lighteval"], + prompt_function=prompt.numeracy, + hf_repo="lighteval/numeracy", + hf_subset="linear_standard", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +numeracy_parabola_example = LightevalTaskConfig( + name="numeracy:parabola_example", + suite=["lighteval"], + prompt_function=prompt.numeracy, + hf_repo="lighteval/numeracy", + hf_subset="parabola_example", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +numeracy_parabola_standard = LightevalTaskConfig( + name="numeracy:parabola_standard", + suite=["lighteval"], + prompt_function=prompt.numeracy, + hf_repo="lighteval/numeracy", + hf_subset="parabola_standard", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +numeracy_paraboloid_example = LightevalTaskConfig( + name="numeracy:paraboloid_example", + suite=["lighteval"], + prompt_function=prompt.numeracy, + hf_repo="lighteval/numeracy", + hf_subset="paraboloid_example", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +numeracy_paraboloid_standard = LightevalTaskConfig( + name="numeracy:paraboloid_standard", + suite=["lighteval"], + prompt_function=prompt.numeracy, + hf_repo="lighteval/numeracy", + hf_subset="paraboloid_standard", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +numeracy_plane_example = LightevalTaskConfig( + name="numeracy:plane_example", + suite=["lighteval"], + prompt_function=prompt.numeracy, + hf_repo="lighteval/numeracy", + hf_subset="plane_example", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + +numeracy_plane_standard = LightevalTaskConfig( + name="numeracy:plane_standard", + suite=["lighteval"], + prompt_function=prompt.numeracy, + hf_repo="lighteval/numeracy", + hf_subset="plane_standard", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/extended/olympiade_bench/main.py b/src/lighteval/tasks/tasks/olympiade_bench/main.py similarity index 100% rename from src/lighteval/tasks/extended/olympiade_bench/main.py rename to src/lighteval/tasks/tasks/olympiade_bench/main.py diff --git a/src/lighteval/tasks/tasks/openbookqa.py b/src/lighteval/tasks/tasks/openbookqa.py new file mode 100644 index 000000000..5de06aa82 --- /dev/null +++ b/src/lighteval/tasks/tasks/openbookqa.py @@ -0,0 +1,58 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +OpenBookQA aims to promote research in advanced question-answering, probing a +deeper understanding of both the topic (with salient facts summarized as an open +book, also provided with the dataset) and the language it is expressed in. In +particular, it contains questions that require multi-step reasoning, use of +additional common and commonsense knowledge, and rich text comprehension. +OpenBookQA is a new kind of question-answering dataset modeled after open book +exams for assessing human understanding of a subject. + + +from: Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering +https://arxiv.org/abs/1809.02789 +""" + +openbookqa = LightevalTaskConfig( + name="openbookqa", + suite=["lighteval"], + prompt_function=prompt.openbookqa_helm, + hf_repo="openbookqa", + hf_subset="main", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/piqa.py b/src/lighteval/tasks/tasks/piqa.py new file mode 100644 index 000000000..1bf3c585e --- /dev/null +++ b/src/lighteval/tasks/tasks/piqa.py @@ -0,0 +1,55 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + +""" +To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? +Questions requiring this kind of physical commonsense pose a challenge to +state-of-the-art natural language understanding systems. The PIQA dataset +introduces the task of physical commonsense reasoning and a corresponding +benchmark dataset Physical Interaction: Question Answering or PIQA. + + +from: PIQA: Reasoning about Physical Commonsense in Natural Language +https://arxiv.org/abs/1911.11641 +""" + +piqa = LightevalTaskConfig( + name="piqa", + suite=["lighteval"], + prompt_function=prompt.piqa_helm, + hf_repo="ybisk/piqa", + hf_subset="plain_text", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/prost.py b/src/lighteval/tasks/tasks/prost.py new file mode 100644 index 000000000..e7acc4969 --- /dev/null +++ b/src/lighteval/tasks/tasks/prost.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +prost = LightevalTaskConfig( + name="prost", + suite=["lighteval"], + prompt_function=prompt.prost, + hf_repo="lighteval/prost", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/pubmedqa.py b/src/lighteval/tasks/tasks/pubmedqa.py new file mode 100644 index 000000000..bccfecafd --- /dev/null +++ b/src/lighteval/tasks/tasks/pubmedqa.py @@ -0,0 +1,50 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +PubMedQA: A Dataset for Biomedical Research Question Answering + +https://pubmedqa.github.io/ +""" + +pubmedqa = LightevalTaskConfig( + name="pubmedqa", + suite=["lighteval"], + prompt_function=prompt.pubmed_qa_helm, + hf_repo="pubmed_qa", + hf_subset="pqa_labeled", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/qa4mre.py b/src/lighteval/tasks/tasks/qa4mre.py new file mode 100644 index 000000000..7a055f8db --- /dev/null +++ b/src/lighteval/tasks/tasks/qa4mre.py @@ -0,0 +1,88 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + +""" +QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation + +https://link.springer.com/chapter/10.1007/978-3-642-40802-1_29 +""" + + +qa4mre_2011 = LightevalTaskConfig( + name="qa4mre:2011", + suite=["lighteval"], + prompt_function=prompt.qa4mre, + hf_repo="qa4mre", + hf_subset="2011.main.EN", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=["\n"], + version=0, +) + + +qa4mre_2012 = LightevalTaskConfig( + name="qa4mre:2012", + suite=["lighteval"], + prompt_function=prompt.qa4mre, + hf_repo="qa4mre", + hf_subset="2012.main.EN", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=["\n"], + version=0, +) + + +qa4mre_2013 = LightevalTaskConfig( + name="qa4mre:2013", + suite=["lighteval"], + prompt_function=prompt.qa4mre, + hf_repo="qa4mre", + hf_subset="2013.main.EN", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[ + Metrics.loglikelihood_acc, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/qasper.py b/src/lighteval/tasks/tasks/qasper.py new file mode 100644 index 000000000..cfc0cb25b --- /dev/null +++ b/src/lighteval/tasks/tasks/qasper.py @@ -0,0 +1,54 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +QASPER is a dataset for question answering on scientific research papers. It +consists of 5,049 questions over 1,585 Natural Language Processing papers. Each +question is written by an NLP practitioner who read only the title and abstract +of the corresponding paper, and the question seeks information present in the +full text. The questions are then answered by a separate set of NLP +practitioners who also provide supporting evidence to answers. + +from: A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers +https://arxiv.org/abs/2105.03011 +""" + +qasper = LightevalTaskConfig( + name="qasper", + suite=["lighteval"], + prompt_function=prompt.qasper, + hf_repo="allenai/qasper", + hf_subset="qasper", + hf_avail_splits=["train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.f1_score], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/quac.py b/src/lighteval/tasks/tasks/quac.py new file mode 100644 index 000000000..4480847fe --- /dev/null +++ b/src/lighteval/tasks/tasks/quac.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +quac = LightevalTaskConfig( + name="quac", + suite=["helm"], + prompt_function=prompt.quac, + hf_repo="lighteval/quac_helm", + hf_subset="default", + hf_avail_splits=["train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=100, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/race_high.py b/src/lighteval/tasks/tasks/race_high.py new file mode 100644 index 000000000..d7f63bf15 --- /dev/null +++ b/src/lighteval/tasks/tasks/race_high.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +race_high = LightevalTaskConfig( + name="race:high", + suite=["lighteval", "race"], + prompt_function=prompt.race, + hf_repo="EleutherAI/race", + hf_subset="high", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/raft.py b/src/lighteval/tasks/tasks/raft.py new file mode 100644 index 000000000..819d909e1 --- /dev/null +++ b/src/lighteval/tasks/tasks/raft.py @@ -0,0 +1,346 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import helm_normalizer +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +raft_ade_corpus_v2_helm = LightevalTaskConfig( + name="raft:ade_corpus_v2", + suite=["helm", "helm_general"], + prompt_function=prompt.raft_ade_corpus_v2, + hf_repo="ought/raft", + hf_subset="ade_corpus_v2", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score_macro, + Metrics.f1_score_micro, + ], + stop_sequence=["\n"], + version=0, +) + +raft_banking_77_helm = LightevalTaskConfig( + name="raft:banking_77", + suite=["helm", "helm_general"], + prompt_function=prompt.raft_banking_77, + hf_repo="ought/raft", + hf_subset="banking_77", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score_macro, + Metrics.f1_score_micro, + ], + stop_sequence=["\n"], + version=0, +) + +raft_neurips_impact_statement_risks_helm = LightevalTaskConfig( + name="raft:neurips_impact_statement_risks", + suite=["helm", "helm_general"], + prompt_function=prompt.raft_neurips_impact_statement_risks, + hf_repo="ought/raft", + hf_subset="neurips_impact_statement_risks", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score_macro, + Metrics.f1_score_micro, + ], + stop_sequence=["\n"], + version=0, +) + +raft_one_stop_english_helm = LightevalTaskConfig( + name="raft:one_stop_english", + suite=["helm", "helm_general"], + prompt_function=prompt.raft_one_stop_english, + hf_repo="ought/raft", + hf_subset="one_stop_english", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score_macro, + Metrics.f1_score_micro, + ], + stop_sequence=["\n"], + version=0, +) + +raft_overruling_helm = LightevalTaskConfig( + name="raft:overruling", + suite=["helm", "helm_general"], + prompt_function=prompt.raft_overruling, + hf_repo="ought/raft", + hf_subset="overruling", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score_macro, + Metrics.f1_score_micro, + ], + stop_sequence=["\n"], + version=0, +) + +raft_semiconductor_org_types_helm = LightevalTaskConfig( + name="raft:semiconductor_org_types", + suite=["helm", "helm_general"], + prompt_function=prompt.raft_semiconductor_org_types, + hf_repo="ought/raft", + hf_subset="semiconductor_org_types", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score_macro, + Metrics.f1_score_micro, + ], + stop_sequence=["\n"], + version=0, +) + +raft_systematic_review_inclusion_helm = LightevalTaskConfig( + name="raft:systematic_review_inclusion", + suite=["helm", "helm_general"], + prompt_function=prompt.raft_systematic_review_inclusion, + hf_repo="ought/raft", + hf_subset="systematic_review_inclusion", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score_macro, + Metrics.f1_score_micro, + ], + stop_sequence=["\n"], + version=0, +) + +raft_tai_safety_research_helm = LightevalTaskConfig( + name="raft:tai_safety_research", + suite=["helm", "helm_general"], + prompt_function=prompt.raft_tai_safety_research, + hf_repo="ought/raft", + hf_subset="tai_safety_research", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score_macro, + Metrics.f1_score_micro, + ], + stop_sequence=["\n"], + version=0, +) + +raft_terms_of_service_helm = LightevalTaskConfig( + name="raft:terms_of_service", + suite=["helm", "helm_general"], + prompt_function=prompt.raft_terms_of_service, + hf_repo="ought/raft", + hf_subset="terms_of_service", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score_macro, + Metrics.f1_score_micro, + ], + stop_sequence=["\n"], + version=0, +) + +raft_tweet_eval_hate_helm = LightevalTaskConfig( + name="raft:tweet_eval_hate", + suite=["helm", "helm_general"], + prompt_function=prompt.raft_tweet_eval_hate, + hf_repo="ought/raft", + hf_subset="tweet_eval_hate", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score_macro, + Metrics.f1_score_micro, + ], + stop_sequence=["\n"], + version=0, +) + +raft_twitter_complaints_helm = LightevalTaskConfig( + name="raft:twitter_complaints", + suite=["helm", "helm_general"], + prompt_function=prompt.raft_twitter_complaints, + hf_repo="ought/raft", + hf_subset="twitter_complaints", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=30, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score_macro, + Metrics.f1_score_micro, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/real_toxicity_prompts.py b/src/lighteval/tasks/tasks/real_toxicity_prompts.py new file mode 100644 index 000000000..b83314681 --- /dev/null +++ b/src/lighteval/tasks/tasks/real_toxicity_prompts.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +real_toxicity_prompts = LightevalTaskConfig( + name="real_toxicity_prompts", + suite=["helm"], + prompt_function=prompt.real_toxicity_prompts, + hf_repo="allenai/real-toxicity-prompts", + hf_subset="default", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/sacrebleu.py b/src/lighteval/tasks/tasks/sacrebleu.py new file mode 100644 index 000000000..b7e67874f --- /dev/null +++ b/src/lighteval/tasks/tasks/sacrebleu.py @@ -0,0 +1,2890 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +iwslt17_ar_en_lighteval = LightevalTaskConfig( + name="iwslt17:ar-en", + suite=["lighteval", "harness_selection"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="iwslt17_ar-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +iwslt17_de_en_lighteval = LightevalTaskConfig( + name="iwslt17:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="iwslt17_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +iwslt17_en_ar_lighteval = LightevalTaskConfig( + name="iwslt17:en-ar", + suite=["lighteval", "harness_selection"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="iwslt17_ar-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +iwslt17_en_de_lighteval = LightevalTaskConfig( + name="iwslt17:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="iwslt17_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +iwslt17_en_fr_lighteval = LightevalTaskConfig( + name="iwslt17:en-fr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="iwslt17_en-fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +iwslt17_en_ja_lighteval = LightevalTaskConfig( + name="iwslt17:en-ja", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="iwslt17_en-ja", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +iwslt17_en_ko_lighteval = LightevalTaskConfig( + name="iwslt17:en-ko", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="iwslt17_en-ko", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +iwslt17_en_zh_lighteval = LightevalTaskConfig( + name="iwslt17:en-zh", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="iwslt17_en-zh", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +iwslt17_fr_en_lighteval = LightevalTaskConfig( + name="iwslt17:fr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="iwslt17_fr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +iwslt17_ja_en_lighteval = LightevalTaskConfig( + name="iwslt17:ja-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="iwslt17_ja-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +iwslt17_ko_en_lighteval = LightevalTaskConfig( + name="iwslt17:ko-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="iwslt17_ko-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +iwslt17_zh_en_lighteval = LightevalTaskConfig( + name="iwslt17:zh-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="iwslt17_zh-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +mtnt2019_en_fr_lighteval = LightevalTaskConfig( + name="mtnt2019:en-fr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="mtnt2019_en-fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=200, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +mtnt2019_en_ja_lighteval = LightevalTaskConfig( + name="mtnt2019:en-ja", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="mtnt2019_en-ja", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=200, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +mtnt2019_fr_en_lighteval = LightevalTaskConfig( + name="mtnt2019:fr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="mtnt2019_fr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=200, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +mtnt2019_ja_en_lighteval = LightevalTaskConfig( + name="mtnt2019:ja-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="mtnt2019_ja-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=200, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt08_cs_en_lighteval = LightevalTaskConfig( + name="wmt08:cs-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt08_cs-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt08_de_en_lighteval = LightevalTaskConfig( + name="wmt08:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt08_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt08_en_cs_lighteval = LightevalTaskConfig( + name="wmt08:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt08_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt08_en_de_lighteval = LightevalTaskConfig( + name="wmt08:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt08_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt08_en_es_lighteval = LightevalTaskConfig( + name="wmt08:en-es", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt08_en-es", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt08_en_fr_lighteval = LightevalTaskConfig( + name="wmt08:en-fr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt08_en-fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt08_en_hu_lighteval = LightevalTaskConfig( + name="wmt08:en-hu", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt08_en-hu", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt08_es_en_lighteval = LightevalTaskConfig( + name="wmt08:es-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt08_es-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt08_fr_en_lighteval = LightevalTaskConfig( + name="wmt08:fr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt08_fr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt08_hu_en_lighteval = LightevalTaskConfig( + name="wmt08:hu-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt08_hu-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt09_cs_en_lighteval = LightevalTaskConfig( + name="wmt09:cs-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt09_cs-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt09_de_en_lighteval = LightevalTaskConfig( + name="wmt09:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt09_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt09_en_cs_lighteval = LightevalTaskConfig( + name="wmt09:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt09_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt09_en_de_lighteval = LightevalTaskConfig( + name="wmt09:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt09_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt09_en_es_lighteval = LightevalTaskConfig( + name="wmt09:en-es", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt09_en-es", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt09_en_fr_lighteval = LightevalTaskConfig( + name="wmt09:en-fr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt09_en-fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt09_en_hu_lighteval = LightevalTaskConfig( + name="wmt09:en-hu", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt09_en-hu", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt09_en_it_lighteval = LightevalTaskConfig( + name="wmt09:en-it", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt09_en-it", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt09_es_en_lighteval = LightevalTaskConfig( + name="wmt09:es-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt09_es-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt09_fr_en_lighteval = LightevalTaskConfig( + name="wmt09:fr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt09_fr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt09_hu_en_lighteval = LightevalTaskConfig( + name="wmt09:hu-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt09_hu-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt09_it_en_lighteval = LightevalTaskConfig( + name="wmt09:it-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt09_it-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt10_cs_en_lighteval = LightevalTaskConfig( + name="wmt10:cs-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt10_cs-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt10_de_en_lighteval = LightevalTaskConfig( + name="wmt10:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt10_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt10_en_cs_lighteval = LightevalTaskConfig( + name="wmt10:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt10_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt10_en_de_lighteval = LightevalTaskConfig( + name="wmt10:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt10_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt10_en_es_lighteval = LightevalTaskConfig( + name="wmt10:en-es", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt10_en-es", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt10_en_fr_lighteval = LightevalTaskConfig( + name="wmt10:en-fr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt10_en-fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt10_es_en_lighteval = LightevalTaskConfig( + name="wmt10:es-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt10_es-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt10_fr_en_lighteval = LightevalTaskConfig( + name="wmt10:fr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt10_fr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt11_cs_en_lighteval = LightevalTaskConfig( + name="wmt11:cs-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt11_cs-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt11_de_en_lighteval = LightevalTaskConfig( + name="wmt11:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt11_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt11_en_cs_lighteval = LightevalTaskConfig( + name="wmt11:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt11_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt11_en_de_lighteval = LightevalTaskConfig( + name="wmt11:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt11_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt11_en_es_lighteval = LightevalTaskConfig( + name="wmt11:en-es", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt11_en-es", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt11_en_fr_lighteval = LightevalTaskConfig( + name="wmt11:en-fr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt11_en-fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt11_es_en_lighteval = LightevalTaskConfig( + name="wmt11:es-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt11_es-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt11_fr_en_lighteval = LightevalTaskConfig( + name="wmt11:fr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt11_fr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt12_cs_en_lighteval = LightevalTaskConfig( + name="wmt12:cs-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt12_cs-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt12_de_en_lighteval = LightevalTaskConfig( + name="wmt12:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt12_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt12_en_cs_lighteval = LightevalTaskConfig( + name="wmt12:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt12_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt12_en_de_lighteval = LightevalTaskConfig( + name="wmt12:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt12_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt12_en_es_lighteval = LightevalTaskConfig( + name="wmt12:en-es", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt12_en-es", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt12_en_fr_lighteval = LightevalTaskConfig( + name="wmt12:en-fr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt12_en-fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt12_es_en_lighteval = LightevalTaskConfig( + name="wmt12:es-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt12_es-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt12_fr_en_lighteval = LightevalTaskConfig( + name="wmt12:fr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt12_fr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt13_cs_en_lighteval = LightevalTaskConfig( + name="wmt13:cs-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt13_cs-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt13_de_en_lighteval = LightevalTaskConfig( + name="wmt13:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt13_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt13_en_cs_lighteval = LightevalTaskConfig( + name="wmt13:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt13_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt13_en_de_lighteval = LightevalTaskConfig( + name="wmt13:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt13_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt13_en_es_lighteval = LightevalTaskConfig( + name="wmt13:en-es", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt13_en-es", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt13_en_fr_lighteval = LightevalTaskConfig( + name="wmt13:en-fr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt13_en-fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt13_en_ru_lighteval = LightevalTaskConfig( + name="wmt13:en-ru", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt13_en-ru", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt13_es_en_lighteval = LightevalTaskConfig( + name="wmt13:es-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt13_es-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt13_fr_en_lighteval = LightevalTaskConfig( + name="wmt13:fr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt13_fr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt13_ru_en_lighteval = LightevalTaskConfig( + name="wmt13:ru-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt13_ru-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt14_cs_en_lighteval = LightevalTaskConfig( + name="wmt14:cs-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt14_cs-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt14_de_en_lighteval = LightevalTaskConfig( + name="wmt14:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt14_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt14_en_cs_lighteval = LightevalTaskConfig( + name="wmt14:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt14_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt14_en_de_lighteval = LightevalTaskConfig( + name="wmt14:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt14_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt14_en_fr_lighteval = LightevalTaskConfig( + name="wmt14:en-fr", + suite=["lighteval", "gpt3_benchmarks"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="wmt14", + hf_subset="fr-en", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt14_en_fr_lighteval = LightevalTaskConfig( + name="wmt14:en-fr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt14_en-fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt14_en_hi_lighteval = LightevalTaskConfig( + name="wmt14:en-hi", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt14_en-hi", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt14_en_ru_lighteval = LightevalTaskConfig( + name="wmt14:en-ru", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt14_en-ru", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt14_fr_en_lighteval = LightevalTaskConfig( + name="wmt14:fr-en", + suite=["lighteval", "gpt3_benchmarks"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="wmt14", + hf_subset="fr-en", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt14_fr_en_lighteval = LightevalTaskConfig( + name="wmt14:fr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt14_fr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt14_hi_en_lighteval = LightevalTaskConfig( + name="wmt14:hi-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt14_hi-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt14_ru_en_lighteval = LightevalTaskConfig( + name="wmt14:ru-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt14_ru-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt15_cs_en_lighteval = LightevalTaskConfig( + name="wmt15:cs-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt15_cs-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt15_de_en_lighteval = LightevalTaskConfig( + name="wmt15:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt15_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt15_en_cs_lighteval = LightevalTaskConfig( + name="wmt15:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt15_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt15_en_de_lighteval = LightevalTaskConfig( + name="wmt15:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt15_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt15_en_fi_lighteval = LightevalTaskConfig( + name="wmt15:en-fi", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt15_en-fi", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt15_en_fr_lighteval = LightevalTaskConfig( + name="wmt15:en-fr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt15_en-fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt15_en_ru_lighteval = LightevalTaskConfig( + name="wmt15:en-ru", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt15_en-ru", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt15_fi_en_lighteval = LightevalTaskConfig( + name="wmt15:fi-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt15_fi-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt15_fr_en_lighteval = LightevalTaskConfig( + name="wmt15:fr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt15_fr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt15_ru_en_lighteval = LightevalTaskConfig( + name="wmt15:ru-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt15_ru-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_cs_en_lighteval = LightevalTaskConfig( + name="wmt16:cs-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt16_cs-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_de_en_lighteval = LightevalTaskConfig( + name="wmt16:de-en", + suite=["lighteval", "gpt3_benchmarks"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="wmt16", + hf_subset="de-en", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_de_en_lighteval = LightevalTaskConfig( + name="wmt16:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt16_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_en_cs_lighteval = LightevalTaskConfig( + name="wmt16:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt16_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_en_de_lighteval = LightevalTaskConfig( + name="wmt16:en-de", + suite=["lighteval", "gpt3_benchmarks"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="wmt16", + hf_subset="de-en", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_en_de_lighteval = LightevalTaskConfig( + name="wmt16:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt16_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_en_fi_lighteval = LightevalTaskConfig( + name="wmt16:en-fi", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt16_en-fi", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_en_ro_lighteval = LightevalTaskConfig( + name="wmt16:en-ro", + suite=["lighteval", "gpt3_benchmarks"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="wmt16", + hf_subset="ro-en", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_en_ro_lighteval = LightevalTaskConfig( + name="wmt16:en-ro", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt16_en-ro", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_en_ru_lighteval = LightevalTaskConfig( + name="wmt16:en-ru", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt16_en-ru", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_en_tr_lighteval = LightevalTaskConfig( + name="wmt16:en-tr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt16_en-tr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_fi_en_lighteval = LightevalTaskConfig( + name="wmt16:fi-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt16_fi-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_ro_en_lighteval = LightevalTaskConfig( + name="wmt16:ro-en", + suite=["lighteval", "gpt3_benchmarks"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="wmt16", + hf_subset="ro-en", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_ro_en_lighteval = LightevalTaskConfig( + name="wmt16:ro-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt16_ro-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_ru_en_lighteval = LightevalTaskConfig( + name="wmt16:ru-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt16_ru-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt16_tr_en_lighteval = LightevalTaskConfig( + name="wmt16:tr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt16_tr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_cs_en_lighteval = LightevalTaskConfig( + name="wmt17:cs-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_cs-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_de_en_lighteval = LightevalTaskConfig( + name="wmt17:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_en_cs_lighteval = LightevalTaskConfig( + name="wmt17:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_en_de_lighteval = LightevalTaskConfig( + name="wmt17:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_en_fi_lighteval = LightevalTaskConfig( + name="wmt17:en-fi", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_en-fi", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_en_lv_lighteval = LightevalTaskConfig( + name="wmt17:en-lv", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_en-lv", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_en_ru_lighteval = LightevalTaskConfig( + name="wmt17:en-ru", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_en-ru", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_en_tr_lighteval = LightevalTaskConfig( + name="wmt17:en-tr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_en-tr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_en_zh_lighteval = LightevalTaskConfig( + name="wmt17:en-zh", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_en-zh", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_fi_en_lighteval = LightevalTaskConfig( + name="wmt17:fi-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_fi-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_lv_en_lighteval = LightevalTaskConfig( + name="wmt17:lv-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_lv-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_ru_en_lighteval = LightevalTaskConfig( + name="wmt17:ru-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_ru-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_tr_en_lighteval = LightevalTaskConfig( + name="wmt17:tr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_tr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt17_zh_en_lighteval = LightevalTaskConfig( + name="wmt17:zh-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt17_zh-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_cs_en_lighteval = LightevalTaskConfig( + name="wmt18:cs-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_cs-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_de_en_lighteval = LightevalTaskConfig( + name="wmt18:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_en_cs_lighteval = LightevalTaskConfig( + name="wmt18:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_en_de_lighteval = LightevalTaskConfig( + name="wmt18:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_en_et_lighteval = LightevalTaskConfig( + name="wmt18:en-et", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_en-et", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_en_fi_lighteval = LightevalTaskConfig( + name="wmt18:en-fi", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_en-fi", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_en_ru_lighteval = LightevalTaskConfig( + name="wmt18:en-ru", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_en-ru", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_en_tr_lighteval = LightevalTaskConfig( + name="wmt18:en-tr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_en-tr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_en_zh_lighteval = LightevalTaskConfig( + name="wmt18:en-zh", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_en-zh", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_et_en_lighteval = LightevalTaskConfig( + name="wmt18:et-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_et-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_fi_en_lighteval = LightevalTaskConfig( + name="wmt18:fi-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_fi-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_ru_en_lighteval = LightevalTaskConfig( + name="wmt18:ru-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_ru-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_tr_en_lighteval = LightevalTaskConfig( + name="wmt18:tr-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_tr-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt18_zh_en_lighteval = LightevalTaskConfig( + name="wmt18:zh-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt18_zh-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_cs_de_lighteval = LightevalTaskConfig( + name="wmt19:cs-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_cs-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_de_cs_lighteval = LightevalTaskConfig( + name="wmt19:de-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_de-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_de_en_lighteval = LightevalTaskConfig( + name="wmt19:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_de_fr_lighteval = LightevalTaskConfig( + name="wmt19:de-fr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_de-fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_en_cs_lighteval = LightevalTaskConfig( + name="wmt19:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_en_de_lighteval = LightevalTaskConfig( + name="wmt19:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_en_fi_lighteval = LightevalTaskConfig( + name="wmt19:en-fi", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_en-fi", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_en_gu_lighteval = LightevalTaskConfig( + name="wmt19:en-gu", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_en-gu", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_en_kk_lighteval = LightevalTaskConfig( + name="wmt19:en-kk", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_en-kk", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_en_lt_lighteval = LightevalTaskConfig( + name="wmt19:en-lt", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_en-lt", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_en_ru_lighteval = LightevalTaskConfig( + name="wmt19:en-ru", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_en-ru", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_en_zh_lighteval = LightevalTaskConfig( + name="wmt19:en-zh", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_en-zh", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_fi_en_lighteval = LightevalTaskConfig( + name="wmt19:fi-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_fi-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_fr_de_lighteval = LightevalTaskConfig( + name="wmt19:fr-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_fr-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_gu_en_lighteval = LightevalTaskConfig( + name="wmt19:gu-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_gu-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_kk_en_lighteval = LightevalTaskConfig( + name="wmt19:kk-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_kk-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_lt_en_lighteval = LightevalTaskConfig( + name="wmt19:lt-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_lt-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_ru_en_lighteval = LightevalTaskConfig( + name="wmt19:ru-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_ru-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt19_zh_en_lighteval = LightevalTaskConfig( + name="wmt19:zh-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt19_zh-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_cs_en_lighteval = LightevalTaskConfig( + name="wmt20:cs-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_cs-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_de_en_lighteval = LightevalTaskConfig( + name="wmt20:de-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_de-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_de_fr_lighteval = LightevalTaskConfig( + name="wmt20:de-fr", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_de-fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_en_cs_lighteval = LightevalTaskConfig( + name="wmt20:en-cs", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_en-cs", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_en_de_lighteval = LightevalTaskConfig( + name="wmt20:en-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_en-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_en_iu_lighteval = LightevalTaskConfig( + name="wmt20:en-iu", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_en-iu", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_en_ja_lighteval = LightevalTaskConfig( + name="wmt20:en-ja", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_en-ja", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_en_km_lighteval = LightevalTaskConfig( + name="wmt20:en-km", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_en-km", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_en_pl_lighteval = LightevalTaskConfig( + name="wmt20:en-pl", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_en-pl", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_en_ps_lighteval = LightevalTaskConfig( + name="wmt20:en-ps", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_en-ps", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_en_ru_lighteval = LightevalTaskConfig( + name="wmt20:en-ru", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_en-ru", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_en_ta_lighteval = LightevalTaskConfig( + name="wmt20:en-ta", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_en-ta", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_en_zh_lighteval = LightevalTaskConfig( + name="wmt20:en-zh", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_en-zh", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_fr_de_lighteval = LightevalTaskConfig( + name="wmt20:fr-de", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_fr-de", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_iu_en_lighteval = LightevalTaskConfig( + name="wmt20:iu-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_iu-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_ja_en_lighteval = LightevalTaskConfig( + name="wmt20:ja-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_ja-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_km_en_lighteval = LightevalTaskConfig( + name="wmt20:km-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_km-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_pl_en_lighteval = LightevalTaskConfig( + name="wmt20:pl-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_pl-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_ps_en_lighteval = LightevalTaskConfig( + name="wmt20:ps-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_ps-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_ru_en_lighteval = LightevalTaskConfig( + name="wmt20:ru-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_ru-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_ta_en_lighteval = LightevalTaskConfig( + name="wmt20:ta-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_ta-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) + +wmt20_zh_en_lighteval = LightevalTaskConfig( + name="wmt20:zh-en", + suite=["lighteval", "sacrebleu"], + prompt_function=prompt.wmt_reverse_alphabetical, + hf_repo="lighteval/sacrebleu_manual", + hf_subset="wmt20_zh-en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.bleu, Metrics.chrf, Metrics.ter], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/sciq.py b/src/lighteval/tasks/tasks/sciq.py new file mode 100644 index 000000000..35dd189b3 --- /dev/null +++ b/src/lighteval/tasks/tasks/sciq.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +sciq = LightevalTaskConfig( + name="sciq", + suite=["lighteval"], + prompt_function=prompt.sciq, + hf_repo="sciq", + hf_subset="default", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/simpleqa.py b/src/lighteval/tasks/tasks/simpleqa.py new file mode 100644 index 000000000..43a16296e --- /dev/null +++ b/src/lighteval/tasks/tasks/simpleqa.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +simpleqa = LightevalTaskConfig( + name="simpleqa", + suite=["lighteval"], + prompt_function=prompt.simpleqa, + hf_repo="lighteval/SimpleQA", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split="few_shot", + few_shots_select=None, + generation_size=2048, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/siqa.py b/src/lighteval/tasks/tasks/siqa.py new file mode 100644 index 000000000..8a38beb01 --- /dev/null +++ b/src/lighteval/tasks/tasks/siqa.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +siqa = LightevalTaskConfig( + name="siqa", + suite=["helm", "commonsense_scenario"], + prompt_function=prompt.siqa, + hf_repo="allenai/social_i_qa", + hf_subset="default", + hf_avail_splits=["train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/squad_v2.py b/src/lighteval/tasks/tasks/squad_v2.py new file mode 100644 index 000000000..b04314c6e --- /dev/null +++ b/src/lighteval/tasks/tasks/squad_v2.py @@ -0,0 +1,56 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +squad_v2 = LightevalTaskConfig( + name="squad_v2", + prompt_function=get_qa_prompt_function( + Language.ENGLISH, + lambda line: { + "question": line["question"], + "context": line["context"], + "choices": [ans for ans in line["answers"]["text"] if len(ans) > 0], + }, + ), + suite=("lighteval",), + hf_repo="rajpurkar/squad_v2", + hf_subset="squad_v2", + hf_filter=lambda line: any(ans for ans in line["answers"]["text"] if len(ans) > 0), + evaluation_splits=("validation",), + few_shots_split="train", + stop_sequence=["\n", "Question:", "question:"], + generation_size=200, + metrics=( + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + Metrics.f1_score(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + ), +) diff --git a/src/lighteval/tasks/tasks/storycloze.py b/src/lighteval/tasks/tasks/storycloze.py new file mode 100644 index 000000000..692504945 --- /dev/null +++ b/src/lighteval/tasks/tasks/storycloze.py @@ -0,0 +1,65 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +"""A Corpus and Cloze Evaluation for Deeper Understanding of +Commonsense Stories + +https://arxiv.org/abs/1604.01696 +""" + +storycloze_2016 = LightevalTaskConfig( + name="storycloze:2016", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="MoE-UNC/story_cloze", + hf_subset="2016", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + + +storycloze_2018 = LightevalTaskConfig( + name="storycloze:2018", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="MoE-UNC/story_cloze", + hf_subset="2018", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/summarization.py b/src/lighteval/tasks/tasks/summarization.py new file mode 100644 index 000000000..153defb43 --- /dev/null +++ b/src/lighteval/tasks/tasks/summarization.py @@ -0,0 +1,106 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization +and: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond + + +https://aclanthology.org/D18-1206/ +https://aclanthology.org/K16-1028/ +""" + +summarization_cnn_dm = LightevalTaskConfig( + name="summarization:cnn-dm", + suite=["lighteval"], + prompt_function=prompt.cnn_dm, + hf_repo="lighteval/summarization", + hf_subset="cnn-dm", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=128, + metrics=[ + Metrics.rouge1, + Metrics.rouge2, + Metrics.rougeL, + Metrics.faithfulness, + Metrics.extractiveness, + Metrics.bert_score, + ], + stop_sequence=["\n"], + version=0, +) + + +summarization_xsum = LightevalTaskConfig( + name="summarization:xsum", + suite=["lighteval"], + prompt_function=prompt.xsum, + hf_repo="lighteval/summarization", + hf_subset="xsum", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=64, + metrics=[ + Metrics.rouge1, + Metrics.rouge2, + Metrics.rougeL, + Metrics.faithfulness, + Metrics.extractiveness, + Metrics.bert_score, + ], + stop_sequence=["\n"], + version=0, +) + + +summarization_xsum_sampled = LightevalTaskConfig( + name="summarization:xsum-sampled", + suite=["lighteval"], + prompt_function=prompt.xsum, + hf_repo="lighteval/summarization", + hf_subset="xsum-sampled", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=64, + metrics=[ + Metrics.rouge1, + Metrics.rouge2, + Metrics.rougeL, + Metrics.faithfulness, + Metrics.extractiveness, + Metrics.bert_score, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/swag.py b/src/lighteval/tasks/tasks/swag.py new file mode 100644 index 000000000..5840d3f60 --- /dev/null +++ b/src/lighteval/tasks/tasks/swag.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +swag = LightevalTaskConfig( + name="swag", + suite=["lighteval"], + prompt_function=prompt.swag, + hf_repo="swag", + hf_subset="regular", + hf_avail_splits=["train", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/synthetic_reasoning.py b/src/lighteval/tasks/tasks/synthetic_reasoning.py new file mode 100644 index 000000000..c450df232 --- /dev/null +++ b/src/lighteval/tasks/tasks/synthetic_reasoning.py @@ -0,0 +1,123 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +from: LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning + +https://arxiv.org/abs/2206.03855 +""" + + +synthetic_reasoning_induction = LightevalTaskConfig( + name="synthetic_reasoning:induction", + suite=["lighteval"], + prompt_function=prompt.synthetic_reasoning, + hf_repo="lighteval/synthetic_reasoning", + hf_subset="induction", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=50, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) + + +synthetic_reasoning_natural_easy = LightevalTaskConfig( + name="synthetic_reasoning:natural_easy", + suite=["lighteval"], + prompt_function=prompt.synthetic_reasoning_natural, + hf_repo="lighteval/synthetic_reasoning_natural", + hf_subset="easy", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + + +synthetic_reasoning_natural_hard = LightevalTaskConfig( + name="synthetic_reasoning:natural_hard", + suite=["lighteval"], + prompt_function=prompt.synthetic_reasoning_natural, + hf_repo="lighteval/synthetic_reasoning_natural", + hf_subset="hard", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) + + +synthetic_reasoning_pattern_match = LightevalTaskConfig( + name="synthetic_reasoning:pattern_match", + suite=["lighteval"], + prompt_function=prompt.synthetic_reasoning, + hf_repo="lighteval/synthetic_reasoning", + hf_subset="pattern_match", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=50, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) + + +synthetic_reasoning_variable_substitution = LightevalTaskConfig( + name="synthetic_reasoning:variable_substitution", + suite=["lighteval"], + prompt_function=prompt.synthetic_reasoning, + hf_repo="lighteval/synthetic_reasoning", + hf_subset="variable_substitution", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation", "test"], + few_shots_split=None, + few_shots_select=None, + generation_size=50, + metrics=[ + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/the_pile.py b/src/lighteval/tasks/tasks/the_pile.py new file mode 100644 index 000000000..f87159f48 --- /dev/null +++ b/src/lighteval/tasks/tasks/the_pile.py @@ -0,0 +1,331 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +the_pile_arxiv_helm = LightevalTaskConfig( + name="the_pile:arxiv", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="arxiv", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_bibliotik_helm = LightevalTaskConfig( + name="the_pile:bibliotik", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="bibliotik", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_commoncrawl_helm = LightevalTaskConfig( + name="the_pile:commoncrawl", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="commoncrawl", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_dm_mathematics_helm = LightevalTaskConfig( + name="the_pile:dm-mathematics", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="dm-mathematics", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_enron_helm = LightevalTaskConfig( + name="the_pile:enron", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="enron", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_europarl_helm = LightevalTaskConfig( + name="the_pile:europarl", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="europarl", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_freelaw_helm = LightevalTaskConfig( + name="the_pile:freelaw", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="freelaw", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_github_helm = LightevalTaskConfig( + name="the_pile:github", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="github", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_gutenberg_helm = LightevalTaskConfig( + name="the_pile:gutenberg", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="gutenberg", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_hackernews_helm = LightevalTaskConfig( + name="the_pile:hackernews", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="hackernews", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_nih_exporter_helm = LightevalTaskConfig( + name="the_pile:nih-exporter", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="nih-exporter", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_opensubtitles_helm = LightevalTaskConfig( + name="the_pile:opensubtitles", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="opensubtitles", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_openwebtext2_helm = LightevalTaskConfig( + name="the_pile:openwebtext2", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="openwebtext2", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + + +the_pile_pubmed_abstracts_helm = LightevalTaskConfig( + name="the_pile:pubmed-abstracts", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="pubmed-abstracts", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_pubmed_central_helm = LightevalTaskConfig( + name="the_pile:pubmed-central", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="pubmed-central", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_stackexchange_helm = LightevalTaskConfig( + name="the_pile:stackexchange", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="stackexchange", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_upsto_helm = LightevalTaskConfig( + name="the_pile:upsto", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="uspto", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_wikipedia_helm = LightevalTaskConfig( + name="the_pile:wikipedia", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="wikipedia", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + +the_pile_youtubesubtitles_helm = LightevalTaskConfig( + name="the_pile:youtubesubtitles", + suite=["helm"], + prompt_function=prompt.the_pile, + hf_repo="lighteval/pile_helm", + hf_subset="youtubesubtitles", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/extended/tiny_benchmarks/main.py b/src/lighteval/tasks/tasks/tiny_benchmarks/main.py similarity index 100% rename from src/lighteval/tasks/extended/tiny_benchmarks/main.py rename to src/lighteval/tasks/tasks/tiny_benchmarks/main.py diff --git a/src/lighteval/tasks/tasks/toxigen.py b/src/lighteval/tasks/tasks/toxigen.py new file mode 100644 index 000000000..13f753a1a --- /dev/null +++ b/src/lighteval/tasks/tasks/toxigen.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +toxigen = LightevalTaskConfig( + name="toxigen", + suite=["lighteval"], + prompt_function=prompt.toxigen, + hf_repo="skg/toxigen-data", + hf_subset="annotated", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/triviaqa.py b/src/lighteval/tasks/tasks/triviaqa.py new file mode 100644 index 000000000..7c7cd62de --- /dev/null +++ b/src/lighteval/tasks/tasks/triviaqa.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +triviaqa = LightevalTaskConfig( + name="triviaqa", + suite=["lighteval"], + prompt_function=prompt.triviaqa, + hf_repo="trivia_qa", + hf_subset="rc.nocontext", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=20, + metrics=[Metrics.exact_match], + stop_sequence=["\n", ".", ","], + version=0, +) diff --git a/src/lighteval/tasks/tasks/truthfulqa.py b/src/lighteval/tasks/tasks/truthfulqa.py new file mode 100644 index 000000000..29cddcfcc --- /dev/null +++ b/src/lighteval/tasks/tasks/truthfulqa.py @@ -0,0 +1,48 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +TruthfulQA: Measuring How Models Mimic Human Falsehoods + +https://arxiv.org/abs/2109.07958 +""" + +truthfulqa_gen = LightevalTaskConfig( + name="truthfulqa:gen", + suite=["lighteval"], + prompt_function=prompt.truthful_qa_generative, + hf_repo="truthful_qa", + hf_subset="generation", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=200, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/twitterAAE.py b/src/lighteval/tasks/tasks/twitterAAE.py new file mode 100644 index 000000000..6145736f7 --- /dev/null +++ b/src/lighteval/tasks/tasks/twitterAAE.py @@ -0,0 +1,65 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +Demographic Dialectal Variation in Social Media: A Case Study of African-American English + +https://aclanthology.org/D16-1120/ +""" + +twitterAAE_aa = LightevalTaskConfig( + name="twitterAAE:aa", + suite=["lighteval"], + prompt_function=prompt.twitter_aae, + hf_repo="lighteval/twitterAAE", + hf_subset="aa", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) + + +twitterAAE_white = LightevalTaskConfig( + name="twitterAAE:white", + suite=["lighteval"], + prompt_function=prompt.twitter_aae, + hf_repo="lighteval/twitterAAE", + hf_subset="white", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/unscramble.py b/src/lighteval/tasks/tasks/unscramble.py new file mode 100644 index 000000000..cff64e898 --- /dev/null +++ b/src/lighteval/tasks/tasks/unscramble.py @@ -0,0 +1,113 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +"""benchmark where we ask the model to unscramble a word, either anagram or +random insertion. +Don't remember where it's from. + +https://huggingface.co/datasets/lighteval/GPT3_unscramble +""" + +unscramble_anagrams1 = LightevalTaskConfig( + name="unscramble:anagrams1", + suite=["lighteval"], + prompt_function=prompt.unscramble, + hf_repo="lighteval/GPT3_unscramble", + hf_subset="default", + hf_avail_splits=["mid_word_1_anagrams"], + evaluation_splits=["mid_word_1_anagrams"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +unscramble_anagrams2 = LightevalTaskConfig( + name="unscramble:anagrams2", + suite=["lighteval"], + prompt_function=prompt.unscramble, + hf_repo="lighteval/GPT3_unscramble", + hf_subset="default", + hf_avail_splits=["mid_word_2_anagrams"], + evaluation_splits=["mid_word_2_anagrams"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +unscramble_cycle_letters = LightevalTaskConfig( + name="unscramble:cycle_letters", + suite=["lighteval"], + prompt_function=prompt.unscramble, + hf_repo="lighteval/GPT3_unscramble", + hf_subset="default", + hf_avail_splits=["cycle_letters_in_word"], + evaluation_splits=["cycle_letters_in_word"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +unscramble_random_insertion = LightevalTaskConfig( + name="unscramble:random_insertion", + suite=["lighteval"], + prompt_function=prompt.unscramble, + hf_repo="lighteval/GPT3_unscramble", + hf_subset="default", + hf_avail_splits=["random_insertion_in_word"], + evaluation_splits=["random_insertion_in_word"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) + +unscramble_reversed_words = LightevalTaskConfig( + name="unscramble:reversed_words", + suite=["lighteval"], + prompt_function=prompt.unscramble, + hf_repo="lighteval/GPT3_unscramble", + hf_subset="default", + hf_avail_splits=["reversed_words"], + evaluation_splits=["reversed_words"], + few_shots_split=None, + few_shots_select=None, + generation_size=5, + metrics=[Metrics.exact_match(sample_params={"strip_strings": False})], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/webqs.py b/src/lighteval/tasks/tasks/webqs.py new file mode 100644 index 000000000..047bac5eb --- /dev/null +++ b/src/lighteval/tasks/tasks/webqs.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +webqs = LightevalTaskConfig( + name="webqs", + suite=["lighteval"], + prompt_function=prompt.webqs, + hf_repo="web_questions", + hf_subset="default", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.exact_match], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/wikifact.py b/src/lighteval/tasks/tasks/wikifact.py new file mode 100644 index 000000000..7a9dd1555 --- /dev/null +++ b/src/lighteval/tasks/tasks/wikifact.py @@ -0,0 +1,2349 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import helm_normalizer +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +wikifact_applies_to_jurisdiction_helm = LightevalTaskConfig( + name="wikifact:applies_to_jurisdiction", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="applies_to_jurisdiction", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_atomic_number_helm = LightevalTaskConfig( + name="wikifact:atomic_number", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="atomic_number", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_author_helm = LightevalTaskConfig( + name="wikifact:author", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="author", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_award_received_helm = LightevalTaskConfig( + name="wikifact:award_received", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="award_received", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_basic_form_of_government_helm = LightevalTaskConfig( + name="wikifact:basic_form_of_government", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="basic_form_of_government", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_capital_helm = LightevalTaskConfig( + name="wikifact:capital", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="capital", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_capital_of_helm = LightevalTaskConfig( + name="wikifact:capital_of", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="capital_of", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_central_bank_helm = LightevalTaskConfig( + name="wikifact:central_bank", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="central_bank", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_composer_helm = LightevalTaskConfig( + name="wikifact:composer", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="composer", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_continent_helm = LightevalTaskConfig( + name="wikifact:continent", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="continent", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_country_helm = LightevalTaskConfig( + name="wikifact:country", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="country", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_country_of_citizenship_helm = LightevalTaskConfig( + name="wikifact:country_of_citizenship", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="country_of_citizenship", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_country_of_origin_helm = LightevalTaskConfig( + name="wikifact:country_of_origin", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="country_of_origin", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_creator_helm = LightevalTaskConfig( + name="wikifact:creator", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="creator", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_currency_helm = LightevalTaskConfig( + name="wikifact:currency", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="currency", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_defendant_helm = LightevalTaskConfig( + name="wikifact:defendant", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="defendant", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_developer_helm = LightevalTaskConfig( + name="wikifact:developer", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="developer", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_diplomatic_relation_helm = LightevalTaskConfig( + name="wikifact:diplomatic_relation", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="diplomatic_relation", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_director_helm = LightevalTaskConfig( + name="wikifact:director", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="director", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_discoverer_or_inventor_helm = LightevalTaskConfig( + name="wikifact:discoverer_or_inventor", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="discoverer_or_inventor", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_drug_or_therapy_used_for_treatment_helm = LightevalTaskConfig( + name="wikifact:drug_or_therapy_used_for_treatment", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="drug_or_therapy_used_for_treatment", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_educated_at_helm = LightevalTaskConfig( + name="wikifact:educated_at", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="educated_at", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_electron_configuration_helm = LightevalTaskConfig( + name="wikifact:electron_configuration", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="electron_configuration", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_employer_helm = LightevalTaskConfig( + name="wikifact:employer", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="employer", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_field_of_work_helm = LightevalTaskConfig( + name="wikifact:field_of_work", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="field_of_work", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_file_extension_helm = LightevalTaskConfig( + name="wikifact:file_extension", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="file_extension", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_genetic_association_helm = LightevalTaskConfig( + name="wikifact:genetic_association", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="genetic_association", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_genre_helm = LightevalTaskConfig( + name="wikifact:genre", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="genre", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_has_part_helm = LightevalTaskConfig( + name="wikifact:has_part", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="has_part", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_head_of_government_helm = LightevalTaskConfig( + name="wikifact:head_of_government", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="head_of_government", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_head_of_state_helm = LightevalTaskConfig( + name="wikifact:head_of_state", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="head_of_state", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_headquarters_location_helm = LightevalTaskConfig( + name="wikifact:headquarters_location", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="headquarters_location", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_industry_helm = LightevalTaskConfig( + name="wikifact:industry", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="industry", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_influenced_by_helm = LightevalTaskConfig( + name="wikifact:influenced_by", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="influenced_by", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_instance_of_helm = LightevalTaskConfig( + name="wikifact:instance_of", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="instance_of", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_instrument_helm = LightevalTaskConfig( + name="wikifact:instrument", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="instrument", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_language_of_work_or_name_helm = LightevalTaskConfig( + name="wikifact:language_of_work_or_name", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="language_of_work_or_name", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_languages_spoken_written_or_signed_helm = LightevalTaskConfig( + name="wikifact:languages_spoken_written_or_signed", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="languages_spoken_written_or_signed", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_laws_applied_helm = LightevalTaskConfig( + name="wikifact:laws_applied", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="laws_applied", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_located_in_the_administrative_territorial_entity_helm = LightevalTaskConfig( + name="wikifact:located_in_the_administrative_territorial_entity", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="located_in_the_administrative_territorial_entity", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_location_helm = LightevalTaskConfig( + name="wikifact:location", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="location", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_location_of_discovery_helm = LightevalTaskConfig( + name="wikifact:location_of_discovery", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="location_of_discovery", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_location_of_formation_helm = LightevalTaskConfig( + name="wikifact:location_of_formation", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="location_of_formation", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_majority_opinion_by_helm = LightevalTaskConfig( + name="wikifact:majority_opinion_by", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="majority_opinion_by", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_manufacturer_helm = LightevalTaskConfig( + name="wikifact:manufacturer", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="manufacturer", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_measured_physical_quantity_helm = LightevalTaskConfig( + name="wikifact:measured_physical_quantity", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="measured_physical_quantity", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_medical_condition_treated_helm = LightevalTaskConfig( + name="wikifact:medical_condition_treated", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="medical_condition_treated", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_member_of_helm = LightevalTaskConfig( + name="wikifact:member_of", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="member_of", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_member_of_political_party_helm = LightevalTaskConfig( + name="wikifact:member_of_political_party", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="member_of_political_party", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_member_of_sports_team_helm = LightevalTaskConfig( + name="wikifact:member_of_sports_team", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="member_of_sports_team", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_movement_helm = LightevalTaskConfig( + name="wikifact:movement", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="movement", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_named_after_helm = LightevalTaskConfig( + name="wikifact:named_after", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="named_after", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_native_language_helm = LightevalTaskConfig( + name="wikifact:native_language", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="native_language", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_number_of_processor_cores_helm = LightevalTaskConfig( + name="wikifact:number_of_processor_cores", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="number_of_processor_cores", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_occupation_helm = LightevalTaskConfig( + name="wikifact:occupation", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="occupation", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_office_held_by_head_of_government_helm = LightevalTaskConfig( + name="wikifact:office_held_by_head_of_government", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="office_held_by_head_of_government", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_office_held_by_head_of_state_helm = LightevalTaskConfig( + name="wikifact:office_held_by_head_of_state", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="office_held_by_head_of_state", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_official_language_helm = LightevalTaskConfig( + name="wikifact:official_language", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="official_language", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_operating_system_helm = LightevalTaskConfig( + name="wikifact:operating_system", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="operating_system", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_original_language_of_film_or_TV_show_helm = LightevalTaskConfig( + name="wikifact:original_language_of_film_or_TV_show", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="original_language_of_film_or_TV_show", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_original_network_helm = LightevalTaskConfig( + name="wikifact:original_network", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="original_network", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_overrules_helm = LightevalTaskConfig( + name="wikifact:overrules", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="overrules", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_owned_by_helm = LightevalTaskConfig( + name="wikifact:owned_by", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="owned_by", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_part_of_helm = LightevalTaskConfig( + name="wikifact:part_of", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="part_of", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_participating_team_helm = LightevalTaskConfig( + name="wikifact:participating_team", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="participating_team", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_place_of_birth_helm = LightevalTaskConfig( + name="wikifact:place_of_birth", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="place_of_birth", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_place_of_death_helm = LightevalTaskConfig( + name="wikifact:place_of_death", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="place_of_death", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_plaintiff_helm = LightevalTaskConfig( + name="wikifact:plaintiff", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="plaintiff", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_position_held_helm = LightevalTaskConfig( + name="wikifact:position_held", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="position_held", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_position_played_on_team_helm = LightevalTaskConfig( + name="wikifact:position_played_on_team", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="position_played_on_team", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_programming_language_helm = LightevalTaskConfig( + name="wikifact:programming_language", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="programming_language", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_recommended_unit_of_measurement_helm = LightevalTaskConfig( + name="wikifact:recommended_unit_of_measurement", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="recommended_unit_of_measurement", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_record_label_helm = LightevalTaskConfig( + name="wikifact:record_label", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="record_label", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_religion_helm = LightevalTaskConfig( + name="wikifact:religion", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="religion", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_repealed_by_helm = LightevalTaskConfig( + name="wikifact:repealed_by", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="repealed_by", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_shares_border_with_helm = LightevalTaskConfig( + name="wikifact:shares_border_with", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="shares_border_with", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_solved_by_helm = LightevalTaskConfig( + name="wikifact:solved_by", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="solved_by", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_statement_describes_helm = LightevalTaskConfig( + name="wikifact:statement_describes", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="statement_describes", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_stock_exchange_helm = LightevalTaskConfig( + name="wikifact:stock_exchange", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="stock_exchange", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_subclass_of_helm = LightevalTaskConfig( + name="wikifact:subclass_of", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="subclass_of", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_subsidiary_helm = LightevalTaskConfig( + name="wikifact:subsidiary", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="subsidiary", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_symptoms_and_signs_helm = LightevalTaskConfig( + name="wikifact:symptoms_and_signs", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="symptoms_and_signs", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_therapeutic_area_helm = LightevalTaskConfig( + name="wikifact:therapeutic_area", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="therapeutic_area", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_time_of_discovery_or_invention_helm = LightevalTaskConfig( + name="wikifact:time_of_discovery_or_invention", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="time_of_discovery_or_invention", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_twinned_administrative_body_helm = LightevalTaskConfig( + name="wikifact:twinned_administrative_body", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="twinned_administrative_body", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) + +wikifact_work_location_helm = LightevalTaskConfig( + name="wikifact:work_location", + suite=["helm"], + prompt_function=prompt.wikifact, + hf_repo="lighteval/wikifact", + hf_subset="work_location", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=8, + metrics=[ + Metrics.exact_match, + Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), + Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), + Metrics.exact_match( + sample_params={ + "normalize_gold": helm_normalizer, + "normalize_pred": helm_normalizer, + "type_exact_match": "prefix", + } + ), + ], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/wikitext.py b/src/lighteval/tasks/tasks/wikitext.py new file mode 100644 index 000000000..4db10b6b7 --- /dev/null +++ b/src/lighteval/tasks/tasks/wikitext.py @@ -0,0 +1,50 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License. + +from: Pointer Sentinel Mixture Models +https://arxiv.org/abs/1609.07843 +""" + + +wikitext_103_document_level = LightevalTaskConfig( + name="wikitext:103:document_level", + suite=["lighteval"], + prompt_function=prompt.wikitext_helm, + hf_repo="EleutherAI/wikitext_document_level", + hf_subset="wikitext-103-raw-v1", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/winogrande.py b/src/lighteval/tasks/tasks/winogrande.py new file mode 100644 index 000000000..a829f43d5 --- /dev/null +++ b/src/lighteval/tasks/tasks/winogrande.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +winogrande = LightevalTaskConfig( + name="winogrande", + suite=["leaderboard"], + prompt_function=prompt.winogrande, + hf_repo="winogrande", + hf_subset="winogrande_xl", + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/wsc273.py b/src/lighteval/tasks/tasks/wsc273.py new file mode 100644 index 000000000..67fc95f82 --- /dev/null +++ b/src/lighteval/tasks/tasks/wsc273.py @@ -0,0 +1,42 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +wsc273 = LightevalTaskConfig( + name="wsc273", + suite=["lighteval"], + prompt_function=prompt.wsc273, + hf_repo="lighteval/winograd_wsc", + hf_subset="wsc273", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/xcopa.py b/src/lighteval/tasks/tasks/xcopa.py new file mode 100644 index 000000000..c2f953af4 --- /dev/null +++ b/src/lighteval/tasks/tasks/xcopa.py @@ -0,0 +1,226 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning The Cross-lingual +Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability +of machine learning models to transfer commonsense reasoning across languages. + +https://arxiv.org/abs/2005.00333 +""" + +xcopa_en = LightevalTaskConfig( + name="xcopa:en", + suite=["lighteval"], + prompt_function=prompt.xcopa_en, + hf_repo="cambridgeltl/xcopa", + hf_subset="default", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xcopa_et = LightevalTaskConfig( + name="xcopa:et", + suite=["lighteval"], + prompt_function=prompt.xcopa_et, + hf_repo="cambridgeltl/xcopa", + hf_subset="et", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xcopa_ht = LightevalTaskConfig( + name="xcopa:ht", + suite=["lighteval"], + prompt_function=prompt.xcopa_ht, + hf_repo="cambridgeltl/xcopa", + hf_subset="ht", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xcopa_it = LightevalTaskConfig( + name="xcopa:it", + suite=["lighteval"], + prompt_function=prompt.xcopa_it, + hf_repo="cambridgeltl/xcopa", + hf_subset="it", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xcopa_id = LightevalTaskConfig( + name="xcopa:id", + suite=["lighteval"], + prompt_function=prompt.xcopa_id, + hf_repo="cambridgeltl/xcopa", + hf_subset="id", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xcopa_qu = LightevalTaskConfig( + name="xcopa:qu", + suite=["lighteval"], + prompt_function=prompt.xcopa_qu, + hf_repo="cambridgeltl/xcopa", + hf_subset="qu", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xcopa_sw = LightevalTaskConfig( + name="xcopa:sw", + suite=["lighteval"], + prompt_function=prompt.xcopa_sw, + hf_repo="cambridgeltl/xcopa", + hf_subset="sw", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xcopa_zh = LightevalTaskConfig( + name="xcopa:zh", + suite=["lighteval"], + prompt_function=prompt.xcopa_zh, + hf_repo="cambridgeltl/xcopa", + hf_subset="zh", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xcopa_ta = LightevalTaskConfig( + name="xcopa:ta", + suite=["lighteval"], + prompt_function=prompt.xcopa_ta, + hf_repo="cambridgeltl/xcopa", + hf_subset="ta", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xcopa_th = LightevalTaskConfig( + name="xcopa:th", + suite=["lighteval"], + prompt_function=prompt.xcopa_th, + hf_repo="cambridgeltl/xcopa", + hf_subset="th", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xcopa_tr = LightevalTaskConfig( + name="xcopa:tr", + suite=["lighteval"], + prompt_function=prompt.xcopa_tr, + hf_repo="cambridgeltl/xcopa", + hf_subset="tr", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xcopa_vi = LightevalTaskConfig( + name="xcopa:vi", + suite=["lighteval"], + prompt_function=prompt.xcopa_vi, + hf_repo="cambridgeltl/xcopa", + hf_subset="vi", + hf_avail_splits=["test", "train", "validation"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/xstory_cloze.py b/src/lighteval/tasks/tasks/xstory_cloze.py new file mode 100644 index 000000000..0043926ab --- /dev/null +++ b/src/lighteval/tasks/tasks/xstory_cloze.py @@ -0,0 +1,208 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +""" +XStoryCloze consists of the professionally translated version of the English +StoryCloze dataset (Spring 2016 version) to 10 non-English languages. This +dataset is released by Meta AI. +""" + +xstory_cloze_en_lighteval = LightevalTaskConfig( + name="xstory_cloze:en", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="juletxara/xstory_cloze", + hf_subset="en", + hf_avail_splits=["training", "eval"], + evaluation_splits=["eval"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xstory_cloze_ru_lighteval = LightevalTaskConfig( + name="xstory_cloze:ru", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="juletxara/xstory_cloze", + hf_subset="ru", + hf_avail_splits=["training", "eval"], + evaluation_splits=["eval"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xstory_cloze_zh_lighteval = LightevalTaskConfig( + name="xstory_cloze:zh", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="juletxara/xstory_cloze", + hf_subset="zh", + hf_avail_splits=["training", "eval"], + evaluation_splits=["eval"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xstory_cloze_es_lighteval = LightevalTaskConfig( + name="xstory_cloze:es", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="juletxara/xstory_cloze", + hf_subset="es", + hf_avail_splits=["training", "eval"], + evaluation_splits=["eval"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xstory_cloze_ar_lighteval = LightevalTaskConfig( + name="xstory_cloze:ar", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="juletxara/xstory_cloze", + hf_subset="ar", + hf_avail_splits=["training", "eval"], + evaluation_splits=["eval"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xstory_cloze_hi_lighteval = LightevalTaskConfig( + name="xstory_cloze:hi", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="juletxara/xstory_cloze", + hf_subset="hi", + hf_avail_splits=["training", "eval"], + evaluation_splits=["eval"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xstory_cloze_id_lighteval = LightevalTaskConfig( + name="xstory_cloze:id", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="juletxara/xstory_cloze", + hf_subset="id", + hf_avail_splits=["training", "eval"], + evaluation_splits=["eval"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xstory_cloze_te_lighteval = LightevalTaskConfig( + name="xstory_cloze:te", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="juletxara/xstory_cloze", + hf_subset="te", + hf_avail_splits=["training", "eval"], + evaluation_splits=["eval"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xstory_cloze_sw_lighteval = LightevalTaskConfig( + name="xstory_cloze:sw", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="juletxara/xstory_cloze", + hf_subset="sw", + hf_avail_splits=["training", "eval"], + evaluation_splits=["eval"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xstory_cloze_eu_lighteval = LightevalTaskConfig( + name="xstory_cloze:eu", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="juletxara/xstory_cloze", + hf_subset="eu", + hf_avail_splits=["training", "eval"], + evaluation_splits=["eval"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xstory_cloze_my_lighteval = LightevalTaskConfig( + name="xstory_cloze:my", + suite=["lighteval"], + prompt_function=prompt.storycloze, + hf_repo="juletxara/xstory_cloze", + hf_subset="my", + hf_avail_splits=["training", "eval"], + evaluation_splits=["eval"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) diff --git a/src/lighteval/tasks/tasks/xwinograd.py b/src/lighteval/tasks/tasks/xwinograd.py new file mode 100644 index 000000000..370718c3d --- /dev/null +++ b/src/lighteval/tasks/tasks/xwinograd.py @@ -0,0 +1,122 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +xwinograd_en_lighteval = LightevalTaskConfig( + name="xwinograd:en", + suite=["lighteval"], + prompt_function=prompt.winogrande, + hf_repo="Muennighoff/xwinograd", + hf_subset="en", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xwinograd_fr_lighteval = LightevalTaskConfig( + name="xwinograd:fr", + suite=["lighteval"], + prompt_function=prompt.winogrande, + hf_repo="Muennighoff/xwinograd", + hf_subset="fr", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xwinograd_jp_lighteval = LightevalTaskConfig( + name="xwinograd:jp", + suite=["lighteval"], + prompt_function=prompt.winogrande, + hf_repo="Muennighoff/xwinograd", + hf_subset="jp", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xwinograd_pt_lighteval = LightevalTaskConfig( + name="xwinograd:pt", + suite=["lighteval"], + prompt_function=prompt.winogrande, + hf_repo="Muennighoff/xwinograd", + hf_subset="pt", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xwinograd_ru_lighteval = LightevalTaskConfig( + name="xwinograd:ru", + suite=["lighteval"], + prompt_function=prompt.winogrande, + hf_repo="Muennighoff/xwinograd", + hf_subset="ru", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +xwinograd_zh_lighteval = LightevalTaskConfig( + name="xwinograd:zh", + suite=["lighteval"], + prompt_function=prompt.winogrande, + hf_repo="Muennighoff/xwinograd", + hf_subset="zh", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) From 0d42edf4f634dfacbf9694be51ddbed1046c9bfe Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Mon, 13 Oct 2025 17:31:59 +0200 Subject: [PATCH 07/43] move tasks to individual files --- src/lighteval/metrics/metrics.py | 100 +++++++-------- .../metrics/utils/extractive_match_utils.py | 7 +- src/lighteval/tasks/__init__.py | 47 +++++++ src/lighteval/tasks/default_prompts.py | 39 +++++- src/lighteval/tasks/registry.py | 46 +------ src/lighteval/tasks/tasks/aime.py | 99 ++++++++++++--- src/lighteval/tasks/tasks/anli.py | 4 +- src/lighteval/tasks/tasks/coqa.py | 4 +- src/lighteval/tasks/tasks/drop_qa.py | 23 ++++ src/lighteval/tasks/tasks/gpqa.py | 120 +++++++++++++----- src/lighteval/tasks/tasks/gsm8k.py | 34 +++-- src/lighteval/tasks/tasks/gsm_plus.py | 36 ++++-- src/lighteval/tasks/tasks/jeopardy.py | 14 +- src/lighteval/tasks/tasks/math_500.py | 39 ++++-- .../tasks/tasks/natural_questions.py | 16 +-- src/lighteval/tasks/tasks/squad_v2.py | 14 +- 16 files changed, 429 insertions(+), 213 deletions(-) diff --git a/src/lighteval/metrics/metrics.py b/src/lighteval/metrics/metrics.py index 2fc5ec0b4..835e70cfc 100644 --- a/src/lighteval/metrics/metrics.py +++ b/src/lighteval/metrics/metrics.py @@ -81,64 +81,64 @@ from lighteval.utils.language import Language -@scorer(metrics=[accuracy(), stderr()]) -def extractive_math_scorer(): - gold_extraction_target = (ExprExtractionConfig(), LatexExtractionConfig(boxed_match_priority=0)) - pred_extraction_target = (ExprExtractionConfig(), LatexExtractionConfig(boxed_match_priority=0)) - language = Language.ENGLISH - fallback_mode = "first_match" - extraction_mode = "first_match" - timeout_seconds = 5 +# @scorer(metrics=[accuracy(), stderr()]) +# def extractive_math_scorer(): +# gold_extraction_target = (ExprExtractionConfig(), LatexExtractionConfig(boxed_match_priority=0)) +# pred_extraction_target = (ExprExtractionConfig(), LatexExtractionConfig(boxed_match_priority=0)) +# language = Language.ENGLISH +# fallback_mode = "first_match" +# extraction_mode = "first_match" +# timeout_seconds = 5 - gold_extraction_regexes = get_extraction_regexes(gold_extraction_target, language) - pred_extraction_regexes = get_extraction_regexes(pred_extraction_target, language) +# gold_extraction_regexes = get_extraction_regexes(gold_extraction_target, language) +# pred_extraction_regexes = get_extraction_regexes(pred_extraction_target, language) - async def score(state: TaskState, target: Target): - extracted_predictions = extract_target_from_pred( - state.output.completion, pred_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds - ) - extracted_gold = extract_target_from_pred( - target.text, gold_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds - ) - return Score( - value="C" if extracted_predictions == extracted_gold else "I", - explanation=state.output.completion, - answer=str(extracted_predictions), - ) +# async def score(state: TaskState, target: Target): +# extracted_predictions = extract_target_from_pred( +# state.output.completion, pred_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds +# ) +# extracted_gold = extract_target_from_pred( +# target.text, gold_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds +# ) +# return Score( +# value="C" if extracted_predictions == extracted_gold else "I", +# explanation=state.output.completion, +# answer=str(extracted_predictions), +# ) - return score +# return score -@scorer(metrics=[accuracy(), stderr()]) -def multichoice_scorer(): - language = Language.ENGLISH - gold_extraction_target = ( - IndicesExtractionConfig(prefix_for_extraction="NativeLetters", try_extract_without_anchor=True), - ) - pred_extraction_target = ( - IndicesExtractionConfig(prefix_for_extraction="NativeLetters", try_extract_without_anchor=True), - ) - fallback_mode = "first_match" - extraction_mode = "first_match" - timeout_seconds = 5 +# @scorer(metrics=[accuracy(), stderr()]) +# def multichoice_scorer(): +# language = Language.ENGLISH +# gold_extraction_target = ( +# IndicesExtractionConfig(prefix_for_extraction="NativeLetters", try_extract_without_anchor=True), +# ) +# pred_extraction_target = ( +# IndicesExtractionConfig(prefix_for_extraction="NativeLetters", try_extract_without_anchor=True), +# ) +# fallback_mode = "first_match" +# extraction_mode = "first_match" +# timeout_seconds = 5 - gold_extraction_regexes = get_extraction_regexes(gold_extraction_target, language, len_choices=4) - pred_extraction_regexes = get_extraction_regexes(pred_extraction_target, language, len_choices=4) +# gold_extraction_regexes = get_extraction_regexes(gold_extraction_target, language, len_choices=4) +# pred_extraction_regexes = get_extraction_regexes(pred_extraction_target, language, len_choices=4) - async def score(state: TaskState, target: Target): - extracted_predictions = extract_target_from_pred( - state.output.completion, pred_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds - ) - extracted_gold = extract_target_from_pred( - target.text, gold_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds - ) - return Score( - value="C" if extracted_predictions == extracted_gold else "I", - explanation=state.output.completion, - answer=str(extracted_predictions), - ) +# async def score(state: TaskState, target: Target): +# extracted_predictions = extract_target_from_pred( +# state.output.completion, pred_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds +# ) +# extracted_gold = extract_target_from_pred( +# target.text, gold_extraction_regexes, fallback_mode, extraction_mode, timeout_seconds +# ) +# return Score( +# value="C" if extracted_predictions == extracted_gold else "I", +# explanation=state.output.completion, +# answer=str(extracted_predictions), +# ) - return score +# return score class Metrics(Enum): diff --git a/src/lighteval/metrics/utils/extractive_match_utils.py b/src/lighteval/metrics/utils/extractive_match_utils.py index a5ef53a6f..64ff6127e 100644 --- a/src/lighteval/metrics/utils/extractive_match_utils.py +++ b/src/lighteval/metrics/utils/extractive_match_utils.py @@ -31,6 +31,7 @@ from sympy.parsing import parse_expr from lighteval.metrics.utils.math_comparison import should_treat_as_complex +from lighteval.tasks.requests import Doc from lighteval.tasks.templates.utils.formulation import ChoicePrefix, get_prefix from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS from lighteval.utils.imports import requires @@ -344,14 +345,16 @@ def lazy_indices_regex( def get_extraction_regexes( - target_types: Sequence[ExtractionTarget], language: Language, len_choices: int = 1 + # target_types: Sequence[ExtractionTarget], language: Language, len_choices: int = 1 + formatted_doc: Doc, target_types: Sequence[ExtractionTarget], language: Language ) -> list[tuple[list[tuple[re.Pattern[str], int]], ExtractionTarget]]: extraction_regexes: list[tuple[list[tuple[re.Pattern[str], int]], ExtractionTarget]] = [ (lazy_latex_regex(target_type, language), target_type) if isinstance(target_type, LatexExtractionConfig) else (lazy_expr_regex(target_type, language), target_type) if isinstance(target_type, ExprExtractionConfig) - else (lazy_indices_regex(target_type, len_choices, language), target_type) + # else (lazy_indices_regex(target_type, len_choices, language), target_type) + else (lazy_indices_regex(target_type, len(formatted_doc.choices), language), target_type) for target_type in target_types ] diff --git a/src/lighteval/tasks/__init__.py b/src/lighteval/tasks/__init__.py index a732db8d0..52388513c 100644 --- a/src/lighteval/tasks/__init__.py +++ b/src/lighteval/tasks/__init__.py @@ -19,3 +19,50 @@ # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. + +""" +Automatically imports all task configs from the tasks/ directory. +This module dynamically loads all Python files in tasks/ and exposes their LightevalTaskConfig objects. +""" + +import importlib +import logging +from pathlib import Path + +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +logger = logging.getLogger(__name__) + + +# Get the tasks directory +TASKS_DIR = Path(__file__).parent / "tasks" + + +def _load_all_task_configs(): + """Load all LightevalTaskConfig objects from all Python files in the tasks/ directory.""" + loaded_configs = {} + + # Get all Python files in the tasks directory (excluding __init__.py and subdirectories) + task_files = [f for f in TASKS_DIR.glob("*.py") if f.name != "__init__.py"] + + for task_file in task_files: + module_name = task_file.stem + # Import the module + module = importlib.import_module(f"lighteval.tasks.tasks.{module_name}") + + # Find all LightevalTaskConfig objects in the module + for attr_name in dir(module): + attr = getattr(module, attr_name) + if isinstance(attr, LightevalTaskConfig): + loaded_configs[attr_name] = attr + + return loaded_configs + + +# Load all configs and add them to module namespace +_configs = _load_all_task_configs() +globals().update(_configs) + +# Clean up +del _configs diff --git a/src/lighteval/tasks/default_prompts.py b/src/lighteval/tasks/default_prompts.py index cb48c936c..482a19c6a 100644 --- a/src/lighteval/tasks/default_prompts.py +++ b/src/lighteval/tasks/default_prompts.py @@ -888,16 +888,41 @@ def gpqa(line, task_name: str = None): ) -def gpqa_instruct(record): +# def gpqa_instruct(record): +# """Prompt template adapted from simple-evals: https://github.com/openai/simple-evals/blob/83ed7640a7d9cd26849bcb3340125002ef14abbe/common.py#L14""" +# gold_index = random.randint(0, 3) +# choices = [record["Incorrect Answer 1"], record["Incorrect Answer 2"], record["Incorrect Answer 3"]] +# choices.insert(gold_index, record["Correct Answer"]) + +# return Sample( +# input=record["Question"].strip(), +# choices=choices, +# target=LETTER_INDICES[gold_index], +# ) + +def gpqa_instruct(line, task_name: str = None): """Prompt template adapted from simple-evals: https://github.com/openai/simple-evals/blob/83ed7640a7d9cd26849bcb3340125002ef14abbe/common.py#L14""" gold_index = random.randint(0, 3) - choices = [record["Incorrect Answer 1"], record["Incorrect Answer 2"], record["Incorrect Answer 3"]] - choices.insert(gold_index, record["Correct Answer"]) + choices = [line["Incorrect Answer 1"], line["Incorrect Answer 2"], line["Incorrect Answer 3"]] + choices.insert(gold_index, line["Correct Answer"]) + instruction = "Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering." + query_template = "{Instruction}\n\n{Question}\n\nA) {A}\nB) {B}\nC) {C}\nD) {D}" + query = query_template.format( + # Stripping to avoid accidental extra whitespaces, present in GPQA + A=choices[0].strip(), + B=choices[1].strip(), + C=choices[2].strip(), + D=choices[3].strip(), + Question=line["Question"].strip(), + Instruction=instruction, + ) - return Sample( - input=record["Question"].strip(), - choices=choices, - target=LETTER_INDICES[gold_index], + return Doc( + task_name=task_name, + query=query, + choices=LETTER_INDICES[: len(choices)], + gold_index=gold_index, + instruction=instruction, ) diff --git a/src/lighteval/tasks/registry.py b/src/lighteval/tasks/registry.py index 95914991c..92110a615 100644 --- a/src/lighteval/tasks/registry.py +++ b/src/lighteval/tasks/registry.py @@ -33,8 +33,7 @@ from pathlib import Path from types import ModuleType -import lighteval.tasks.default_tasks as default_tasks -from lighteval.tasks.extended import AVAILABLE_EXTENDED_TASKS_MODULES +import lighteval.tasks as default_tasks from lighteval.tasks.lighteval_task import LightevalTask, LightevalTaskConfig @@ -227,51 +226,18 @@ def _load_full_registry(self) -> dict[str, LightevalTaskConfig]: Example: { - "lighteval|arc_easy": LightevalTaskConfig(name="arc_easy", suite="lighteval", ...), + "arc_easy": LightevalTaskConfig(name="arc_easy", suite="lighteval", ...), } """ - custom_tasks_registry = {} - custom_tasks_module = [] - custom_task_configs = [] - if self._custom_tasks is not None: - custom_tasks_module.append(Registry.create_custom_tasks_module(custom_tasks=self._custom_tasks)) - - # Need to load extended tasks - if self._load_extended: - for extended_task_module in AVAILABLE_EXTENDED_TASKS_MODULES: - custom_tasks_module.append(extended_task_module) - - # Need to load community tasks - if self._load_community: - community_modules = load_community_tasks() - for community_task_module in community_modules: - custom_tasks_module.append(community_task_module) + return Registry.create_task_config_dict() # Need to load multilingual tasks if self._load_multilingual: - import lighteval.tasks.multilingual.tasks as multilingual_tasks - - custom_tasks_module.append(multilingual_tasks) - - # We load all - for module in custom_tasks_module: - custom_task_configs.extend(module.TASKS_TABLE) - logger.info(f"Found {len(module.TASKS_TABLE)} custom tasks in {module.__file__}") - - if len(custom_task_configs) > 0: - custom_tasks_registry = Registry.create_task_config_dict(meta_table=custom_task_configs) - - default_tasks_registry = Registry.create_task_config_dict() - - # Check the overlap between default_tasks_registry and custom_tasks_registry - intersection = set(default_tasks_registry.keys()).intersection(set(custom_tasks_registry.keys())) - if len(intersection) > 0: - logger.warning( - f"Following tasks ({intersection}) exists both in the default and custom tasks. Will use the custom ones on conflict." - ) + pass + tasks_registry = {} - return {**default_tasks_registry, **custom_tasks_registry} + return tasks_registry def _update_task_configs(self) -> dict[str, LightevalTaskConfig]: # noqa: C901 """ diff --git a/src/lighteval/tasks/tasks/aime.py b/src/lighteval/tasks/tasks/aime.py index eb4e7c0fc..d4d50e357 100644 --- a/src/lighteval/tasks/tasks/aime.py +++ b/src/lighteval/tasks/tasks/aime.py @@ -21,32 +21,93 @@ # SOFTWARE. import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics, extractive_math_scorer -from lighteval.tasks.lighteval_task import LightevalTaskConfig, LightevalTaskConfig_inspect +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig -aime24 = LightevalTaskConfig_inspect( +# aime24 = LightevalTaskConfig_inspect( +# name="aime24", +# prompt_function=prompt.aime_prompt_fn, +# dataset_repo="HuggingFaceH4/aime_2024", +# dataset_subset="default", +# dataset_split="train", +# scorers=[extractive_math_scorer()], +# system_prompt="ASNWER USING THE FORMAT $ANSWER$", +# epochs=16, +# epochs_reducer="pass_at_4", +# ) + + +# aime25 = LightevalTaskConfig_inspect( +# name="aime25", +# prompt_function=prompt.aime_prompt_fn, +# dataset_repo="yentinglin/aime_2025", +# dataset_subset="default", +# dataset_split="train", +# dataset_revision="main", +# scorers=[extractive_math_scorer()], +# system_prompt="ASNWER USING THE FORMAT $ANSWER$", +# epochs=16, +# epochs_reducer="pass_at_4", +# ) + + +aime24 = LightevalTaskConfig( name="aime24", + suite=["lighteval"], prompt_function=prompt.aime_prompt_fn, - dataset_repo="HuggingFaceH4/aime_2024", - dataset_subset="default", - dataset_split="train", - scorers=[extractive_math_scorer()], - system_prompt="ASNWER USING THE FORMAT $ANSWER$", - epochs=16, - epochs_reducer="pass_at_4", + hf_repo="HuggingFaceH4/aime_2024", + hf_subset="default", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.pass_at_k_math(sample_params={"k": 1})], + version=2, ) +aime24_gpassk = LightevalTaskConfig( + name="aime24_gpassk", + suite=["lighteval"], + prompt_function=prompt.aime_prompt_fn, + hf_repo="HuggingFaceH4/aime_2024", + hf_subset="default", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=8192, + metrics=[Metrics.g_pass_at_k_math(sample_params={"k": 16, "n": 48})], + version=1, +) -aime25 = LightevalTaskConfig_inspect( +aime25 = LightevalTaskConfig( name="aime25", + suite=["lighteval"], + prompt_function=prompt.aime_prompt_fn, + hf_repo="yentinglin/aime_2025", + hf_subset="default", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=10000, + metrics=[Metrics.pass_at_k_math(sample_params={"k": 1, "n": 1})], + version=2, +) + +aime25_gpassk = LightevalTaskConfig( + name="aime25_gpassk", + suite=["lighteval"], prompt_function=prompt.aime_prompt_fn, - dataset_repo="yentinglin/aime_2025", - dataset_subset="default", - dataset_split="train", - dataset_revision="main", - scorers=[extractive_math_scorer()], - system_prompt="ASNWER USING THE FORMAT $ANSWER$", - epochs=16, - epochs_reducer="pass_at_4", + hf_repo="yentinglin/aime_2025", + hf_subset="default", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=8192, + metrics=[Metrics.g_pass_at_k_math(sample_params={"k": 16, "n": 48})], + version=1, ) diff --git a/src/lighteval/tasks/tasks/anli.py b/src/lighteval/tasks/tasks/anli.py index 69d05da49..dcdea20ab 100644 --- a/src/lighteval/tasks/tasks/anli.py +++ b/src/lighteval/tasks/tasks/anli.py @@ -21,8 +21,8 @@ # SOFTWARE. import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics, extractive_math_scorer -from lighteval.tasks.lighteval_task import LightevalTaskConfig, LightevalTaskConfig_inspect +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig anli_r1 = LightevalTaskConfig( diff --git a/src/lighteval/tasks/tasks/coqa.py b/src/lighteval/tasks/tasks/coqa.py index 22146cf66..3e0adf375 100644 --- a/src/lighteval/tasks/tasks/coqa.py +++ b/src/lighteval/tasks/tasks/coqa.py @@ -36,7 +36,5 @@ stop_sequence=["\n", "Question:", "question:"], generation_size=100, version=1, - metrics=( - Metrics.exact_match - ), + metrics=[Metrics.exact_match] ) diff --git a/src/lighteval/tasks/tasks/drop_qa.py b/src/lighteval/tasks/tasks/drop_qa.py index 49ea79eac..1a441ebf4 100644 --- a/src/lighteval/tasks/tasks/drop_qa.py +++ b/src/lighteval/tasks/tasks/drop_qa.py @@ -23,6 +23,8 @@ import lighteval.tasks.default_prompts as prompt from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.utils.language import Language drop_qa = LightevalTaskConfig( @@ -39,3 +41,24 @@ + line["answer"]["spans"] + [prompt.get_drop_date(line["answer"].get("date"))], ) + ), + }, + ), + suite=("lighteval",), + hf_repo="lighteval/drop_harness", + hf_subset="default", + hf_filter=lambda line: list( + filter( + lambda x: x, + [line["answer"].get("number")] + + line["answer"]["spans"] + + [prompt.get_drop_date(line["answer"].get("date"))], + ) + ), + evaluation_splits=("validation",), + few_shots_split="train", + generation_size=250, + stop_sequence=["Question:", "question:", "\n"], + metrics=[Metrics.exact_match], + version=1, +) \ No newline at end of file diff --git a/src/lighteval/tasks/tasks/gpqa.py b/src/lighteval/tasks/tasks/gpqa.py index e2adc6040..6c8d1496a 100644 --- a/src/lighteval/tasks/tasks/gpqa.py +++ b/src/lighteval/tasks/tasks/gpqa.py @@ -20,45 +20,103 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from inspect_ai.scorer import choice -from inspect_ai.solver import multiple_choice - import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import multichoice_scorer -from lighteval.tasks.lighteval_task import LightevalTaskConfig_inspect +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + +# gpqa_diamond = LightevalTaskConfig_inspect( +# name="gpqa:diamond", +# prompt_function=prompt.gpqa_instruct, +# dataset_repo="Idavidrein/gpqa", +# dataset_subset="gpqa_diamond", +# dataset_split="train", +# scorers=[multichoice_scorer(), choice()], +# solvers=[multiple_choice()], +# system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", +# ) + + +# gpqa_extended = LightevalTaskConfig_inspect( +# name="gpqa:extended", +# prompt_function=prompt.gpqa_instruct, +# dataset_repo="Idavidrein/gpqa", +# dataset_subset="gpqa_extended", +# dataset_split="train", +# scorers=[multichoice_scorer(), choice()], +# solvers=[multiple_choice()], +# system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", +# ) + +# gpqa_main = LightevalTaskConfig_inspect( +# name="gpqa:main", +# prompt_function=prompt.gpqa_instruct, +# dataset_repo="Idavidrein/gpqa", +# dataset_subset="gpqa_main", +# dataset_split="train", +# scorers=[multichoice_scorer(), choice()], +# solvers=[multiple_choice()], +# system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", +# ) -gpqa_diamond = LightevalTaskConfig_inspect( +gpqa_lighteval = LightevalTaskConfig( + name="gpqa:mc", + suite=["lighteval"], + prompt_function=prompt.gpqa, + hf_repo="Idavidrein/gpqa", + hf_subset="gpqa_main", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) +gpqa_diamond_instruct_lighteval = LightevalTaskConfig( name="gpqa:diamond", + suite=["lighteval"], prompt_function=prompt.gpqa_instruct, - dataset_repo="Idavidrein/gpqa", - dataset_subset="gpqa_diamond", - dataset_split="train", - scorers=[multichoice_scorer(), choice()], - solvers=[multiple_choice()], - system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", + hf_repo="Idavidrein/gpqa", + hf_subset="gpqa_diamond", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=32768, # needed for reasoning models like R1 + metrics=[Metrics.gpqa_instruct_pass_at_k(sample_params={"k": 1})], + stop_sequence=[], # no stop sequence, will use eos token + version=1, ) - - -gpqa_extended = LightevalTaskConfig_inspect( +gpqa_extended_instruct_lighteval = LightevalTaskConfig( name="gpqa:extended", + suite=["lighteval"], prompt_function=prompt.gpqa_instruct, - dataset_repo="Idavidrein/gpqa", - dataset_subset="gpqa_extended", - dataset_split="train", - scorers=[multichoice_scorer(), choice()], - solvers=[multiple_choice()], - system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", + hf_repo="Idavidrein/gpqa", + hf_subset="gpqa_extended", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=32768, # needed for reasoning models like R1 + metrics=[Metrics.gpqa_instruct_metric], + stop_sequence=[], # no stop sequence, will use eos token + version=0, ) - - -gpqa_main = LightevalTaskConfig_inspect( +gpqa_main_instruct_lighteval = LightevalTaskConfig( name="gpqa:main", + suite=["lighteval"], prompt_function=prompt.gpqa_instruct, - dataset_repo="Idavidrein/gpqa", - dataset_subset="gpqa_main", - dataset_split="train", - scorers=[multichoice_scorer(), choice()], - solvers=[multiple_choice()], - system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", -) + hf_repo="Idavidrein/gpqa", + hf_subset="gpqa_main", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + generation_size=32768, # needed for reasoning models like R1 + metrics=[Metrics.gpqa_instruct_metric], + stop_sequence=[], # no stop sequence, will use eos token + version=0, +) \ No newline at end of file diff --git a/src/lighteval/tasks/tasks/gsm8k.py b/src/lighteval/tasks/tasks/gsm8k.py index 31bed67fb..c2ba53842 100644 --- a/src/lighteval/tasks/tasks/gsm8k.py +++ b/src/lighteval/tasks/tasks/gsm8k.py @@ -25,13 +25,31 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -gsm8k = LightevalTaskConfig_inspect( +# gsm8k = LightevalTaskConfig_inspect( +# name="gsm8k", +# prompt_function=prompt.gsm8k, +# dataset_repo="openai/gsm8k", +# dataset_subset="main", +# dataset_split="train", +# dataset_revision="main", +# scorers=[extractive_math_scorer()], +# system_prompt="ANSWER USING THE FORMAT $ANSWER$", +# ) + +gsm8k_lighteval = LightevalTaskConfig( name="gsm8k", + suite=["lighteval"], prompt_function=prompt.gsm8k, - dataset_repo="openai/gsm8k", - dataset_subset="main", - dataset_split="train", - dataset_revision="main", - scorers=[extractive_math_scorer()], - system_prompt="ANSWER USING THE FORMAT $ANSWER$", -) + hf_repo="openai/gsm8k", + hf_subset="main", + hf_avail_splits=["train", "test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select="random_sampling_from_train", + generation_size=256, + metrics=[ + Metrics.expr_gold_metric, + ], + stop_sequence=["Question:"], + version=0, +) \ No newline at end of file diff --git a/src/lighteval/tasks/tasks/gsm_plus.py b/src/lighteval/tasks/tasks/gsm_plus.py index c386324bd..952006cae 100644 --- a/src/lighteval/tasks/tasks/gsm_plus.py +++ b/src/lighteval/tasks/tasks/gsm_plus.py @@ -21,18 +21,34 @@ # SOFTWARE. import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics, extractive_math_scorer -from lighteval.tasks.lighteval_task import LightevalTaskConfig, LightevalTaskConfig_inspect +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig -gsm_plus = LightevalTaskConfig_inspect( +# gsm_plus = LightevalTaskConfig_inspect( +# name="gsm_plus", +# prompt_function=prompt.gsm_plus, +# dataset_repo="qintongli/GSM-Plus", +# dataset_subset="default", +# dataset_split="test", +# system_prompt="ANSWER USING THE FORMAT $ANSWER$", +# epochs=48, +# epochs_reducer="pass_at_16", +# scorers=[extractive_math_scorer(), model_graded_fact()] +# ) + +gsm_plus = LightevalTaskConfig( name="gsm_plus", + suite=["lighteval"], prompt_function=prompt.gsm_plus, - dataset_repo="qintongli/GSM-Plus", - dataset_subset="default", - dataset_split="test", - system_prompt="ANSWER USING THE FORMAT $ANSWER$", - epochs=48, - epochs_reducer="pass_at_16", - scorers=[extractive_math_scorer(), model_graded_fact()] + hf_repo="qintongli/GSM-Plus", + hf_subset="default", + hf_avail_splits=["test", "testmini"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=None, + metrics=[Metrics.expr_gold_metric], + stop_sequence=None, + version=0, ) diff --git a/src/lighteval/tasks/tasks/jeopardy.py b/src/lighteval/tasks/tasks/jeopardy.py index 331cf5671..d582a0008 100644 --- a/src/lighteval/tasks/tasks/jeopardy.py +++ b/src/lighteval/tasks/tasks/jeopardy.py @@ -23,6 +23,8 @@ import lighteval.tasks.default_prompts as prompt from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.utils.language import Language jeopardy = LightevalTaskConfig( @@ -41,14 +43,6 @@ few_shots_split="train", generation_size=250, stop_sequence=["\n", "Question:", "question:"], - metrics=( - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ), + metrics=[Metrics.exact_match], + version=1, ) diff --git a/src/lighteval/tasks/tasks/math_500.py b/src/lighteval/tasks/tasks/math_500.py index c5213dfc9..1e8ef1d7e 100644 --- a/src/lighteval/tasks/tasks/math_500.py +++ b/src/lighteval/tasks/tasks/math_500.py @@ -21,18 +21,35 @@ # SOFTWARE. import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics, extractive_math_scorer -from lighteval.tasks.lighteval_task import LightevalTaskConfig, LightevalTaskConfig_inspect +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig -math_500 = LightevalTaskConfig_inspect( +# math_500 = LightevalTaskConfig_inspect( +# name="math_500", +# prompt_function=prompt.math_500, +# dataset_repo="HuggingFaceH4/MATH-500", +# dataset_subset="default", +# dataset_split="test", +# scorers=[extractive_math_scorer()], +# system_prompt="Solve the following math problem efficiently and clearly. The last line of your response should be of the following format: 'Therefore, the final answer is: $\\boxed{{ANSWER}}$. I hope it is correct' (without quotes) where ANSWER is just the final number or expression that solves the problem. Think step by step before answering.", +# epochs=48, +# epochs_reducer="pass_at_16", +# ) + +math_500 = LightevalTaskConfig( name="math_500", + suite=["lighteval"], prompt_function=prompt.math_500, - dataset_repo="HuggingFaceH4/MATH-500", - dataset_subset="default", - dataset_split="test", - scorers=[extractive_math_scorer()], - system_prompt="Solve the following math problem efficiently and clearly. The last line of your response should be of the following format: 'Therefore, the final answer is: $\\boxed{{ANSWER}}$. I hope it is correct' (without quotes) where ANSWER is just the final number or expression that solves the problem. Think step by step before answering.", - epochs=48, - epochs_reducer="pass_at_16", -) + hf_repo="HuggingFaceH4/MATH-500", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=32768, + metrics=[ + Metrics.pass_at_k_math(sample_params={"k": 1, "n": 1}), + ], + version=2, +) \ No newline at end of file diff --git a/src/lighteval/tasks/tasks/natural_questions.py b/src/lighteval/tasks/tasks/natural_questions.py index b2f0e4f0c..92d4eb46f 100644 --- a/src/lighteval/tasks/tasks/natural_questions.py +++ b/src/lighteval/tasks/tasks/natural_questions.py @@ -23,6 +23,8 @@ import lighteval.tasks.default_prompts as prompt from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.utils.language import Language natural_questions = LightevalTaskConfig( @@ -38,14 +40,8 @@ few_shots_split="few_shot", generation_size=250, stop_sequence=["\n", "Question:", "question:"], - metrics=( - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ), + metrics=[ + Metrics.exact_match + ], + version=1, ) diff --git a/src/lighteval/tasks/tasks/squad_v2.py b/src/lighteval/tasks/tasks/squad_v2.py index b04314c6e..96744f2c3 100644 --- a/src/lighteval/tasks/tasks/squad_v2.py +++ b/src/lighteval/tasks/tasks/squad_v2.py @@ -23,6 +23,8 @@ import lighteval.tasks.default_prompts as prompt from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.utils.language import Language squad_v2 = LightevalTaskConfig( @@ -43,14 +45,6 @@ few_shots_split="train", stop_sequence=["\n", "Question:", "question:"], generation_size=200, - metrics=( - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - ), + metrics=[Metrics.exact_match], + version=1, ) From 6cc3c041f91c13875f88626354d94788fec8a331 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Mon, 13 Oct 2025 18:18:19 +0200 Subject: [PATCH 08/43] enable extended tasks as well --- src/lighteval/tasks/__init__.py | 17 +++- .../tasks/tasks/ifbench/evaluation_lib.py | 2 +- .../tasks/tasks/ifbench/instructions.py | 2 +- .../tasks/ifbench/instructions_registry.py | 2 +- src/lighteval/tasks/tasks/ifbench/main.py | 2 +- .../tasks/tasks/ifeval/instructions.py | 2 +- .../tasks/ifeval/instructions_registry.py | 2 +- src/lighteval/tasks/tasks/ifeval/main.py | 83 +++++++++---------- src/lighteval/tasks/tasks/lcb/main.py | 6 +- .../tasks/tasks/mix_eval/judge_prompts.py | 2 +- src/lighteval/tasks/tasks/mix_eval/main.py | 8 +- src/lighteval/tasks/tasks/mt_bench/main.py | 2 +- 12 files changed, 72 insertions(+), 58 deletions(-) diff --git a/src/lighteval/tasks/__init__.py b/src/lighteval/tasks/__init__.py index 52388513c..4d72419a4 100644 --- a/src/lighteval/tasks/__init__.py +++ b/src/lighteval/tasks/__init__.py @@ -43,9 +43,12 @@ def _load_all_task_configs(): """Load all LightevalTaskConfig objects from all Python files in the tasks/ directory.""" loaded_configs = {} - # Get all Python files in the tasks directory (excluding __init__.py and subdirectories) + # Get all Python files in the tasks directory (excluding __init__.py) task_files = [f for f in TASKS_DIR.glob("*.py") if f.name != "__init__.py"] + # Also get all subdirectories with main.py files + task_subdirs = [d for d in TASKS_DIR.iterdir() if d.is_dir() and (d / "main.py").exists()] + for task_file in task_files: module_name = task_file.stem # Import the module @@ -57,6 +60,18 @@ def _load_all_task_configs(): if isinstance(attr, LightevalTaskConfig): loaded_configs[attr_name] = attr + # Load from subdirectories' main.py files + for task_dir in task_subdirs: + module_name = task_dir.name + # Import the main.py from the subdirectory + module = importlib.import_module(f"lighteval.tasks.tasks.{module_name}.main") + + # Find all LightevalTaskConfig objects in the module + for attr_name in dir(module): + attr = getattr(module, attr_name) + if isinstance(attr, LightevalTaskConfig): + loaded_configs[attr_name] = attr + return loaded_configs diff --git a/src/lighteval/tasks/tasks/ifbench/evaluation_lib.py b/src/lighteval/tasks/tasks/ifbench/evaluation_lib.py index 493362866..2c4b761e8 100644 --- a/src/lighteval/tasks/tasks/ifbench/evaluation_lib.py +++ b/src/lighteval/tasks/tasks/ifbench/evaluation_lib.py @@ -20,7 +20,7 @@ import json from typing import Dict, Optional, Union -import lighteval.tasks.extended.ifbench.instructions_registry as instructions_registry +import lighteval.tasks.tasks.ifbench.instructions_registry as instructions_registry @dataclasses.dataclass diff --git a/src/lighteval/tasks/tasks/ifbench/instructions.py b/src/lighteval/tasks/tasks/ifbench/instructions.py index 0c4f0a9a0..c15a0dd02 100644 --- a/src/lighteval/tasks/tasks/ifbench/instructions.py +++ b/src/lighteval/tasks/tasks/ifbench/instructions.py @@ -35,7 +35,7 @@ if is_package_available("spacy"): import spacy -import lighteval.tasks.extended.ifeval.instructions_utils as instructions_util +import lighteval.tasks.tasks.ifeval.instructions_utils as instructions_util logger = logging.getLogger(__name__) diff --git a/src/lighteval/tasks/tasks/ifbench/instructions_registry.py b/src/lighteval/tasks/tasks/ifbench/instructions_registry.py index b47494dd2..b146bd06d 100644 --- a/src/lighteval/tasks/tasks/ifbench/instructions_registry.py +++ b/src/lighteval/tasks/tasks/ifbench/instructions_registry.py @@ -14,7 +14,7 @@ """Registry of all instructions.""" -import lighteval.tasks.extended.ifbench.instructions as instructions +import lighteval.tasks.tasks.ifbench.instructions as instructions INSTRUCTION_DICT = { diff --git a/src/lighteval/tasks/tasks/ifbench/main.py b/src/lighteval/tasks/tasks/ifbench/main.py index 6f948203a..b67c497ae 100644 --- a/src/lighteval/tasks/tasks/ifbench/main.py +++ b/src/lighteval/tasks/tasks/ifbench/main.py @@ -30,9 +30,9 @@ SampleLevelMetricGrouping, ) from lighteval.models.model_output import ModelResponse -from lighteval.tasks.extended.ifbench import evaluation_lib from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.requests import Doc, SamplingMethod +from lighteval.tasks.tasks.ifbench import evaluation_lib def ifbench_prompt(line, task_name: str = ""): diff --git a/src/lighteval/tasks/tasks/ifeval/instructions.py b/src/lighteval/tasks/tasks/ifeval/instructions.py index 06b7cf85c..70a87e893 100644 --- a/src/lighteval/tasks/tasks/ifeval/instructions.py +++ b/src/lighteval/tasks/tasks/ifeval/instructions.py @@ -27,7 +27,7 @@ if is_package_available("langdetect"): import langdetect -import lighteval.tasks.extended.ifeval.instructions_utils as instructions_util +import lighteval.tasks.tasks.ifeval.instructions_utils as instructions_util logger = logging.getLogger(__name__) diff --git a/src/lighteval/tasks/tasks/ifeval/instructions_registry.py b/src/lighteval/tasks/tasks/ifeval/instructions_registry.py index 62becfbaa..4dada73d4 100644 --- a/src/lighteval/tasks/tasks/ifeval/instructions_registry.py +++ b/src/lighteval/tasks/tasks/ifeval/instructions_registry.py @@ -14,7 +14,7 @@ """Registry of all instructions.""" -import lighteval.tasks.extended.ifeval.instructions as instructions +import lighteval.tasks.tasks.ifeval.instructions as instructions _KEYWORD = "keywords:" diff --git a/src/lighteval/tasks/tasks/ifeval/main.py b/src/lighteval/tasks/tasks/ifeval/main.py index 50867c5e3..26124cdd9 100644 --- a/src/lighteval/tasks/tasks/ifeval/main.py +++ b/src/lighteval/tasks/tasks/ifeval/main.py @@ -22,13 +22,12 @@ import numpy as np -from inspect_ai.dataset import Sample -from inspect_ai.scorer import Score, Target, accuracy, scorer, stderr -from inspect_ai.solver import TaskState -import lighteval.tasks.extended.ifeval.instructions_registry as instructions_registry +import lighteval.tasks.tasks.ifeval.instructions_registry as instructions_registry from lighteval.metrics.metrics_sample import SampleLevelComputation -from lighteval.metrics.utils.metric_utils import SampleLevelMetricGrouping +from lighteval.metrics.utils.metric_utils import ( + SampleLevelMetricGrouping, +) from lighteval.models.model_output import ModelResponse from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.requests import Doc, SamplingMethod @@ -37,35 +36,37 @@ # Very specific task where there are no precise outputs but instead we test if the format obeys rules @requires("langdetect") -def ifeval_prompt(record): - metadata = {"instruction_id_list": record["instruction_id_list"], "kwargs": record["kwargs"]} - - return Sample( - input=record["prompt"], - metadata=metadata, +def ifeval_prompt(line, task_name: str = ""): + return Doc( + task_name=task_name, + query=line["prompt"], + choices=[""], + gold_index=0, + instruction="", + specific={"instructions_id_list": line["instruction_id_list"], "kwargs": line["kwargs"]}, ) submetric_names = [ "prompt_level_strict_acc", + "inst_level_strict_acc", "prompt_level_loose_acc", + "inst_level_loose_acc", +] + +REASONING_TAG_PAIRS = [ + ("", ""), ] -@scorer( - metrics={ - "prompt_level_strict_acc": [accuracy(), stderr()], - "prompt_level_loose_acc": [accuracy(), stderr()], - } -) -def ifeval_scorer(): - async def score(state: TaskState, target: Target): - response = state.output.completion +class IFEvalMetrics(SampleLevelComputation): + def compute(self, doc: Doc, model_response: ModelResponse, **kwargs) -> dict: + response = model_response.final_text[0] # Strict instructions - instruction_list = state.metadata["instruction_id_list"] - all_kwargs = state.metadata["kwargs"] - prompt = state.input + instruction_list = doc.specific["instructions_id_list"] + all_kwargs = doc.specific["kwargs"] + prompt = doc.query # Loose instructions r = response.split("\n") @@ -116,19 +117,12 @@ async def score(state: TaskState, target: Target): is_following_list_loose.append(is_following) - return Score( - value={ - "prompt_level_strict_acc": int(all(is_following_list_strict)), - "prompt_level_loose_acc": int(all(is_following_list_loose)), - } - ) - - return score - - -class IFEvalMetrics(SampleLevelComputation): - def compute(self, doc: Doc, model_response: ModelResponse, **kwargs) -> dict: - pass + return { + "prompt_level_strict_acc": int(all(is_following_list_strict)), + "inst_level_strict_acc": is_following_list_strict, + "prompt_level_loose_acc": int(all(is_following_list_loose)), + "inst_level_loose_acc": is_following_list_loose, + } @requires("langdetect") @@ -155,12 +149,17 @@ def agg_inst_level_acc(items): ifeval = LightevalTaskConfig( name="ifeval", prompt_function=ifeval_prompt, - dataset_repo="google/IFEval", - dataset_subset="default", - dataset_split="train", - dataset_revision="main", - metrics=[], - system_prompt="FOLLOW THE INSTRUCTIONS STRICTLY.", + suite=["extended"], + hf_repo="google/IFEval", + hf_subset="default", + metrics=[ifeval_metrics], + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split="train", + few_shots_select="random_sampling", + generation_size=1280, + stop_sequence=[], # no stop sequence, will use eot token + version="0.1", ) diff --git a/src/lighteval/tasks/tasks/lcb/main.py b/src/lighteval/tasks/tasks/lcb/main.py index 299ae9073..3feae4fec 100644 --- a/src/lighteval/tasks/tasks/lcb/main.py +++ b/src/lighteval/tasks/tasks/lcb/main.py @@ -38,13 +38,13 @@ from lighteval.metrics.metrics import Metrics, SampleLevelMetric from lighteval.metrics.metrics_sample import SampleLevelComputation from lighteval.models.model_output import ModelResponse -from lighteval.tasks.extended.lcb.codegen_metrics import ( +from lighteval.tasks.lighteval_task import Doc, LightevalTaskConfig +from lighteval.tasks.requests import SamplingMethod +from lighteval.tasks.tasks.lcb.codegen_metrics import ( codegen_metrics, extract_code, translate_private_test_cases, ) -from lighteval.tasks.lighteval_task import Doc, LightevalTaskConfig -from lighteval.tasks.requests import SamplingMethod def prepare_prompt(line: dict[str, Any]) -> str: diff --git a/src/lighteval/tasks/tasks/mix_eval/judge_prompts.py b/src/lighteval/tasks/tasks/mix_eval/judge_prompts.py index ab2a03405..b4925678f 100644 --- a/src/lighteval/tasks/tasks/mix_eval/judge_prompts.py +++ b/src/lighteval/tasks/tasks/mix_eval/judge_prompts.py @@ -20,7 +20,7 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from lighteval.tasks.extended.mix_eval.prompts import parse_options +from lighteval.tasks.tasks.mix_eval.prompts import parse_options def flow_judge_for_freeform_template(question, options, answer, gold): diff --git a/src/lighteval/tasks/tasks/mix_eval/main.py b/src/lighteval/tasks/tasks/mix_eval/main.py index 2d9b7569a..4aa86ddcb 100644 --- a/src/lighteval/tasks/tasks/mix_eval/main.py +++ b/src/lighteval/tasks/tasks/mix_eval/main.py @@ -27,15 +27,15 @@ from lighteval.metrics.metrics_sample import JudgeLLMMixEval from lighteval.metrics.utils.metric_utils import SampleLevelMetricGrouping -from lighteval.tasks.extended.mix_eval.judge_prompts import ( +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc, SamplingMethod +from lighteval.tasks.tasks.mix_eval.judge_prompts import ( flow_judge_for_freeform_template, flow_judge_for_multichoice_template, gpt_judge_for_closeended_freeform, gpt_judge_for_closeended_multiplechoice, ) -from lighteval.tasks.extended.mix_eval.prompts import construct_prompt_freeform, construct_prompt_multichoice -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc, SamplingMethod +from lighteval.tasks.tasks.mix_eval.prompts import construct_prompt_freeform, construct_prompt_multichoice logger = logging.getLogger(__name__) diff --git a/src/lighteval/tasks/tasks/mt_bench/main.py b/src/lighteval/tasks/tasks/mt_bench/main.py index e32194747..eb322e5cc 100644 --- a/src/lighteval/tasks/tasks/mt_bench/main.py +++ b/src/lighteval/tasks/tasks/mt_bench/main.py @@ -25,7 +25,7 @@ from lighteval.tasks.requests import Doc, SamplingMethod from lighteval.metrics.metrics_sample import JudgeLLMMTBench from lighteval.metrics.utils.metric_utils import SampleLevelMetricGrouping -from lighteval.tasks.extended.mt_bench.judge_prompt_templates import ( +from lighteval.tasks.tasks.mt_bench.judge_prompt_templates import ( flow_judge_prompt_mt_bench_with_ref, flow_judge_prompt_mt_bench_without_ref, ) From 4c38951d913499c3e753f4c0da5ecaf7d75fbf2a Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Mon, 13 Oct 2025 18:31:10 +0200 Subject: [PATCH 09/43] run precomit hook --- README.md | 2 +- src/lighteval/main_inspect.py | 42 ++++-- src/lighteval/metrics/metrics.py | 4 - .../metrics/utils/extractive_match_utils.py | 4 +- src/lighteval/tasks/default_prompts.py | 15 +- .../tasks/multilingual/tasks/acva.py | 34 +---- .../tasks/multilingual/tasks/afri_mgsm.py | 38 +---- .../tasks/multilingual/tasks/afri_mmlu.py | 31 +--- .../tasks/multilingual/tasks/afri_xnli.py | 36 +---- .../tasks/multilingual/tasks/arabic_arc.py | 32 +--- .../tasks/multilingual/tasks/arabic_mmlu.py | 30 +--- .../tasks/multilingual/tasks/arcd.py | 37 +---- .../tasks/multilingual/tasks/belebele.py | 33 +---- src/lighteval/tasks/multilingual/tasks/c3.py | 35 +---- .../tasks/multilingual/tasks/ceval.py | 31 +--- .../tasks/multilingual/tasks/chegeka.py | 37 +---- .../tasks/multilingual/tasks/chinese_squad.py | 37 +---- .../tasks/multilingual/tasks/cmath.py | 38 +---- .../tasks/multilingual/tasks/cmmlu.py | 32 +--- .../tasks/multilingual/tasks/cmnli.py | 35 +---- .../tasks/multilingual/tasks/cmrc2018.py | 37 +---- .../tasks/multilingual/tasks/copa_indic.py | 33 +---- .../tasks/multilingual/tasks/enem.py | 31 +--- .../tasks/multilingual/tasks/exams.py | 140 ++++++++++++++---- .../tasks/multilingual/tasks/faquad.py | 37 +---- .../tasks/multilingual/tasks/flores200.py | 38 +---- .../tasks/multilingual/tasks/fquad_v2.py | 37 +---- .../tasks/multilingual/tasks/french_boolq.py | 34 +---- .../multilingual/tasks/french_triviqa.py | 37 +---- .../tasks/multilingual/tasks/germanquad.py | 37 +---- .../tasks/multilingual/tasks/global_mmlu.py | 29 +--- .../tasks/multilingual/tasks/hellaswag_hin.py | 35 +---- .../tasks/multilingual/tasks/hellaswag_tel.py | 35 +---- .../tasks/multilingual/tasks/hellaswag_tha.py | 35 +---- .../tasks/multilingual/tasks/hellaswag_tur.py | 35 +---- .../tasks/multilingual/tasks/hindi_arc.py | 32 +--- .../tasks/multilingual/tasks/hindi_boolq.py | 32 +--- .../tasks/multilingual/tasks/indicqa.py | 35 +---- .../tasks/multilingual/tasks/kenswquad.py | 37 +---- .../tasks/multilingual/tasks/m3exams.py | 28 +--- .../multilingual/tasks/mathlogicqa_rus.py | 34 +---- .../tasks/multilingual/tasks/meta_mmlu.py | 29 +--- .../tasks/multilingual/tasks/mgsm.py | 36 +---- .../tasks/multilingual/tasks/mintaka.py | 35 +---- .../multilingual/tasks/mlmm_arc_challenge.py | 30 +--- .../multilingual/tasks/mlmm_hellaswag.py | 33 +---- .../tasks/multilingual/tasks/mlmm_mmlu.py | 29 +--- .../multilingual/tasks/mlmm_truthfulqa.py | 32 +--- .../tasks/multilingual/tasks/mlqa.py | 35 +---- .../tasks/multilingual/tasks/oab_exams.py | 34 +---- .../tasks/multilingual/tasks/ocnli.py | 35 +---- .../tasks/multilingual/tasks/openai_mmlu.py | 31 +--- .../tasks/multilingual/tasks/openbook_ara.py | 32 +--- .../tasks/multilingual/tasks/openbook_es.py | 34 +---- .../tasks/multilingual/tasks/openbook_rus.py | 34 +---- .../tasks/multilingual/tasks/parus.py | 35 +---- .../tasks/multilingual/tasks/paws_x.py | 33 +---- .../tasks/multilingual/tasks/piqa_ar.py | 32 +--- src/lighteval/tasks/multilingual/tasks/rcb.py | 35 +---- .../tasks/multilingual/tasks/sber_squad.py | 37 +---- .../tasks/multilingual/tasks/soqal.py | 32 +--- .../tasks/multilingual/tasks/squad_es.py | 37 +---- .../tasks/multilingual/tasks/squad_it.py | 37 +---- .../tasks/multilingual/tasks/swahili_arc.py | 32 +--- .../tasks/multilingual/tasks/thai_exams.py | 32 +--- .../tasks/multilingual/tasks/thaiqa.py | 37 +---- .../tasks/multilingual/tasks/tquad_v2.py | 37 +---- .../tasks/multilingual/tasks/turkish_arc.py | 32 +--- .../tasks/multilingual/tasks/turkish_mmlu.py | 30 +--- .../tasks/multilingual/tasks/tydiqa.py | 37 +---- .../tasks/multilingual/tasks/worldtree_rus.py | 34 +---- .../tasks/multilingual/tasks/xcodah.py | 29 +--- .../tasks/multilingual/tasks/xcopa.py | 33 +---- .../tasks/multilingual/tasks/xcsqa.py | 31 +--- .../tasks/multilingual/tasks/xnli.py | 34 +---- .../tasks/multilingual/tasks/xnli2.py | 33 +---- .../tasks/multilingual/tasks/xnli_indic.py | 33 +---- .../tasks/multilingual/tasks/xquad.py | 35 +---- .../tasks/multilingual/tasks/xstory.py | 31 +--- .../tasks/multilingual/tasks/xwinograd.py | 28 +--- src/lighteval/tasks/tasks/agieval.py | 2 +- src/lighteval/tasks/tasks/bigbench_hard.py | 1 + src/lighteval/tasks/tasks/coqa.py | 2 +- src/lighteval/tasks/tasks/drop_qa.py | 2 +- .../tasks/tasks/entity_data_imputation.py | 1 + src/lighteval/tasks/tasks/gpqa.py | 2 +- src/lighteval/tasks/tasks/gsm8k.py | 2 +- src/lighteval/tasks/tasks/imdb.py | 1 + src/lighteval/tasks/tasks/jeopardy.py | 1 - src/lighteval/tasks/tasks/lambada.py | 1 + src/lighteval/tasks/tasks/math_500.py | 2 +- .../tasks/tasks/natural_questions.py | 5 +- src/lighteval/tasks/tasks/piqa.py | 1 + src/lighteval/tasks/tasks/qa4mre.py | 1 + src/lighteval/tasks/tasks/squad_v2.py | 1 - 95 files changed, 327 insertions(+), 2404 deletions(-) diff --git a/README.md b/README.md index 8fa4dbe7f..503efd75c 100644 --- a/README.md +++ b/README.md @@ -62,7 +62,7 @@ Lighteval supports **7,000+ evaluation tasks** across multiple domains and langu ### 🌍 **Multilingual Evaluation** - **Cross-lingual**: XTREME, Flores200 (200 languages), XCOPA, XQuAD -- **Language-specific**: +- **Language-specific**: - **Arabic**: ArabicMMLU - **Filipino**: FilBench - **French**: IFEval-fr, GPQA-fr, BAC-fr diff --git a/src/lighteval/main_inspect.py b/src/lighteval/main_inspect.py index a9e0eaa9f..4e7f97bc7 100644 --- a/src/lighteval/main_inspect.py +++ b/src/lighteval/main_inspect.py @@ -53,24 +53,44 @@ def get_task(lighteval_task_config: LightevalTaskConfig): model_args = { - "max-tokens", "system-message", "temperature", "top-p", "top-k", "frequence-penalty", - "presence-penalty", "logit-bias", "seed", "stop-seqs", "num-choices", "best-of", "log-probs", "top-logprobs", - "cache-prompt", "reasoning-effort", "reasoning-tokens", "reasoning-history", "response-format", "parallel-tool-calls", "max-tool-output", - "internal-tools", "max-retries", "timeout" + "max-tokens", + "system-message", + "temperature", + "top-p", + "top-k", + "frequence-penalty", + "presence-penalty", + "logit-bias", + "seed", + "stop-seqs", + "num-choices", + "best-of", + "log-probs", + "top-logprobs", + "cache-prompt", + "reasoning-effort", + "reasoning-tokens", + "reasoning-history", + "response-format", + "parallel-tool-calls", + "max-tool-output", + "internal-tools", + "max-retries", + "timeout", } def main(): MODEL = ["openai/gpt-4o"] all_tasks = [ - #default_tasks.gsm8k_lighteval, - #default_tasks.aime25, - #default_tasks.aime24, - #default_tasks.math_500, + # default_tasks.gsm8k_lighteval, + # default_tasks.aime25, + # default_tasks.aime24, + # default_tasks.math_500, default_tasks.gsm_plus, - #default_tasks.gpqa_diamond, - #default_tasks.gpqa_extended, - #default_tasks.gpqa_main, + # default_tasks.gpqa_diamond, + # default_tasks.gpqa_extended, + # default_tasks.gpqa_main, ] # default_tasksifeval] all_tasks = [get_task(task) for task in all_tasks] diff --git a/src/lighteval/metrics/metrics.py b/src/lighteval/metrics/metrics.py index 835e70cfc..0fdddc727 100644 --- a/src/lighteval/metrics/metrics.py +++ b/src/lighteval/metrics/metrics.py @@ -25,8 +25,6 @@ import numpy as np from aenum import Enum -from inspect_ai.scorer import Score, Target, accuracy, exact, scorer, stderr -from inspect_ai.solver import TaskState from lighteval.metrics.dynamic_metrics import MultilingualExtractiveMatchMetric from lighteval.metrics.harness_compatibility.drop import DropMetrics @@ -68,8 +66,6 @@ ExprExtractionConfig, IndicesExtractionConfig, LatexExtractionConfig, - extract_target_from_pred, - get_extraction_regexes, ) from lighteval.metrics.utils.metric_utils import ( CorpusLevelMetric, diff --git a/src/lighteval/metrics/utils/extractive_match_utils.py b/src/lighteval/metrics/utils/extractive_match_utils.py index 64ff6127e..3eb4508bb 100644 --- a/src/lighteval/metrics/utils/extractive_match_utils.py +++ b/src/lighteval/metrics/utils/extractive_match_utils.py @@ -346,7 +346,9 @@ def lazy_indices_regex( def get_extraction_regexes( # target_types: Sequence[ExtractionTarget], language: Language, len_choices: int = 1 - formatted_doc: Doc, target_types: Sequence[ExtractionTarget], language: Language + formatted_doc: Doc, + target_types: Sequence[ExtractionTarget], + language: Language, ) -> list[tuple[list[tuple[re.Pattern[str], int]], ExtractionTarget]]: extraction_regexes: list[tuple[list[tuple[re.Pattern[str], int]], ExtractionTarget]] = [ (lazy_latex_regex(target_type, language), target_type) diff --git a/src/lighteval/tasks/default_prompts.py b/src/lighteval/tasks/default_prompts.py index 482a19c6a..7e935a0df 100644 --- a/src/lighteval/tasks/default_prompts.py +++ b/src/lighteval/tasks/default_prompts.py @@ -900,6 +900,7 @@ def gpqa(line, task_name: str = None): # target=LETTER_INDICES[gold_index], # ) + def gpqa_instruct(line, task_name: str = None): """Prompt template adapted from simple-evals: https://github.com/openai/simple-evals/blob/83ed7640a7d9cd26849bcb3340125002ef14abbe/common.py#L14""" gold_index = random.randint(0, 3) @@ -933,7 +934,7 @@ def gsm_plus(record): # they are a bit trickier to eval with regular text extraction. return Sample( - input=record['question'], + input=record["question"], target=record["answer"], ) @@ -1442,20 +1443,22 @@ def lsat_qa(line, task_name: str = None): ) -def math_500(record): +def math_500(line, task_name: str = None): # Prompt template adapted from # - simple-evals: https://github.com/openai/simple-evals/blob/6e84f4e2aed6b60f6a0c7b8f06bbbf4bfde72e58/math_eval.py#L17 # - Llama 3: https://huggingface.co/datasets/meta-llama/Llama-3.2-1B-Instruct-evals/viewer/Llama-3.2-1B-Instruct-evals__math__details?views%5B%5D=llama_32_1b_instruct_evals__math__details # Note that it is important to have the final answer in a box for math-verify to work correctly - MATH_QUERY_TEMPLATE = """ Solve the following math problem efficiently and clearly. The last line of your response should be of the following format: 'Therefore, the final answer is: $\\boxed{{ANSWER}}$. I hope it is correct' (without quotes) where ANSWER is just the final number or expression that solves the problem. Think step by step before answering. +{Question} """.strip() - return Sample( - input=record["problem"], - target=record["solution"], + return Doc( + task_name=task_name, + query=MATH_QUERY_TEMPLATE.format(Question=line["problem"]), + gold_index=0, + choices=[line["solution"]], ) diff --git a/src/lighteval/tasks/multilingual/tasks/acva.py b/src/lighteval/tasks/multilingual/tasks/acva.py index 4950c0124..262eb7394 100644 --- a/src/lighteval/tasks/multilingual/tasks/acva.py +++ b/src/lighteval/tasks/multilingual/tasks/acva.py @@ -20,49 +20,17 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, - HybridFormulation, - MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py b/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py index c1c0a9df0..c09d50e2d 100644 --- a/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py +++ b/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py @@ -20,49 +20,13 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # African MGSM: MGSM for African Languages diff --git a/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py b/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py index f63195b2d..511f0cfc1 100644 --- a/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py @@ -21,48 +21,21 @@ # SOFTWARE. from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # African MMLU: African Massive Multitask Language Understanding diff --git a/src/lighteval/tasks/multilingual/tasks/afri_xnli.py b/src/lighteval/tasks/multilingual/tasks/afri_xnli.py index 6b21f50b6..b7e5a48e7 100644 --- a/src/lighteval/tasks/multilingual/tasks/afri_xnli.py +++ b/src/lighteval/tasks/multilingual/tasks/afri_xnli.py @@ -20,49 +20,21 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language + # African XNLI: African XNLI # From https://arxiv.org/abs/2406.03368. Human translated MMLU. diff --git a/src/lighteval/tasks/multilingual/tasks/arabic_arc.py b/src/lighteval/tasks/multilingual/tasks/arabic_arc.py index 08b1d7455..d2a19e8da 100644 --- a/src/lighteval/tasks/multilingual/tasks/arabic_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/arabic_arc.py @@ -20,49 +20,23 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, ) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py b/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py index a7b1fd35b..582800db7 100644 --- a/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py @@ -20,49 +20,21 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/arcd.py b/src/lighteval/tasks/multilingual/tasks/arcd.py index dc898735c..25172500f 100644 --- a/src/lighteval/tasks/multilingual/tasks/arcd.py +++ b/src/lighteval/tasks/multilingual/tasks/arcd.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ARCD: Arabic Reading Comprehension Dataset. diff --git a/src/lighteval/tasks/multilingual/tasks/belebele.py b/src/lighteval/tasks/multilingual/tasks/belebele.py index a25a18f85..ed9f61eae 100644 --- a/src/lighteval/tasks/multilingual/tasks/belebele.py +++ b/src/lighteval/tasks/multilingual/tasks/belebele.py @@ -20,49 +20,22 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import iso_639_3_ind_to_iso_639_3_macro # Belebele: A large-scale reading comprehension dataset covering 122 languages. diff --git a/src/lighteval/tasks/multilingual/tasks/c3.py b/src/lighteval/tasks/multilingual/tasks/c3.py index bb86ea683..19a4d1dd3 100644 --- a/src/lighteval/tasks/multilingual/tasks/c3.py +++ b/src/lighteval/tasks/multilingual/tasks/c3.py @@ -20,49 +20,20 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # C3: A Chinese Challenge Corpus for Cross-lingual and Cross-modal Tasks diff --git a/src/lighteval/tasks/multilingual/tasks/ceval.py b/src/lighteval/tasks/multilingual/tasks/ceval.py index c34b9b770..37503e313 100644 --- a/src/lighteval/tasks/multilingual/tasks/ceval.py +++ b/src/lighteval/tasks/multilingual/tasks/ceval.py @@ -21,48 +21,23 @@ # SOFTWARE. from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, ) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/chegeka.py b/src/lighteval/tasks/multilingual/tasks/chegeka.py index 59b1a0de3..50ceb7fc3 100644 --- a/src/lighteval/tasks/multilingual/tasks/chegeka.py +++ b/src/lighteval/tasks/multilingual/tasks/chegeka.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/chinese_squad.py b/src/lighteval/tasks/multilingual/tasks/chinese_squad.py index 5fc233cc9..34152a67c 100644 --- a/src/lighteval/tasks/multilingual/tasks/chinese_squad.py +++ b/src/lighteval/tasks/multilingual/tasks/chinese_squad.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ChineseSquad: A reading comprehension dataset for Chinese. diff --git a/src/lighteval/tasks/multilingual/tasks/cmath.py b/src/lighteval/tasks/multilingual/tasks/cmath.py index ab66f015f..dd23ba79e 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmath.py +++ b/src/lighteval/tasks/multilingual/tasks/cmath.py @@ -20,49 +20,13 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/cmmlu.py b/src/lighteval/tasks/multilingual/tasks/cmmlu.py index bd7ff232d..bb9c5c39f 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/cmmlu.py @@ -20,49 +20,21 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/cmnli.py b/src/lighteval/tasks/multilingual/tasks/cmnli.py index d5631eb47..7c6371613 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmnli.py +++ b/src/lighteval/tasks/multilingual/tasks/cmnli.py @@ -20,49 +20,20 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # https://arxiv.org/abs/2004.05986 diff --git a/src/lighteval/tasks/multilingual/tasks/cmrc2018.py b/src/lighteval/tasks/multilingual/tasks/cmrc2018.py index 0c8c7a81c..2f4dd36ab 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmrc2018.py +++ b/src/lighteval/tasks/multilingual/tasks/cmrc2018.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # CMRC 2018: A span-extraction machine reading comprehension dataset for Chinese. diff --git a/src/lighteval/tasks/multilingual/tasks/copa_indic.py b/src/lighteval/tasks/multilingual/tasks/copa_indic.py index 55727c586..fb6366f94 100644 --- a/src/lighteval/tasks/multilingual/tasks/copa_indic.py +++ b/src/lighteval/tasks/multilingual/tasks/copa_indic.py @@ -20,49 +20,22 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # IndicCOPA: COPA for Indic Languages diff --git a/src/lighteval/tasks/multilingual/tasks/enem.py b/src/lighteval/tasks/multilingual/tasks/enem.py index 3ba56a9e4..9eb6e4f6a 100644 --- a/src/lighteval/tasks/multilingual/tasks/enem.py +++ b/src/lighteval/tasks/multilingual/tasks/enem.py @@ -21,48 +21,23 @@ # SOFTWARE. from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, ) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ENEM (Exame Nacional do Ensino Médio) is a standardized Brazilian national secondary diff --git a/src/lighteval/tasks/multilingual/tasks/exams.py b/src/lighteval/tasks/multilingual/tasks/exams.py index 7529c3a0c..1fce34c22 100644 --- a/src/lighteval/tasks/multilingual/tasks/exams.py +++ b/src/lighteval/tasks/multilingual/tasks/exams.py @@ -21,52 +21,138 @@ # SOFTWARE. from functools import partial -from itertools import permutations from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] +exams_subjects_by_lang: dict[Language, set[str]] = { + Language.ARABIC: {"Biology", "Islamic Studies", "Physics", "Science", "Social"}, + Language.BULGARIAN: {"Biology", "Chemistry", "Geography", "History", "Philosophy", "Physics"}, + Language.CROATIAN: { + "Biology", + "Chemistry", + "Ethics", + "Fine Arts", + "Geography", + "Geology", + "History", + "Informatics", + "Philosophy", + "Physics", + "Politics", + "Psychology", + "Religion", + "Sociology", + }, + Language.HUNGARIAN: { + "Agriculture", + "Agriculture (Mechanical knowledge)", + "Biology", + "Chemistry", + "Economics", + "Economics & Marketing", + "Economics Basics (Business)", + "Economics Basics (Theoretical)", + "Forestry", + "Geography", + "Landscaping", + "Physics", + "Politics", + "Tourism", + }, + Language.ITALIAN: { + "Biology", + "Chemistry", + "Ethics", + "Geography", + "Geology", + "History", + "Informatics", + "Philosophy", + "Physics", + "Politics", + "Psychology", + "Sociology", + }, + Language.SERBIAN: { + "Biology", + "Chemistry", + "Ethics", + "Geography", + "Geology", + "History", + "Informatics", + "Philosophy", + "Physics", + "Politics", + "Psychology", + "Religion", + "Sociology", + }, + Language.FRENCH: {"Economics", "Economics & Marketing", "Economics Basics (Theoretical)", "Geography", "Physics"}, + Language.GERMAN: { + "Chemistry", + "Economics", + "Economics & Marketing", + "Economics Basics (Theoretical)", + "Geography", + "Physics", + "Tourism", + }, + Language.SPANISH: {"Geography", "Physics"}, + Language.LITHUANIAN: {"Geology", "History"}, + Language.ALBANIAN: { + "Biology", + "Business", + "Chemistry", + "Fine Arts", + "History", + "Philosophy", + "Physics", + "Sociology", + }, + Language.MACEDONIAN: { + "Biology", + "Business", + "Chemistry", + "Fine Arts", + "History", + "Philosophy", + "Physics", + "Sociology", + }, + Language.TURKISH: { + "Biology", + "Business", + "Chemistry", + "Geography", + "History", + "Philosophy", + "Physics", + "Sociology", + }, + Language.POLISH: {"Professional"}, + Language.PORTUGUESE: {"Biology", "Economics", "Geology", "Philosophy"}, + Language.VIETNAMESE: {"Biology", "Chemistry", "Citizenship", "Geography", "History", "Physics"}, +} + exams_tasks = [ LightevalTaskConfig( diff --git a/src/lighteval/tasks/multilingual/tasks/faquad.py b/src/lighteval/tasks/multilingual/tasks/faquad.py index 997d8b5ab..47c896b93 100644 --- a/src/lighteval/tasks/multilingual/tasks/faquad.py +++ b/src/lighteval/tasks/multilingual/tasks/faquad.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # FaQuAD: A Portuguese Reading Comprehension Dataset diff --git a/src/lighteval/tasks/multilingual/tasks/flores200.py b/src/lighteval/tasks/multilingual/tasks/flores200.py index 6b1fcaa6d..7e84d6b27 100644 --- a/src/lighteval/tasks/multilingual/tasks/flores200.py +++ b/src/lighteval/tasks/multilingual/tasks/flores200.py @@ -20,49 +20,15 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial from itertools import permutations -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag - -from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, -) from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, - HybridFormulation, - MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language, manage_duplicate_language_codes TASKS_TABLE = [] @@ -283,8 +249,6 @@ def flores_adapter(lang1, lang2): } - - flores200_tasks = [ LightevalTaskConfig( name=f"flores200:{lang1}-{lang2}", diff --git a/src/lighteval/tasks/multilingual/tasks/fquad_v2.py b/src/lighteval/tasks/multilingual/tasks/fquad_v2.py index 4fbee8d2f..9deae5e65 100644 --- a/src/lighteval/tasks/multilingual/tasks/fquad_v2.py +++ b/src/lighteval/tasks/multilingual/tasks/fquad_v2.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # FQuAD v2: French Question Answering Dataset version 2. diff --git a/src/lighteval/tasks/multilingual/tasks/french_boolq.py b/src/lighteval/tasks/multilingual/tasks/french_boolq.py index 2b9a595a7..856209f61 100644 --- a/src/lighteval/tasks/multilingual/tasks/french_boolq.py +++ b/src/lighteval/tasks/multilingual/tasks/french_boolq.py @@ -20,49 +20,17 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, - HybridFormulation, - MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/french_triviqa.py b/src/lighteval/tasks/multilingual/tasks/french_triviqa.py index 6628d6ce5..b203e96f5 100644 --- a/src/lighteval/tasks/multilingual/tasks/french_triviqa.py +++ b/src/lighteval/tasks/multilingual/tasks/french_triviqa.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/germanquad.py b/src/lighteval/tasks/multilingual/tasks/germanquad.py index 093756287..096862449 100644 --- a/src/lighteval/tasks/multilingual/tasks/germanquad.py +++ b/src/lighteval/tasks/multilingual/tasks/germanquad.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # GermanQuAD: High-quality German QA dataset with 13,722 questions diff --git a/src/lighteval/tasks/multilingual/tasks/global_mmlu.py b/src/lighteval/tasks/multilingual/tasks/global_mmlu.py index 5c60f2c9a..40d23d459 100644 --- a/src/lighteval/tasks/multilingual/tasks/global_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/global_mmlu.py @@ -21,48 +21,23 @@ # SOFTWARE. from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # Translated MMLU using both professional and non-professional translators. Contains tags for cultural sensitivity. diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py index 2cc5cb56c..038fe26c1 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py @@ -20,49 +20,20 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py index 744df1bfa..2df720beb 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py @@ -20,49 +20,20 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py index d63227fb1..e97772341 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py @@ -20,49 +20,20 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # Hellaswag Thai diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py index 5155b49cd..2b0f3f696 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py @@ -20,49 +20,20 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # Hellaswag Turkish diff --git a/src/lighteval/tasks/multilingual/tasks/hindi_arc.py b/src/lighteval/tasks/multilingual/tasks/hindi_arc.py index 80e6bb05f..7f8f4ebcc 100644 --- a/src/lighteval/tasks/multilingual/tasks/hindi_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/hindi_arc.py @@ -20,49 +20,21 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py b/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py index 8bd44b02e..b7b019543 100644 --- a/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py +++ b/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py @@ -20,49 +20,19 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, - HybridFormulation, - MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/indicqa.py b/src/lighteval/tasks/multilingual/tasks/indicqa.py index cefcfcb0d..1dacf5c55 100644 --- a/src/lighteval/tasks/multilingual/tasks/indicqa.py +++ b/src/lighteval/tasks/multilingual/tasks/indicqa.py @@ -20,49 +20,16 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # IndicQA: A reading comprehension dataset for 11 Indian languages. diff --git a/src/lighteval/tasks/multilingual/tasks/kenswquad.py b/src/lighteval/tasks/multilingual/tasks/kenswquad.py index 5e4ab4f1a..4da082d5b 100644 --- a/src/lighteval/tasks/multilingual/tasks/kenswquad.py +++ b/src/lighteval/tasks/multilingual/tasks/kenswquad.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # KenSwQuAD: A question answering dataset for Kenyan Swahili. diff --git a/src/lighteval/tasks/multilingual/tasks/m3exams.py b/src/lighteval/tasks/multilingual/tasks/m3exams.py index 147164861..e9c92ded6 100644 --- a/src/lighteval/tasks/multilingual/tasks/m3exams.py +++ b/src/lighteval/tasks/multilingual/tasks/m3exams.py @@ -21,48 +21,26 @@ # SOFTWARE. from functools import partial -from itertools import permutations from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, ) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # M3Exam: Multitask Multilingual Multimodal Evaluation Benchmark diff --git a/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py b/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py index df8629dc8..719a14c32 100644 --- a/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py +++ b/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py @@ -20,49 +20,21 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ------------------------------- Math Tasks ------------------------------- # diff --git a/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py b/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py index a7c1a5bc6..ecf91526c 100644 --- a/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py @@ -21,48 +21,23 @@ # SOFTWARE. from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # Meta MMLU: A multilingual version of MMLU (using google translation) diff --git a/src/lighteval/tasks/multilingual/tasks/mgsm.py b/src/lighteval/tasks/multilingual/tasks/mgsm.py index 511220d6d..aeee28ca4 100644 --- a/src/lighteval/tasks/multilingual/tasks/mgsm.py +++ b/src/lighteval/tasks/multilingual/tasks/mgsm.py @@ -20,49 +20,15 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/mintaka.py b/src/lighteval/tasks/multilingual/tasks/mintaka.py index 4978dd2d3..a1dc4c22d 100644 --- a/src/lighteval/tasks/multilingual/tasks/mintaka.py +++ b/src/lighteval/tasks/multilingual/tasks/mintaka.py @@ -20,49 +20,16 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py b/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py index c57634fc1..f591fa55c 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py @@ -20,49 +20,23 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ---------------------------- ARC ---------------------------- # diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py b/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py index 3530758c8..1475e6580 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py @@ -20,49 +20,22 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ------------------------------- Hellaswag Tasks ------------------------------- # diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py b/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py index 34567bd38..d29e6c803 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py @@ -21,48 +21,23 @@ # SOFTWARE. from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # MLMM MMLU: Another multilingual version of MMLU diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py b/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py index 73d5ac5cd..bac700255 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py @@ -21,48 +21,22 @@ # SOFTWARE. from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ---------------------------- TruthfulQA ---------------------------- # diff --git a/src/lighteval/tasks/multilingual/tasks/mlqa.py b/src/lighteval/tasks/multilingual/tasks/mlqa.py index 93d68e45d..0011333b5 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlqa.py +++ b/src/lighteval/tasks/multilingual/tasks/mlqa.py @@ -20,49 +20,16 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. diff --git a/src/lighteval/tasks/multilingual/tasks/oab_exams.py b/src/lighteval/tasks/multilingual/tasks/oab_exams.py index 060aeccad..d1de02ccf 100644 --- a/src/lighteval/tasks/multilingual/tasks/oab_exams.py +++ b/src/lighteval/tasks/multilingual/tasks/oab_exams.py @@ -20,49 +20,21 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # OAB Exams: A collection of questions from the Brazilian Bar Association exam diff --git a/src/lighteval/tasks/multilingual/tasks/ocnli.py b/src/lighteval/tasks/multilingual/tasks/ocnli.py index 5f8ebfd7c..112bceb99 100644 --- a/src/lighteval/tasks/multilingual/tasks/ocnli.py +++ b/src/lighteval/tasks/multilingual/tasks/ocnli.py @@ -20,49 +20,20 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # Native Chinese NLI dataset based. diff --git a/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py b/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py index 4bb81e120..01d98ed60 100644 --- a/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py @@ -21,48 +21,21 @@ # SOFTWARE. from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_ara.py b/src/lighteval/tasks/multilingual/tasks/openbook_ara.py index d8e786415..a1cb13eac 100644 --- a/src/lighteval/tasks/multilingual/tasks/openbook_ara.py +++ b/src/lighteval/tasks/multilingual/tasks/openbook_ara.py @@ -20,49 +20,23 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, ) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ------------------------------- OpenBookQA ------------------------------- # diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_es.py b/src/lighteval/tasks/multilingual/tasks/openbook_es.py index b8478c8cc..3e23178bc 100644 --- a/src/lighteval/tasks/multilingual/tasks/openbook_es.py +++ b/src/lighteval/tasks/multilingual/tasks/openbook_es.py @@ -20,49 +20,21 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # Spanish version of OpenBookQA from BSC Language Technology group diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_rus.py b/src/lighteval/tasks/multilingual/tasks/openbook_rus.py index 708cf932e..366027413 100644 --- a/src/lighteval/tasks/multilingual/tasks/openbook_rus.py +++ b/src/lighteval/tasks/multilingual/tasks/openbook_rus.py @@ -20,49 +20,21 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # The Russian version is part of the MERA (Multilingual Enhanced Russian NLP Architectures) project. diff --git a/src/lighteval/tasks/multilingual/tasks/parus.py b/src/lighteval/tasks/multilingual/tasks/parus.py index 84aa081db..546907377 100644 --- a/src/lighteval/tasks/multilingual/tasks/parus.py +++ b/src/lighteval/tasks/multilingual/tasks/parus.py @@ -20,49 +20,20 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # PARus: Plausible Alternatives for Russian diff --git a/src/lighteval/tasks/multilingual/tasks/paws_x.py b/src/lighteval/tasks/multilingual/tasks/paws_x.py index bfdf5331c..09aa28288 100644 --- a/src/lighteval/tasks/multilingual/tasks/paws_x.py +++ b/src/lighteval/tasks/multilingual/tasks/paws_x.py @@ -20,49 +20,22 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification diff --git a/src/lighteval/tasks/multilingual/tasks/piqa_ar.py b/src/lighteval/tasks/multilingual/tasks/piqa_ar.py index 3ae26314d..850d22604 100644 --- a/src/lighteval/tasks/multilingual/tasks/piqa_ar.py +++ b/src/lighteval/tasks/multilingual/tasks/piqa_ar.py @@ -20,49 +20,23 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, ) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ------------------------------- PIQA ------------------------------- # diff --git a/src/lighteval/tasks/multilingual/tasks/rcb.py b/src/lighteval/tasks/multilingual/tasks/rcb.py index 300081342..686a4cc77 100644 --- a/src/lighteval/tasks/multilingual/tasks/rcb.py +++ b/src/lighteval/tasks/multilingual/tasks/rcb.py @@ -20,49 +20,20 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # Russian Commitment Bank (RCB) is a large-scale NLI dataset with Russian sentences, diff --git a/src/lighteval/tasks/multilingual/tasks/sber_squad.py b/src/lighteval/tasks/multilingual/tasks/sber_squad.py index 39e231904..69ba737a9 100644 --- a/src/lighteval/tasks/multilingual/tasks/sber_squad.py +++ b/src/lighteval/tasks/multilingual/tasks/sber_squad.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # SberQuAD: A large-scale Russian reading comprehension dataset. diff --git a/src/lighteval/tasks/multilingual/tasks/soqal.py b/src/lighteval/tasks/multilingual/tasks/soqal.py index 3a129db2b..bacc503fa 100644 --- a/src/lighteval/tasks/multilingual/tasks/soqal.py +++ b/src/lighteval/tasks/multilingual/tasks/soqal.py @@ -20,49 +20,23 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, ) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # SOQAL: A large-scale Arabic reading comprehension dataset. diff --git a/src/lighteval/tasks/multilingual/tasks/squad_es.py b/src/lighteval/tasks/multilingual/tasks/squad_es.py index ed671879f..aba9ba49e 100644 --- a/src/lighteval/tasks/multilingual/tasks/squad_es.py +++ b/src/lighteval/tasks/multilingual/tasks/squad_es.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # SQuAD-es: Spanish translation of the Stanford Question Answering Dataset diff --git a/src/lighteval/tasks/multilingual/tasks/squad_it.py b/src/lighteval/tasks/multilingual/tasks/squad_it.py index 9835ee278..8bdacc23b 100644 --- a/src/lighteval/tasks/multilingual/tasks/squad_it.py +++ b/src/lighteval/tasks/multilingual/tasks/squad_it.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # SQuAD-it: Italian translation of the SQuAD dataset diff --git a/src/lighteval/tasks/multilingual/tasks/swahili_arc.py b/src/lighteval/tasks/multilingual/tasks/swahili_arc.py index 9ef95fd85..3fae78ce6 100644 --- a/src/lighteval/tasks/multilingual/tasks/swahili_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/swahili_arc.py @@ -20,49 +20,21 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/thai_exams.py b/src/lighteval/tasks/multilingual/tasks/thai_exams.py index 4ea9f1e3a..d86aae20a 100644 --- a/src/lighteval/tasks/multilingual/tasks/thai_exams.py +++ b/src/lighteval/tasks/multilingual/tasks/thai_exams.py @@ -20,49 +20,23 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, ) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/thaiqa.py b/src/lighteval/tasks/multilingual/tasks/thaiqa.py index d74308dcf..1aeb4f15d 100644 --- a/src/lighteval/tasks/multilingual/tasks/thaiqa.py +++ b/src/lighteval/tasks/multilingual/tasks/thaiqa.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ThaiQA: A question answering dataset for the Thai language. diff --git a/src/lighteval/tasks/multilingual/tasks/tquad_v2.py b/src/lighteval/tasks/multilingual/tasks/tquad_v2.py index d51100130..fe91615db 100644 --- a/src/lighteval/tasks/multilingual/tasks/tquad_v2.py +++ b/src/lighteval/tasks/multilingual/tasks/tquad_v2.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # TQuAD v2: Turkish Question Answering Dataset version 2. diff --git a/src/lighteval/tasks/multilingual/tasks/turkish_arc.py b/src/lighteval/tasks/multilingual/tasks/turkish_arc.py index b56ce5254..e7c8db845 100644 --- a/src/lighteval/tasks/multilingual/tasks/turkish_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/turkish_arc.py @@ -20,49 +20,21 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # Turkish ARC diff --git a/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py b/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py index 22a680336..539e02277 100644 --- a/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py @@ -20,49 +20,21 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/tydiqa.py b/src/lighteval/tasks/multilingual/tasks/tydiqa.py index 28adb0ecc..930773ecc 100644 --- a/src/lighteval/tasks/multilingual/tasks/tydiqa.py +++ b/src/lighteval/tasks/multilingual/tasks/tydiqa.py @@ -20,49 +20,14 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # Other QA tasks for RC diff --git a/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py b/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py index f392df227..9c85a0b60 100644 --- a/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py +++ b/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py @@ -20,49 +20,21 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # WorldTree is a dataset for multi-hop inference in science question answering. diff --git a/src/lighteval/tasks/multilingual/tasks/xcodah.py b/src/lighteval/tasks/multilingual/tasks/xcodah.py index d6551c4ae..1d184f2dc 100644 --- a/src/lighteval/tasks/multilingual/tasks/xcodah.py +++ b/src/lighteval/tasks/multilingual/tasks/xcodah.py @@ -21,48 +21,25 @@ # SOFTWARE. from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, xcodah_adapter, ) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ------------------------------- Continuation Tasks ------------------------------- # diff --git a/src/lighteval/tasks/multilingual/tasks/xcopa.py b/src/lighteval/tasks/multilingual/tasks/xcopa.py index 87dd2d6e1..eeec3f05d 100644 --- a/src/lighteval/tasks/multilingual/tasks/xcopa.py +++ b/src/lighteval/tasks/multilingual/tasks/xcopa.py @@ -20,49 +20,22 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ------------------------------- Copa Tasks ------------------------------- # diff --git a/src/lighteval/tasks/multilingual/tasks/xcsqa.py b/src/lighteval/tasks/multilingual/tasks/xcsqa.py index 89d884cc0..3274c3230 100644 --- a/src/lighteval/tasks/multilingual/tasks/xcsqa.py +++ b/src/lighteval/tasks/multilingual/tasks/xcsqa.py @@ -20,49 +20,22 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ------------------------------- XCSQA ------------------------------- # diff --git a/src/lighteval/tasks/multilingual/tasks/xnli.py b/src/lighteval/tasks/multilingual/tasks/xnli.py index 5c6c689bf..7ce8c16a3 100644 --- a/src/lighteval/tasks/multilingual/tasks/xnli.py +++ b/src/lighteval/tasks/multilingual/tasks/xnli.py @@ -20,49 +20,23 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language + # ------------------------------- NLI Tasks ------------------------------- # # NLI (Natural Language Inference) tasks involve determining the logical relationship diff --git a/src/lighteval/tasks/multilingual/tasks/xnli2.py b/src/lighteval/tasks/multilingual/tasks/xnli2.py index 786605e64..44fb05858 100644 --- a/src/lighteval/tasks/multilingual/tasks/xnli2.py +++ b/src/lighteval/tasks/multilingual/tasks/xnli2.py @@ -20,49 +20,24 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language + # Improvement on XNLI with better translation, from our experience models tend to # perform better on XNLI2.0 than XNLI diff --git a/src/lighteval/tasks/multilingual/tasks/xnli_indic.py b/src/lighteval/tasks/multilingual/tasks/xnli_indic.py index 91f6036ca..4a9b81a57 100644 --- a/src/lighteval/tasks/multilingual/tasks/xnli_indic.py +++ b/src/lighteval/tasks/multilingual/tasks/xnli_indic.py @@ -20,49 +20,22 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # Another variant of XNLI, with emphasis on Indic languages diff --git a/src/lighteval/tasks/multilingual/tasks/xquad.py b/src/lighteval/tasks/multilingual/tasks/xquad.py index c2d7304cb..e0d8c65b5 100644 --- a/src/lighteval/tasks/multilingual/tasks/xquad.py +++ b/src/lighteval/tasks/multilingual/tasks/xquad.py @@ -20,49 +20,16 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ------------------------------- RC Tasks ------------------------------- # diff --git a/src/lighteval/tasks/multilingual/tasks/xstory.py b/src/lighteval/tasks/multilingual/tasks/xstory.py index a9ff92cf6..5d12f43d1 100644 --- a/src/lighteval/tasks/multilingual/tasks/xstory.py +++ b/src/lighteval/tasks/multilingual/tasks/xstory.py @@ -21,48 +21,23 @@ # SOFTWARE. from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, -) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/xwinograd.py b/src/lighteval/tasks/multilingual/tasks/xwinograd.py index 03028a5f4..c37abf6e2 100644 --- a/src/lighteval/tasks/multilingual/tasks/xwinograd.py +++ b/src/lighteval/tasks/multilingual/tasks/xwinograd.py @@ -21,48 +21,24 @@ # SOFTWARE. from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, - MultilingualQuasiExactMatchMetric, - MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.metrics.normalizations import LogProbCharNorm, LogProbTokenNorm from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, - get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, winogrand_adapter, - xcodah_adapter, ) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function from lighteval.tasks.templates.utils.formulation import ( CFFormulation, HybridFormulation, MCFFormulation, ) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language # ------------------------------- Winogrande Tasks ------------------------------- # diff --git a/src/lighteval/tasks/tasks/agieval.py b/src/lighteval/tasks/tasks/agieval.py index d6dbf2f93..ece7cca1a 100644 --- a/src/lighteval/tasks/tasks/agieval.py +++ b/src/lighteval/tasks/tasks/agieval.py @@ -34,7 +34,7 @@ high-standard admission and qualification exams intended for general human test-takers, such as general college admission tests (e.g., Chinese College Entrance Exam (Gaokao) and American SAT), law school admission tests, math -competitions, lawyer qualification tests, and national civil service exams. +competitions, lawyer qualification tests, and national civil service exams. https://arxiv.org/abs/2304.06364 """ diff --git a/src/lighteval/tasks/tasks/bigbench_hard.py b/src/lighteval/tasks/tasks/bigbench_hard.py index 12b6ffd91..e2b730975 100644 --- a/src/lighteval/tasks/tasks/bigbench_hard.py +++ b/src/lighteval/tasks/tasks/bigbench_hard.py @@ -24,6 +24,7 @@ from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig + """ hardest subset of bigbench benchmark. """ diff --git a/src/lighteval/tasks/tasks/coqa.py b/src/lighteval/tasks/tasks/coqa.py index 3e0adf375..2d8b9aaa6 100644 --- a/src/lighteval/tasks/tasks/coqa.py +++ b/src/lighteval/tasks/tasks/coqa.py @@ -36,5 +36,5 @@ stop_sequence=["\n", "Question:", "question:"], generation_size=100, version=1, - metrics=[Metrics.exact_match] + metrics=[Metrics.exact_match], ) diff --git a/src/lighteval/tasks/tasks/drop_qa.py b/src/lighteval/tasks/tasks/drop_qa.py index 1a441ebf4..dd5646583 100644 --- a/src/lighteval/tasks/tasks/drop_qa.py +++ b/src/lighteval/tasks/tasks/drop_qa.py @@ -61,4 +61,4 @@ stop_sequence=["Question:", "question:", "\n"], metrics=[Metrics.exact_match], version=1, -) \ No newline at end of file +) diff --git a/src/lighteval/tasks/tasks/entity_data_imputation.py b/src/lighteval/tasks/tasks/entity_data_imputation.py index 807a839c2..d1f5f088e 100644 --- a/src/lighteval/tasks/tasks/entity_data_imputation.py +++ b/src/lighteval/tasks/tasks/entity_data_imputation.py @@ -24,6 +24,7 @@ from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig + """ Capturing Semantics for Imputation with Pre-trained Language Models diff --git a/src/lighteval/tasks/tasks/gpqa.py b/src/lighteval/tasks/tasks/gpqa.py index 6c8d1496a..9db5fc7d4 100644 --- a/src/lighteval/tasks/tasks/gpqa.py +++ b/src/lighteval/tasks/tasks/gpqa.py @@ -119,4 +119,4 @@ metrics=[Metrics.gpqa_instruct_metric], stop_sequence=[], # no stop sequence, will use eos token version=0, -) \ No newline at end of file +) diff --git a/src/lighteval/tasks/tasks/gsm8k.py b/src/lighteval/tasks/tasks/gsm8k.py index c2ba53842..2dd7ee06b 100644 --- a/src/lighteval/tasks/tasks/gsm8k.py +++ b/src/lighteval/tasks/tasks/gsm8k.py @@ -52,4 +52,4 @@ ], stop_sequence=["Question:"], version=0, -) \ No newline at end of file +) diff --git a/src/lighteval/tasks/tasks/imdb.py b/src/lighteval/tasks/tasks/imdb.py index cf8ada89e..b21fe53ec 100644 --- a/src/lighteval/tasks/tasks/imdb.py +++ b/src/lighteval/tasks/tasks/imdb.py @@ -24,6 +24,7 @@ from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig + """ The IMDB benchmark for sentiment analysis in movie review, from: Learning Word Vectors for Sentiment Analysis diff --git a/src/lighteval/tasks/tasks/jeopardy.py b/src/lighteval/tasks/tasks/jeopardy.py index d582a0008..d7b647245 100644 --- a/src/lighteval/tasks/tasks/jeopardy.py +++ b/src/lighteval/tasks/tasks/jeopardy.py @@ -20,7 +20,6 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -import lighteval.tasks.default_prompts as prompt from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.templates.qa import get_qa_prompt_function diff --git a/src/lighteval/tasks/tasks/lambada.py b/src/lighteval/tasks/tasks/lambada.py index 29e87b854..978ae6663 100644 --- a/src/lighteval/tasks/tasks/lambada.py +++ b/src/lighteval/tasks/tasks/lambada.py @@ -24,6 +24,7 @@ from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig + """ The LAMBADA dataset: Word prediction requiring a broad discourse context diff --git a/src/lighteval/tasks/tasks/math_500.py b/src/lighteval/tasks/tasks/math_500.py index 1e8ef1d7e..de798682b 100644 --- a/src/lighteval/tasks/tasks/math_500.py +++ b/src/lighteval/tasks/tasks/math_500.py @@ -52,4 +52,4 @@ Metrics.pass_at_k_math(sample_params={"k": 1, "n": 1}), ], version=2, -) \ No newline at end of file +) diff --git a/src/lighteval/tasks/tasks/natural_questions.py b/src/lighteval/tasks/tasks/natural_questions.py index 92d4eb46f..da3a66629 100644 --- a/src/lighteval/tasks/tasks/natural_questions.py +++ b/src/lighteval/tasks/tasks/natural_questions.py @@ -20,7 +20,6 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -import lighteval.tasks.default_prompts as prompt from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.templates.qa import get_qa_prompt_function @@ -40,8 +39,6 @@ few_shots_split="few_shot", generation_size=250, stop_sequence=["\n", "Question:", "question:"], - metrics=[ - Metrics.exact_match - ], + metrics=[Metrics.exact_match], version=1, ) diff --git a/src/lighteval/tasks/tasks/piqa.py b/src/lighteval/tasks/tasks/piqa.py index 1bf3c585e..fbd1ea3a4 100644 --- a/src/lighteval/tasks/tasks/piqa.py +++ b/src/lighteval/tasks/tasks/piqa.py @@ -24,6 +24,7 @@ from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig + """ To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to diff --git a/src/lighteval/tasks/tasks/qa4mre.py b/src/lighteval/tasks/tasks/qa4mre.py index 7a055f8db..5ab61f346 100644 --- a/src/lighteval/tasks/tasks/qa4mre.py +++ b/src/lighteval/tasks/tasks/qa4mre.py @@ -24,6 +24,7 @@ from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig + """ QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation diff --git a/src/lighteval/tasks/tasks/squad_v2.py b/src/lighteval/tasks/tasks/squad_v2.py index 96744f2c3..d19ef7aa4 100644 --- a/src/lighteval/tasks/tasks/squad_v2.py +++ b/src/lighteval/tasks/tasks/squad_v2.py @@ -20,7 +20,6 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -import lighteval.tasks.default_prompts as prompt from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.templates.qa import get_qa_prompt_function From d2fd5e1ebaf5b60e849d2e326d72d2fbc56ee0e3 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Mon, 13 Oct 2025 18:33:04 +0200 Subject: [PATCH 10/43] fix mkqa --- .../tasks/multilingual/tasks/mkqa.py | 40 +++++-------------- 1 file changed, 10 insertions(+), 30 deletions(-) diff --git a/src/lighteval/tasks/multilingual/tasks/mkqa.py b/src/lighteval/tasks/multilingual/tasks/mkqa.py index 2950e3e4d..b899d67dc 100644 --- a/src/lighteval/tasks/multilingual/tasks/mkqa.py +++ b/src/lighteval/tasks/multilingual/tasks/mkqa.py @@ -21,54 +21,34 @@ # SOFTWARE. from functools import partial -from itertools import permutations -from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag from lighteval.metrics.dynamic_metrics import ( - LogLikelihoodAccMetric, MultilingualQuasiExactMatchMetric, MultilingualQuasiF1ScoreMetric, ) -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm, LogProbPMINorm, LogProbTokenNorm -from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.multilingual.adapters import ( - agieval_adapter, - alghafa_adapter, - ceval_adapter, - enem_adapter, - get_m3exam_adapter, get_mkqa_adapter, - sciqa_adapter, - thai_exams_adapter, - winogrand_adapter, - xcodah_adapter, ) -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation, normalize_subset -from lighteval.tasks.templates.boolq import get_boolq_prompt_function -from lighteval.tasks.templates.continuation import get_continuation_prompt_function -from lighteval.tasks.templates.copa import get_copa_prompt_function -from lighteval.tasks.templates.hellaswag import get_hellaswag_prompt_function -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.tasks.templates.utils.translation_literals import TRANSLATION_LITERALS -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro, manage_duplicate_language_codes +from lighteval.utils.language import Language TASKS_TABLE = [] MKQA_TASK_TO_ID = { + "entity": 0, + "long_answer": 1, + # "unanswerable": 2, + "date": 3, + "number": 4, + "number_with_unit": 5, + "short_phrase": 6, + "binary": 7, +} mkqa_tasks = [ From 2ddb0f9433f8aff560dcb5dc07d02d8b46f70b82 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Mon, 13 Oct 2025 18:43:50 +0200 Subject: [PATCH 11/43] chaange extended suite to lighteval --- src/lighteval/tasks/tasks/gsm8k.py | 10 ++++++++++ src/lighteval/tasks/tasks/ifbench/main.py | 4 ++-- src/lighteval/tasks/tasks/ifeval/main.py | 2 +- src/lighteval/tasks/tasks/lcb/main.py | 2 +- src/lighteval/tasks/tasks/mix_eval/main.py | 8 ++++---- src/lighteval/tasks/tasks/mt_bench/main.py | 2 +- src/lighteval/tasks/tasks/olympiade_bench/main.py | 2 +- src/lighteval/tasks/tasks/tiny_benchmarks/main.py | 2 +- 8 files changed, 21 insertions(+), 11 deletions(-) diff --git a/src/lighteval/tasks/tasks/gsm8k.py b/src/lighteval/tasks/tasks/gsm8k.py index 2dd7ee06b..f69708ca7 100644 --- a/src/lighteval/tasks/tasks/gsm8k.py +++ b/src/lighteval/tasks/tasks/gsm8k.py @@ -25,6 +25,16 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +GSM8K is a dataset of 8,000+ high-quality, single-step arithmetic word problems. + +https://huggingface.co/datasets/openai/gsm8k + +languages: en +fields: math, reasoning +""" + + # gsm8k = LightevalTaskConfig_inspect( # name="gsm8k", # prompt_function=prompt.gsm8k, diff --git a/src/lighteval/tasks/tasks/ifbench/main.py b/src/lighteval/tasks/tasks/ifbench/main.py index b67c497ae..45aaca708 100644 --- a/src/lighteval/tasks/tasks/ifbench/main.py +++ b/src/lighteval/tasks/tasks/ifbench/main.py @@ -104,7 +104,7 @@ def agg_inst_level_acc(items): ifbench_test = LightevalTaskConfig( name="ifbench_test", prompt_function=ifbench_prompt, - suite=["extended"], + suite=["lighteval"], hf_repo="allenai/IFBench_test", hf_subset="default", metrics=[ifbench_metrics], @@ -121,7 +121,7 @@ def agg_inst_level_acc(items): ifbench_multiturn = LightevalTaskConfig( name="ifbench_multiturn", prompt_function=ifbench_prompt, - suite=["extended"], + suite=["lighteval"], hf_repo="allenai/IFBench_multi-turn", hf_subset="default", metrics=[ifbench_metrics], diff --git a/src/lighteval/tasks/tasks/ifeval/main.py b/src/lighteval/tasks/tasks/ifeval/main.py index 26124cdd9..a1fafdbb4 100644 --- a/src/lighteval/tasks/tasks/ifeval/main.py +++ b/src/lighteval/tasks/tasks/ifeval/main.py @@ -149,7 +149,7 @@ def agg_inst_level_acc(items): ifeval = LightevalTaskConfig( name="ifeval", prompt_function=ifeval_prompt, - suite=["extended"], + suite=["lighteval"], hf_repo="google/IFEval", hf_subset="default", metrics=[ifeval_metrics], diff --git a/src/lighteval/tasks/tasks/lcb/main.py b/src/lighteval/tasks/tasks/lcb/main.py index 3feae4fec..506668162 100644 --- a/src/lighteval/tasks/tasks/lcb/main.py +++ b/src/lighteval/tasks/tasks/lcb/main.py @@ -154,7 +154,7 @@ def compute(self, model_response: ModelResponse, doc: Doc, **kwargs) -> dict: name = "lcb:codegeneration" if subset == "v4_v5" else f"lcb:codegeneration_{subset}" task = LightevalTaskConfig( name=name, - suite=["extended"], + suite=["lighteval"], prompt_function=lcb_codegeneration_prompt_fn, hf_repo="lighteval/code_generation_lite", hf_subset=subset, # https://github.com/LiveCodeBench/LiveCodeBench/tree/main?tab=readme-ov-file#dataset-versions diff --git a/src/lighteval/tasks/tasks/mix_eval/main.py b/src/lighteval/tasks/tasks/mix_eval/main.py index 4aa86ddcb..e173c0672 100644 --- a/src/lighteval/tasks/tasks/mix_eval/main.py +++ b/src/lighteval/tasks/tasks/mix_eval/main.py @@ -174,7 +174,7 @@ def mean_dv_5(x): mixeval_freeform_easy = LightevalTaskConfig( name="mixeval_easy:freeform", prompt_function=mixeval_freeform_prompt, - suite=["extended"], + suite=["lighteval"], hf_repo="MixEval/MixEval", hf_subset="MixEval", metrics=[llm_judge_mixeval_freeform_flow_judge, llm_judge_mixeval_freeform_gpt_judge], @@ -191,7 +191,7 @@ def mean_dv_5(x): mixeval_multichoice_easy = LightevalTaskConfig( name="mixeval_easy:multichoice", prompt_function=mixeval_multichoice_prompt, - suite=["extended"], + suite=["lighteval"], hf_repo="MixEval/MixEval", hf_subset="MixEval", metrics=[llm_judge_mixeval_multichoice_flow_judge, llm_judge_mixeval_multichoice_gpt_judge], @@ -207,7 +207,7 @@ def mean_dv_5(x): mixeval_freeform_hard = LightevalTaskConfig( name="mixeval_hard:freeform", prompt_function=mixeval_freeform_prompt, - suite=["extended"], + suite=["lighteval"], hf_repo="MixEval/MixEval", hf_subset="MixEval_Hard", metrics=[llm_judge_mixeval_freeform_flow_judge, llm_judge_mixeval_freeform_gpt_judge], @@ -224,7 +224,7 @@ def mean_dv_5(x): mixeval_multichoice_hard = LightevalTaskConfig( name="mixeval_hard:multichoice", prompt_function=mixeval_multichoice_prompt, - suite=["extended"], + suite=["lighteval"], hf_repo="MixEval/MixEval", hf_subset="MixEval_Hard", metrics=[llm_judge_mixeval_multichoice_flow_judge, llm_judge_mixeval_multichoice_gpt_judge], diff --git a/src/lighteval/tasks/tasks/mt_bench/main.py b/src/lighteval/tasks/tasks/mt_bench/main.py index eb322e5cc..30ef3cc16 100644 --- a/src/lighteval/tasks/tasks/mt_bench/main.py +++ b/src/lighteval/tasks/tasks/mt_bench/main.py @@ -80,7 +80,7 @@ def flow_judge_mt_bench_prompt(question, answer, options, gold): task = LightevalTaskConfig( name="mt_bench", prompt_function=mt_bench_prompt, # must be defined in the file or imported from src/lighteval/tasks/tasks_prompt_formatting.py - suite=["extended"], + suite=["lighteval"], hf_repo="lighteval/mt-bench", hf_subset="default", hf_avail_splits=["train"], diff --git a/src/lighteval/tasks/tasks/olympiade_bench/main.py b/src/lighteval/tasks/tasks/olympiade_bench/main.py index d9fe0d2bc..d753f970b 100644 --- a/src/lighteval/tasks/tasks/olympiade_bench/main.py +++ b/src/lighteval/tasks/tasks/olympiade_bench/main.py @@ -224,7 +224,7 @@ def olympiad_bench_prompt(line, task_name: str = None): LightevalTaskConfig( name="olympiad_bench:" + subset, prompt_function=olympiad_bench_prompt, - suite=["extended"], + suite=["lighteval"], hf_repo="Hothan/OlympiadBench", hf_subset=subset, metrics=[metric], diff --git a/src/lighteval/tasks/tasks/tiny_benchmarks/main.py b/src/lighteval/tasks/tasks/tiny_benchmarks/main.py index 44e05d0cc..e8305f9e2 100644 --- a/src/lighteval/tasks/tasks/tiny_benchmarks/main.py +++ b/src/lighteval/tasks/tasks/tiny_benchmarks/main.py @@ -249,7 +249,7 @@ def compute_corpus(self, y_input): task = LightevalTaskConfig( name=f"tiny:{name}", prompt_function=task["prompt"], - suite=["extended"], + suite=["lighteval"], hf_repo=task["dataset"], hf_subset=task["subset"], hf_avail_splits=task["splits"], From ee9712284cca6f986ad5b95f31eade666309071b Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Tue, 14 Oct 2025 10:57:17 +0200 Subject: [PATCH 12/43] chaange extended suite to lighteval --- src/lighteval/tasks/default_prompts.py | 12 +- src/lighteval/tasks/tasks/babi_qa.py | 6 +- src/lighteval/tasks/tasks/bigbench_hard.py | 36 +- src/lighteval/tasks/tasks/commonsenseqa.py | 5 +- src/lighteval/tasks/tasks/covid_dialogue.py | 5 +- src/lighteval/tasks/tasks/glue.py | 34 +- src/lighteval/tasks/tasks/gpqa.py | 8 +- src/lighteval/tasks/tasks/gsm8k.py | 2 +- src/lighteval/tasks/tasks/me_q_sum.py | 6 +- src/lighteval/tasks/tasks/narrativeqa.py | 5 +- src/lighteval/tasks/tasks/quac.py | 6 +- src/lighteval/tasks/tasks/race_high.py | 2 +- src/lighteval/tasks/tasks/raft.py | 170 +- .../tasks/tasks/real_toxicity_prompts.py | 5 +- src/lighteval/tasks/tasks/sacrebleu.py | 722 ++++---- src/lighteval/tasks/tasks/siqa.py | 5 +- src/lighteval/tasks/tasks/the_pile.py | 41 +- src/lighteval/tasks/tasks/wikifact.py | 1469 +++-------------- src/lighteval/tasks/tasks/xstory_cloze.py | 22 +- src/lighteval/tasks/tasks/xwinograd.py | 12 +- 20 files changed, 768 insertions(+), 1805 deletions(-) diff --git a/src/lighteval/tasks/default_prompts.py b/src/lighteval/tasks/default_prompts.py index 7e935a0df..43f3658da 100644 --- a/src/lighteval/tasks/default_prompts.py +++ b/src/lighteval/tasks/default_prompts.py @@ -330,7 +330,7 @@ def bbh_harness(line, task_name: str = None): ) -def bbh_lighteval(line, task_name: str = None): +def bbh(line, task_name: str = None): line = {k: v for k, v in line.items() if v is not None} query = line.get("task_prefix", "") @@ -349,16 +349,6 @@ def bbh_lighteval(line, task_name: str = None): ) -def bbh(line, instruction, choices, task_name: str = None): - return Doc( - task_name=task_name, - query=f"{instruction}Q: {line['input']}\nA:", - choices=choices, - gold_index=choices.index(line["target"]), - instruction=instruction, - ) - - def bbh_boolean_expressions(line, task_name: str = None): instruction = "Evaluate the result of a random Boolean expression.\n\n" choices = ["False", "True"] diff --git a/src/lighteval/tasks/tasks/babi_qa.py b/src/lighteval/tasks/tasks/babi_qa.py index 14df9a7aa..618611f4b 100644 --- a/src/lighteval/tasks/tasks/babi_qa.py +++ b/src/lighteval/tasks/tasks/babi_qa.py @@ -25,9 +25,13 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +helm task +""" + babi_qa = LightevalTaskConfig( name="babi_qa", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.babi_qa, hf_repo="facebook/babi_qa", hf_subset="en-valid-qa1", diff --git a/src/lighteval/tasks/tasks/bigbench_hard.py b/src/lighteval/tasks/tasks/bigbench_hard.py index e2b730975..896891fba 100644 --- a/src/lighteval/tasks/tasks/bigbench_hard.py +++ b/src/lighteval/tasks/tasks/bigbench_hard.py @@ -33,7 +33,7 @@ causal_judgment = LightevalTaskConfig( name="bigbench_hard:causal_judgment", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="causal_judgement", hf_avail_splits=["train"], @@ -49,7 +49,7 @@ date_understanding = LightevalTaskConfig( name="bigbench_hard:date_understanding", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="date_understanding", hf_avail_splits=["train"], @@ -65,7 +65,7 @@ disambiguation_qa = LightevalTaskConfig( name="bigbench_hard:disambiguation_qa", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="disambiguation_qa", hf_avail_splits=["train"], @@ -81,7 +81,7 @@ geometric_shapes = LightevalTaskConfig( name="bigbench_hard:geometric_shapes", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="geometric_shapes", hf_avail_splits=["train"], @@ -97,7 +97,7 @@ logical_deduction_five_objects = LightevalTaskConfig( name="bigbench_hard:logical_deduction_five_objects", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="logical_deduction_five_objects", hf_avail_splits=["train"], @@ -113,7 +113,7 @@ logical_deduction_seven_objects = LightevalTaskConfig( name="bigbench_hard:logical_deduction_seven_objects", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="logical_deduction_seven_objects", hf_avail_splits=["train"], @@ -129,7 +129,7 @@ logical_deduction_three_objects = LightevalTaskConfig( name="bigbench_hard:logical_deduction_three_objects", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="logical_deduction_three_objects", hf_avail_splits=["train"], @@ -145,7 +145,7 @@ movie_recommendation = LightevalTaskConfig( name="bigbench_hard:movie_recommendation", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="movie_recommendation", hf_avail_splits=["train"], @@ -161,7 +161,7 @@ navigate = LightevalTaskConfig( name="bigbench_hard:navigate", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="navigate", hf_avail_splits=["train"], @@ -177,7 +177,7 @@ reasoning_about_colored_objects = LightevalTaskConfig( name="bigbench_hard:reasoning_about_colored_objects", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="reasoning_about_colored_objects", hf_avail_splits=["train"], @@ -193,7 +193,7 @@ ruin_names = LightevalTaskConfig( name="bigbench_hard:ruin_names", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="ruin_names", hf_avail_splits=["train"], @@ -209,7 +209,7 @@ salient_translation_error_detection = LightevalTaskConfig( name="bigbench_hard:salient_translation_error_detection", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="salient_translation_error_detection", hf_avail_splits=["train"], @@ -225,7 +225,7 @@ snarks = LightevalTaskConfig( name="bigbench_hard:snarks", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="snarks", hf_avail_splits=["train"], @@ -241,7 +241,7 @@ sports_understanding = LightevalTaskConfig( name="bigbench_hard:sports_understanding", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="sports_understanding", hf_avail_splits=["train"], @@ -257,7 +257,7 @@ temporal_sequences = LightevalTaskConfig( name="bigbench_hard:temporal_sequences", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="temporal_sequences", hf_avail_splits=["train"], @@ -273,7 +273,7 @@ tracking_shuffled_objects_five_objects = LightevalTaskConfig( name="bigbench_hard:tracking_shuffled_objects_five_objects", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="tracking_shuffled_objects_five_objects", hf_avail_splits=["train"], @@ -289,7 +289,7 @@ tracking_shuffled_objects_seven_objects = LightevalTaskConfig( name="bigbench_hard:tracking_shuffled_objects_seven_objects", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="tracking_shuffled_objects_seven_objects", hf_avail_splits=["train"], @@ -305,7 +305,7 @@ tracking_shuffled_objects_three_objects = LightevalTaskConfig( name="bigbench_hard:tracking_shuffled_objects_three_objects", suite=["lighteval"], - prompt_function=prompt.bbh_lighteval, + prompt_function=prompt.bbh, hf_repo="lighteval/bbh", hf_subset="tracking_shuffled_objects_three_objects", hf_avail_splits=["train"], diff --git a/src/lighteval/tasks/tasks/commonsenseqa.py b/src/lighteval/tasks/tasks/commonsenseqa.py index e97fba27d..dfc2b3dd8 100644 --- a/src/lighteval/tasks/tasks/commonsenseqa.py +++ b/src/lighteval/tasks/tasks/commonsenseqa.py @@ -25,9 +25,12 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +helm task +""" commonsenseqa = LightevalTaskConfig( name="commonsenseqa", - suite=["helm", "commonsense_scenario"], + suite=["lighteval"], prompt_function=prompt.commonsense_qa, hf_repo="commonsense_qa", hf_subset="default", diff --git a/src/lighteval/tasks/tasks/covid_dialogue.py b/src/lighteval/tasks/tasks/covid_dialogue.py index ed54ae170..3131ed043 100644 --- a/src/lighteval/tasks/tasks/covid_dialogue.py +++ b/src/lighteval/tasks/tasks/covid_dialogue.py @@ -25,9 +25,12 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +helm task +""" covid_dialogue = LightevalTaskConfig( name="covid_dialogue", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.covid_dialogue, hf_repo="lighteval/covid_dialogue", hf_subset="default", diff --git a/src/lighteval/tasks/tasks/glue.py b/src/lighteval/tasks/tasks/glue.py index c75acb95e..35f5c621d 100644 --- a/src/lighteval/tasks/tasks/glue.py +++ b/src/lighteval/tasks/tasks/glue.py @@ -25,7 +25,7 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -glue_cola_lighteval = LightevalTaskConfig( +glue_cola = LightevalTaskConfig( name="glue:cola", suite=["lighteval"], prompt_function=prompt.cola, @@ -41,7 +41,7 @@ version=0, ) -glue_mnli_lighteval = LightevalTaskConfig( +glue_mnli = LightevalTaskConfig( name="glue:mnli", suite=["lighteval"], prompt_function=prompt.mnli, @@ -57,7 +57,7 @@ version=0, ) -glue_mnli_mismatched_lighteval = LightevalTaskConfig( +glue_mnli_mismatched = LightevalTaskConfig( name="glue:mnli_mismatched", suite=["lighteval"], prompt_function=prompt.mnli, @@ -73,7 +73,7 @@ version=0, ) -glue_mrpc_lighteval = LightevalTaskConfig( +glue_mrpc = LightevalTaskConfig( name="glue:mrpc", suite=["lighteval"], prompt_function=prompt.mrpc, @@ -89,7 +89,7 @@ version=0, ) -glue_qnli_lighteval = LightevalTaskConfig( +glue_qnli = LightevalTaskConfig( name="glue:qnli", suite=["lighteval"], prompt_function=prompt.qnli, @@ -105,7 +105,7 @@ version=0, ) -glue_qqp_lighteval = LightevalTaskConfig( +glue_qqp = LightevalTaskConfig( name="glue:qqp", suite=["lighteval"], prompt_function=prompt.qqp, @@ -121,7 +121,7 @@ version=0, ) -glue_rte_lighteval = LightevalTaskConfig( +glue_rte = LightevalTaskConfig( name="glue:rte", suite=["lighteval"], prompt_function=prompt.rte, @@ -137,7 +137,7 @@ version=0, ) -glue_sst2_lighteval = LightevalTaskConfig( +glue_sst2 = LightevalTaskConfig( name="glue:sst2", suite=["lighteval"], prompt_function=prompt.sst, @@ -153,7 +153,7 @@ version=0, ) -glue_stsb_lighteval = LightevalTaskConfig( +glue_stsb = LightevalTaskConfig( name="glue:stsb", suite=["lighteval"], prompt_function=prompt.stsb, @@ -169,7 +169,7 @@ version=0, ) -glue_wnli_lighteval = LightevalTaskConfig( +glue_wnli = LightevalTaskConfig( name="glue:wnli", suite=["lighteval"], prompt_function=prompt.wnli, @@ -185,7 +185,7 @@ version=0, ) -super_glue_boolq_lighteval = LightevalTaskConfig( +super_glue_boolq = LightevalTaskConfig( name="super_glue:boolq", suite=["lighteval"], prompt_function=prompt.boolq_harness, @@ -201,7 +201,7 @@ version=0, ) -super_glue_cb_lighteval = LightevalTaskConfig( +super_glue_cb = LightevalTaskConfig( name="super_glue:cb", suite=["lighteval"], prompt_function=prompt.cb, @@ -217,7 +217,7 @@ version=0, ) -super_glue_copa_lighteval = LightevalTaskConfig( +super_glue_copa = LightevalTaskConfig( name="super_glue:copa", suite=["lighteval"], prompt_function=prompt.copa, @@ -233,7 +233,7 @@ version=0, ) -super_glue_rte_lighteval = LightevalTaskConfig( +super_glue_rte = LightevalTaskConfig( name="super_glue:rte", suite=["lighteval"], prompt_function=prompt.rte, @@ -249,7 +249,7 @@ version=0, ) -super_glue_multirc_lighteval = LightevalTaskConfig( +super_glue_multirc = LightevalTaskConfig( name="super_glue:multirc", suite=["lighteval"], prompt_function=prompt.multirc, @@ -265,7 +265,7 @@ version=0, ) -super_glue_wic_lighteval = LightevalTaskConfig( +super_glue_wic = LightevalTaskConfig( name="super_glue:wic", suite=["lighteval"], prompt_function=prompt.wic, @@ -281,7 +281,7 @@ version=0, ) -super_glue_wsc_lighteval = LightevalTaskConfig( +super_glue_wsc = LightevalTaskConfig( name="super_glue:wsc", suite=["lighteval"], prompt_function=prompt.wsc, diff --git a/src/lighteval/tasks/tasks/gpqa.py b/src/lighteval/tasks/tasks/gpqa.py index 9db5fc7d4..d1bd72625 100644 --- a/src/lighteval/tasks/tasks/gpqa.py +++ b/src/lighteval/tasks/tasks/gpqa.py @@ -60,7 +60,7 @@ # system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", # ) -gpqa_lighteval = LightevalTaskConfig( +gpqa = LightevalTaskConfig( name="gpqa:mc", suite=["lighteval"], prompt_function=prompt.gpqa, @@ -75,7 +75,7 @@ stop_sequence=["\n"], version=0, ) -gpqa_diamond_instruct_lighteval = LightevalTaskConfig( +gpqa_diamond_instruct = LightevalTaskConfig( name="gpqa:diamond", suite=["lighteval"], prompt_function=prompt.gpqa_instruct, @@ -90,7 +90,7 @@ stop_sequence=[], # no stop sequence, will use eos token version=1, ) -gpqa_extended_instruct_lighteval = LightevalTaskConfig( +gpqa_extended_instruct = LightevalTaskConfig( name="gpqa:extended", suite=["lighteval"], prompt_function=prompt.gpqa_instruct, @@ -105,7 +105,7 @@ stop_sequence=[], # no stop sequence, will use eos token version=0, ) -gpqa_main_instruct_lighteval = LightevalTaskConfig( +gpqa_main_instruct = LightevalTaskConfig( name="gpqa:main", suite=["lighteval"], prompt_function=prompt.gpqa_instruct, diff --git a/src/lighteval/tasks/tasks/gsm8k.py b/src/lighteval/tasks/tasks/gsm8k.py index f69708ca7..e9d25d74e 100644 --- a/src/lighteval/tasks/tasks/gsm8k.py +++ b/src/lighteval/tasks/tasks/gsm8k.py @@ -46,7 +46,7 @@ # system_prompt="ANSWER USING THE FORMAT $ANSWER$", # ) -gsm8k_lighteval = LightevalTaskConfig( +gsm8k = LightevalTaskConfig( name="gsm8k", suite=["lighteval"], prompt_function=prompt.gsm8k, diff --git a/src/lighteval/tasks/tasks/me_q_sum.py b/src/lighteval/tasks/tasks/me_q_sum.py index 15dc20df6..168b55cdf 100644 --- a/src/lighteval/tasks/tasks/me_q_sum.py +++ b/src/lighteval/tasks/tasks/me_q_sum.py @@ -25,9 +25,13 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +helm task +""" + me_q_sum = LightevalTaskConfig( name="me_q_sum", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.me_q_sum, hf_repo="lighteval/me_q_sum", hf_subset="default", diff --git a/src/lighteval/tasks/tasks/narrativeqa.py b/src/lighteval/tasks/tasks/narrativeqa.py index 305bbfd26..54692d7f0 100644 --- a/src/lighteval/tasks/tasks/narrativeqa.py +++ b/src/lighteval/tasks/tasks/narrativeqa.py @@ -25,9 +25,12 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +helm task +""" narrativeqa = LightevalTaskConfig( name="narrativeqa", - suite=["helm", "helm_general"], + suite=["lighteval"], prompt_function=prompt.narrativeqa, hf_repo="lighteval/narrative_qa_helm", hf_subset="default", diff --git a/src/lighteval/tasks/tasks/quac.py b/src/lighteval/tasks/tasks/quac.py index 4480847fe..12acbb0f3 100644 --- a/src/lighteval/tasks/tasks/quac.py +++ b/src/lighteval/tasks/tasks/quac.py @@ -25,9 +25,13 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +helm task +""" + quac = LightevalTaskConfig( name="quac", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.quac, hf_repo="lighteval/quac_helm", hf_subset="default", diff --git a/src/lighteval/tasks/tasks/race_high.py b/src/lighteval/tasks/tasks/race_high.py index d7f63bf15..88eb81b25 100644 --- a/src/lighteval/tasks/tasks/race_high.py +++ b/src/lighteval/tasks/tasks/race_high.py @@ -27,7 +27,7 @@ race_high = LightevalTaskConfig( name="race:high", - suite=["lighteval", "race"], + suite=["lighteval"], prompt_function=prompt.race, hf_repo="EleutherAI/race", hf_subset="high", diff --git a/src/lighteval/tasks/tasks/raft.py b/src/lighteval/tasks/tasks/raft.py index 819d909e1..25efb5bd6 100644 --- a/src/lighteval/tasks/tasks/raft.py +++ b/src/lighteval/tasks/tasks/raft.py @@ -22,13 +22,16 @@ import lighteval.tasks.default_prompts as prompt from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import helm_normalizer from lighteval.tasks.lighteval_task import LightevalTaskConfig -raft_ade_corpus_v2_helm = LightevalTaskConfig( +""" +helm task +""" + +raft_ade_corpus_v2 = LightevalTaskConfig( name="raft:ade_corpus_v2", - suite=["helm", "helm_general"], + suite=["lighteval"], prompt_function=prompt.raft_ade_corpus_v2, hf_repo="ought/raft", hf_subset="ade_corpus_v2", @@ -39,25 +42,14 @@ generation_size=30, metrics=[ Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, ], stop_sequence=["\n"], version=0, ) -raft_banking_77_helm = LightevalTaskConfig( +raft_banking_77 = LightevalTaskConfig( name="raft:banking_77", - suite=["helm", "helm_general"], + suite=["lighteval"], prompt_function=prompt.raft_banking_77, hf_repo="ought/raft", hf_subset="banking_77", @@ -68,25 +60,14 @@ generation_size=30, metrics=[ Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, ], stop_sequence=["\n"], version=0, ) -raft_neurips_impact_statement_risks_helm = LightevalTaskConfig( +raft_neurips_impact_statement_risks = LightevalTaskConfig( name="raft:neurips_impact_statement_risks", - suite=["helm", "helm_general"], + suite=["lighteval"], prompt_function=prompt.raft_neurips_impact_statement_risks, hf_repo="ought/raft", hf_subset="neurips_impact_statement_risks", @@ -97,25 +78,14 @@ generation_size=30, metrics=[ Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, ], stop_sequence=["\n"], version=0, ) -raft_one_stop_english_helm = LightevalTaskConfig( +raft_one_stop_english = LightevalTaskConfig( name="raft:one_stop_english", - suite=["helm", "helm_general"], + suite=["lighteval"], prompt_function=prompt.raft_one_stop_english, hf_repo="ought/raft", hf_subset="one_stop_english", @@ -126,25 +96,14 @@ generation_size=30, metrics=[ Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, ], stop_sequence=["\n"], version=0, ) -raft_overruling_helm = LightevalTaskConfig( +raft_overruling = LightevalTaskConfig( name="raft:overruling", - suite=["helm", "helm_general"], + suite=["lighteval"], prompt_function=prompt.raft_overruling, hf_repo="ought/raft", hf_subset="overruling", @@ -155,25 +114,14 @@ generation_size=30, metrics=[ Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, ], stop_sequence=["\n"], version=0, ) -raft_semiconductor_org_types_helm = LightevalTaskConfig( +raft_semiconductor_org_types = LightevalTaskConfig( name="raft:semiconductor_org_types", - suite=["helm", "helm_general"], + suite=["lighteval"], prompt_function=prompt.raft_semiconductor_org_types, hf_repo="ought/raft", hf_subset="semiconductor_org_types", @@ -184,25 +132,14 @@ generation_size=30, metrics=[ Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, ], stop_sequence=["\n"], version=0, ) -raft_systematic_review_inclusion_helm = LightevalTaskConfig( +raft_systematic_review_inclusion = LightevalTaskConfig( name="raft:systematic_review_inclusion", - suite=["helm", "helm_general"], + suite=["lighteval"], prompt_function=prompt.raft_systematic_review_inclusion, hf_repo="ought/raft", hf_subset="systematic_review_inclusion", @@ -213,25 +150,14 @@ generation_size=30, metrics=[ Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, ], stop_sequence=["\n"], version=0, ) -raft_tai_safety_research_helm = LightevalTaskConfig( +raft_tai_safety_research = LightevalTaskConfig( name="raft:tai_safety_research", - suite=["helm", "helm_general"], + suite=["lighteval"], prompt_function=prompt.raft_tai_safety_research, hf_repo="ought/raft", hf_subset="tai_safety_research", @@ -242,25 +168,14 @@ generation_size=30, metrics=[ Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, ], stop_sequence=["\n"], version=0, ) -raft_terms_of_service_helm = LightevalTaskConfig( +raft_terms_of_service = LightevalTaskConfig( name="raft:terms_of_service", - suite=["helm", "helm_general"], + suite=["lighteval"], prompt_function=prompt.raft_terms_of_service, hf_repo="ought/raft", hf_subset="terms_of_service", @@ -271,25 +186,14 @@ generation_size=30, metrics=[ Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, ], stop_sequence=["\n"], version=0, ) -raft_tweet_eval_hate_helm = LightevalTaskConfig( +raft_tweet_eval_hate = LightevalTaskConfig( name="raft:tweet_eval_hate", - suite=["helm", "helm_general"], + suite=["lighteval"], prompt_function=prompt.raft_tweet_eval_hate, hf_repo="ought/raft", hf_subset="tweet_eval_hate", @@ -300,25 +204,14 @@ generation_size=30, metrics=[ Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, ], stop_sequence=["\n"], version=0, ) -raft_twitter_complaints_helm = LightevalTaskConfig( +raft_twitter_complaints = LightevalTaskConfig( name="raft:twitter_complaints", - suite=["helm", "helm_general"], + suite=["lighteval"], prompt_function=prompt.raft_twitter_complaints, hf_repo="ought/raft", hf_subset="twitter_complaints", @@ -329,17 +222,6 @@ generation_size=30, metrics=[ Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - Metrics.f1_score_macro, - Metrics.f1_score_micro, ], stop_sequence=["\n"], version=0, diff --git a/src/lighteval/tasks/tasks/real_toxicity_prompts.py b/src/lighteval/tasks/tasks/real_toxicity_prompts.py index b83314681..ceb38284b 100644 --- a/src/lighteval/tasks/tasks/real_toxicity_prompts.py +++ b/src/lighteval/tasks/tasks/real_toxicity_prompts.py @@ -25,9 +25,12 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +"""helm task +""" + real_toxicity_prompts = LightevalTaskConfig( name="real_toxicity_prompts", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.real_toxicity_prompts, hf_repo="allenai/real-toxicity-prompts", hf_subset="default", diff --git a/src/lighteval/tasks/tasks/sacrebleu.py b/src/lighteval/tasks/tasks/sacrebleu.py index b7e67874f..224446f66 100644 --- a/src/lighteval/tasks/tasks/sacrebleu.py +++ b/src/lighteval/tasks/tasks/sacrebleu.py @@ -20,14 +20,18 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -import lighteval.tasks.default_prompts as prompt from lighteval.metrics.metrics import Metrics +from lighteval.tasks import default_prompts as prompt from lighteval.tasks.lighteval_task import LightevalTaskConfig -iwslt17_ar_en_lighteval = LightevalTaskConfig( +""" +tasks from sacrebleu +""" + +iwslt17_ar_en = LightevalTaskConfig( name="iwslt17:ar-en", - suite=["lighteval", "harness_selection"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="iwslt17_ar-en", @@ -41,9 +45,9 @@ version=0, ) -iwslt17_de_en_lighteval = LightevalTaskConfig( +iwslt17_de_en = LightevalTaskConfig( name="iwslt17:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="iwslt17_de-en", @@ -57,9 +61,9 @@ version=0, ) -iwslt17_en_ar_lighteval = LightevalTaskConfig( +iwslt17_en_ar = LightevalTaskConfig( name="iwslt17:en-ar", - suite=["lighteval", "harness_selection"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="iwslt17_ar-en", @@ -73,9 +77,9 @@ version=0, ) -iwslt17_en_de_lighteval = LightevalTaskConfig( +iwslt17_en_de = LightevalTaskConfig( name="iwslt17:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="iwslt17_en-de", @@ -89,9 +93,9 @@ version=0, ) -iwslt17_en_fr_lighteval = LightevalTaskConfig( +iwslt17_en_fr = LightevalTaskConfig( name="iwslt17:en-fr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="iwslt17_en-fr", @@ -105,9 +109,9 @@ version=0, ) -iwslt17_en_ja_lighteval = LightevalTaskConfig( +iwslt17_en_ja = LightevalTaskConfig( name="iwslt17:en-ja", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="iwslt17_en-ja", @@ -121,9 +125,9 @@ version=0, ) -iwslt17_en_ko_lighteval = LightevalTaskConfig( +iwslt17_en_ko = LightevalTaskConfig( name="iwslt17:en-ko", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="iwslt17_en-ko", @@ -137,9 +141,9 @@ version=0, ) -iwslt17_en_zh_lighteval = LightevalTaskConfig( +iwslt17_en_zh = LightevalTaskConfig( name="iwslt17:en-zh", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="iwslt17_en-zh", @@ -153,9 +157,9 @@ version=0, ) -iwslt17_fr_en_lighteval = LightevalTaskConfig( +iwslt17_fr_en = LightevalTaskConfig( name="iwslt17:fr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="iwslt17_fr-en", @@ -169,9 +173,9 @@ version=0, ) -iwslt17_ja_en_lighteval = LightevalTaskConfig( +iwslt17_ja_en = LightevalTaskConfig( name="iwslt17:ja-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="iwslt17_ja-en", @@ -185,9 +189,9 @@ version=0, ) -iwslt17_ko_en_lighteval = LightevalTaskConfig( +iwslt17_ko_en = LightevalTaskConfig( name="iwslt17:ko-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="iwslt17_ko-en", @@ -201,9 +205,9 @@ version=0, ) -iwslt17_zh_en_lighteval = LightevalTaskConfig( +iwslt17_zh_en = LightevalTaskConfig( name="iwslt17:zh-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="iwslt17_zh-en", @@ -217,9 +221,9 @@ version=0, ) -mtnt2019_en_fr_lighteval = LightevalTaskConfig( +mtnt2019_en_fr = LightevalTaskConfig( name="mtnt2019:en-fr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="mtnt2019_en-fr", @@ -233,9 +237,9 @@ version=0, ) -mtnt2019_en_ja_lighteval = LightevalTaskConfig( +mtnt2019_en_ja = LightevalTaskConfig( name="mtnt2019:en-ja", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="mtnt2019_en-ja", @@ -249,9 +253,9 @@ version=0, ) -mtnt2019_fr_en_lighteval = LightevalTaskConfig( +mtnt2019_fr_en = LightevalTaskConfig( name="mtnt2019:fr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="mtnt2019_fr-en", @@ -265,9 +269,9 @@ version=0, ) -mtnt2019_ja_en_lighteval = LightevalTaskConfig( +mtnt2019_ja_en = LightevalTaskConfig( name="mtnt2019:ja-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="mtnt2019_ja-en", @@ -281,9 +285,9 @@ version=0, ) -wmt08_cs_en_lighteval = LightevalTaskConfig( +wmt08_cs_en = LightevalTaskConfig( name="wmt08:cs-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt08_cs-en", @@ -297,9 +301,9 @@ version=0, ) -wmt08_de_en_lighteval = LightevalTaskConfig( +wmt08_de_en = LightevalTaskConfig( name="wmt08:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt08_de-en", @@ -313,9 +317,9 @@ version=0, ) -wmt08_en_cs_lighteval = LightevalTaskConfig( +wmt08_en_cs = LightevalTaskConfig( name="wmt08:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt08_en-cs", @@ -329,9 +333,9 @@ version=0, ) -wmt08_en_de_lighteval = LightevalTaskConfig( +wmt08_en_de = LightevalTaskConfig( name="wmt08:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt08_en-de", @@ -345,9 +349,9 @@ version=0, ) -wmt08_en_es_lighteval = LightevalTaskConfig( +wmt08_en_es = LightevalTaskConfig( name="wmt08:en-es", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt08_en-es", @@ -361,9 +365,9 @@ version=0, ) -wmt08_en_fr_lighteval = LightevalTaskConfig( +wmt08_en_fr = LightevalTaskConfig( name="wmt08:en-fr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt08_en-fr", @@ -377,9 +381,9 @@ version=0, ) -wmt08_en_hu_lighteval = LightevalTaskConfig( +wmt08_en_hu = LightevalTaskConfig( name="wmt08:en-hu", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt08_en-hu", @@ -393,9 +397,9 @@ version=0, ) -wmt08_es_en_lighteval = LightevalTaskConfig( +wmt08_es_en = LightevalTaskConfig( name="wmt08:es-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt08_es-en", @@ -409,9 +413,9 @@ version=0, ) -wmt08_fr_en_lighteval = LightevalTaskConfig( +wmt08_fr_en = LightevalTaskConfig( name="wmt08:fr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt08_fr-en", @@ -425,9 +429,9 @@ version=0, ) -wmt08_hu_en_lighteval = LightevalTaskConfig( +wmt08_hu_en = LightevalTaskConfig( name="wmt08:hu-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt08_hu-en", @@ -441,9 +445,9 @@ version=0, ) -wmt09_cs_en_lighteval = LightevalTaskConfig( +wmt09_cs_en = LightevalTaskConfig( name="wmt09:cs-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt09_cs-en", @@ -457,9 +461,9 @@ version=0, ) -wmt09_de_en_lighteval = LightevalTaskConfig( +wmt09_de_en = LightevalTaskConfig( name="wmt09:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt09_de-en", @@ -473,9 +477,9 @@ version=0, ) -wmt09_en_cs_lighteval = LightevalTaskConfig( +wmt09_en_cs = LightevalTaskConfig( name="wmt09:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt09_en-cs", @@ -489,9 +493,9 @@ version=0, ) -wmt09_en_de_lighteval = LightevalTaskConfig( +wmt09_en_de = LightevalTaskConfig( name="wmt09:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt09_en-de", @@ -505,9 +509,9 @@ version=0, ) -wmt09_en_es_lighteval = LightevalTaskConfig( +wmt09_en_es = LightevalTaskConfig( name="wmt09:en-es", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt09_en-es", @@ -521,9 +525,9 @@ version=0, ) -wmt09_en_fr_lighteval = LightevalTaskConfig( +wmt09_en_fr = LightevalTaskConfig( name="wmt09:en-fr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt09_en-fr", @@ -537,9 +541,9 @@ version=0, ) -wmt09_en_hu_lighteval = LightevalTaskConfig( +wmt09_en_hu = LightevalTaskConfig( name="wmt09:en-hu", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt09_en-hu", @@ -553,9 +557,9 @@ version=0, ) -wmt09_en_it_lighteval = LightevalTaskConfig( +wmt09_en_it = LightevalTaskConfig( name="wmt09:en-it", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt09_en-it", @@ -569,9 +573,9 @@ version=0, ) -wmt09_es_en_lighteval = LightevalTaskConfig( +wmt09_es_en = LightevalTaskConfig( name="wmt09:es-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt09_es-en", @@ -585,9 +589,9 @@ version=0, ) -wmt09_fr_en_lighteval = LightevalTaskConfig( +wmt09_fr_en = LightevalTaskConfig( name="wmt09:fr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt09_fr-en", @@ -601,9 +605,9 @@ version=0, ) -wmt09_hu_en_lighteval = LightevalTaskConfig( +wmt09_hu_en = LightevalTaskConfig( name="wmt09:hu-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt09_hu-en", @@ -617,9 +621,9 @@ version=0, ) -wmt09_it_en_lighteval = LightevalTaskConfig( +wmt09_it_en = LightevalTaskConfig( name="wmt09:it-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt09_it-en", @@ -633,9 +637,9 @@ version=0, ) -wmt10_cs_en_lighteval = LightevalTaskConfig( +wmt10_cs_en = LightevalTaskConfig( name="wmt10:cs-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt10_cs-en", @@ -649,9 +653,9 @@ version=0, ) -wmt10_de_en_lighteval = LightevalTaskConfig( +wmt10_de_en = LightevalTaskConfig( name="wmt10:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt10_de-en", @@ -665,9 +669,9 @@ version=0, ) -wmt10_en_cs_lighteval = LightevalTaskConfig( +wmt10_en_cs = LightevalTaskConfig( name="wmt10:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt10_en-cs", @@ -681,9 +685,9 @@ version=0, ) -wmt10_en_de_lighteval = LightevalTaskConfig( +wmt10_en_de = LightevalTaskConfig( name="wmt10:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt10_en-de", @@ -697,9 +701,9 @@ version=0, ) -wmt10_en_es_lighteval = LightevalTaskConfig( +wmt10_en_es = LightevalTaskConfig( name="wmt10:en-es", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt10_en-es", @@ -713,9 +717,9 @@ version=0, ) -wmt10_en_fr_lighteval = LightevalTaskConfig( +wmt10_en_fr = LightevalTaskConfig( name="wmt10:en-fr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt10_en-fr", @@ -729,9 +733,9 @@ version=0, ) -wmt10_es_en_lighteval = LightevalTaskConfig( +wmt10_es_en = LightevalTaskConfig( name="wmt10:es-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt10_es-en", @@ -745,9 +749,9 @@ version=0, ) -wmt10_fr_en_lighteval = LightevalTaskConfig( +wmt10_fr_en = LightevalTaskConfig( name="wmt10:fr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt10_fr-en", @@ -761,9 +765,9 @@ version=0, ) -wmt11_cs_en_lighteval = LightevalTaskConfig( +wmt11_cs_en = LightevalTaskConfig( name="wmt11:cs-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt11_cs-en", @@ -777,9 +781,9 @@ version=0, ) -wmt11_de_en_lighteval = LightevalTaskConfig( +wmt11_de_en = LightevalTaskConfig( name="wmt11:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt11_de-en", @@ -793,9 +797,9 @@ version=0, ) -wmt11_en_cs_lighteval = LightevalTaskConfig( +wmt11_en_cs = LightevalTaskConfig( name="wmt11:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt11_en-cs", @@ -809,9 +813,9 @@ version=0, ) -wmt11_en_de_lighteval = LightevalTaskConfig( +wmt11_en_de = LightevalTaskConfig( name="wmt11:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt11_en-de", @@ -825,9 +829,9 @@ version=0, ) -wmt11_en_es_lighteval = LightevalTaskConfig( +wmt11_en_es = LightevalTaskConfig( name="wmt11:en-es", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt11_en-es", @@ -841,9 +845,9 @@ version=0, ) -wmt11_en_fr_lighteval = LightevalTaskConfig( +wmt11_en_fr = LightevalTaskConfig( name="wmt11:en-fr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt11_en-fr", @@ -857,9 +861,9 @@ version=0, ) -wmt11_es_en_lighteval = LightevalTaskConfig( +wmt11_es_en = LightevalTaskConfig( name="wmt11:es-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt11_es-en", @@ -873,9 +877,9 @@ version=0, ) -wmt11_fr_en_lighteval = LightevalTaskConfig( +wmt11_fr_en = LightevalTaskConfig( name="wmt11:fr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt11_fr-en", @@ -889,9 +893,9 @@ version=0, ) -wmt12_cs_en_lighteval = LightevalTaskConfig( +wmt12_cs_en = LightevalTaskConfig( name="wmt12:cs-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt12_cs-en", @@ -905,9 +909,9 @@ version=0, ) -wmt12_de_en_lighteval = LightevalTaskConfig( +wmt12_de_en = LightevalTaskConfig( name="wmt12:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt12_de-en", @@ -921,9 +925,9 @@ version=0, ) -wmt12_en_cs_lighteval = LightevalTaskConfig( +wmt12_en_cs = LightevalTaskConfig( name="wmt12:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt12_en-cs", @@ -937,9 +941,9 @@ version=0, ) -wmt12_en_de_lighteval = LightevalTaskConfig( +wmt12_en_de = LightevalTaskConfig( name="wmt12:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt12_en-de", @@ -953,9 +957,9 @@ version=0, ) -wmt12_en_es_lighteval = LightevalTaskConfig( +wmt12_en_es = LightevalTaskConfig( name="wmt12:en-es", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt12_en-es", @@ -969,9 +973,9 @@ version=0, ) -wmt12_en_fr_lighteval = LightevalTaskConfig( +wmt12_en_fr = LightevalTaskConfig( name="wmt12:en-fr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt12_en-fr", @@ -985,9 +989,9 @@ version=0, ) -wmt12_es_en_lighteval = LightevalTaskConfig( +wmt12_es_en = LightevalTaskConfig( name="wmt12:es-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt12_es-en", @@ -1001,9 +1005,9 @@ version=0, ) -wmt12_fr_en_lighteval = LightevalTaskConfig( +wmt12_fr_en = LightevalTaskConfig( name="wmt12:fr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt12_fr-en", @@ -1017,9 +1021,9 @@ version=0, ) -wmt13_cs_en_lighteval = LightevalTaskConfig( +wmt13_cs_en = LightevalTaskConfig( name="wmt13:cs-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt13_cs-en", @@ -1033,9 +1037,9 @@ version=0, ) -wmt13_de_en_lighteval = LightevalTaskConfig( +wmt13_de_en = LightevalTaskConfig( name="wmt13:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt13_de-en", @@ -1049,9 +1053,9 @@ version=0, ) -wmt13_en_cs_lighteval = LightevalTaskConfig( +wmt13_en_cs = LightevalTaskConfig( name="wmt13:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt13_en-cs", @@ -1065,9 +1069,9 @@ version=0, ) -wmt13_en_de_lighteval = LightevalTaskConfig( +wmt13_en_de = LightevalTaskConfig( name="wmt13:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt13_en-de", @@ -1081,9 +1085,9 @@ version=0, ) -wmt13_en_es_lighteval = LightevalTaskConfig( +wmt13_en_es = LightevalTaskConfig( name="wmt13:en-es", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt13_en-es", @@ -1097,9 +1101,9 @@ version=0, ) -wmt13_en_fr_lighteval = LightevalTaskConfig( +wmt13_en_fr = LightevalTaskConfig( name="wmt13:en-fr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt13_en-fr", @@ -1113,9 +1117,9 @@ version=0, ) -wmt13_en_ru_lighteval = LightevalTaskConfig( +wmt13_en_ru = LightevalTaskConfig( name="wmt13:en-ru", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt13_en-ru", @@ -1129,9 +1133,9 @@ version=0, ) -wmt13_es_en_lighteval = LightevalTaskConfig( +wmt13_es_en = LightevalTaskConfig( name="wmt13:es-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt13_es-en", @@ -1145,9 +1149,9 @@ version=0, ) -wmt13_fr_en_lighteval = LightevalTaskConfig( +wmt13_fr_en = LightevalTaskConfig( name="wmt13:fr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt13_fr-en", @@ -1161,9 +1165,9 @@ version=0, ) -wmt13_ru_en_lighteval = LightevalTaskConfig( +wmt13_ru_en = LightevalTaskConfig( name="wmt13:ru-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt13_ru-en", @@ -1177,9 +1181,9 @@ version=0, ) -wmt14_cs_en_lighteval = LightevalTaskConfig( +wmt14_cs_en = LightevalTaskConfig( name="wmt14:cs-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt14_cs-en", @@ -1193,9 +1197,9 @@ version=0, ) -wmt14_de_en_lighteval = LightevalTaskConfig( +wmt14_de_en = LightevalTaskConfig( name="wmt14:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt14_de-en", @@ -1209,9 +1213,9 @@ version=0, ) -wmt14_en_cs_lighteval = LightevalTaskConfig( +wmt14_en_cs = LightevalTaskConfig( name="wmt14:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt14_en-cs", @@ -1225,9 +1229,9 @@ version=0, ) -wmt14_en_de_lighteval = LightevalTaskConfig( +wmt14_en_de = LightevalTaskConfig( name="wmt14:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt14_en-de", @@ -1241,9 +1245,9 @@ version=0, ) -wmt14_en_fr_lighteval = LightevalTaskConfig( +wmt14_en_fr = LightevalTaskConfig( name="wmt14:en-fr", - suite=["lighteval", "gpt3_benchmarks"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="wmt14", hf_subset="fr-en", @@ -1257,9 +1261,9 @@ version=0, ) -wmt14_en_fr_lighteval = LightevalTaskConfig( +wmt14_en_fr = LightevalTaskConfig( name="wmt14:en-fr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt14_en-fr", @@ -1273,9 +1277,9 @@ version=0, ) -wmt14_en_hi_lighteval = LightevalTaskConfig( +wmt14_en_hi = LightevalTaskConfig( name="wmt14:en-hi", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt14_en-hi", @@ -1289,9 +1293,9 @@ version=0, ) -wmt14_en_ru_lighteval = LightevalTaskConfig( +wmt14_en_ru = LightevalTaskConfig( name="wmt14:en-ru", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt14_en-ru", @@ -1305,9 +1309,9 @@ version=0, ) -wmt14_fr_en_lighteval = LightevalTaskConfig( +wmt14_fr_en = LightevalTaskConfig( name="wmt14:fr-en", - suite=["lighteval", "gpt3_benchmarks"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="wmt14", hf_subset="fr-en", @@ -1321,9 +1325,9 @@ version=0, ) -wmt14_fr_en_lighteval = LightevalTaskConfig( +wmt14_fr_en = LightevalTaskConfig( name="wmt14:fr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt14_fr-en", @@ -1337,9 +1341,9 @@ version=0, ) -wmt14_hi_en_lighteval = LightevalTaskConfig( +wmt14_hi_en = LightevalTaskConfig( name="wmt14:hi-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt14_hi-en", @@ -1353,9 +1357,9 @@ version=0, ) -wmt14_ru_en_lighteval = LightevalTaskConfig( +wmt14_ru_en = LightevalTaskConfig( name="wmt14:ru-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt14_ru-en", @@ -1369,9 +1373,9 @@ version=0, ) -wmt15_cs_en_lighteval = LightevalTaskConfig( +wmt15_cs_en = LightevalTaskConfig( name="wmt15:cs-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt15_cs-en", @@ -1385,9 +1389,9 @@ version=0, ) -wmt15_de_en_lighteval = LightevalTaskConfig( +wmt15_de_en = LightevalTaskConfig( name="wmt15:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt15_de-en", @@ -1401,9 +1405,9 @@ version=0, ) -wmt15_en_cs_lighteval = LightevalTaskConfig( +wmt15_en_cs = LightevalTaskConfig( name="wmt15:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt15_en-cs", @@ -1417,9 +1421,9 @@ version=0, ) -wmt15_en_de_lighteval = LightevalTaskConfig( +wmt15_en_de = LightevalTaskConfig( name="wmt15:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt15_en-de", @@ -1433,9 +1437,9 @@ version=0, ) -wmt15_en_fi_lighteval = LightevalTaskConfig( +wmt15_en_fi = LightevalTaskConfig( name="wmt15:en-fi", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt15_en-fi", @@ -1449,9 +1453,9 @@ version=0, ) -wmt15_en_fr_lighteval = LightevalTaskConfig( +wmt15_en_fr = LightevalTaskConfig( name="wmt15:en-fr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt15_en-fr", @@ -1465,9 +1469,9 @@ version=0, ) -wmt15_en_ru_lighteval = LightevalTaskConfig( +wmt15_en_ru = LightevalTaskConfig( name="wmt15:en-ru", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt15_en-ru", @@ -1481,9 +1485,9 @@ version=0, ) -wmt15_fi_en_lighteval = LightevalTaskConfig( +wmt15_fi_en = LightevalTaskConfig( name="wmt15:fi-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt15_fi-en", @@ -1497,9 +1501,9 @@ version=0, ) -wmt15_fr_en_lighteval = LightevalTaskConfig( +wmt15_fr_en = LightevalTaskConfig( name="wmt15:fr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt15_fr-en", @@ -1513,9 +1517,9 @@ version=0, ) -wmt15_ru_en_lighteval = LightevalTaskConfig( +wmt15_ru_en = LightevalTaskConfig( name="wmt15:ru-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt15_ru-en", @@ -1529,9 +1533,9 @@ version=0, ) -wmt16_cs_en_lighteval = LightevalTaskConfig( +wmt16_cs_en = LightevalTaskConfig( name="wmt16:cs-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt16_cs-en", @@ -1545,9 +1549,9 @@ version=0, ) -wmt16_de_en_lighteval = LightevalTaskConfig( +wmt16_de_en = LightevalTaskConfig( name="wmt16:de-en", - suite=["lighteval", "gpt3_benchmarks"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="wmt16", hf_subset="de-en", @@ -1561,9 +1565,9 @@ version=0, ) -wmt16_de_en_lighteval = LightevalTaskConfig( +wmt16_de_en = LightevalTaskConfig( name="wmt16:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt16_de-en", @@ -1577,9 +1581,9 @@ version=0, ) -wmt16_en_cs_lighteval = LightevalTaskConfig( +wmt16_en_cs = LightevalTaskConfig( name="wmt16:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt16_en-cs", @@ -1593,9 +1597,9 @@ version=0, ) -wmt16_en_de_lighteval = LightevalTaskConfig( +wmt16_en_de = LightevalTaskConfig( name="wmt16:en-de", - suite=["lighteval", "gpt3_benchmarks"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="wmt16", hf_subset="de-en", @@ -1609,9 +1613,9 @@ version=0, ) -wmt16_en_de_lighteval = LightevalTaskConfig( +wmt16_en_de = LightevalTaskConfig( name="wmt16:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt16_en-de", @@ -1625,9 +1629,9 @@ version=0, ) -wmt16_en_fi_lighteval = LightevalTaskConfig( +wmt16_en_fi = LightevalTaskConfig( name="wmt16:en-fi", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt16_en-fi", @@ -1641,9 +1645,9 @@ version=0, ) -wmt16_en_ro_lighteval = LightevalTaskConfig( +wmt16_en_ro = LightevalTaskConfig( name="wmt16:en-ro", - suite=["lighteval", "gpt3_benchmarks"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="wmt16", hf_subset="ro-en", @@ -1657,9 +1661,9 @@ version=0, ) -wmt16_en_ro_lighteval = LightevalTaskConfig( +wmt16_en_ro = LightevalTaskConfig( name="wmt16:en-ro", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt16_en-ro", @@ -1673,9 +1677,9 @@ version=0, ) -wmt16_en_ru_lighteval = LightevalTaskConfig( +wmt16_en_ru = LightevalTaskConfig( name="wmt16:en-ru", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt16_en-ru", @@ -1689,9 +1693,9 @@ version=0, ) -wmt16_en_tr_lighteval = LightevalTaskConfig( +wmt16_en_tr = LightevalTaskConfig( name="wmt16:en-tr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt16_en-tr", @@ -1705,9 +1709,9 @@ version=0, ) -wmt16_fi_en_lighteval = LightevalTaskConfig( +wmt16_fi_en = LightevalTaskConfig( name="wmt16:fi-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt16_fi-en", @@ -1721,9 +1725,9 @@ version=0, ) -wmt16_ro_en_lighteval = LightevalTaskConfig( +wmt16_ro_en = LightevalTaskConfig( name="wmt16:ro-en", - suite=["lighteval", "gpt3_benchmarks"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="wmt16", hf_subset="ro-en", @@ -1737,9 +1741,9 @@ version=0, ) -wmt16_ro_en_lighteval = LightevalTaskConfig( +wmt16_ro_en = LightevalTaskConfig( name="wmt16:ro-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt16_ro-en", @@ -1753,9 +1757,9 @@ version=0, ) -wmt16_ru_en_lighteval = LightevalTaskConfig( +wmt16_ru_en = LightevalTaskConfig( name="wmt16:ru-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt16_ru-en", @@ -1769,9 +1773,9 @@ version=0, ) -wmt16_tr_en_lighteval = LightevalTaskConfig( +wmt16_tr_en = LightevalTaskConfig( name="wmt16:tr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt16_tr-en", @@ -1785,9 +1789,9 @@ version=0, ) -wmt17_cs_en_lighteval = LightevalTaskConfig( +wmt17_cs_en = LightevalTaskConfig( name="wmt17:cs-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_cs-en", @@ -1801,9 +1805,9 @@ version=0, ) -wmt17_de_en_lighteval = LightevalTaskConfig( +wmt17_de_en = LightevalTaskConfig( name="wmt17:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_de-en", @@ -1817,9 +1821,9 @@ version=0, ) -wmt17_en_cs_lighteval = LightevalTaskConfig( +wmt17_en_cs = LightevalTaskConfig( name="wmt17:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_en-cs", @@ -1833,9 +1837,9 @@ version=0, ) -wmt17_en_de_lighteval = LightevalTaskConfig( +wmt17_en_de = LightevalTaskConfig( name="wmt17:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_en-de", @@ -1849,9 +1853,9 @@ version=0, ) -wmt17_en_fi_lighteval = LightevalTaskConfig( +wmt17_en_fi = LightevalTaskConfig( name="wmt17:en-fi", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_en-fi", @@ -1865,9 +1869,9 @@ version=0, ) -wmt17_en_lv_lighteval = LightevalTaskConfig( +wmt17_en_lv = LightevalTaskConfig( name="wmt17:en-lv", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_en-lv", @@ -1881,9 +1885,9 @@ version=0, ) -wmt17_en_ru_lighteval = LightevalTaskConfig( +wmt17_en_ru = LightevalTaskConfig( name="wmt17:en-ru", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_en-ru", @@ -1897,9 +1901,9 @@ version=0, ) -wmt17_en_tr_lighteval = LightevalTaskConfig( +wmt17_en_tr = LightevalTaskConfig( name="wmt17:en-tr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_en-tr", @@ -1913,9 +1917,9 @@ version=0, ) -wmt17_en_zh_lighteval = LightevalTaskConfig( +wmt17_en_zh = LightevalTaskConfig( name="wmt17:en-zh", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_en-zh", @@ -1929,9 +1933,9 @@ version=0, ) -wmt17_fi_en_lighteval = LightevalTaskConfig( +wmt17_fi_en = LightevalTaskConfig( name="wmt17:fi-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_fi-en", @@ -1945,9 +1949,9 @@ version=0, ) -wmt17_lv_en_lighteval = LightevalTaskConfig( +wmt17_lv_en = LightevalTaskConfig( name="wmt17:lv-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_lv-en", @@ -1961,9 +1965,9 @@ version=0, ) -wmt17_ru_en_lighteval = LightevalTaskConfig( +wmt17_ru_en = LightevalTaskConfig( name="wmt17:ru-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_ru-en", @@ -1977,9 +1981,9 @@ version=0, ) -wmt17_tr_en_lighteval = LightevalTaskConfig( +wmt17_tr_en = LightevalTaskConfig( name="wmt17:tr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_tr-en", @@ -1993,9 +1997,9 @@ version=0, ) -wmt17_zh_en_lighteval = LightevalTaskConfig( +wmt17_zh_en = LightevalTaskConfig( name="wmt17:zh-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt17_zh-en", @@ -2009,9 +2013,9 @@ version=0, ) -wmt18_cs_en_lighteval = LightevalTaskConfig( +wmt18_cs_en = LightevalTaskConfig( name="wmt18:cs-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_cs-en", @@ -2025,9 +2029,9 @@ version=0, ) -wmt18_de_en_lighteval = LightevalTaskConfig( +wmt18_de_en = LightevalTaskConfig( name="wmt18:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_de-en", @@ -2041,9 +2045,9 @@ version=0, ) -wmt18_en_cs_lighteval = LightevalTaskConfig( +wmt18_en_cs = LightevalTaskConfig( name="wmt18:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_en-cs", @@ -2057,9 +2061,9 @@ version=0, ) -wmt18_en_de_lighteval = LightevalTaskConfig( +wmt18_en_de = LightevalTaskConfig( name="wmt18:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_en-de", @@ -2073,9 +2077,9 @@ version=0, ) -wmt18_en_et_lighteval = LightevalTaskConfig( +wmt18_en_et = LightevalTaskConfig( name="wmt18:en-et", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_en-et", @@ -2089,9 +2093,9 @@ version=0, ) -wmt18_en_fi_lighteval = LightevalTaskConfig( +wmt18_en_fi = LightevalTaskConfig( name="wmt18:en-fi", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_en-fi", @@ -2105,9 +2109,9 @@ version=0, ) -wmt18_en_ru_lighteval = LightevalTaskConfig( +wmt18_en_ru = LightevalTaskConfig( name="wmt18:en-ru", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_en-ru", @@ -2121,9 +2125,9 @@ version=0, ) -wmt18_en_tr_lighteval = LightevalTaskConfig( +wmt18_en_tr = LightevalTaskConfig( name="wmt18:en-tr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_en-tr", @@ -2137,9 +2141,9 @@ version=0, ) -wmt18_en_zh_lighteval = LightevalTaskConfig( +wmt18_en_zh = LightevalTaskConfig( name="wmt18:en-zh", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_en-zh", @@ -2153,9 +2157,9 @@ version=0, ) -wmt18_et_en_lighteval = LightevalTaskConfig( +wmt18_et_en = LightevalTaskConfig( name="wmt18:et-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_et-en", @@ -2169,9 +2173,9 @@ version=0, ) -wmt18_fi_en_lighteval = LightevalTaskConfig( +wmt18_fi_en = LightevalTaskConfig( name="wmt18:fi-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_fi-en", @@ -2185,9 +2189,9 @@ version=0, ) -wmt18_ru_en_lighteval = LightevalTaskConfig( +wmt18_ru_en = LightevalTaskConfig( name="wmt18:ru-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_ru-en", @@ -2201,9 +2205,9 @@ version=0, ) -wmt18_tr_en_lighteval = LightevalTaskConfig( +wmt18_tr_en = LightevalTaskConfig( name="wmt18:tr-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_tr-en", @@ -2217,9 +2221,9 @@ version=0, ) -wmt18_zh_en_lighteval = LightevalTaskConfig( +wmt18_zh_en = LightevalTaskConfig( name="wmt18:zh-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt18_zh-en", @@ -2233,9 +2237,9 @@ version=0, ) -wmt19_cs_de_lighteval = LightevalTaskConfig( +wmt19_cs_de = LightevalTaskConfig( name="wmt19:cs-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_cs-de", @@ -2249,9 +2253,9 @@ version=0, ) -wmt19_de_cs_lighteval = LightevalTaskConfig( +wmt19_de_cs = LightevalTaskConfig( name="wmt19:de-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_de-cs", @@ -2265,9 +2269,9 @@ version=0, ) -wmt19_de_en_lighteval = LightevalTaskConfig( +wmt19_de_en = LightevalTaskConfig( name="wmt19:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_de-en", @@ -2281,9 +2285,9 @@ version=0, ) -wmt19_de_fr_lighteval = LightevalTaskConfig( +wmt19_de_fr = LightevalTaskConfig( name="wmt19:de-fr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_de-fr", @@ -2297,9 +2301,9 @@ version=0, ) -wmt19_en_cs_lighteval = LightevalTaskConfig( +wmt19_en_cs = LightevalTaskConfig( name="wmt19:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_en-cs", @@ -2313,9 +2317,9 @@ version=0, ) -wmt19_en_de_lighteval = LightevalTaskConfig( +wmt19_en_de = LightevalTaskConfig( name="wmt19:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_en-de", @@ -2329,9 +2333,9 @@ version=0, ) -wmt19_en_fi_lighteval = LightevalTaskConfig( +wmt19_en_fi = LightevalTaskConfig( name="wmt19:en-fi", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_en-fi", @@ -2345,9 +2349,9 @@ version=0, ) -wmt19_en_gu_lighteval = LightevalTaskConfig( +wmt19_en_gu = LightevalTaskConfig( name="wmt19:en-gu", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_en-gu", @@ -2361,9 +2365,9 @@ version=0, ) -wmt19_en_kk_lighteval = LightevalTaskConfig( +wmt19_en_kk = LightevalTaskConfig( name="wmt19:en-kk", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_en-kk", @@ -2377,9 +2381,9 @@ version=0, ) -wmt19_en_lt_lighteval = LightevalTaskConfig( +wmt19_en_lt = LightevalTaskConfig( name="wmt19:en-lt", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_en-lt", @@ -2393,9 +2397,9 @@ version=0, ) -wmt19_en_ru_lighteval = LightevalTaskConfig( +wmt19_en_ru = LightevalTaskConfig( name="wmt19:en-ru", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_en-ru", @@ -2409,9 +2413,9 @@ version=0, ) -wmt19_en_zh_lighteval = LightevalTaskConfig( +wmt19_en_zh = LightevalTaskConfig( name="wmt19:en-zh", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_en-zh", @@ -2425,9 +2429,9 @@ version=0, ) -wmt19_fi_en_lighteval = LightevalTaskConfig( +wmt19_fi_en = LightevalTaskConfig( name="wmt19:fi-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_fi-en", @@ -2441,9 +2445,9 @@ version=0, ) -wmt19_fr_de_lighteval = LightevalTaskConfig( +wmt19_fr_de = LightevalTaskConfig( name="wmt19:fr-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_fr-de", @@ -2457,9 +2461,9 @@ version=0, ) -wmt19_gu_en_lighteval = LightevalTaskConfig( +wmt19_gu_en = LightevalTaskConfig( name="wmt19:gu-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_gu-en", @@ -2473,9 +2477,9 @@ version=0, ) -wmt19_kk_en_lighteval = LightevalTaskConfig( +wmt19_kk_en = LightevalTaskConfig( name="wmt19:kk-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_kk-en", @@ -2489,9 +2493,9 @@ version=0, ) -wmt19_lt_en_lighteval = LightevalTaskConfig( +wmt19_lt_en = LightevalTaskConfig( name="wmt19:lt-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_lt-en", @@ -2505,9 +2509,9 @@ version=0, ) -wmt19_ru_en_lighteval = LightevalTaskConfig( +wmt19_ru_en = LightevalTaskConfig( name="wmt19:ru-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_ru-en", @@ -2521,9 +2525,9 @@ version=0, ) -wmt19_zh_en_lighteval = LightevalTaskConfig( +wmt19_zh_en = LightevalTaskConfig( name="wmt19:zh-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt19_zh-en", @@ -2537,9 +2541,9 @@ version=0, ) -wmt20_cs_en_lighteval = LightevalTaskConfig( +wmt20_cs_en = LightevalTaskConfig( name="wmt20:cs-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_cs-en", @@ -2553,9 +2557,9 @@ version=0, ) -wmt20_de_en_lighteval = LightevalTaskConfig( +wmt20_de_en = LightevalTaskConfig( name="wmt20:de-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_de-en", @@ -2569,9 +2573,9 @@ version=0, ) -wmt20_de_fr_lighteval = LightevalTaskConfig( +wmt20_de_fr = LightevalTaskConfig( name="wmt20:de-fr", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_de-fr", @@ -2585,9 +2589,9 @@ version=0, ) -wmt20_en_cs_lighteval = LightevalTaskConfig( +wmt20_en_cs = LightevalTaskConfig( name="wmt20:en-cs", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_en-cs", @@ -2601,9 +2605,9 @@ version=0, ) -wmt20_en_de_lighteval = LightevalTaskConfig( +wmt20_en_de = LightevalTaskConfig( name="wmt20:en-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_en-de", @@ -2617,9 +2621,9 @@ version=0, ) -wmt20_en_iu_lighteval = LightevalTaskConfig( +wmt20_en_iu = LightevalTaskConfig( name="wmt20:en-iu", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_en-iu", @@ -2633,9 +2637,9 @@ version=0, ) -wmt20_en_ja_lighteval = LightevalTaskConfig( +wmt20_en_ja = LightevalTaskConfig( name="wmt20:en-ja", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_en-ja", @@ -2649,9 +2653,9 @@ version=0, ) -wmt20_en_km_lighteval = LightevalTaskConfig( +wmt20_en_km = LightevalTaskConfig( name="wmt20:en-km", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_en-km", @@ -2665,9 +2669,9 @@ version=0, ) -wmt20_en_pl_lighteval = LightevalTaskConfig( +wmt20_en_pl = LightevalTaskConfig( name="wmt20:en-pl", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_en-pl", @@ -2681,9 +2685,9 @@ version=0, ) -wmt20_en_ps_lighteval = LightevalTaskConfig( +wmt20_en_ps = LightevalTaskConfig( name="wmt20:en-ps", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_en-ps", @@ -2697,9 +2701,9 @@ version=0, ) -wmt20_en_ru_lighteval = LightevalTaskConfig( +wmt20_en_ru = LightevalTaskConfig( name="wmt20:en-ru", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_en-ru", @@ -2713,9 +2717,9 @@ version=0, ) -wmt20_en_ta_lighteval = LightevalTaskConfig( +wmt20_en_ta = LightevalTaskConfig( name="wmt20:en-ta", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_en-ta", @@ -2729,9 +2733,9 @@ version=0, ) -wmt20_en_zh_lighteval = LightevalTaskConfig( +wmt20_en_zh = LightevalTaskConfig( name="wmt20:en-zh", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_en-zh", @@ -2745,9 +2749,9 @@ version=0, ) -wmt20_fr_de_lighteval = LightevalTaskConfig( +wmt20_fr_de = LightevalTaskConfig( name="wmt20:fr-de", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_fr-de", @@ -2761,9 +2765,9 @@ version=0, ) -wmt20_iu_en_lighteval = LightevalTaskConfig( +wmt20_iu_en = LightevalTaskConfig( name="wmt20:iu-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_iu-en", @@ -2777,9 +2781,9 @@ version=0, ) -wmt20_ja_en_lighteval = LightevalTaskConfig( +wmt20_ja_en = LightevalTaskConfig( name="wmt20:ja-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_ja-en", @@ -2793,9 +2797,9 @@ version=0, ) -wmt20_km_en_lighteval = LightevalTaskConfig( +wmt20_km_en = LightevalTaskConfig( name="wmt20:km-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_km-en", @@ -2809,9 +2813,9 @@ version=0, ) -wmt20_pl_en_lighteval = LightevalTaskConfig( +wmt20_pl_en = LightevalTaskConfig( name="wmt20:pl-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_pl-en", @@ -2825,9 +2829,9 @@ version=0, ) -wmt20_ps_en_lighteval = LightevalTaskConfig( +wmt20_ps_en = LightevalTaskConfig( name="wmt20:ps-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_ps-en", @@ -2841,9 +2845,9 @@ version=0, ) -wmt20_ru_en_lighteval = LightevalTaskConfig( +wmt20_ru_en = LightevalTaskConfig( name="wmt20:ru-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_ru-en", @@ -2857,9 +2861,9 @@ version=0, ) -wmt20_ta_en_lighteval = LightevalTaskConfig( +wmt20_ta_en = LightevalTaskConfig( name="wmt20:ta-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_ta-en", @@ -2873,9 +2877,9 @@ version=0, ) -wmt20_zh_en_lighteval = LightevalTaskConfig( +wmt20_zh_en = LightevalTaskConfig( name="wmt20:zh-en", - suite=["lighteval", "sacrebleu"], + suite=["lighteval"], prompt_function=prompt.wmt_reverse_alphabetical, hf_repo="lighteval/sacrebleu_manual", hf_subset="wmt20_zh-en", diff --git a/src/lighteval/tasks/tasks/siqa.py b/src/lighteval/tasks/tasks/siqa.py index 8a38beb01..a6f5ad044 100644 --- a/src/lighteval/tasks/tasks/siqa.py +++ b/src/lighteval/tasks/tasks/siqa.py @@ -25,9 +25,12 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +helm task +""" siqa = LightevalTaskConfig( name="siqa", - suite=["helm", "commonsense_scenario"], + suite=["lighteval"], prompt_function=prompt.siqa, hf_repo="allenai/social_i_qa", hf_subset="default", diff --git a/src/lighteval/tasks/tasks/the_pile.py b/src/lighteval/tasks/tasks/the_pile.py index f87159f48..4fa86bec6 100644 --- a/src/lighteval/tasks/tasks/the_pile.py +++ b/src/lighteval/tasks/tasks/the_pile.py @@ -25,9 +25,12 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +helm task +""" the_pile_arxiv_helm = LightevalTaskConfig( name="the_pile:arxiv", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="arxiv", @@ -43,7 +46,7 @@ the_pile_bibliotik_helm = LightevalTaskConfig( name="the_pile:bibliotik", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="bibliotik", @@ -59,7 +62,7 @@ the_pile_commoncrawl_helm = LightevalTaskConfig( name="the_pile:commoncrawl", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="commoncrawl", @@ -75,7 +78,7 @@ the_pile_dm_mathematics_helm = LightevalTaskConfig( name="the_pile:dm-mathematics", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="dm-mathematics", @@ -91,7 +94,7 @@ the_pile_enron_helm = LightevalTaskConfig( name="the_pile:enron", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="enron", @@ -107,7 +110,7 @@ the_pile_europarl_helm = LightevalTaskConfig( name="the_pile:europarl", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="europarl", @@ -123,7 +126,7 @@ the_pile_freelaw_helm = LightevalTaskConfig( name="the_pile:freelaw", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="freelaw", @@ -139,7 +142,7 @@ the_pile_github_helm = LightevalTaskConfig( name="the_pile:github", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="github", @@ -155,7 +158,7 @@ the_pile_gutenberg_helm = LightevalTaskConfig( name="the_pile:gutenberg", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="gutenberg", @@ -171,7 +174,7 @@ the_pile_hackernews_helm = LightevalTaskConfig( name="the_pile:hackernews", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="hackernews", @@ -187,7 +190,7 @@ the_pile_nih_exporter_helm = LightevalTaskConfig( name="the_pile:nih-exporter", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="nih-exporter", @@ -203,7 +206,7 @@ the_pile_opensubtitles_helm = LightevalTaskConfig( name="the_pile:opensubtitles", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="opensubtitles", @@ -219,7 +222,7 @@ the_pile_openwebtext2_helm = LightevalTaskConfig( name="the_pile:openwebtext2", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="openwebtext2", @@ -236,7 +239,7 @@ the_pile_pubmed_abstracts_helm = LightevalTaskConfig( name="the_pile:pubmed-abstracts", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="pubmed-abstracts", @@ -252,7 +255,7 @@ the_pile_pubmed_central_helm = LightevalTaskConfig( name="the_pile:pubmed-central", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="pubmed-central", @@ -268,7 +271,7 @@ the_pile_stackexchange_helm = LightevalTaskConfig( name="the_pile:stackexchange", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="stackexchange", @@ -284,7 +287,7 @@ the_pile_upsto_helm = LightevalTaskConfig( name="the_pile:upsto", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="uspto", @@ -300,7 +303,7 @@ the_pile_wikipedia_helm = LightevalTaskConfig( name="the_pile:wikipedia", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="wikipedia", @@ -316,7 +319,7 @@ the_pile_youtubesubtitles_helm = LightevalTaskConfig( name="the_pile:youtubesubtitles", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.the_pile, hf_repo="lighteval/pile_helm", hf_subset="youtubesubtitles", diff --git a/src/lighteval/tasks/tasks/wikifact.py b/src/lighteval/tasks/tasks/wikifact.py index 7a9dd1555..0332e1b31 100644 --- a/src/lighteval/tasks/tasks/wikifact.py +++ b/src/lighteval/tasks/tasks/wikifact.py @@ -20,15 +20,18 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -import lighteval.tasks.default_prompts as prompt from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import helm_normalizer +from lighteval.tasks import default_prompts as prompt from lighteval.tasks.lighteval_task import LightevalTaskConfig -wikifact_applies_to_jurisdiction_helm = LightevalTaskConfig( +""" +helm task +""" + +wikifact_applies_to_jurisdiction = LightevalTaskConfig( name="wikifact:applies_to_jurisdiction", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="applies_to_jurisdiction", @@ -37,25 +40,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_atomic_number_helm = LightevalTaskConfig( +wikifact_atomic_number = LightevalTaskConfig( name="wikifact:atomic_number", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="atomic_number", @@ -64,25 +56,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_author_helm = LightevalTaskConfig( +wikifact_author = LightevalTaskConfig( name="wikifact:author", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="author", @@ -91,25 +72,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_award_received_helm = LightevalTaskConfig( +wikifact_award_received = LightevalTaskConfig( name="wikifact:award_received", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="award_received", @@ -118,25 +88,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_basic_form_of_government_helm = LightevalTaskConfig( +wikifact_basic_form_of_government = LightevalTaskConfig( name="wikifact:basic_form_of_government", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="basic_form_of_government", @@ -145,25 +104,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_capital_helm = LightevalTaskConfig( +wikifact_capital = LightevalTaskConfig( name="wikifact:capital", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="capital", @@ -172,25 +120,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_capital_of_helm = LightevalTaskConfig( +wikifact_capital_of = LightevalTaskConfig( name="wikifact:capital_of", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="capital_of", @@ -199,25 +136,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_central_bank_helm = LightevalTaskConfig( +wikifact_central_bank = LightevalTaskConfig( name="wikifact:central_bank", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="central_bank", @@ -226,25 +152,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_composer_helm = LightevalTaskConfig( +wikifact_composer = LightevalTaskConfig( name="wikifact:composer", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="composer", @@ -253,25 +168,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_continent_helm = LightevalTaskConfig( +wikifact_continent = LightevalTaskConfig( name="wikifact:continent", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="continent", @@ -280,25 +184,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_country_helm = LightevalTaskConfig( +wikifact_country = LightevalTaskConfig( name="wikifact:country", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="country", @@ -307,25 +200,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_country_of_citizenship_helm = LightevalTaskConfig( +wikifact_country_of_citizenship = LightevalTaskConfig( name="wikifact:country_of_citizenship", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="country_of_citizenship", @@ -334,25 +216,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_country_of_origin_helm = LightevalTaskConfig( +wikifact_country_of_origin = LightevalTaskConfig( name="wikifact:country_of_origin", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="country_of_origin", @@ -361,25 +232,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_creator_helm = LightevalTaskConfig( +wikifact_creator = LightevalTaskConfig( name="wikifact:creator", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="creator", @@ -388,25 +248,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_currency_helm = LightevalTaskConfig( +wikifact_currency = LightevalTaskConfig( name="wikifact:currency", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="currency", @@ -415,25 +264,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_defendant_helm = LightevalTaskConfig( +wikifact_defendant = LightevalTaskConfig( name="wikifact:defendant", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="defendant", @@ -442,25 +280,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_developer_helm = LightevalTaskConfig( +wikifact_developer = LightevalTaskConfig( name="wikifact:developer", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="developer", @@ -469,25 +296,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_diplomatic_relation_helm = LightevalTaskConfig( +wikifact_diplomatic_relation = LightevalTaskConfig( name="wikifact:diplomatic_relation", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="diplomatic_relation", @@ -496,25 +312,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_director_helm = LightevalTaskConfig( +wikifact_director = LightevalTaskConfig( name="wikifact:director", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="director", @@ -523,25 +328,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_discoverer_or_inventor_helm = LightevalTaskConfig( +wikifact_discoverer_or_inventor = LightevalTaskConfig( name="wikifact:discoverer_or_inventor", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="discoverer_or_inventor", @@ -550,25 +344,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_drug_or_therapy_used_for_treatment_helm = LightevalTaskConfig( +wikifact_drug_or_therapy_used_for_treatment = LightevalTaskConfig( name="wikifact:drug_or_therapy_used_for_treatment", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="drug_or_therapy_used_for_treatment", @@ -577,25 +360,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_educated_at_helm = LightevalTaskConfig( +wikifact_educated_at = LightevalTaskConfig( name="wikifact:educated_at", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="educated_at", @@ -604,25 +376,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_electron_configuration_helm = LightevalTaskConfig( +wikifact_electron_configuration = LightevalTaskConfig( name="wikifact:electron_configuration", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="electron_configuration", @@ -631,25 +392,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_employer_helm = LightevalTaskConfig( +wikifact_employer = LightevalTaskConfig( name="wikifact:employer", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="employer", @@ -658,25 +408,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_field_of_work_helm = LightevalTaskConfig( +wikifact_field_of_work = LightevalTaskConfig( name="wikifact:field_of_work", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="field_of_work", @@ -685,25 +424,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_file_extension_helm = LightevalTaskConfig( +wikifact_file_extension = LightevalTaskConfig( name="wikifact:file_extension", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="file_extension", @@ -712,25 +440,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_genetic_association_helm = LightevalTaskConfig( +wikifact_genetic_association = LightevalTaskConfig( name="wikifact:genetic_association", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="genetic_association", @@ -739,25 +456,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_genre_helm = LightevalTaskConfig( +wikifact_genre = LightevalTaskConfig( name="wikifact:genre", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="genre", @@ -766,25 +472,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_has_part_helm = LightevalTaskConfig( +wikifact_has_part = LightevalTaskConfig( name="wikifact:has_part", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="has_part", @@ -793,25 +488,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_head_of_government_helm = LightevalTaskConfig( +wikifact_head_of_government = LightevalTaskConfig( name="wikifact:head_of_government", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="head_of_government", @@ -820,25 +504,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_head_of_state_helm = LightevalTaskConfig( +wikifact_head_of_state = LightevalTaskConfig( name="wikifact:head_of_state", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="head_of_state", @@ -847,25 +520,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_headquarters_location_helm = LightevalTaskConfig( +wikifact_headquarters_location = LightevalTaskConfig( name="wikifact:headquarters_location", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="headquarters_location", @@ -874,25 +536,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_industry_helm = LightevalTaskConfig( +wikifact_industry = LightevalTaskConfig( name="wikifact:industry", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="industry", @@ -901,25 +552,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_influenced_by_helm = LightevalTaskConfig( +wikifact_influenced_by = LightevalTaskConfig( name="wikifact:influenced_by", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="influenced_by", @@ -928,25 +568,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_instance_of_helm = LightevalTaskConfig( +wikifact_instance_of = LightevalTaskConfig( name="wikifact:instance_of", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="instance_of", @@ -955,25 +584,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_instrument_helm = LightevalTaskConfig( +wikifact_instrument = LightevalTaskConfig( name="wikifact:instrument", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="instrument", @@ -982,25 +600,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_language_of_work_or_name_helm = LightevalTaskConfig( +wikifact_language_of_work_or_name = LightevalTaskConfig( name="wikifact:language_of_work_or_name", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="language_of_work_or_name", @@ -1009,25 +616,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_languages_spoken_written_or_signed_helm = LightevalTaskConfig( +wikifact_languages_spoken_written_or_signed = LightevalTaskConfig( name="wikifact:languages_spoken_written_or_signed", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="languages_spoken_written_or_signed", @@ -1036,25 +632,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_laws_applied_helm = LightevalTaskConfig( +wikifact_laws_applied = LightevalTaskConfig( name="wikifact:laws_applied", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="laws_applied", @@ -1063,25 +648,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_located_in_the_administrative_territorial_entity_helm = LightevalTaskConfig( +wikifact_located_in_the_administrative_territorial_entity = LightevalTaskConfig( name="wikifact:located_in_the_administrative_territorial_entity", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="located_in_the_administrative_territorial_entity", @@ -1090,25 +664,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_location_helm = LightevalTaskConfig( +wikifact_location = LightevalTaskConfig( name="wikifact:location", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="location", @@ -1117,25 +680,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_location_of_discovery_helm = LightevalTaskConfig( +wikifact_location_of_discovery = LightevalTaskConfig( name="wikifact:location_of_discovery", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="location_of_discovery", @@ -1144,25 +696,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_location_of_formation_helm = LightevalTaskConfig( +wikifact_location_of_formation = LightevalTaskConfig( name="wikifact:location_of_formation", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="location_of_formation", @@ -1171,25 +712,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_majority_opinion_by_helm = LightevalTaskConfig( +wikifact_majority_opinion_by = LightevalTaskConfig( name="wikifact:majority_opinion_by", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="majority_opinion_by", @@ -1198,25 +728,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_manufacturer_helm = LightevalTaskConfig( +wikifact_manufacturer = LightevalTaskConfig( name="wikifact:manufacturer", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="manufacturer", @@ -1225,25 +744,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_measured_physical_quantity_helm = LightevalTaskConfig( +wikifact_measured_physical_quantity = LightevalTaskConfig( name="wikifact:measured_physical_quantity", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="measured_physical_quantity", @@ -1252,25 +760,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_medical_condition_treated_helm = LightevalTaskConfig( +wikifact_medical_condition_treated = LightevalTaskConfig( name="wikifact:medical_condition_treated", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="medical_condition_treated", @@ -1279,25 +776,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_member_of_helm = LightevalTaskConfig( +wikifact_member_of = LightevalTaskConfig( name="wikifact:member_of", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="member_of", @@ -1306,25 +792,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_member_of_political_party_helm = LightevalTaskConfig( +wikifact_member_of_political_party = LightevalTaskConfig( name="wikifact:member_of_political_party", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="member_of_political_party", @@ -1333,25 +808,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_member_of_sports_team_helm = LightevalTaskConfig( +wikifact_member_of_sports_team = LightevalTaskConfig( name="wikifact:member_of_sports_team", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="member_of_sports_team", @@ -1360,25 +824,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_movement_helm = LightevalTaskConfig( +wikifact_movement = LightevalTaskConfig( name="wikifact:movement", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="movement", @@ -1387,25 +840,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_named_after_helm = LightevalTaskConfig( +wikifact_named_after = LightevalTaskConfig( name="wikifact:named_after", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="named_after", @@ -1414,25 +856,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_native_language_helm = LightevalTaskConfig( +wikifact_native_language = LightevalTaskConfig( name="wikifact:native_language", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="native_language", @@ -1441,25 +872,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_number_of_processor_cores_helm = LightevalTaskConfig( +wikifact_number_of_processor_cores = LightevalTaskConfig( name="wikifact:number_of_processor_cores", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="number_of_processor_cores", @@ -1468,25 +888,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_occupation_helm = LightevalTaskConfig( +wikifact_occupation = LightevalTaskConfig( name="wikifact:occupation", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="occupation", @@ -1495,25 +904,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_office_held_by_head_of_government_helm = LightevalTaskConfig( +wikifact_office_held_by_head_of_government = LightevalTaskConfig( name="wikifact:office_held_by_head_of_government", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="office_held_by_head_of_government", @@ -1522,25 +920,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_office_held_by_head_of_state_helm = LightevalTaskConfig( +wikifact_office_held_by_head_of_state = LightevalTaskConfig( name="wikifact:office_held_by_head_of_state", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="office_held_by_head_of_state", @@ -1549,25 +936,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_official_language_helm = LightevalTaskConfig( +wikifact_official_language = LightevalTaskConfig( name="wikifact:official_language", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="official_language", @@ -1576,25 +952,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_operating_system_helm = LightevalTaskConfig( +wikifact_operating_system = LightevalTaskConfig( name="wikifact:operating_system", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="operating_system", @@ -1603,25 +968,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_original_language_of_film_or_TV_show_helm = LightevalTaskConfig( +wikifact_original_language_of_film_or_TV_show = LightevalTaskConfig( name="wikifact:original_language_of_film_or_TV_show", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="original_language_of_film_or_TV_show", @@ -1630,25 +984,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_original_network_helm = LightevalTaskConfig( +wikifact_original_network = LightevalTaskConfig( name="wikifact:original_network", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="original_network", @@ -1657,25 +1000,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_overrules_helm = LightevalTaskConfig( +wikifact_overrules = LightevalTaskConfig( name="wikifact:overrules", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="overrules", @@ -1684,25 +1016,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_owned_by_helm = LightevalTaskConfig( +wikifact_owned_by = LightevalTaskConfig( name="wikifact:owned_by", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="owned_by", @@ -1711,25 +1032,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_part_of_helm = LightevalTaskConfig( +wikifact_part_of = LightevalTaskConfig( name="wikifact:part_of", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="part_of", @@ -1738,25 +1048,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_participating_team_helm = LightevalTaskConfig( +wikifact_participating_team = LightevalTaskConfig( name="wikifact:participating_team", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="participating_team", @@ -1765,25 +1064,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_place_of_birth_helm = LightevalTaskConfig( +wikifact_place_of_birth = LightevalTaskConfig( name="wikifact:place_of_birth", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="place_of_birth", @@ -1792,25 +1080,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_place_of_death_helm = LightevalTaskConfig( +wikifact_place_of_death = LightevalTaskConfig( name="wikifact:place_of_death", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="place_of_death", @@ -1819,25 +1096,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_plaintiff_helm = LightevalTaskConfig( +wikifact_plaintiff = LightevalTaskConfig( name="wikifact:plaintiff", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="plaintiff", @@ -1846,25 +1112,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_position_held_helm = LightevalTaskConfig( +wikifact_position_held = LightevalTaskConfig( name="wikifact:position_held", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="position_held", @@ -1873,25 +1128,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_position_played_on_team_helm = LightevalTaskConfig( +wikifact_position_played_on_team = LightevalTaskConfig( name="wikifact:position_played_on_team", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="position_played_on_team", @@ -1900,25 +1144,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_programming_language_helm = LightevalTaskConfig( +wikifact_programming_language = LightevalTaskConfig( name="wikifact:programming_language", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="programming_language", @@ -1927,25 +1160,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_recommended_unit_of_measurement_helm = LightevalTaskConfig( +wikifact_recommended_unit_of_measurement = LightevalTaskConfig( name="wikifact:recommended_unit_of_measurement", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="recommended_unit_of_measurement", @@ -1954,25 +1176,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_record_label_helm = LightevalTaskConfig( +wikifact_record_label = LightevalTaskConfig( name="wikifact:record_label", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="record_label", @@ -1981,25 +1192,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_religion_helm = LightevalTaskConfig( +wikifact_religion = LightevalTaskConfig( name="wikifact:religion", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="religion", @@ -2008,25 +1208,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_repealed_by_helm = LightevalTaskConfig( +wikifact_repealed_by = LightevalTaskConfig( name="wikifact:repealed_by", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="repealed_by", @@ -2035,25 +1224,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_shares_border_with_helm = LightevalTaskConfig( +wikifact_shares_border_with = LightevalTaskConfig( name="wikifact:shares_border_with", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="shares_border_with", @@ -2062,25 +1240,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_solved_by_helm = LightevalTaskConfig( +wikifact_solved_by = LightevalTaskConfig( name="wikifact:solved_by", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="solved_by", @@ -2089,25 +1256,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_statement_describes_helm = LightevalTaskConfig( +wikifact_statement_describes = LightevalTaskConfig( name="wikifact:statement_describes", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="statement_describes", @@ -2116,25 +1272,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_stock_exchange_helm = LightevalTaskConfig( +wikifact_stock_exchange = LightevalTaskConfig( name="wikifact:stock_exchange", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="stock_exchange", @@ -2143,25 +1288,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_subclass_of_helm = LightevalTaskConfig( +wikifact_subclass_of = LightevalTaskConfig( name="wikifact:subclass_of", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="subclass_of", @@ -2170,25 +1304,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_subsidiary_helm = LightevalTaskConfig( +wikifact_subsidiary = LightevalTaskConfig( name="wikifact:subsidiary", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="subsidiary", @@ -2197,25 +1320,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_symptoms_and_signs_helm = LightevalTaskConfig( +wikifact_symptoms_and_signs = LightevalTaskConfig( name="wikifact:symptoms_and_signs", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="symptoms_and_signs", @@ -2224,25 +1336,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_therapeutic_area_helm = LightevalTaskConfig( +wikifact_therapeutic_area = LightevalTaskConfig( name="wikifact:therapeutic_area", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="therapeutic_area", @@ -2251,25 +1352,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_time_of_discovery_or_invention_helm = LightevalTaskConfig( +wikifact_time_of_discovery_or_invention = LightevalTaskConfig( name="wikifact:time_of_discovery_or_invention", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="time_of_discovery_or_invention", @@ -2278,25 +1368,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_twinned_administrative_body_helm = LightevalTaskConfig( +wikifact_twinned_administrative_body = LightevalTaskConfig( name="wikifact:twinned_administrative_body", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="twinned_administrative_body", @@ -2305,25 +1384,14 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) -wikifact_work_location_helm = LightevalTaskConfig( +wikifact_work_location = LightevalTaskConfig( name="wikifact:work_location", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.wikifact, hf_repo="lighteval/wikifact", hf_subset="work_location", @@ -2332,18 +1400,7 @@ few_shots_split=None, few_shots_select=None, generation_size=8, - metrics=[ - Metrics.exact_match, - Metrics.exact_match(sample_params={"normalize_gold": helm_normalizer, "normalize_pred": helm_normalizer}), - Metrics.exact_match(sample_params={"type_exact_match": "prefix"}), - Metrics.exact_match( - sample_params={ - "normalize_gold": helm_normalizer, - "normalize_pred": helm_normalizer, - "type_exact_match": "prefix", - } - ), - ], + metrics=[Metrics.exact_match], stop_sequence=["\n"], version=0, ) diff --git a/src/lighteval/tasks/tasks/xstory_cloze.py b/src/lighteval/tasks/tasks/xstory_cloze.py index 0043926ab..e6f8a2685 100644 --- a/src/lighteval/tasks/tasks/xstory_cloze.py +++ b/src/lighteval/tasks/tasks/xstory_cloze.py @@ -31,7 +31,7 @@ dataset is released by Meta AI. """ -xstory_cloze_en_lighteval = LightevalTaskConfig( +xstory_cloze_en = LightevalTaskConfig( name="xstory_cloze:en", suite=["lighteval"], prompt_function=prompt.storycloze, @@ -47,7 +47,7 @@ version=0, ) -xstory_cloze_ru_lighteval = LightevalTaskConfig( +xstory_cloze_ru = LightevalTaskConfig( name="xstory_cloze:ru", suite=["lighteval"], prompt_function=prompt.storycloze, @@ -63,7 +63,7 @@ version=0, ) -xstory_cloze_zh_lighteval = LightevalTaskConfig( +xstory_cloze_zh = LightevalTaskConfig( name="xstory_cloze:zh", suite=["lighteval"], prompt_function=prompt.storycloze, @@ -79,7 +79,7 @@ version=0, ) -xstory_cloze_es_lighteval = LightevalTaskConfig( +xstory_cloze_es = LightevalTaskConfig( name="xstory_cloze:es", suite=["lighteval"], prompt_function=prompt.storycloze, @@ -95,7 +95,7 @@ version=0, ) -xstory_cloze_ar_lighteval = LightevalTaskConfig( +xstory_cloze_ar = LightevalTaskConfig( name="xstory_cloze:ar", suite=["lighteval"], prompt_function=prompt.storycloze, @@ -111,7 +111,7 @@ version=0, ) -xstory_cloze_hi_lighteval = LightevalTaskConfig( +xstory_cloze_hi = LightevalTaskConfig( name="xstory_cloze:hi", suite=["lighteval"], prompt_function=prompt.storycloze, @@ -127,7 +127,7 @@ version=0, ) -xstory_cloze_id_lighteval = LightevalTaskConfig( +xstory_cloze_id = LightevalTaskConfig( name="xstory_cloze:id", suite=["lighteval"], prompt_function=prompt.storycloze, @@ -143,7 +143,7 @@ version=0, ) -xstory_cloze_te_lighteval = LightevalTaskConfig( +xstory_cloze_te = LightevalTaskConfig( name="xstory_cloze:te", suite=["lighteval"], prompt_function=prompt.storycloze, @@ -159,7 +159,7 @@ version=0, ) -xstory_cloze_sw_lighteval = LightevalTaskConfig( +xstory_cloze_sw = LightevalTaskConfig( name="xstory_cloze:sw", suite=["lighteval"], prompt_function=prompt.storycloze, @@ -175,7 +175,7 @@ version=0, ) -xstory_cloze_eu_lighteval = LightevalTaskConfig( +xstory_cloze_eu = LightevalTaskConfig( name="xstory_cloze:eu", suite=["lighteval"], prompt_function=prompt.storycloze, @@ -191,7 +191,7 @@ version=0, ) -xstory_cloze_my_lighteval = LightevalTaskConfig( +xstory_cloze_my = LightevalTaskConfig( name="xstory_cloze:my", suite=["lighteval"], prompt_function=prompt.storycloze, diff --git a/src/lighteval/tasks/tasks/xwinograd.py b/src/lighteval/tasks/tasks/xwinograd.py index 370718c3d..6b87a23e8 100644 --- a/src/lighteval/tasks/tasks/xwinograd.py +++ b/src/lighteval/tasks/tasks/xwinograd.py @@ -25,7 +25,7 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -xwinograd_en_lighteval = LightevalTaskConfig( +xwinograd_en = LightevalTaskConfig( name="xwinograd:en", suite=["lighteval"], prompt_function=prompt.winogrande, @@ -41,7 +41,7 @@ version=0, ) -xwinograd_fr_lighteval = LightevalTaskConfig( +xwinograd_fr = LightevalTaskConfig( name="xwinograd:fr", suite=["lighteval"], prompt_function=prompt.winogrande, @@ -57,7 +57,7 @@ version=0, ) -xwinograd_jp_lighteval = LightevalTaskConfig( +xwinograd_jp = LightevalTaskConfig( name="xwinograd:jp", suite=["lighteval"], prompt_function=prompt.winogrande, @@ -73,7 +73,7 @@ version=0, ) -xwinograd_pt_lighteval = LightevalTaskConfig( +xwinograd_pt = LightevalTaskConfig( name="xwinograd:pt", suite=["lighteval"], prompt_function=prompt.winogrande, @@ -89,7 +89,7 @@ version=0, ) -xwinograd_ru_lighteval = LightevalTaskConfig( +xwinograd_ru = LightevalTaskConfig( name="xwinograd:ru", suite=["lighteval"], prompt_function=prompt.winogrande, @@ -105,7 +105,7 @@ version=0, ) -xwinograd_zh_lighteval = LightevalTaskConfig( +xwinograd_zh = LightevalTaskConfig( name="xwinograd:zh", suite=["lighteval"], prompt_function=prompt.winogrande, From e2c8e226c5fee7fd736c614af35b20bc2b19fb9e Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Tue, 14 Oct 2025 14:52:31 +0200 Subject: [PATCH 13/43] add metdata to tasks --- src/lighteval/tasks/tasks/agieval.py | 10 +++- src/lighteval/tasks/tasks/aime.py | 38 ++++++--------- src/lighteval/tasks/tasks/anli.py | 21 ++++++-- src/lighteval/tasks/tasks/arc.py | 5 +- src/lighteval/tasks/tasks/arc_agi_2.py | 21 ++++++++ src/lighteval/tasks/tasks/arithmetic.py | 7 +++ src/lighteval/tasks/tasks/asdiv.py | 12 +++++ src/lighteval/tasks/tasks/babi_qa.py | 10 +++- src/lighteval/tasks/tasks/bbq.py | 8 +++- src/lighteval/tasks/tasks/bigbench.py | 8 +++- src/lighteval/tasks/tasks/blimp.py | 7 ++- src/lighteval/tasks/tasks/bold.py | 8 +++- src/lighteval/tasks/tasks/boolq.py | 14 ++++++ src/lighteval/tasks/tasks/civil_comments.py | 10 +++- src/lighteval/tasks/tasks/commonsenseqa.py | 15 +++++- src/lighteval/tasks/tasks/coqa.py | 14 ++++++ src/lighteval/tasks/tasks/covid_dialogue.py | 11 ++++- src/lighteval/tasks/tasks/drop_qa.py | 13 +++++ src/lighteval/tasks/tasks/dyck_language.py | 11 +++++ .../tasks/tasks/entity_data_imputation.py | 7 ++- src/lighteval/tasks/tasks/entitymatching.py | 5 ++ src/lighteval/tasks/tasks/ethics.py | 11 ++++- src/lighteval/tasks/tasks/glue.py | 16 +++++++ src/lighteval/tasks/tasks/gpqa.py | 48 +++++++------------ src/lighteval/tasks/tasks/gsm8k.py | 22 ++++----- src/lighteval/tasks/tasks/gsm_plus.py | 26 +++++----- src/lighteval/tasks/tasks/headqa.py | 8 ++++ src/lighteval/tasks/tasks/hellaswag.py | 11 ++++- src/lighteval/tasks/tasks/imdb.py | 8 ++++ src/lighteval/tasks/tasks/jeopardy.py | 10 ++++ src/lighteval/tasks/tasks/lambada.py | 17 +++++-- .../tasks/tasks/legal_summarization.py | 13 ++++- src/lighteval/tasks/tasks/legalsupport.py | 16 ++++++- src/lighteval/tasks/tasks/lexglue.py | 8 ++++ src/lighteval/tasks/tasks/lextreme.py | 11 ++++- src/lighteval/tasks/tasks/logiqa.py | 18 +++++++ src/lighteval/tasks/tasks/lsat_qa.py | 13 +++++ src/lighteval/tasks/tasks/math.py | 9 +++- src/lighteval/tasks/tasks/math_500.py | 25 +++++----- src/lighteval/tasks/tasks/mathqa.py | 17 +++++++ src/lighteval/tasks/tasks/me_q_sum.py | 46 ------------------ src/lighteval/tasks/tasks/med.py | 10 +++- src/lighteval/tasks/tasks/med_dialog.py | 14 ++++++ src/lighteval/tasks/tasks/mgsm.py | 31 +++++++----- src/lighteval/tasks/tasks/mmlu.py | 14 ++++++ src/lighteval/tasks/tasks/mmlu_redux.py | 14 ++++++ src/lighteval/tasks/tasks/mmmu_pro.py | 7 +++ src/lighteval/tasks/tasks/musr.py | 12 ++++- src/lighteval/tasks/tasks/narrativeqa.py | 15 +++++- .../tasks/tasks/natural_questions.py | 16 +++++++ src/lighteval/tasks/tasks/numeracy.py | 14 ++++++ src/lighteval/tasks/tasks/openbookqa.py | 20 ++++---- src/lighteval/tasks/tasks/piqa.py | 15 +++--- src/lighteval/tasks/tasks/prost.py | 18 +++++++ src/lighteval/tasks/tasks/pubmedqa.py | 10 +++- src/lighteval/tasks/tasks/qa4mre.py | 14 +++++- src/lighteval/tasks/tasks/qasper.py | 9 +++- src/lighteval/tasks/tasks/quac.py | 12 ++++- src/lighteval/tasks/tasks/race_high.py | 18 +++++++ src/lighteval/tasks/tasks/raft.py | 13 ++++- .../tasks/tasks/real_toxicity_prompts.py | 13 ++++- src/lighteval/tasks/tasks/sacrebleu.py | 10 ++++ src/lighteval/tasks/tasks/sciq.py | 18 +++++++ src/lighteval/tasks/tasks/simpleqa.py | 15 ++++++ src/lighteval/tasks/tasks/siqa.py | 24 +++++++++- src/lighteval/tasks/tasks/squad_v2.py | 23 +++++++++ src/lighteval/tasks/tasks/storycloze.py | 11 ++++- src/lighteval/tasks/tasks/summarization.py | 12 ++++- src/lighteval/tasks/tasks/swag.py | 21 ++++++++ .../tasks/tasks/synthetic_reasoning.py | 10 +++- src/lighteval/tasks/tasks/the_pile.py | 13 ++++- src/lighteval/tasks/tasks/toxigen.py | 15 ++++++ src/lighteval/tasks/tasks/triviaqa.py | 18 +++++++ src/lighteval/tasks/tasks/truthfulqa.py | 8 ++++ src/lighteval/tasks/tasks/twitterAAE.py | 8 ++++ src/lighteval/tasks/tasks/unscramble.py | 12 ++++- src/lighteval/tasks/tasks/webqs.py | 20 +++++++- src/lighteval/tasks/tasks/wikifact.py | 12 ++++- src/lighteval/tasks/tasks/wikitext.py | 14 +++++- src/lighteval/tasks/tasks/winogrande.py | 20 +++++++- src/lighteval/tasks/tasks/wsc273.py | 42 ---------------- src/lighteval/tasks/tasks/xcopa.py | 8 ++++ src/lighteval/tasks/tasks/xstory_cloze.py | 10 ++++ src/lighteval/tasks/tasks/xwinograd.py | 14 ++++++ 84 files changed, 1005 insertions(+), 250 deletions(-) delete mode 100644 src/lighteval/tasks/tasks/me_q_sum.py delete mode 100644 src/lighteval/tasks/tasks/wsc273.py diff --git a/src/lighteval/tasks/tasks/agieval.py b/src/lighteval/tasks/tasks/agieval.py index ece7cca1a..34a5138c5 100644 --- a/src/lighteval/tasks/tasks/agieval.py +++ b/src/lighteval/tasks/tasks/agieval.py @@ -26,8 +26,7 @@ """ -AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models - +abstract: AGIEval is a human-centric benchmark specifically designed to evaluate the general abilities of foundation models in tasks pertinent to human cognition and problem-solving. This benchmark is derived from 20 official, public, and @@ -36,6 +35,13 @@ Entrance Exam (Gaokao) and American SAT), law school admission tests, math competitions, lawyer qualification tests, and national civil service exams. +languages: +en, zh + +tags: +math, reasoning, law, language, history, chemistry, biology, geography, physics + +paper: https://arxiv.org/abs/2304.06364 """ diff --git a/src/lighteval/tasks/tasks/aime.py b/src/lighteval/tasks/tasks/aime.py index d4d50e357..2defbc70b 100644 --- a/src/lighteval/tasks/tasks/aime.py +++ b/src/lighteval/tasks/tasks/aime.py @@ -25,32 +25,22 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -# aime24 = LightevalTaskConfig_inspect( -# name="aime24", -# prompt_function=prompt.aime_prompt_fn, -# dataset_repo="HuggingFaceH4/aime_2024", -# dataset_subset="default", -# dataset_split="train", -# scorers=[extractive_math_scorer()], -# system_prompt="ASNWER USING THE FORMAT $ANSWER$", -# epochs=16, -# epochs_reducer="pass_at_4", -# ) +""" +abstract: +The American Invitational Mathematics Examination (AIME) is a prestigious, +invite-only mathematics competition for high-school students who perform in the +top 5% of the AMC 12 mathematics exam. It involves 15 questions of increasing +difficulty, with the answer to every question being a single integer from 0 to +999. The median score is historically between 4 and 6 questions correct (out of +the 15 possible). Two versions of the test are given every year (thirty +questions total). +languages: +en -# aime25 = LightevalTaskConfig_inspect( -# name="aime25", -# prompt_function=prompt.aime_prompt_fn, -# dataset_repo="yentinglin/aime_2025", -# dataset_subset="default", -# dataset_split="train", -# dataset_revision="main", -# scorers=[extractive_math_scorer()], -# system_prompt="ASNWER USING THE FORMAT $ANSWER$", -# epochs=16, -# epochs_reducer="pass_at_4", -# ) - +paper: +https://maa.org/aime-thresholds-are-available/ +""" aime24 = LightevalTaskConfig( name="aime24", diff --git a/src/lighteval/tasks/tasks/anli.py b/src/lighteval/tasks/tasks/anli.py index dcdea20ab..9f70c1401 100644 --- a/src/lighteval/tasks/tasks/anli.py +++ b/src/lighteval/tasks/tasks/anli.py @@ -25,11 +25,26 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI +benchmark dataset, The dataset is collected via an iterative, adversarial +human-and-model-in-the-loop procedure. ANLI is much more difficult than its +predecessors including SNLI and MNLI. It contains three rounds. Each round has +train/dev/test splits. + +languages: +en + +paper: +https://arxiv.org/abs/1910.14599 +""" + anli_r1 = LightevalTaskConfig( name="anli:r1", suite=["lighteval"], prompt_function=prompt.anli, - hf_repo="anli", + hf_repo="facebook/anli", hf_subset="plain_text", hf_avail_splits=["train_r1", "dev_r1", "test_r1"], evaluation_splits=["test_r1"], @@ -46,7 +61,7 @@ name="anli:r2", suite=["lighteval"], prompt_function=prompt.anli, - hf_repo="anli", + hf_repo="facebook/anli", hf_subset="plain_text", hf_avail_splits=["train_r2", "dev_r2", "test_r2"], evaluation_splits=["test_r2"], @@ -63,7 +78,7 @@ name="anli:r3", suite=["lighteval"], prompt_function=prompt.anli, - hf_repo="anli", + hf_repo="facebook/anli", hf_subset="plain_text", hf_avail_splits=["train_r3", "dev_r3", "test_r3"], evaluation_splits=["test_r3"], diff --git a/src/lighteval/tasks/tasks/arc.py b/src/lighteval/tasks/tasks/arc.py index e1b6253f3..53416215d 100644 --- a/src/lighteval/tasks/tasks/arc.py +++ b/src/lighteval/tasks/tasks/arc.py @@ -26,14 +26,17 @@ """ +abstract: 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm -from: Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge +languages: +en +paper: https://arxiv.org/abs/1803.05457 """ diff --git a/src/lighteval/tasks/tasks/arc_agi_2.py b/src/lighteval/tasks/tasks/arc_agi_2.py index bb5eada84..3f120c931 100644 --- a/src/lighteval/tasks/tasks/arc_agi_2.py +++ b/src/lighteval/tasks/tasks/arc_agi_2.py @@ -25,6 +25,27 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +ARC-AGI tasks are a series of three to five input and output tasks followed by a +final task with only the input listed. Each task tests the utilization of a +specific learned skill based on a minimal number of cognitive priors. +In their native form, tasks are a JSON lists of integers. These JSON can also be +represented visually as a grid of colors using an ARC-AGI task viewer. You can +view an example of a task here. +A successful submission is a pixel-perfect description (color and position) of +the final task's output. +100% of tasks in the ARC-AGI-2 dataset were solved by a minimim of 2 people in +less than or equal to 2 attempts (many were solved more). ARC-AGI-2 is more +difficult for AI. + +languages: +en + +paper: +https://arcprize.org/guide +""" + arc_agi_2 = LightevalTaskConfig( name="arc_agi_2", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/arithmetic.py b/src/lighteval/tasks/tasks/arithmetic.py index 3f1d44cf3..977b64941 100644 --- a/src/lighteval/tasks/tasks/arithmetic.py +++ b/src/lighteval/tasks/tasks/arithmetic.py @@ -26,8 +26,15 @@ """ +abstract: A small battery of 10 tests that involve asking language models a simple arithmetic problem in natural language. + +languages: +en + +paper: +https://arxiv.org/abs/2005.14165 """ arithmetic_1dc = LightevalTaskConfig( diff --git a/src/lighteval/tasks/tasks/asdiv.py b/src/lighteval/tasks/tasks/asdiv.py index f078d49c3..3dadcdac0 100644 --- a/src/lighteval/tasks/tasks/asdiv.py +++ b/src/lighteval/tasks/tasks/asdiv.py @@ -25,6 +25,18 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +ASDiv is a dataset for arithmetic reasoning that contains 2,000+ questions +covering addition, subtraction, multiplication, and division. + +languages: +en + +paper: +https://arxiv.org/abs/2410.12853 +""" + asdiv = LightevalTaskConfig( name="asdiv", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/babi_qa.py b/src/lighteval/tasks/tasks/babi_qa.py index 618611f4b..99e0f8ff8 100644 --- a/src/lighteval/tasks/tasks/babi_qa.py +++ b/src/lighteval/tasks/tasks/babi_qa.py @@ -26,7 +26,15 @@ """ -helm task +abstract: +The bAbI benchmark for measuring understanding and reasoning, evaluates reading +comprehension via question answering. + +languages: +en + +paper: +https://arxiv.org/abs/1502.05698 """ babi_qa = LightevalTaskConfig( diff --git a/src/lighteval/tasks/tasks/bbq.py b/src/lighteval/tasks/tasks/bbq.py index bcfaf5faf..1932ca8eb 100644 --- a/src/lighteval/tasks/tasks/bbq.py +++ b/src/lighteval/tasks/tasks/bbq.py @@ -26,8 +26,14 @@ """ -BBQ: A hand-built bias benchmark for question answering +abstract: +The Bias Benchmark for Question Answering (BBQ) for measuring social bias in +question answering in ambiguous and unambigous context . +languages: +en + +paper: https://arxiv.org/abs/2110.08193 """ diff --git a/src/lighteval/tasks/tasks/bigbench.py b/src/lighteval/tasks/tasks/bigbench.py index 604ab67a0..6cfc2f8ef 100644 --- a/src/lighteval/tasks/tasks/bigbench.py +++ b/src/lighteval/tasks/tasks/bigbench.py @@ -25,9 +25,15 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -"""Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models +""" +abstract: +Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models 166 tasks from bigbench benchmark. +languages: +en + +paper: https://arxiv.org/abs/2206.04615 """ diff --git a/src/lighteval/tasks/tasks/blimp.py b/src/lighteval/tasks/tasks/blimp.py index 4c33792f7..403185511 100644 --- a/src/lighteval/tasks/tasks/blimp.py +++ b/src/lighteval/tasks/tasks/blimp.py @@ -25,12 +25,17 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -"""BLiMP is a challenge set for evaluating what language models (LMs) know +"""abstract: +BLiMP is a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each containing 1000 minimal pairs isolating specific contrasts in syntax, morphology, or semantics. The data is automatically generated according to expert-crafted grammars. +languages: +en + +paper: https://arxiv.org/abs/1912.00582 """ diff --git a/src/lighteval/tasks/tasks/bold.py b/src/lighteval/tasks/tasks/bold.py index 612faac86..c3d8509aa 100644 --- a/src/lighteval/tasks/tasks/bold.py +++ b/src/lighteval/tasks/tasks/bold.py @@ -26,8 +26,14 @@ """ -BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation +abstract: +The Bias in Open-Ended Language Generation Dataset (BOLD) for measuring biases +and toxicity in open-ended language generation. +languages: +en + +paper: https://dl.acm.org/doi/10.1145/3442188.3445924 """ diff --git a/src/lighteval/tasks/tasks/boolq.py b/src/lighteval/tasks/tasks/boolq.py index 61874d734..e98bf82b6 100644 --- a/src/lighteval/tasks/tasks/boolq.py +++ b/src/lighteval/tasks/tasks/boolq.py @@ -25,6 +25,20 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +The BoolQ benchmark for binary (yes/no) question answering. + +languages: +en + +tags: +Question-Answering, + +paper: +https://arxiv.org/abs/1905.11946 +""" + boolq = LightevalTaskConfig( name="boolq", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/civil_comments.py b/src/lighteval/tasks/tasks/civil_comments.py index e72a8ba16..28e598701 100644 --- a/src/lighteval/tasks/tasks/civil_comments.py +++ b/src/lighteval/tasks/tasks/civil_comments.py @@ -26,8 +26,16 @@ """ -Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification. +abstract: +The CivilComments benchmark for toxicity detection. +languages: +en + +tags: +toxicity, bias + +paper: https://arxiv.org/abs/1903.04561 """ diff --git a/src/lighteval/tasks/tasks/commonsenseqa.py b/src/lighteval/tasks/tasks/commonsenseqa.py index dfc2b3dd8..950be63e7 100644 --- a/src/lighteval/tasks/tasks/commonsenseqa.py +++ b/src/lighteval/tasks/tasks/commonsenseqa.py @@ -26,8 +26,21 @@ """ -helm task +abstract: +CommonsenseQA is a new multiple-choice question answering dataset that requires +different types of commonsense knowledge to predict the correct answers . It +contains 12,102 questions with one correct answer and four distractor answers. +The dataset is provided in two major training/validation/testing set splits: +"Random split" which is the main evaluation split, and "Question token split", +see paper for details. + +languages: +en + +paper: +https://arxiv.org/abs/1811.00937 """ + commonsenseqa = LightevalTaskConfig( name="commonsenseqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/coqa.py b/src/lighteval/tasks/tasks/coqa.py index 2d8b9aaa6..43be5c5c6 100644 --- a/src/lighteval/tasks/tasks/coqa.py +++ b/src/lighteval/tasks/tasks/coqa.py @@ -25,6 +25,20 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +CoQA is a large-scale dataset for building Conversational Question Answering +systems. The goal of the CoQA challenge is to measure the ability of machines to +understand a text passage and answer a series of interconnected questions that +appear in a conversation. + +languages: +en + +paper: +https://arxiv.org/abs/1808.07042 +""" + coqa_first_question = LightevalTaskConfig( name="coqa", prompt_function=prompt.coqa, diff --git a/src/lighteval/tasks/tasks/covid_dialogue.py b/src/lighteval/tasks/tasks/covid_dialogue.py index 3131ed043..b3e114eb0 100644 --- a/src/lighteval/tasks/tasks/covid_dialogue.py +++ b/src/lighteval/tasks/tasks/covid_dialogue.py @@ -26,8 +26,17 @@ """ -helm task +abstract: +The COVID-19 Dialogue dataset is a collection of 500+ dialogues between +doctors and patients during the COVID-19 pandemic. + +languages: +en + +paper: +https://arxiv.org/abs/2004.06561 """ + covid_dialogue = LightevalTaskConfig( name="covid_dialogue", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/drop_qa.py b/src/lighteval/tasks/tasks/drop_qa.py index dd5646583..94d18da47 100644 --- a/src/lighteval/tasks/tasks/drop_qa.py +++ b/src/lighteval/tasks/tasks/drop_qa.py @@ -27,6 +27,19 @@ from lighteval.utils.language import Language +""" +abstract: +The DROP dataset is a new question-answering dataset designed to evaluate the +ability of language models to answer complex questions that require reasoning +over multiple sentences. + +languages: +en + +paper: +https://arxiv.org/abs/1810.00505 +""" + drop_qa = LightevalTaskConfig( name="drop", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/tasks/dyck_language.py b/src/lighteval/tasks/tasks/dyck_language.py index 439cf0b1b..4f1ee985e 100644 --- a/src/lighteval/tasks/tasks/dyck_language.py +++ b/src/lighteval/tasks/tasks/dyck_language.py @@ -25,6 +25,17 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +Scenario testing hierarchical reasoning through the Dyck formal languages. + +languages: +en + +paper: +https://aclanthology.org/W19-3905/ +""" + dyck_language_2 = LightevalTaskConfig( name="dyck_language:2", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/entity_data_imputation.py b/src/lighteval/tasks/tasks/entity_data_imputation.py index d1f5f088e..93497af2c 100644 --- a/src/lighteval/tasks/tasks/entity_data_imputation.py +++ b/src/lighteval/tasks/tasks/entity_data_imputation.py @@ -26,8 +26,13 @@ """ -Capturing Semantics for Imputation with Pre-trained Language Models +abstract: +Scenario that tests the ability to impute missing entities in a data table. +languages: +en + +paper: https://ieeexplore.ieee.org/document/9458712 """ diff --git a/src/lighteval/tasks/tasks/entitymatching.py b/src/lighteval/tasks/tasks/entitymatching.py index 6f68fa62f..b35f204eb 100644 --- a/src/lighteval/tasks/tasks/entitymatching.py +++ b/src/lighteval/tasks/tasks/entitymatching.py @@ -26,8 +26,13 @@ """ +abstract: Simple entity matching benchmark. +languages: +en + +paper: https://dl.acm.org/doi/10.14778/3007263.3007314 """ diff --git a/src/lighteval/tasks/tasks/ethics.py b/src/lighteval/tasks/tasks/ethics.py index 7663451f6..292e6df32 100644 --- a/src/lighteval/tasks/tasks/ethics.py +++ b/src/lighteval/tasks/tasks/ethics.py @@ -26,8 +26,17 @@ """ -from: Aligning AI With Shared Human Values +abstract: +The Ethics benchmark for evaluating the ability of language models to reason about +ethical issues. +languages: +en + +tags: +ethics, morality, commonsense, justice, utilitarianism, virtue + +paper: https://arxiv.org/abs/2008.02275 """ diff --git a/src/lighteval/tasks/tasks/glue.py b/src/lighteval/tasks/tasks/glue.py index 35f5c621d..cad7d22ed 100644 --- a/src/lighteval/tasks/tasks/glue.py +++ b/src/lighteval/tasks/tasks/glue.py @@ -25,6 +25,22 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +The General Language Understanding Evaluation (GLUE) benchmark is a collection +of resources for training, evaluating, and analyzing natural language +understanding systems. + +languages: +en + +tags: + + +paper: +https://arxiv.org/abs/1804.07461 +""" + glue_cola = LightevalTaskConfig( name="glue:cola", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/gpqa.py b/src/lighteval/tasks/tasks/gpqa.py index d1bd72625..9cb78c09f 100644 --- a/src/lighteval/tasks/tasks/gpqa.py +++ b/src/lighteval/tasks/tasks/gpqa.py @@ -25,40 +25,25 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -# gpqa_diamond = LightevalTaskConfig_inspect( -# name="gpqa:diamond", -# prompt_function=prompt.gpqa_instruct, -# dataset_repo="Idavidrein/gpqa", -# dataset_subset="gpqa_diamond", -# dataset_split="train", -# scorers=[multichoice_scorer(), choice()], -# solvers=[multiple_choice()], -# system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", -# ) +""" +abstract: +GPQA is a dataset of 448 expert-written multiple-choice questions in biology, +physics, and chemistry, designed to test graduate-level reasoning. The questions +are extremely difficult—PhD-level experts score about 65%, skilled non-experts +34% (even with web access), and GPT-4 around 39%. GPQA aims to support research +on scalable oversight, helping humans evaluate and trust AI systems that may +exceed human expertise. +languages: +en -# gpqa_extended = LightevalTaskConfig_inspect( -# name="gpqa:extended", -# prompt_function=prompt.gpqa_instruct, -# dataset_repo="Idavidrein/gpqa", -# dataset_subset="gpqa_extended", -# dataset_split="train", -# scorers=[multichoice_scorer(), choice()], -# solvers=[multiple_choice()], -# system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", -# ) +tags: +biology, physics, chemistry, reasoning, graduate-level +paper: +https://arxiv.org/abs/2311.12022 +""" -# gpqa_main = LightevalTaskConfig_inspect( -# name="gpqa:main", -# prompt_function=prompt.gpqa_instruct, -# dataset_repo="Idavidrein/gpqa", -# dataset_subset="gpqa_main", -# dataset_split="train", -# scorers=[multichoice_scorer(), choice()], -# solvers=[multiple_choice()], -# system_prompt="Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.", -# ) gpqa = LightevalTaskConfig( name="gpqa:mc", @@ -75,6 +60,7 @@ stop_sequence=["\n"], version=0, ) + gpqa_diamond_instruct = LightevalTaskConfig( name="gpqa:diamond", suite=["lighteval"], @@ -90,6 +76,7 @@ stop_sequence=[], # no stop sequence, will use eos token version=1, ) + gpqa_extended_instruct = LightevalTaskConfig( name="gpqa:extended", suite=["lighteval"], @@ -105,6 +92,7 @@ stop_sequence=[], # no stop sequence, will use eos token version=0, ) + gpqa_main_instruct = LightevalTaskConfig( name="gpqa:main", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/gsm8k.py b/src/lighteval/tasks/tasks/gsm8k.py index e9d25d74e..89b47d615 100644 --- a/src/lighteval/tasks/tasks/gsm8k.py +++ b/src/lighteval/tasks/tasks/gsm8k.py @@ -26,25 +26,19 @@ """ +abstract: GSM8K is a dataset of 8,000+ high-quality, single-step arithmetic word problems. -https://huggingface.co/datasets/openai/gsm8k +languages: +en -languages: en -fields: math, reasoning -""" +tags: +math, reasoning +paper: +https://arxiv.org/abs/2110.14168 +""" -# gsm8k = LightevalTaskConfig_inspect( -# name="gsm8k", -# prompt_function=prompt.gsm8k, -# dataset_repo="openai/gsm8k", -# dataset_subset="main", -# dataset_split="train", -# dataset_revision="main", -# scorers=[extractive_math_scorer()], -# system_prompt="ANSWER USING THE FORMAT $ANSWER$", -# ) gsm8k = LightevalTaskConfig( name="gsm8k", diff --git a/src/lighteval/tasks/tasks/gsm_plus.py b/src/lighteval/tasks/tasks/gsm_plus.py index 952006cae..8a9c14739 100644 --- a/src/lighteval/tasks/tasks/gsm_plus.py +++ b/src/lighteval/tasks/tasks/gsm_plus.py @@ -25,17 +25,21 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -# gsm_plus = LightevalTaskConfig_inspect( -# name="gsm_plus", -# prompt_function=prompt.gsm_plus, -# dataset_repo="qintongli/GSM-Plus", -# dataset_subset="default", -# dataset_split="test", -# system_prompt="ANSWER USING THE FORMAT $ANSWER$", -# epochs=48, -# epochs_reducer="pass_at_16", -# scorers=[extractive_math_scorer(), model_graded_fact()] -# ) +""" +abstract: +GSM-Plus is an adversarial extension of GSM8K that tests the robustness of LLMs' +mathematical reasoning by introducing varied perturbations to grade-school math +problems. + +languages: +en + +tags: +math, reasoning + +paper: +https://arxiv.org/abs/2402.19255 +""" gsm_plus = LightevalTaskConfig( name="gsm_plus", diff --git a/src/lighteval/tasks/tasks/headqa.py b/src/lighteval/tasks/tasks/headqa.py index 9ffda300c..c0c369eb8 100644 --- a/src/lighteval/tasks/tasks/headqa.py +++ b/src/lighteval/tasks/tasks/headqa.py @@ -26,12 +26,20 @@ """ +abstract: HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio de Sanidad, Consumo y Bienestar Social, who also provides direct access to the exams of the last 5 years. +languages: +en, es + +tags: +health, reasoning + +paper: https://arxiv.org/abs/1906.04701 """ diff --git a/src/lighteval/tasks/tasks/hellaswag.py b/src/lighteval/tasks/tasks/hellaswag.py index e05d1707a..be7e6d3a6 100644 --- a/src/lighteval/tasks/tasks/hellaswag.py +++ b/src/lighteval/tasks/tasks/hellaswag.py @@ -26,8 +26,17 @@ """ -HellaSwag: Can a Machine Really Finish Your Sentence? +abstract: +HellaSwag is a commonsense inference benchmark designed to challenge language +models with adversarially filtered multiple-choice questions. +languages: +en + +tags: +commonsense + +paper: https://arxiv.org/abs/1905.07830 """ diff --git a/src/lighteval/tasks/tasks/imdb.py b/src/lighteval/tasks/tasks/imdb.py index b21fe53ec..a18b45f09 100644 --- a/src/lighteval/tasks/tasks/imdb.py +++ b/src/lighteval/tasks/tasks/imdb.py @@ -26,9 +26,17 @@ """ +abstract: The IMDB benchmark for sentiment analysis in movie review, from: Learning Word Vectors for Sentiment Analysis +languages: +en + +tags: +sentiment-analysis + +paper: https://aclanthology.org/P11-1015/ """ diff --git a/src/lighteval/tasks/tasks/jeopardy.py b/src/lighteval/tasks/tasks/jeopardy.py index d7b647245..c4d7c1116 100644 --- a/src/lighteval/tasks/tasks/jeopardy.py +++ b/src/lighteval/tasks/tasks/jeopardy.py @@ -26,6 +26,16 @@ from lighteval.utils.language import Language +""" +abstract: +Jeopardy is a dataset of questions and answers from the Jeopardy game show. + +languages: +en + +paper: +""" + jeopardy = LightevalTaskConfig( name="jeopardy", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/tasks/lambada.py b/src/lighteval/tasks/tasks/lambada.py index 978ae6663..0811b5572 100644 --- a/src/lighteval/tasks/tasks/lambada.py +++ b/src/lighteval/tasks/tasks/lambada.py @@ -26,8 +26,19 @@ """ -The LAMBADA dataset: Word prediction requiring a broad discourse context +abstract: +LAMBADA is a benchmark for testing language models’ ability to understand broad +narrative context. Each passage requires predicting its final word—easy for +humans given the full passage but impossible from just the last sentence. +Success demands long-range discourse comprehension. +languages: +en + +tags: +reading-comprehension + +paper: https://arxiv.org/abs/1606.06031 """ @@ -35,7 +46,7 @@ name="lambada:standard", suite=["lighteval"], prompt_function=prompt.lambada, - hf_repo="lambada", + hf_repo="cimec/lambada", hf_subset="plain_text", hf_avail_splits=["train", "test", "validation"], evaluation_splits=["test"], @@ -52,7 +63,7 @@ name="lambada:standard_cloze", suite=["lighteval"], prompt_function=prompt.lambada_cloze, - hf_repo="lambada", + hf_repo="cimec/lambada", hf_subset="plain_text", hf_avail_splits=["train", "test", "validation"], evaluation_splits=["test"], diff --git a/src/lighteval/tasks/tasks/legal_summarization.py b/src/lighteval/tasks/tasks/legal_summarization.py index ae84e50c2..921fe35bb 100644 --- a/src/lighteval/tasks/tasks/legal_summarization.py +++ b/src/lighteval/tasks/tasks/legal_summarization.py @@ -26,8 +26,19 @@ """ -Cant find this one in HELM, just a paper. +abstract: +LegalSummarization is a dataset for legal summarization. +languages: +en + +paper: +https://arxiv.org/abs/2210.13448 + +tags: +legal, summarization + +paper: https://arxiv.org/abs/2210.13448 """ diff --git a/src/lighteval/tasks/tasks/legalsupport.py b/src/lighteval/tasks/tasks/legalsupport.py index 1d6a753dc..50d987971 100644 --- a/src/lighteval/tasks/tasks/legalsupport.py +++ b/src/lighteval/tasks/tasks/legalsupport.py @@ -25,9 +25,23 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +Measures fine-grained legal reasoning through reverse entailment. + +languages: +en + +tags: +legal, reasoning + +paper: + +""" + legalsupport = LightevalTaskConfig( name="legalsupport", - suite=["helm"], + suite=["lighteval"], prompt_function=prompt.legal_support, hf_repo="lighteval/LegalSupport", hf_subset="default", diff --git a/src/lighteval/tasks/tasks/lexglue.py b/src/lighteval/tasks/tasks/lexglue.py index c8abbce31..59ae7b2c0 100644 --- a/src/lighteval/tasks/tasks/lexglue.py +++ b/src/lighteval/tasks/tasks/lexglue.py @@ -26,8 +26,16 @@ """ +abstract: LexGLUE: A Benchmark Dataset for Legal Language Understanding in English +languages: +en + +tags: +legal, language-understanding + +paper: https://arxiv.org/abs/2110.00976 """ diff --git a/src/lighteval/tasks/tasks/lextreme.py b/src/lighteval/tasks/tasks/lextreme.py index 336fff7c3..cda907918 100644 --- a/src/lighteval/tasks/tasks/lextreme.py +++ b/src/lighteval/tasks/tasks/lextreme.py @@ -25,8 +25,17 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -"""LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain +""" +abstract: +LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain + +languages: +bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv + +tags: +legal +paper: https://arxiv.org/abs/2301.13126 """ diff --git a/src/lighteval/tasks/tasks/logiqa.py b/src/lighteval/tasks/tasks/logiqa.py index 2f96cbbe3..d5df47dc2 100644 --- a/src/lighteval/tasks/tasks/logiqa.py +++ b/src/lighteval/tasks/tasks/logiqa.py @@ -25,6 +25,24 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +LogiQA is a machine reading comprehension dataset focused on testing logical +reasoning abilities. It contains 8,678 expert-written multiple-choice questions +covering various types of deductive reasoning. While humans perform strongly, +state-of-the-art models lag far behind, making LogiQA a benchmark for advancing +logical reasoning in NLP systems. + +languages: +en + +tags: +reading-comprehension + +paper: +https://arxiv.org/abs/2007.08124 +""" + logiqa = LightevalTaskConfig( name="logiqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/lsat_qa.py b/src/lighteval/tasks/tasks/lsat_qa.py index 3ef5a31c0..f8ab78344 100644 --- a/src/lighteval/tasks/tasks/lsat_qa.py +++ b/src/lighteval/tasks/tasks/lsat_qa.py @@ -25,6 +25,19 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +Questions from law school admission tests. + +languages: +en + +tags: +legal, qa + +paper: +""" + lsat_qa = LightevalTaskConfig( name="lsat_qa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/math.py b/src/lighteval/tasks/tasks/math.py index 870360057..015f6c45e 100644 --- a/src/lighteval/tasks/tasks/math.py +++ b/src/lighteval/tasks/tasks/math.py @@ -31,7 +31,14 @@ Each problem in MATH has a full step-by-step solution, which can be used to teach models to generate answer derivations and explanations. -https://arxiv.org/abs/2103.03874 +languages: +en + +tags: +math + +paper: +https://arxiv.org/abs/2305.20050 """ math_algebra = LightevalTaskConfig( diff --git a/src/lighteval/tasks/tasks/math_500.py b/src/lighteval/tasks/tasks/math_500.py index de798682b..e96235ef1 100644 --- a/src/lighteval/tasks/tasks/math_500.py +++ b/src/lighteval/tasks/tasks/math_500.py @@ -25,17 +25,20 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -# math_500 = LightevalTaskConfig_inspect( -# name="math_500", -# prompt_function=prompt.math_500, -# dataset_repo="HuggingFaceH4/MATH-500", -# dataset_subset="default", -# dataset_split="test", -# scorers=[extractive_math_scorer()], -# system_prompt="Solve the following math problem efficiently and clearly. The last line of your response should be of the following format: 'Therefore, the final answer is: $\\boxed{{ANSWER}}$. I hope it is correct' (without quotes) where ANSWER is just the final number or expression that solves the problem. Think step by step before answering.", -# epochs=48, -# epochs_reducer="pass_at_16", -# ) +""" +abstract: +This dataset contains a subset of 500 problems from the MATH benchmark that +OpenAI created in their Let's Verify Step by Step paper. + +languages: +en + +tags: +math + +paper: +https://arxiv.org/abs/2305.20050 +""" math_500 = LightevalTaskConfig( name="math_500", diff --git a/src/lighteval/tasks/tasks/mathqa.py b/src/lighteval/tasks/tasks/mathqa.py index b1e3cf569..d8bb81bac 100644 --- a/src/lighteval/tasks/tasks/mathqa.py +++ b/src/lighteval/tasks/tasks/mathqa.py @@ -25,6 +25,23 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +large-scale dataset of math word problems. Our dataset is gathered by using a +new representation language to annotate over the AQuA-RAT dataset with +fully-specified operational programs. AQuA-RAT has provided the questions, +options, rationale, and the correct options. + +languages: +en + +tags: +math + +paper: +https://arxiv.org/abs/1905.13319 +""" + mathqa = LightevalTaskConfig( name="mathqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/me_q_sum.py b/src/lighteval/tasks/tasks/me_q_sum.py deleted file mode 100644 index 168b55cdf..000000000 --- a/src/lighteval/tasks/tasks/me_q_sum.py +++ /dev/null @@ -1,46 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - -""" -helm task -""" - -me_q_sum = LightevalTaskConfig( - name="me_q_sum", - suite=["lighteval"], - prompt_function=prompt.me_q_sum, - hf_repo="lighteval/me_q_sum", - hf_subset="default", - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["validation", "test"], - few_shots_split=None, - few_shots_select=None, - generation_size=128, - metrics=[Metrics.exact_match], - stop_sequence=["\n"], - version=0, -) diff --git a/src/lighteval/tasks/tasks/med.py b/src/lighteval/tasks/tasks/med.py index def107ef8..a27b9b81a 100644 --- a/src/lighteval/tasks/tasks/med.py +++ b/src/lighteval/tasks/tasks/med.py @@ -26,8 +26,16 @@ """ -MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering +abstract: +A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering +languages: +en + +tags: +health, qa + +paper: https://medmcqa.github.io/ """ diff --git a/src/lighteval/tasks/tasks/med_dialog.py b/src/lighteval/tasks/tasks/med_dialog.py index 0e99730c9..cb8052a14 100644 --- a/src/lighteval/tasks/tasks/med_dialog.py +++ b/src/lighteval/tasks/tasks/med_dialog.py @@ -25,6 +25,20 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +A collection of medical dialogue datasets. + +languages: +en + +tags: +health, dialog + +paper: + +""" + med_dialog_healthcaremagic = LightevalTaskConfig( name="med_dialog:healthcaremagic", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/mgsm.py b/src/lighteval/tasks/tasks/mgsm.py index 95a979f81..369faf709 100644 --- a/src/lighteval/tasks/tasks/mgsm.py +++ b/src/lighteval/tasks/tasks/mgsm.py @@ -26,12 +26,19 @@ """ +abstract: Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems. The same 250 problems from GSM8K are each translated via human annotators in 10 languages. -language list: en, es, fr, de, ru, zh, ja, th, sw, bn, te +languages: +en, es, fr, de, ru, zh, ja, th, sw, bn, te + +tags: +math + +paper: https://arxiv.org/abs/2210.03057 """ @@ -47,7 +54,7 @@ few_shots_select=None, generation_size=None, metrics=[Metrics.exact_match], - stop_sequence=["\n", "=", "Question="], + stop_sequence=None, version=0, ) @@ -63,7 +70,7 @@ few_shots_select=None, generation_size=None, metrics=[Metrics.exact_match], - stop_sequence=["\n", "=", "Pregunta="], + stop_sequence=None, version=0, ) @@ -79,7 +86,7 @@ few_shots_select=None, generation_size=None, metrics=[Metrics.exact_match], - stop_sequence=["\n", "=", "Question="], + stop_sequence=None, version=0, ) @@ -95,7 +102,7 @@ few_shots_select=None, generation_size=None, metrics=[Metrics.exact_match], - stop_sequence=["\n", "=", "Frage="], + stop_sequence=None, version=0, ) @@ -111,7 +118,7 @@ few_shots_select=None, generation_size=None, metrics=[Metrics.exact_match], - stop_sequence=["\n", "=", "\u0417\u0430\u0434\u0430\u0447\u0430="], + stop_sequence=None, version=0, ) @@ -127,7 +134,7 @@ few_shots_select=None, generation_size=None, metrics=[Metrics.exact_match], - stop_sequence=["\n", "=", "\u95ee\u9898="], + stop_sequence=None, version=0, ) @@ -143,7 +150,7 @@ few_shots_select=None, generation_size=None, metrics=[Metrics.exact_match], - stop_sequence=["\n", "=", "\u554f\u984c="], + stop_sequence=None, version=0, ) @@ -159,7 +166,7 @@ few_shots_select=None, generation_size=None, metrics=[Metrics.exact_match], - stop_sequence=["\n", "=", "\u0e42\u0e08\u0e17\u0e22\u0e4c="], + stop_sequence=None, version=0, ) @@ -175,7 +182,7 @@ few_shots_select=None, generation_size=None, metrics=[Metrics.exact_match], - stop_sequence=["\n", "=", "Swali="], + stop_sequence=None, version=0, ) @@ -191,7 +198,7 @@ few_shots_select=None, generation_size=None, metrics=[Metrics.exact_match], - stop_sequence=["\n", "=", "\u09aa\u09cd\u09b0\u09b6\u09cd\u09a8="], + stop_sequence=None, version=0, ) @@ -207,6 +214,6 @@ few_shots_select=None, generation_size=None, metrics=[Metrics.exact_match], - stop_sequence=["\n", "=", "\u0c2a\u0c4d\u0c30\u0c36\u0c4d\u0c28="], + stop_sequence=None, version=0, ) diff --git a/src/lighteval/tasks/tasks/mmlu.py b/src/lighteval/tasks/tasks/mmlu.py index f1f6e4352..1efb38a7b 100644 --- a/src/lighteval/tasks/tasks/mmlu.py +++ b/src/lighteval/tasks/tasks/mmlu.py @@ -25,6 +25,20 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +MMMLU is a benchmark of general-knowledge and English language understanding. + +languages: +en + +tags: +general-knowledge, qa + +paper: +https://arxiv.org/abs/2009.03300 +""" + mmlu_abstract_algebra = LightevalTaskConfig( name="mmlu:abstract_algebra", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/mmlu_redux.py b/src/lighteval/tasks/tasks/mmlu_redux.py index 27ef9fab1..73558953b 100644 --- a/src/lighteval/tasks/tasks/mmlu_redux.py +++ b/src/lighteval/tasks/tasks/mmlu_redux.py @@ -25,6 +25,20 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +MMLU-Redux is a subset of 5,700 manually re-annotated questions across 57 MMLU subjects. + +languages: +en + +tags: +general-knowledge, qa + +paper: +https://arxiv.org/abs/2406.04127 +""" + _MMLU_REDUX_2_SUBSETS = [ "abstract_algebra", "anatomy", diff --git a/src/lighteval/tasks/tasks/mmmu_pro.py b/src/lighteval/tasks/tasks/mmmu_pro.py index b83b2ea2c..38c98e835 100644 --- a/src/lighteval/tasks/tasks/mmmu_pro.py +++ b/src/lighteval/tasks/tasks/mmmu_pro.py @@ -30,6 +30,13 @@ true understanding capabilities of advanced AI models across multiple modalities. +languages: +en + +tags: +multimodal, qa, general-knowledge + +paper: https://arxiv.org/abs/2409.02813 """ diff --git a/src/lighteval/tasks/tasks/musr.py b/src/lighteval/tasks/tasks/musr.py index 950370fdf..a1e440c8a 100644 --- a/src/lighteval/tasks/tasks/musr.py +++ b/src/lighteval/tasks/tasks/musr.py @@ -26,8 +26,18 @@ """ -MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning +abstract: +MuSR is a benchmark for evaluating multistep reasoning in natural language +narratives. Built using a neurosymbolic synthetic-to-natural generation process, +it features complex, realistic tasks—such as long-form murder mysteries. +languages: +en + +tags: +reasoning, long-context + +paper: https://arxiv.org/abs/2310.16049 """ diff --git a/src/lighteval/tasks/tasks/narrativeqa.py b/src/lighteval/tasks/tasks/narrativeqa.py index 54692d7f0..2823fbe63 100644 --- a/src/lighteval/tasks/tasks/narrativeqa.py +++ b/src/lighteval/tasks/tasks/narrativeqa.py @@ -26,8 +26,21 @@ """ -helm task +abstract: +NarrativeQA is a reading comprehension benchmark that tests deep understanding +of full narratives—books and movie scripts—rather than shallow text matching. To +answer its questions, models must integrate information across entire stories. + +languages: +en + +tags: +reading-comprehension + +paper: +https://aclanthology.org/Q18-1023/ """ + narrativeqa = LightevalTaskConfig( name="narrativeqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/natural_questions.py b/src/lighteval/tasks/tasks/natural_questions.py index da3a66629..4bb4c098f 100644 --- a/src/lighteval/tasks/tasks/natural_questions.py +++ b/src/lighteval/tasks/tasks/natural_questions.py @@ -26,6 +26,22 @@ from lighteval.utils.language import Language +""" +abstract: +This dataset is a collection of question-answer pairs from the Natural Questions +dataset. See Natural Questions for additional information. This dataset can be +used directly with Sentence Transformers to train embedding models. + +languages: +en + +tags: +general-knowledge, qa + +paper: +https://ai.google.com/research/NaturalQuestions +""" + natural_questions = LightevalTaskConfig( name="natural_questions", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/tasks/numeracy.py b/src/lighteval/tasks/tasks/numeracy.py index b5f535cc7..4c35ee9b4 100644 --- a/src/lighteval/tasks/tasks/numeracy.py +++ b/src/lighteval/tasks/tasks/numeracy.py @@ -25,6 +25,20 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +Numeracy is a benchmark for evaluating the ability of language models to reason about mathematics. + +languages: +en + +tags: +math + +paper: + +""" + numeracy_linear_example = LightevalTaskConfig( name="numeracy:linear_example", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/openbookqa.py b/src/lighteval/tasks/tasks/openbookqa.py index 5de06aa82..8115e4890 100644 --- a/src/lighteval/tasks/tasks/openbookqa.py +++ b/src/lighteval/tasks/tasks/openbookqa.py @@ -26,16 +26,20 @@ """ -OpenBookQA aims to promote research in advanced question-answering, probing a -deeper understanding of both the topic (with salient facts summarized as an open -book, also provided with the dataset) and the language it is expressed in. In -particular, it contains questions that require multi-step reasoning, use of -additional common and commonsense knowledge, and rich text comprehension. -OpenBookQA is a new kind of question-answering dataset modeled after open book -exams for assessing human understanding of a subject. +abstract: +OpenBookQA is a question-answering dataset modeled after open-book exams for +assessing human understanding of a subject. It contains multiple-choice +questions that require combining facts from a given open book with broad common +knowledge. The task tests language models' ability to leverage provided +information and apply common sense reasoning. +languages: +en -from: Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering +tags: +reading-comprehension, qa + +paper: https://arxiv.org/abs/1809.02789 """ diff --git a/src/lighteval/tasks/tasks/piqa.py b/src/lighteval/tasks/tasks/piqa.py index fbd1ea3a4..d382503de 100644 --- a/src/lighteval/tasks/tasks/piqa.py +++ b/src/lighteval/tasks/tasks/piqa.py @@ -26,14 +26,17 @@ """ -To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? -Questions requiring this kind of physical commonsense pose a challenge to -state-of-the-art natural language understanding systems. The PIQA dataset -introduces the task of physical commonsense reasoning and a corresponding -benchmark dataset Physical Interaction: Question Answering or PIQA. +abstract: +PIQA is a benchmark for testing physical commonsense reasoning. It contains +questions requiring this kind of physical commonsense reasoning. +languages: +en -from: PIQA: Reasoning about Physical Commonsense in Natural Language +tags: +reasoning, physical-commonsense, qa + +paper: https://arxiv.org/abs/1911.11641 """ diff --git a/src/lighteval/tasks/tasks/prost.py b/src/lighteval/tasks/tasks/prost.py index e7acc4969..0e81419b3 100644 --- a/src/lighteval/tasks/tasks/prost.py +++ b/src/lighteval/tasks/tasks/prost.py @@ -25,6 +25,24 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +PROST is a benchmark for testing physical reasoning about objects through space +and time. It includes 18,736 multiple-choice questions covering 10 core physics +concepts, designed to probe models in zero-shot settings. Results show that even +large pretrained models struggle with physical reasoning and are sensitive to +question phrasing, underscoring their limited real-world understanding. + +languages: +en + +tags: +reasoning, qa, physical-commonsense + +paper: +https://arxiv.org/abs/2106.03634 +""" + prost = LightevalTaskConfig( name="prost", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/pubmedqa.py b/src/lighteval/tasks/tasks/pubmedqa.py index bccfecafd..df710f1d4 100644 --- a/src/lighteval/tasks/tasks/pubmedqa.py +++ b/src/lighteval/tasks/tasks/pubmedqa.py @@ -26,8 +26,16 @@ """ -PubMedQA: A Dataset for Biomedical Research Question Answering +abstract: +PubMedQA is a dataset for biomedical research question answering. +languages: +en + +tags: +qa, health, biomedical + +paper: https://pubmedqa.github.io/ """ diff --git a/src/lighteval/tasks/tasks/qa4mre.py b/src/lighteval/tasks/tasks/qa4mre.py index 5ab61f346..fdd425d2f 100644 --- a/src/lighteval/tasks/tasks/qa4mre.py +++ b/src/lighteval/tasks/tasks/qa4mre.py @@ -26,8 +26,20 @@ """ -QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation +abstract: +QA4MRE is a machine reading comprehension benchmark from the CLEF 2011-2013 +challenges. It evaluates systems' ability to answer questions requiring deep +understanding of short texts, supported by external background knowledge. +Covering tasks like modality, negation, biomedical reading, and entrance exams, +QA4MRE tests reasoning beyond surface-level text matching. +languages: +en + +tags: +reading-comprehension, qa, health, biomedical + +paper: https://link.springer.com/chapter/10.1007/978-3-642-40802-1_29 """ diff --git a/src/lighteval/tasks/tasks/qasper.py b/src/lighteval/tasks/tasks/qasper.py index cfc0cb25b..615f5362e 100644 --- a/src/lighteval/tasks/tasks/qasper.py +++ b/src/lighteval/tasks/tasks/qasper.py @@ -26,6 +26,7 @@ """ +abstract: QASPER is a dataset for question answering on scientific research papers. It consists of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract @@ -33,7 +34,13 @@ full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers. -from: A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers +languages: +en + +tags: +qa, scientific + +paper: https://arxiv.org/abs/2105.03011 """ diff --git a/src/lighteval/tasks/tasks/quac.py b/src/lighteval/tasks/tasks/quac.py index 12acbb0f3..518ed1f6f 100644 --- a/src/lighteval/tasks/tasks/quac.py +++ b/src/lighteval/tasks/tasks/quac.py @@ -26,7 +26,17 @@ """ -helm task +abstract: +The QuAC benchmark for question answering in the context of dialogues. + +languages: +en + +tags: +qa, dialogue + +paper: +https://aclanthology.org/D18-1241/ """ quac = LightevalTaskConfig( diff --git a/src/lighteval/tasks/tasks/race_high.py b/src/lighteval/tasks/tasks/race_high.py index 88eb81b25..f803b72fd 100644 --- a/src/lighteval/tasks/tasks/race_high.py +++ b/src/lighteval/tasks/tasks/race_high.py @@ -25,6 +25,24 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +RACE is a large-scale reading comprehension dataset with more than 28,000 +passages and nearly 100,000 questions. The dataset is collected from English +examinations in China, which are designed for middle school and high school +students. The dataset can be served as the training and test sets for machine +comprehension. + +languages: +en + +tags: +reading-comprehension + +paper: +https://aclanthology.org/D17-1082/ +""" + race_high = LightevalTaskConfig( name="race:high", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/raft.py b/src/lighteval/tasks/tasks/raft.py index 25efb5bd6..7155e9850 100644 --- a/src/lighteval/tasks/tasks/raft.py +++ b/src/lighteval/tasks/tasks/raft.py @@ -26,7 +26,18 @@ """ -helm task +abstract: +The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text +classification tasks. + +languages: +en + +tags: +text-classification + +paper: +https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html """ raft_ade_corpus_v2 = LightevalTaskConfig( diff --git a/src/lighteval/tasks/tasks/real_toxicity_prompts.py b/src/lighteval/tasks/tasks/real_toxicity_prompts.py index ceb38284b..a643c829f 100644 --- a/src/lighteval/tasks/tasks/real_toxicity_prompts.py +++ b/src/lighteval/tasks/tasks/real_toxicity_prompts.py @@ -25,7 +25,18 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -"""helm task +""" +abstract: +The RealToxicityPrompts dataset for measuring toxicity in prompted model generations + +languages: +en + +tags: +toxicity + +paper: +https://aclanthology.org/2020.findings-emnlp.301/ """ real_toxicity_prompts = LightevalTaskConfig( diff --git a/src/lighteval/tasks/tasks/sacrebleu.py b/src/lighteval/tasks/tasks/sacrebleu.py index 224446f66..c338343d5 100644 --- a/src/lighteval/tasks/tasks/sacrebleu.py +++ b/src/lighteval/tasks/tasks/sacrebleu.py @@ -26,7 +26,17 @@ """ +abstract: tasks from sacrebleu + +languages: +en, de, fr, ja, ko, zh, ar + +tags: +translation + +paper: +https://github.com/mjpost/sacrebleu """ iwslt17_ar_en = LightevalTaskConfig( diff --git a/src/lighteval/tasks/tasks/sciq.py b/src/lighteval/tasks/tasks/sciq.py index 35dd189b3..b3d6d7c59 100644 --- a/src/lighteval/tasks/tasks/sciq.py +++ b/src/lighteval/tasks/tasks/sciq.py @@ -25,6 +25,24 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +The SciQ dataset contains 13,679 crowdsourced science exam questions about +Physics, Chemistry and Biology, among others. The questions are in +multiple-choice format with 4 answer options each. For the majority of the +questions, an additional paragraph with supporting evidence for the correct +answer is provided. + +languages: +en + +tags: +physics, chemistry, biology, reasoning, multiple-choice, qa + +paper: +https://arxiv.org/abs/1707.06209 +""" + sciq = LightevalTaskConfig( name="sciq", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/simpleqa.py b/src/lighteval/tasks/tasks/simpleqa.py index 43a16296e..b072bc196 100644 --- a/src/lighteval/tasks/tasks/simpleqa.py +++ b/src/lighteval/tasks/tasks/simpleqa.py @@ -25,6 +25,21 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +A factuality benchmark called SimpleQA that measures the ability for language +models to answer short, fact-seeking questions. + +languages: +en + +tags: +qa, factuality, general-knowledge + +paper: +https://openai.com/index/introducing-simpleqa/ +""" + simpleqa = LightevalTaskConfig( name="simpleqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/siqa.py b/src/lighteval/tasks/tasks/siqa.py index a6f5ad044..5d1c0bc0a 100644 --- a/src/lighteval/tasks/tasks/siqa.py +++ b/src/lighteval/tasks/tasks/siqa.py @@ -26,8 +26,30 @@ """ -helm task +abstract: +We introduce Social IQa: Social Interaction QA, a new question-answering +benchmark for testing social commonsense intelligence. Contrary to many prior +benchmarks that focus on physical or taxonomic knowledge, Social IQa focuses on +reasoning about people's actions and their social implications. For example, +given an action like "Jesse saw a concert" and a question like "Why did Jesse do +this?", humans can easily infer that Jesse wanted "to see their favorite +performer" or "to enjoy the music", and not "to see what's happening inside" or +"to see if it works". The actions in Social IQa span a wide variety of social +situations, and answer candidates contain both human-curated answers and +adversarially-filtered machine-generated candidates. Social IQa contains over +37,000 QA pairs for evaluating models' abilities to reason about the social +implications of everyday events and situations. + +languages: +en + +tags: +qa, social-intelligence, commonsense + +paper: + """ + siqa = LightevalTaskConfig( name="siqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/squad_v2.py b/src/lighteval/tasks/tasks/squad_v2.py index d19ef7aa4..7c44e9c1d 100644 --- a/src/lighteval/tasks/tasks/squad_v2.py +++ b/src/lighteval/tasks/tasks/squad_v2.py @@ -26,6 +26,29 @@ from lighteval.utils.language import Language +""" +abstract: +Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, +consisting of questions posed by crowdworkers on a set of Wikipedia articles, +where the answer to every question is a segment of text, or span, from the +corresponding reading passage, or the question might be unanswerable. + +SQuAD 2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 +unanswerable questions written adversarially by crowdworkers to look similar to +answerable ones. To do well on SQuAD2.0, systems must not only answer questions +when possible, but also determine when no answer is supported by the paragraph +and abstain from answering. + +languages: +en + +tags: +qa + +paper: +https://arxiv.org/abs/1806.03822 +""" + squad_v2 = LightevalTaskConfig( name="squad_v2", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/tasks/storycloze.py b/src/lighteval/tasks/tasks/storycloze.py index 692504945..e87d5d4f2 100644 --- a/src/lighteval/tasks/tasks/storycloze.py +++ b/src/lighteval/tasks/tasks/storycloze.py @@ -25,9 +25,18 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -"""A Corpus and Cloze Evaluation for Deeper Understanding of +""" +abstract: +A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories +languages: +en + +tags: +commonsense, reading-comprehension + +paper: https://arxiv.org/abs/1604.01696 """ diff --git a/src/lighteval/tasks/tasks/summarization.py b/src/lighteval/tasks/tasks/summarization.py index 153defb43..a07907f46 100644 --- a/src/lighteval/tasks/tasks/summarization.py +++ b/src/lighteval/tasks/tasks/summarization.py @@ -26,10 +26,18 @@ """ -Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization -and: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond +abstract: +Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural +Networks for Extreme Summarization and: Abstractive Text Summarization using +Sequence-to-sequence RNNs and Beyond +languages: +en +tags: +summarization + +paper: https://aclanthology.org/D18-1206/ https://aclanthology.org/K16-1028/ """ diff --git a/src/lighteval/tasks/tasks/swag.py b/src/lighteval/tasks/tasks/swag.py index 5840d3f60..5d6d8d793 100644 --- a/src/lighteval/tasks/tasks/swag.py +++ b/src/lighteval/tasks/tasks/swag.py @@ -25,6 +25,27 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +The dataset consists of 113k multiple choice questions about grounded situations +(73k training, 20k validation, 20k test). Each question is a video caption from +LSMDC or ActivityNet Captions, with four answer choices about what might happen +next in the scene. The correct answer is the (real) video caption for the next +event in the video; the three incorrect answers are adversarially generated and +human verified, so as to fool machines but not humans. SWAG aims to be a +benchmark for evaluating grounded commonsense NLI and for learning +representations. + +languages: +en + +tags: +commonsense, grounded-commonsense, nli + +paper: +https://arxiv.org/abs/1808.05326 +""" + swag = LightevalTaskConfig( name="swag", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/synthetic_reasoning.py b/src/lighteval/tasks/tasks/synthetic_reasoning.py index c450df232..38e438e83 100644 --- a/src/lighteval/tasks/tasks/synthetic_reasoning.py +++ b/src/lighteval/tasks/tasks/synthetic_reasoning.py @@ -26,8 +26,16 @@ """ -from: LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning +abstract: +LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning +languages: +en + +tags: +reasoning, math + +paper: https://arxiv.org/abs/2206.03855 """ diff --git a/src/lighteval/tasks/tasks/the_pile.py b/src/lighteval/tasks/tasks/the_pile.py index 4fa86bec6..b90352967 100644 --- a/src/lighteval/tasks/tasks/the_pile.py +++ b/src/lighteval/tasks/tasks/the_pile.py @@ -26,8 +26,19 @@ """ -helm task +abstract: +The Pile corpus for measuring lanugage model performance across various domains. + +languages: +en + +tags: +language-modeling + +paper: +https://arxiv.org/abs/2101.00027 """ + the_pile_arxiv_helm = LightevalTaskConfig( name="the_pile:arxiv", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/toxigen.py b/src/lighteval/tasks/tasks/toxigen.py index 13f753a1a..5458e9cd4 100644 --- a/src/lighteval/tasks/tasks/toxigen.py +++ b/src/lighteval/tasks/tasks/toxigen.py @@ -25,6 +25,21 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +This dataset is for implicit hate speech detection. All instances were generated +using GPT-3 and the methods described in our paper. + +languages: +en + +tags: +toxicity + +paper: +https://arxiv.org/abs/2203.09509 +""" + toxigen = LightevalTaskConfig( name="toxigen", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/triviaqa.py b/src/lighteval/tasks/tasks/triviaqa.py index 7c7cd62de..58ec26a25 100644 --- a/src/lighteval/tasks/tasks/triviaqa.py +++ b/src/lighteval/tasks/tasks/triviaqa.py @@ -25,6 +25,24 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +TriviaqQA is a reading comprehension dataset containing over 650K +question-answer-evidence triples. TriviaqQA includes 95K question-answer pairs +authored by trivia enthusiasts and independently gathered evidence documents, +six per question on average, that provide high quality distant supervision for +answering the questions. + +languages: +en + +tags: +reading-comprehension + +paper: +https://arxiv.org/abs/1705.03551 +""" + triviaqa = LightevalTaskConfig( name="triviaqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/truthfulqa.py b/src/lighteval/tasks/tasks/truthfulqa.py index 29cddcfcc..90671984c 100644 --- a/src/lighteval/tasks/tasks/truthfulqa.py +++ b/src/lighteval/tasks/tasks/truthfulqa.py @@ -26,8 +26,16 @@ """ +abstract: TruthfulQA: Measuring How Models Mimic Human Falsehoods +languages: +en + +tags: +truthfulness + +paper: https://arxiv.org/abs/2109.07958 """ diff --git a/src/lighteval/tasks/tasks/twitterAAE.py b/src/lighteval/tasks/tasks/twitterAAE.py index 6145736f7..7024f4a47 100644 --- a/src/lighteval/tasks/tasks/twitterAAE.py +++ b/src/lighteval/tasks/tasks/twitterAAE.py @@ -26,8 +26,16 @@ """ +abstract: Demographic Dialectal Variation in Social Media: A Case Study of African-American English +languages: +en + +tags: +dialectal, social-media + +paper: https://aclanthology.org/D16-1120/ """ diff --git a/src/lighteval/tasks/tasks/unscramble.py b/src/lighteval/tasks/tasks/unscramble.py index cff64e898..cb7a6293b 100644 --- a/src/lighteval/tasks/tasks/unscramble.py +++ b/src/lighteval/tasks/tasks/unscramble.py @@ -25,10 +25,18 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig -"""benchmark where we ask the model to unscramble a word, either anagram or +""" +abstract: +Benchmark where we ask the model to unscramble a word, either anagram or random insertion. -Don't remember where it's from. +languages: +en + +tags: +unscrambling, anagram, random insertion, reversed words + +paper: https://huggingface.co/datasets/lighteval/GPT3_unscramble """ diff --git a/src/lighteval/tasks/tasks/webqs.py b/src/lighteval/tasks/tasks/webqs.py index 047bac5eb..609a71486 100644 --- a/src/lighteval/tasks/tasks/webqs.py +++ b/src/lighteval/tasks/tasks/webqs.py @@ -25,11 +25,29 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +This dataset consists of 6,642 question/answer pairs. The questions are supposed +to be answerable by Freebase, a large knowledge graph. The questions are mostly +centered around a single named entity. The questions are popular ones asked on +the web. + +languages: +en + +tags: +qa + +paper: +https://aclanthology.org/D13-1160.pdf +""" + + webqs = LightevalTaskConfig( name="webqs", suite=["lighteval"], prompt_function=prompt.webqs, - hf_repo="web_questions", + hf_repo="stanfordnlp/web_questions", hf_subset="default", hf_avail_splits=["train", "test"], evaluation_splits=["test"], diff --git a/src/lighteval/tasks/tasks/wikifact.py b/src/lighteval/tasks/tasks/wikifact.py index 0332e1b31..2ac4f68a4 100644 --- a/src/lighteval/tasks/tasks/wikifact.py +++ b/src/lighteval/tasks/tasks/wikifact.py @@ -26,7 +26,17 @@ """ -helm task +abstract: +Extensively test factual knowledge. + +languages: +en + +tags: +factuality, knowledge + +paper: +https://aclanthology.org/D19-1250/ """ wikifact_applies_to_jurisdiction = LightevalTaskConfig( diff --git a/src/lighteval/tasks/tasks/wikitext.py b/src/lighteval/tasks/tasks/wikitext.py index 4db10b6b7..a84a005e0 100644 --- a/src/lighteval/tasks/tasks/wikitext.py +++ b/src/lighteval/tasks/tasks/wikitext.py @@ -26,9 +26,19 @@ """ -The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License. +abstract: +The WikiText language modeling dataset is a collection of over 100 million +tokens extracted from the set of verified Good and Featured articles on +Wikipedia. The dataset is available under the Creative Commons +Attribution-ShareAlike License. -from: Pointer Sentinel Mixture Models +languages: +en + +tags: +language-modeling + +paper: https://arxiv.org/abs/1609.07843 """ diff --git a/src/lighteval/tasks/tasks/winogrande.py b/src/lighteval/tasks/tasks/winogrande.py index a829f43d5..4f1efa29b 100644 --- a/src/lighteval/tasks/tasks/winogrande.py +++ b/src/lighteval/tasks/tasks/winogrande.py @@ -25,11 +25,29 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +WinoGrande is a new collection of 44k problems, inspired by Winograd Schema +Challenge (Levesque, Davis, and Morgenstern 2011), but adjusted to improve the +scale and robustness against the dataset-specific bias. Formulated as a +fill-in-a-blank task with binary options, the goal is to choose the right option +for a given sentence which requires commonsense reasoning. + +languages: +en + +tags: +commonsense, commonsense-reasoning + +paper: +https://arxiv.org/abs/1907.10641 +""" + winogrande = LightevalTaskConfig( name="winogrande", suite=["leaderboard"], prompt_function=prompt.winogrande, - hf_repo="winogrande", + hf_repo="allenai/winogrande", hf_subset="winogrande_xl", hf_avail_splits=["train", "test", "validation"], evaluation_splits=["validation"], diff --git a/src/lighteval/tasks/tasks/wsc273.py b/src/lighteval/tasks/tasks/wsc273.py deleted file mode 100644 index 67fc95f82..000000000 --- a/src/lighteval/tasks/tasks/wsc273.py +++ /dev/null @@ -1,42 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - -wsc273 = LightevalTaskConfig( - name="wsc273", - suite=["lighteval"], - prompt_function=prompt.wsc273, - hf_repo="lighteval/winograd_wsc", - hf_subset="wsc273", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=-1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) diff --git a/src/lighteval/tasks/tasks/xcopa.py b/src/lighteval/tasks/tasks/xcopa.py index c2f953af4..34b7af187 100644 --- a/src/lighteval/tasks/tasks/xcopa.py +++ b/src/lighteval/tasks/tasks/xcopa.py @@ -26,10 +26,18 @@ """ +abstract: XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across languages. +languages: +en + +tags: +commonsense, commonsense-reasoning + +paper: https://arxiv.org/abs/2005.00333 """ diff --git a/src/lighteval/tasks/tasks/xstory_cloze.py b/src/lighteval/tasks/tasks/xstory_cloze.py index e6f8a2685..b7bdfd3ec 100644 --- a/src/lighteval/tasks/tasks/xstory_cloze.py +++ b/src/lighteval/tasks/tasks/xstory_cloze.py @@ -26,9 +26,19 @@ """ +abstract: XStoryCloze consists of the professionally translated version of the English StoryCloze dataset (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI. + +languages: +en, ru, zh, es, ar, hi, id, te, sw, eu, my + +tags: +commonsense, commonsense-reasoning, multilingual + +paper: + """ xstory_cloze_en = LightevalTaskConfig( diff --git a/src/lighteval/tasks/tasks/xwinograd.py b/src/lighteval/tasks/tasks/xwinograd.py index 6b87a23e8..53e48ae43 100644 --- a/src/lighteval/tasks/tasks/xwinograd.py +++ b/src/lighteval/tasks/tasks/xwinograd.py @@ -25,6 +25,20 @@ from lighteval.tasks.lighteval_task import LightevalTaskConfig +""" +abstract: +Multilingual winograd schema challenge as used in Crosslingual Generalization through Multitask Finetuning. + +languages: +en, fr, jp, pt, ru, zh + +tags: +commonsense, commonsense-reasoning, multilingual + +paper: +https://arxiv.org/abs/2211.01786 +""" + xwinograd_en = LightevalTaskConfig( name="xwinograd:en", suite=["lighteval"], From c980ddbe012b1fa5d9dcb05126379c3bf12952ba Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Tue, 14 Oct 2025 15:00:36 +0200 Subject: [PATCH 14/43] add metdata to tasks --- src/lighteval/tasks/tasks/hle/main.py | 18 +++++++++++++++ src/lighteval/tasks/tasks/ifbench/main.py | 15 ++++++++++++ src/lighteval/tasks/tasks/ifeval/main.py | 16 +++++++++++++ src/lighteval/tasks/tasks/lcb/main.py | 23 ++++++++++++------- src/lighteval/tasks/tasks/mix_eval/main.py | 19 +++++++++++++++ .../tasks/tasks/olympiade_bench/main.py | 15 ++++++++++++ .../tasks/tasks/tiny_benchmarks/main.py | 14 +++++++++-- 7 files changed, 110 insertions(+), 10 deletions(-) diff --git a/src/lighteval/tasks/tasks/hle/main.py b/src/lighteval/tasks/tasks/hle/main.py index 1e2540984..f83363ae6 100644 --- a/src/lighteval/tasks/tasks/hle/main.py +++ b/src/lighteval/tasks/tasks/hle/main.py @@ -36,6 +36,24 @@ from lighteval.tasks.requests import Doc, SamplingMethod +""" +abstract: +Humanity's Last Exam (HLE) is a global collaborative effort, with questions from +nearly 1,000 subject expert contributors affiliated with over 500 institutions +across 50 countries - comprised mostly of professors, researchers, and graduate +degree holders. + +languages: +en + +tags: +qa, reasoning, general-knowledge + +paper: +https://arxiv.org/abs/2501.14249 +""" + + logger = logging.getLogger(__name__) diff --git a/src/lighteval/tasks/tasks/ifbench/main.py b/src/lighteval/tasks/tasks/ifbench/main.py index 45aaca708..44a375cf5 100644 --- a/src/lighteval/tasks/tasks/ifbench/main.py +++ b/src/lighteval/tasks/tasks/ifbench/main.py @@ -35,6 +35,21 @@ from lighteval.tasks.tasks.ifbench import evaluation_lib +""" +abstract: +Challenging benchmark for precise instruction following. + +languages: +en + +tags: +instruction-following + +paper: +https://arxiv.org/abs/2507.02833 +""" + + def ifbench_prompt(line, task_name: str = ""): return Doc( task_name=task_name, diff --git a/src/lighteval/tasks/tasks/ifeval/main.py b/src/lighteval/tasks/tasks/ifeval/main.py index a1fafdbb4..25304f572 100644 --- a/src/lighteval/tasks/tasks/ifeval/main.py +++ b/src/lighteval/tasks/tasks/ifeval/main.py @@ -34,6 +34,22 @@ from lighteval.utils.imports import requires +""" +abstract: +Very specific task where there are no precise outputs but instead we test if the +format obeys rules. + +languages: +en + +tags: +instruction-following + +paper: +https://arxiv.org/abs/2311.07911 +""" + + # Very specific task where there are no precise outputs but instead we test if the format obeys rules @requires("langdetect") def ifeval_prompt(line, task_name: str = ""): diff --git a/src/lighteval/tasks/tasks/lcb/main.py b/src/lighteval/tasks/tasks/lcb/main.py index 506668162..99e74b3c6 100644 --- a/src/lighteval/tasks/tasks/lcb/main.py +++ b/src/lighteval/tasks/tasks/lcb/main.py @@ -19,14 +19,21 @@ # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -"""Usage: -lighteval vllm \ - "pretrained=deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B,dtype=bfloat16,data_parallel_size=8,max_model_length=32768,gpu_memory_utilization=0.8,generation_parameters={temperature:0.6,top_p:0.95}" \ - "extended|lcb:codegeneration|0" - -lighteval vllm \ - "pretrained=Qwen/Qwen2.5-Coder-3B-Instruct,dtype=bfloat16,data_parallel_size=8,max_model_length=32768,gpu_memory_utilization=0.8,generation_parameters={temperature:0.2,top_p:0.95}" \ - "extended|lcb:codegeneration|0" +""" +abstract: +LiveCodeBench collects problems from periodic contests on LeetCode, AtCoder, and +Codeforces platforms and uses them for constructing a holistic benchmark for +evaluating Code LLMs across variety of code-related scenarios continuously over +time. + +languages: +en + +tags: +code-generation + +paper: +https://livecodebench.github.io/ """ import json diff --git a/src/lighteval/tasks/tasks/mix_eval/main.py b/src/lighteval/tasks/tasks/mix_eval/main.py index e173c0672..7a94223b3 100644 --- a/src/lighteval/tasks/tasks/mix_eval/main.py +++ b/src/lighteval/tasks/tasks/mix_eval/main.py @@ -38,6 +38,25 @@ from lighteval.tasks.tasks.mix_eval.prompts import construct_prompt_freeform, construct_prompt_multichoice +""" +abstract: +Ground-truth-based dynamic benchmark derived from off-the-shelf benchmark +mixtures, which evaluates LLMs with a highly capable model ranking (i.e., 0.96 +correlation with Chatbot Arena) while running locally and quickly (6% the time +and cost of running MMLU), with its queries being stably and effortlessly +updated every month to avoid contamination. + +languages: +en + +tags: +general-knowledge, reasoning, qa + +paper: +https://mixeval.github.io/ +""" + + logger = logging.getLogger(__name__) diff --git a/src/lighteval/tasks/tasks/olympiade_bench/main.py b/src/lighteval/tasks/tasks/olympiade_bench/main.py index d753f970b..fd63177a1 100644 --- a/src/lighteval/tasks/tasks/olympiade_bench/main.py +++ b/src/lighteval/tasks/tasks/olympiade_bench/main.py @@ -34,6 +34,21 @@ from lighteval.utils.language import Language +""" +abstract: +OlympiadBench is a benchmark for evaluating the performance of language models +on olympiad problems. + +languages: +en, zh + +tags: +math, reasoning, language + +paper: +https://arxiv.org/abs/2402.14008 +""" + chinese_answer_type_dict = {"Numerical": "数值", "Expression": "表达式", "Equation": "方程", "Interval": "区间"} english_answer_type_dict = { "Numerical": "a numerical value", diff --git a/src/lighteval/tasks/tasks/tiny_benchmarks/main.py b/src/lighteval/tasks/tasks/tiny_benchmarks/main.py index e8305f9e2..e00e8b586 100644 --- a/src/lighteval/tasks/tasks/tiny_benchmarks/main.py +++ b/src/lighteval/tasks/tasks/tiny_benchmarks/main.py @@ -21,9 +21,19 @@ # SOFTWARE. # ruff: noqa: F405, F403, F401 -"""See https://github.com/felipemaiapolo/tinyBenchmarks/ for the original code. +""" +abstract: +TinyBenchmarks is a benchmark for evaluating the performance of language models +on tiny benchmarks. + +languages: +en + +tags: +general-knowledge, reasoning, qa -Test with `python run_evals_accelerate.py --model_args "pretrained=EleutherAI/pythia-70m" --tasks "extended|tiny:winogrande|0,extended|tiny:gsm8k|0,extended|tiny:hellaswag|0,extended|tiny:arc|0,extended|tiny:truthfulqa|0" --extended_tasks extended_tasks --output_dir "./evals"` +paper: +https://arxiv.org/abs/2402.14992 """ import os From 57fe39088814bdce4b85b01a79dfc57bf12929d5 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Tue, 14 Oct 2025 15:06:01 +0200 Subject: [PATCH 15/43] remove license notice and put docstring on top of file --- pyproject.toml | 2 +- src/lighteval/tasks/tasks/agieval.py | 32 +++-------------- src/lighteval/tasks/tasks/aime.py | 32 +++-------------- src/lighteval/tasks/tasks/anli.py | 32 +++-------------- src/lighteval/tasks/tasks/arc.py | 32 +++-------------- src/lighteval/tasks/tasks/arc_agi_2.py | 32 +++-------------- src/lighteval/tasks/tasks/arithmetic.py | 32 +++-------------- src/lighteval/tasks/tasks/asdiv.py | 32 +++-------------- src/lighteval/tasks/tasks/babi_qa.py | 32 +++-------------- src/lighteval/tasks/tasks/bbq.py | 32 +++-------------- src/lighteval/tasks/tasks/bigbench.py | 31 +++------------- src/lighteval/tasks/tasks/bigbench_hard.py | 29 ++------------- src/lighteval/tasks/tasks/blimp.py | 32 +++-------------- src/lighteval/tasks/tasks/bold.py | 32 +++-------------- src/lighteval/tasks/tasks/boolq.py | 32 +++-------------- src/lighteval/tasks/tasks/civil_comments.py | 32 +++-------------- src/lighteval/tasks/tasks/commonsenseqa.py | 32 +++-------------- src/lighteval/tasks/tasks/coqa.py | 32 +++-------------- src/lighteval/tasks/tasks/covid_dialogue.py | 32 +++-------------- src/lighteval/tasks/tasks/drop_qa.py | 36 ++++--------------- src/lighteval/tasks/tasks/dyck_language.py | 32 +++-------------- .../tasks/tasks/entity_data_imputation.py | 32 +++-------------- src/lighteval/tasks/tasks/entitymatching.py | 32 +++-------------- src/lighteval/tasks/tasks/ethics.py | 32 +++-------------- src/lighteval/tasks/tasks/glue.py | 32 +++-------------- src/lighteval/tasks/tasks/gpqa.py | 31 +++------------- src/lighteval/tasks/tasks/gsm8k.py | 31 +++------------- src/lighteval/tasks/tasks/gsm_plus.py | 32 +++-------------- src/lighteval/tasks/tasks/headqa.py | 31 +++------------- src/lighteval/tasks/tasks/hellaswag.py | 32 +++-------------- src/lighteval/tasks/tasks/hle/main.py | 23 ------------ src/lighteval/tasks/tasks/ifbench/main.py | 23 ------------ src/lighteval/tasks/tasks/ifeval/main.py | 23 ------------ src/lighteval/tasks/tasks/imdb.py | 31 +++------------- src/lighteval/tasks/tasks/jeopardy.py | 34 ++++-------------- src/lighteval/tasks/tasks/lambada.py | 32 +++-------------- .../tasks/tasks/lcb/codegen_metrics.py | 21 ----------- src/lighteval/tasks/tasks/lcb/main.py | 21 ----------- .../tasks/tasks/legal_summarization.py | 32 +++-------------- src/lighteval/tasks/tasks/legalsupport.py | 32 +++-------------- src/lighteval/tasks/tasks/lexglue.py | 32 +++-------------- src/lighteval/tasks/tasks/lextreme.py | 32 +++-------------- src/lighteval/tasks/tasks/logiqa.py | 32 +++-------------- src/lighteval/tasks/tasks/lsat_qa.py | 32 +++-------------- src/lighteval/tasks/tasks/math.py | 32 +++-------------- src/lighteval/tasks/tasks/math_500.py | 32 +++-------------- src/lighteval/tasks/tasks/mathqa.py | 32 +++-------------- src/lighteval/tasks/tasks/med.py | 32 +++-------------- src/lighteval/tasks/tasks/med_dialog.py | 32 +++-------------- src/lighteval/tasks/tasks/mgsm.py | 32 +++-------------- .../tasks/tasks/mix_eval/judge_prompts.py | 22 ------------ src/lighteval/tasks/tasks/mix_eval/main.py | 22 ------------ src/lighteval/tasks/tasks/mix_eval/prompts.py | 22 ------------ src/lighteval/tasks/tasks/mmlu.py | 32 +++-------------- src/lighteval/tasks/tasks/mmlu_redux.py | 32 +++-------------- src/lighteval/tasks/tasks/mmmu_pro.py | 32 +++-------------- .../tasks/mt_bench/judge_prompt_templates.py | 23 ------------ src/lighteval/tasks/tasks/mt_bench/main.py | 22 ------------ src/lighteval/tasks/tasks/musr.py | 32 +++-------------- src/lighteval/tasks/tasks/narrativeqa.py | 32 +++-------------- .../tasks/tasks/natural_questions.py | 34 ++++-------------- src/lighteval/tasks/tasks/numeracy.py | 32 +++-------------- .../tasks/tasks/olympiade_bench/main.py | 23 ------------ src/lighteval/tasks/tasks/openbookqa.py | 32 +++-------------- src/lighteval/tasks/tasks/piqa.py | 32 +++-------------- src/lighteval/tasks/tasks/prost.py | 32 +++-------------- src/lighteval/tasks/tasks/pubmedqa.py | 32 +++-------------- src/lighteval/tasks/tasks/qa4mre.py | 31 +++------------- src/lighteval/tasks/tasks/qasper.py | 32 +++-------------- src/lighteval/tasks/tasks/quac.py | 32 +++-------------- src/lighteval/tasks/tasks/race_high.py | 32 +++-------------- src/lighteval/tasks/tasks/raft.py | 32 +++-------------- .../tasks/tasks/real_toxicity_prompts.py | 32 +++-------------- src/lighteval/tasks/tasks/sacrebleu.py | 32 +++-------------- src/lighteval/tasks/tasks/sciq.py | 32 +++-------------- src/lighteval/tasks/tasks/simpleqa.py | 32 +++-------------- src/lighteval/tasks/tasks/siqa.py | 32 +++-------------- src/lighteval/tasks/tasks/squad_v2.py | 34 ++++-------------- src/lighteval/tasks/tasks/storycloze.py | 32 +++-------------- src/lighteval/tasks/tasks/summarization.py | 32 +++-------------- src/lighteval/tasks/tasks/swag.py | 32 +++-------------- .../tasks/tasks/synthetic_reasoning.py | 31 +++------------- src/lighteval/tasks/tasks/the_pile.py | 32 +++-------------- .../tasks/tasks/tiny_benchmarks/main.py | 22 ------------ src/lighteval/tasks/tasks/toxigen.py | 32 +++-------------- src/lighteval/tasks/tasks/triviaqa.py | 32 +++-------------- src/lighteval/tasks/tasks/truthfulqa.py | 32 +++-------------- src/lighteval/tasks/tasks/twitterAAE.py | 32 +++-------------- src/lighteval/tasks/tasks/unscramble.py | 32 +++-------------- src/lighteval/tasks/tasks/webqs.py | 31 +++------------- src/lighteval/tasks/tasks/wikifact.py | 32 +++-------------- src/lighteval/tasks/tasks/wikitext.py | 31 +++------------- src/lighteval/tasks/tasks/winogrande.py | 32 +++-------------- src/lighteval/tasks/tasks/xcopa.py | 32 +++-------------- src/lighteval/tasks/tasks/xstory_cloze.py | 32 +++-------------- src/lighteval/tasks/tasks/xwinograd.py | 32 +++-------------- 96 files changed, 410 insertions(+), 2513 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 45b88d1f2..c75af3b95 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ line-length = 119 [tool.ruff.lint] # Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default. # Never enforce `E501` (line length violations). -ignore = ["E501", "D100", "D101", "D102", "D103", "D104", "D415", "D105", "DOC501", "DOC201"] +ignore = ["E501", "D100", "D101", "D102", "D103", "D104", "D415", "D105", "DOC501", "DOC201", "CPY001"] select = ["C", "E", "F", "I", "W", "CPY", "D417", "DOC"] preview = true diff --git a/src/lighteval/tasks/tasks/agieval.py b/src/lighteval/tasks/tasks/agieval.py index 34a5138c5..97f79dc22 100644 --- a/src/lighteval/tasks/tasks/agieval.py +++ b/src/lighteval/tasks/tasks/agieval.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: AGIEval is a human-centric benchmark specifically designed to evaluate the @@ -45,6 +18,11 @@ https://arxiv.org/abs/2304.06364 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + agieval_aqua_rat = LightevalTaskConfig( name="agieval:aqua-rat", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/aime.py b/src/lighteval/tasks/tasks/aime.py index 2defbc70b..86eddf8c8 100644 --- a/src/lighteval/tasks/tasks/aime.py +++ b/src/lighteval/tasks/tasks/aime.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The American Invitational Mathematics Examination (AIME) is a prestigious, @@ -42,6 +15,11 @@ https://maa.org/aime-thresholds-are-available/ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + aime24 = LightevalTaskConfig( name="aime24", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/anli.py b/src/lighteval/tasks/tasks/anli.py index 9f70c1401..14df3d020 100644 --- a/src/lighteval/tasks/tasks/anli.py +++ b/src/lighteval/tasks/tasks/anli.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI @@ -40,6 +13,11 @@ https://arxiv.org/abs/1910.14599 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + anli_r1 = LightevalTaskConfig( name="anli:r1", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/arc.py b/src/lighteval/tasks/tasks/arc.py index 53416215d..f508c0dc0 100644 --- a/src/lighteval/tasks/tasks/arc.py +++ b/src/lighteval/tasks/tasks/arc.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: 7,787 genuine grade-school level, multiple-choice science questions, assembled @@ -40,6 +13,11 @@ https://arxiv.org/abs/1803.05457 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + arc_challenge = LightevalTaskConfig( name="arc:challenge", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/arc_agi_2.py b/src/lighteval/tasks/tasks/arc_agi_2.py index 3f120c931..b4c272e4c 100644 --- a/src/lighteval/tasks/tasks/arc_agi_2.py +++ b/src/lighteval/tasks/tasks/arc_agi_2.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: ARC-AGI tasks are a series of three to five input and output tasks followed by a @@ -46,6 +19,11 @@ https://arcprize.org/guide """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + arc_agi_2 = LightevalTaskConfig( name="arc_agi_2", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/arithmetic.py b/src/lighteval/tasks/tasks/arithmetic.py index 977b64941..1ba1dc290 100644 --- a/src/lighteval/tasks/tasks/arithmetic.py +++ b/src/lighteval/tasks/tasks/arithmetic.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: A small battery of 10 tests that involve asking language models a simple @@ -37,6 +10,11 @@ https://arxiv.org/abs/2005.14165 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + arithmetic_1dc = LightevalTaskConfig( name="arithmetic:1dc", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/asdiv.py b/src/lighteval/tasks/tasks/asdiv.py index 3dadcdac0..dfe3dfcdf 100644 --- a/src/lighteval/tasks/tasks/asdiv.py +++ b/src/lighteval/tasks/tasks/asdiv.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: ASDiv is a dataset for arithmetic reasoning that contains 2,000+ questions @@ -37,6 +10,11 @@ https://arxiv.org/abs/2410.12853 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + asdiv = LightevalTaskConfig( name="asdiv", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/babi_qa.py b/src/lighteval/tasks/tasks/babi_qa.py index 99e0f8ff8..8e9282020 100644 --- a/src/lighteval/tasks/tasks/babi_qa.py +++ b/src/lighteval/tasks/tasks/babi_qa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The bAbI benchmark for measuring understanding and reasoning, evaluates reading @@ -37,6 +10,11 @@ https://arxiv.org/abs/1502.05698 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + babi_qa = LightevalTaskConfig( name="babi_qa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/bbq.py b/src/lighteval/tasks/tasks/bbq.py index 1932ca8eb..dfc3bb751 100644 --- a/src/lighteval/tasks/tasks/bbq.py +++ b/src/lighteval/tasks/tasks/bbq.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The Bias Benchmark for Question Answering (BBQ) for measuring social bias in @@ -37,6 +10,11 @@ https://arxiv.org/abs/2110.08193 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + bbq = LightevalTaskConfig( name="bbq", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/bigbench.py b/src/lighteval/tasks/tasks/bigbench.py index 6cfc2f8ef..67825a36b 100644 --- a/src/lighteval/tasks/tasks/bigbench.py +++ b/src/lighteval/tasks/tasks/bigbench.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models @@ -37,6 +10,10 @@ https://arxiv.org/abs/2206.04615 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + abstract_narrative_understanding = LightevalTaskConfig( name="bigbench:abstract_narrative_understanding", diff --git a/src/lighteval/tasks/tasks/bigbench_hard.py b/src/lighteval/tasks/tasks/bigbench_hard.py index 896891fba..9061cf3bf 100644 --- a/src/lighteval/tasks/tasks/bigbench_hard.py +++ b/src/lighteval/tasks/tasks/bigbench_hard.py @@ -1,35 +1,12 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +""" +hardest subset of bigbench benchmark. +""" import lighteval.tasks.default_prompts as prompt from lighteval.metrics.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig -""" -hardest subset of bigbench benchmark. -""" - - causal_judgment = LightevalTaskConfig( name="bigbench_hard:causal_judgment", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/blimp.py b/src/lighteval/tasks/tasks/blimp.py index 403185511..42e4ba1ff 100644 --- a/src/lighteval/tasks/tasks/blimp.py +++ b/src/lighteval/tasks/tasks/blimp.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """abstract: BLiMP is a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 @@ -39,6 +12,11 @@ https://arxiv.org/abs/1912.00582 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + blimp_adjunct_island = LightevalTaskConfig( name="blimp:adjunct_island", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/bold.py b/src/lighteval/tasks/tasks/bold.py index c3d8509aa..93a2be85e 100644 --- a/src/lighteval/tasks/tasks/bold.py +++ b/src/lighteval/tasks/tasks/bold.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The Bias in Open-Ended Language Generation Dataset (BOLD) for measuring biases @@ -37,6 +10,11 @@ https://dl.acm.org/doi/10.1145/3442188.3445924 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + bold = LightevalTaskConfig( name="bold", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/boolq.py b/src/lighteval/tasks/tasks/boolq.py index e98bf82b6..bbbbc2e60 100644 --- a/src/lighteval/tasks/tasks/boolq.py +++ b/src/lighteval/tasks/tasks/boolq.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The BoolQ benchmark for binary (yes/no) question answering. @@ -39,6 +12,11 @@ https://arxiv.org/abs/1905.11946 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + boolq = LightevalTaskConfig( name="boolq", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/civil_comments.py b/src/lighteval/tasks/tasks/civil_comments.py index 28e598701..7a50597ac 100644 --- a/src/lighteval/tasks/tasks/civil_comments.py +++ b/src/lighteval/tasks/tasks/civil_comments.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The CivilComments benchmark for toxicity detection. @@ -39,6 +12,11 @@ https://arxiv.org/abs/1903.04561 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + civil_comments = LightevalTaskConfig( name="civil_comments", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/commonsenseqa.py b/src/lighteval/tasks/tasks/commonsenseqa.py index 950be63e7..2e5ffaee6 100644 --- a/src/lighteval/tasks/tasks/commonsenseqa.py +++ b/src/lighteval/tasks/tasks/commonsenseqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: CommonsenseQA is a new multiple-choice question answering dataset that requires @@ -41,6 +14,11 @@ https://arxiv.org/abs/1811.00937 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + commonsenseqa = LightevalTaskConfig( name="commonsenseqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/coqa.py b/src/lighteval/tasks/tasks/coqa.py index 43be5c5c6..af3e43c27 100644 --- a/src/lighteval/tasks/tasks/coqa.py +++ b/src/lighteval/tasks/tasks/coqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: CoQA is a large-scale dataset for building Conversational Question Answering @@ -39,6 +12,11 @@ https://arxiv.org/abs/1808.07042 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + coqa_first_question = LightevalTaskConfig( name="coqa", prompt_function=prompt.coqa, diff --git a/src/lighteval/tasks/tasks/covid_dialogue.py b/src/lighteval/tasks/tasks/covid_dialogue.py index b3e114eb0..f6dda67fe 100644 --- a/src/lighteval/tasks/tasks/covid_dialogue.py +++ b/src/lighteval/tasks/tasks/covid_dialogue.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The COVID-19 Dialogue dataset is a collection of 500+ dialogues between @@ -37,6 +10,11 @@ https://arxiv.org/abs/2004.06561 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + covid_dialogue = LightevalTaskConfig( name="covid_dialogue", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/drop_qa.py b/src/lighteval/tasks/tasks/drop_qa.py index 94d18da47..6e07f4c21 100644 --- a/src/lighteval/tasks/tasks/drop_qa.py +++ b/src/lighteval/tasks/tasks/drop_qa.py @@ -1,32 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.utils.language import Language - - """ abstract: The DROP dataset is a new question-answering dataset designed to evaluate the @@ -40,6 +11,13 @@ https://arxiv.org/abs/1810.00505 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.utils.language import Language + + drop_qa = LightevalTaskConfig( name="drop", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/tasks/dyck_language.py b/src/lighteval/tasks/tasks/dyck_language.py index 4f1ee985e..20f97a576 100644 --- a/src/lighteval/tasks/tasks/dyck_language.py +++ b/src/lighteval/tasks/tasks/dyck_language.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Scenario testing hierarchical reasoning through the Dyck formal languages. @@ -36,6 +9,11 @@ https://aclanthology.org/W19-3905/ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + dyck_language_2 = LightevalTaskConfig( name="dyck_language:2", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/entity_data_imputation.py b/src/lighteval/tasks/tasks/entity_data_imputation.py index 93497af2c..185d4d341 100644 --- a/src/lighteval/tasks/tasks/entity_data_imputation.py +++ b/src/lighteval/tasks/tasks/entity_data_imputation.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Scenario that tests the ability to impute missing entities in a data table. @@ -36,6 +9,11 @@ https://ieeexplore.ieee.org/document/9458712 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + entity_data_imputation_Buy = LightevalTaskConfig( name="entity_data_imputation:Buy", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/entitymatching.py b/src/lighteval/tasks/tasks/entitymatching.py index b35f204eb..916ebc3a0 100644 --- a/src/lighteval/tasks/tasks/entitymatching.py +++ b/src/lighteval/tasks/tasks/entitymatching.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Simple entity matching benchmark. @@ -36,6 +9,11 @@ https://dl.acm.org/doi/10.14778/3007263.3007314 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + entity_matching_Abt_Buy = LightevalTaskConfig( name="entity_matching:Abt_Buy", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/ethics.py b/src/lighteval/tasks/tasks/ethics.py index 292e6df32..eb4e1009a 100644 --- a/src/lighteval/tasks/tasks/ethics.py +++ b/src/lighteval/tasks/tasks/ethics.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The Ethics benchmark for evaluating the ability of language models to reason about @@ -40,6 +13,11 @@ https://arxiv.org/abs/2008.02275 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + ethics_commonsense = LightevalTaskConfig( name="ethics:commonsense", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/glue.py b/src/lighteval/tasks/tasks/glue.py index cad7d22ed..1a6a6d513 100644 --- a/src/lighteval/tasks/tasks/glue.py +++ b/src/lighteval/tasks/tasks/glue.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The General Language Understanding Evaluation (GLUE) benchmark is a collection @@ -41,6 +14,11 @@ https://arxiv.org/abs/1804.07461 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + glue_cola = LightevalTaskConfig( name="glue:cola", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/gpqa.py b/src/lighteval/tasks/tasks/gpqa.py index 9cb78c09f..a612929c1 100644 --- a/src/lighteval/tasks/tasks/gpqa.py +++ b/src/lighteval/tasks/tasks/gpqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: GPQA is a dataset of 448 expert-written multiple-choice questions in biology, @@ -44,6 +17,10 @@ https://arxiv.org/abs/2311.12022 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + gpqa = LightevalTaskConfig( name="gpqa:mc", diff --git a/src/lighteval/tasks/tasks/gsm8k.py b/src/lighteval/tasks/tasks/gsm8k.py index 89b47d615..b35c1f9f9 100644 --- a/src/lighteval/tasks/tasks/gsm8k.py +++ b/src/lighteval/tasks/tasks/gsm8k.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: GSM8K is a dataset of 8,000+ high-quality, single-step arithmetic word problems. @@ -39,6 +12,10 @@ https://arxiv.org/abs/2110.14168 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + gsm8k = LightevalTaskConfig( name="gsm8k", diff --git a/src/lighteval/tasks/tasks/gsm_plus.py b/src/lighteval/tasks/tasks/gsm_plus.py index 8a9c14739..bce5a6e7b 100644 --- a/src/lighteval/tasks/tasks/gsm_plus.py +++ b/src/lighteval/tasks/tasks/gsm_plus.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: GSM-Plus is an adversarial extension of GSM8K that tests the robustness of LLMs' @@ -41,6 +14,11 @@ https://arxiv.org/abs/2402.19255 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + gsm_plus = LightevalTaskConfig( name="gsm_plus", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/headqa.py b/src/lighteval/tasks/tasks/headqa.py index c0c369eb8..812f21935 100644 --- a/src/lighteval/tasks/tasks/headqa.py +++ b/src/lighteval/tasks/tasks/headqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to @@ -43,6 +16,10 @@ https://arxiv.org/abs/1906.04701 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + headqa_en = LightevalTaskConfig( name="headqa:en", diff --git a/src/lighteval/tasks/tasks/hellaswag.py b/src/lighteval/tasks/tasks/hellaswag.py index be7e6d3a6..e2d29ee1a 100644 --- a/src/lighteval/tasks/tasks/hellaswag.py +++ b/src/lighteval/tasks/tasks/hellaswag.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: HellaSwag is a commonsense inference benchmark designed to challenge language @@ -40,6 +13,11 @@ https://arxiv.org/abs/1905.07830 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + hellaswag = LightevalTaskConfig( name="hellaswag", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/hle/main.py b/src/lighteval/tasks/tasks/hle/main.py index f83363ae6..105048d47 100644 --- a/src/lighteval/tasks/tasks/hle/main.py +++ b/src/lighteval/tasks/tasks/hle/main.py @@ -1,26 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - - import logging import math from typing import List, Literal diff --git a/src/lighteval/tasks/tasks/ifbench/main.py b/src/lighteval/tasks/tasks/ifbench/main.py index 44a375cf5..a47930103 100644 --- a/src/lighteval/tasks/tasks/ifbench/main.py +++ b/src/lighteval/tasks/tasks/ifbench/main.py @@ -1,26 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - - import numpy as np from aenum import extend_enum diff --git a/src/lighteval/tasks/tasks/ifeval/main.py b/src/lighteval/tasks/tasks/ifeval/main.py index 25304f572..babab7695 100644 --- a/src/lighteval/tasks/tasks/ifeval/main.py +++ b/src/lighteval/tasks/tasks/ifeval/main.py @@ -1,26 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - - import numpy as np import lighteval.tasks.tasks.ifeval.instructions_registry as instructions_registry diff --git a/src/lighteval/tasks/tasks/imdb.py b/src/lighteval/tasks/tasks/imdb.py index a18b45f09..05cdd6cb8 100644 --- a/src/lighteval/tasks/tasks/imdb.py +++ b/src/lighteval/tasks/tasks/imdb.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The IMDB benchmark for sentiment analysis in movie review, from: @@ -40,6 +13,10 @@ https://aclanthology.org/P11-1015/ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + imdb = LightevalTaskConfig( name="imdb", diff --git a/src/lighteval/tasks/tasks/jeopardy.py b/src/lighteval/tasks/tasks/jeopardy.py index c4d7c1116..487dcc118 100644 --- a/src/lighteval/tasks/tasks/jeopardy.py +++ b/src/lighteval/tasks/tasks/jeopardy.py @@ -1,31 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.utils.language import Language - - """ abstract: Jeopardy is a dataset of questions and answers from the Jeopardy game show. @@ -36,6 +8,12 @@ paper: """ +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.utils.language import Language + + jeopardy = LightevalTaskConfig( name="jeopardy", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/tasks/lambada.py b/src/lighteval/tasks/tasks/lambada.py index 0811b5572..1d6cd5ee8 100644 --- a/src/lighteval/tasks/tasks/lambada.py +++ b/src/lighteval/tasks/tasks/lambada.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: LAMBADA is a benchmark for testing language models’ ability to understand broad @@ -42,6 +15,11 @@ https://arxiv.org/abs/1606.06031 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + lambada_standard = LightevalTaskConfig( name="lambada:standard", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/lcb/codegen_metrics.py b/src/lighteval/tasks/tasks/lcb/codegen_metrics.py index 98fad8858..cec88a7aa 100644 --- a/src/lighteval/tasks/tasks/lcb/codegen_metrics.py +++ b/src/lighteval/tasks/tasks/lcb/codegen_metrics.py @@ -1,24 +1,3 @@ -# MIT License - -# Copyright (c) 2025 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. """This module contains helper functions copied and modified from https://github.com/LiveCodeBench/LiveCodeBench and diff --git a/src/lighteval/tasks/tasks/lcb/main.py b/src/lighteval/tasks/tasks/lcb/main.py index 99e74b3c6..52842429f 100644 --- a/src/lighteval/tasks/tasks/lcb/main.py +++ b/src/lighteval/tasks/tasks/lcb/main.py @@ -1,24 +1,3 @@ -# MIT License - -# Copyright (c) 2025 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. """ abstract: LiveCodeBench collects problems from periodic contests on LeetCode, AtCoder, and diff --git a/src/lighteval/tasks/tasks/legal_summarization.py b/src/lighteval/tasks/tasks/legal_summarization.py index 921fe35bb..a886caff8 100644 --- a/src/lighteval/tasks/tasks/legal_summarization.py +++ b/src/lighteval/tasks/tasks/legal_summarization.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: LegalSummarization is a dataset for legal summarization. @@ -42,6 +15,11 @@ https://arxiv.org/abs/2210.13448 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + legal_summarization_billsum = LightevalTaskConfig( name="legal_summarization:billsum", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/legalsupport.py b/src/lighteval/tasks/tasks/legalsupport.py index 50d987971..215699613 100644 --- a/src/lighteval/tasks/tasks/legalsupport.py +++ b/src/lighteval/tasks/tasks/legalsupport.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Measures fine-grained legal reasoning through reverse entailment. @@ -39,6 +12,11 @@ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + legalsupport = LightevalTaskConfig( name="legalsupport", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/lexglue.py b/src/lighteval/tasks/tasks/lexglue.py index 59ae7b2c0..b6d4f75fa 100644 --- a/src/lighteval/tasks/tasks/lexglue.py +++ b/src/lighteval/tasks/tasks/lexglue.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: LexGLUE: A Benchmark Dataset for Legal Language Understanding in English @@ -39,6 +12,11 @@ https://arxiv.org/abs/2110.00976 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + lexglue_case_hold = LightevalTaskConfig( name="lexglue:case_hold", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/lextreme.py b/src/lighteval/tasks/tasks/lextreme.py index cda907918..2e3a233f8 100644 --- a/src/lighteval/tasks/tasks/lextreme.py +++ b/src/lighteval/tasks/tasks/lextreme.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain @@ -39,6 +12,11 @@ https://arxiv.org/abs/2301.13126 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + lextreme_brazilian_court_decisions_judgment = LightevalTaskConfig( name="lextreme:brazilian_court_decisions_judgment", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/logiqa.py b/src/lighteval/tasks/tasks/logiqa.py index d5df47dc2..99e9bde1a 100644 --- a/src/lighteval/tasks/tasks/logiqa.py +++ b/src/lighteval/tasks/tasks/logiqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: LogiQA is a machine reading comprehension dataset focused on testing logical @@ -43,6 +16,11 @@ https://arxiv.org/abs/2007.08124 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + logiqa = LightevalTaskConfig( name="logiqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/lsat_qa.py b/src/lighteval/tasks/tasks/lsat_qa.py index f8ab78344..8b12b06ac 100644 --- a/src/lighteval/tasks/tasks/lsat_qa.py +++ b/src/lighteval/tasks/tasks/lsat_qa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Questions from law school admission tests. @@ -38,6 +11,11 @@ paper: """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + lsat_qa = LightevalTaskConfig( name="lsat_qa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/math.py b/src/lighteval/tasks/tasks/math.py index 015f6c45e..00529bdea 100644 --- a/src/lighteval/tasks/tasks/math.py +++ b/src/lighteval/tasks/tasks/math.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. @@ -41,6 +14,11 @@ https://arxiv.org/abs/2305.20050 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + math_algebra = LightevalTaskConfig( name="math:algebra", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/math_500.py b/src/lighteval/tasks/tasks/math_500.py index e96235ef1..202e2e4d4 100644 --- a/src/lighteval/tasks/tasks/math_500.py +++ b/src/lighteval/tasks/tasks/math_500.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: This dataset contains a subset of 500 problems from the MATH benchmark that @@ -40,6 +13,11 @@ https://arxiv.org/abs/2305.20050 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + math_500 = LightevalTaskConfig( name="math_500", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/mathqa.py b/src/lighteval/tasks/tasks/mathqa.py index d8bb81bac..2c9060571 100644 --- a/src/lighteval/tasks/tasks/mathqa.py +++ b/src/lighteval/tasks/tasks/mathqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: large-scale dataset of math word problems. Our dataset is gathered by using a @@ -42,6 +15,11 @@ https://arxiv.org/abs/1905.13319 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + mathqa = LightevalTaskConfig( name="mathqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/med.py b/src/lighteval/tasks/tasks/med.py index a27b9b81a..16aaa3ee6 100644 --- a/src/lighteval/tasks/tasks/med.py +++ b/src/lighteval/tasks/tasks/med.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering @@ -39,6 +12,11 @@ https://medmcqa.github.io/ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + med_mcqa = LightevalTaskConfig( name="med_mcqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/med_dialog.py b/src/lighteval/tasks/tasks/med_dialog.py index cb8052a14..c60cf656a 100644 --- a/src/lighteval/tasks/tasks/med_dialog.py +++ b/src/lighteval/tasks/tasks/med_dialog.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: A collection of medical dialogue datasets. @@ -39,6 +12,11 @@ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + med_dialog_healthcaremagic = LightevalTaskConfig( name="med_dialog:healthcaremagic", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/mgsm.py b/src/lighteval/tasks/tasks/mgsm.py index 369faf709..260cc799d 100644 --- a/src/lighteval/tasks/tasks/mgsm.py +++ b/src/lighteval/tasks/tasks/mgsm.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school @@ -42,6 +15,11 @@ https://arxiv.org/abs/2210.03057 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + mgsm_en = LightevalTaskConfig( name="mgsm:en", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/mix_eval/judge_prompts.py b/src/lighteval/tasks/tasks/mix_eval/judge_prompts.py index b4925678f..48850b820 100644 --- a/src/lighteval/tasks/tasks/mix_eval/judge_prompts.py +++ b/src/lighteval/tasks/tasks/mix_eval/judge_prompts.py @@ -1,25 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - from lighteval.tasks.tasks.mix_eval.prompts import parse_options diff --git a/src/lighteval/tasks/tasks/mix_eval/main.py b/src/lighteval/tasks/tasks/mix_eval/main.py index 7a94223b3..aa68661bc 100644 --- a/src/lighteval/tasks/tasks/mix_eval/main.py +++ b/src/lighteval/tasks/tasks/mix_eval/main.py @@ -1,25 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - import logging import re diff --git a/src/lighteval/tasks/tasks/mix_eval/prompts.py b/src/lighteval/tasks/tasks/mix_eval/prompts.py index d5cb2f06b..bd859a967 100644 --- a/src/lighteval/tasks/tasks/mix_eval/prompts.py +++ b/src/lighteval/tasks/tasks/mix_eval/prompts.py @@ -1,25 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team and MixEval team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - MULTI_CHOICE_PROMPT = "Answer with the option letter from the given choices directly." FREE_FORM_PROMPT = "Answer the question shortly." # FREE_FORM_PROMPT_QUAC = "Answer the question using a short excerpt (span) from the given text." diff --git a/src/lighteval/tasks/tasks/mmlu.py b/src/lighteval/tasks/tasks/mmlu.py index 1efb38a7b..fb4e7d73f 100644 --- a/src/lighteval/tasks/tasks/mmlu.py +++ b/src/lighteval/tasks/tasks/mmlu.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: MMMLU is a benchmark of general-knowledge and English language understanding. @@ -39,6 +12,11 @@ https://arxiv.org/abs/2009.03300 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + mmlu_abstract_algebra = LightevalTaskConfig( name="mmlu:abstract_algebra", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/mmlu_redux.py b/src/lighteval/tasks/tasks/mmlu_redux.py index 73558953b..afd176241 100644 --- a/src/lighteval/tasks/tasks/mmlu_redux.py +++ b/src/lighteval/tasks/tasks/mmlu_redux.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: MMLU-Redux is a subset of 5,700 manually re-annotated questions across 57 MMLU subjects. @@ -39,6 +12,11 @@ https://arxiv.org/abs/2406.04127 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + _MMLU_REDUX_2_SUBSETS = [ "abstract_algebra", "anatomy", diff --git a/src/lighteval/tasks/tasks/mmmu_pro.py b/src/lighteval/tasks/tasks/mmmu_pro.py index 38c98e835..bc08d48e7 100644 --- a/src/lighteval/tasks/tasks/mmmu_pro.py +++ b/src/lighteval/tasks/tasks/mmmu_pro.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ MMMU-Pro is an enhanced multimodal benchmark designed to rigorously assess the true understanding capabilities of advanced AI models across multiple @@ -40,6 +13,11 @@ https://arxiv.org/abs/2409.02813 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + mmmu_pro_standard_4_options = LightevalTaskConfig( name="mmmu_pro:standard-4", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/mt_bench/judge_prompt_templates.py b/src/lighteval/tasks/tasks/mt_bench/judge_prompt_templates.py index ea3ca41f4..e76de1b2d 100644 --- a/src/lighteval/tasks/tasks/mt_bench/judge_prompt_templates.py +++ b/src/lighteval/tasks/tasks/mt_bench/judge_prompt_templates.py @@ -1,26 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - - def flow_judge_prompt_mt_bench_without_ref(question, options, answer, gold): return [ { diff --git a/src/lighteval/tasks/tasks/mt_bench/main.py b/src/lighteval/tasks/tasks/mt_bench/main.py index 30ef3cc16..cd8212f70 100644 --- a/src/lighteval/tasks/tasks/mt_bench/main.py +++ b/src/lighteval/tasks/tasks/mt_bench/main.py @@ -1,25 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - # ruff: noqa: F405, F403, F401, I001 from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.requests import Doc, SamplingMethod diff --git a/src/lighteval/tasks/tasks/musr.py b/src/lighteval/tasks/tasks/musr.py index a1e440c8a..030a09f82 100644 --- a/src/lighteval/tasks/tasks/musr.py +++ b/src/lighteval/tasks/tasks/musr.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: MuSR is a benchmark for evaluating multistep reasoning in natural language @@ -41,6 +14,11 @@ https://arxiv.org/abs/2310.16049 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + musr_murder_mysteries = LightevalTaskConfig( name="musr:murder_mysteries", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/narrativeqa.py b/src/lighteval/tasks/tasks/narrativeqa.py index 2823fbe63..b71ed7791 100644 --- a/src/lighteval/tasks/tasks/narrativeqa.py +++ b/src/lighteval/tasks/tasks/narrativeqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: NarrativeQA is a reading comprehension benchmark that tests deep understanding @@ -41,6 +14,11 @@ https://aclanthology.org/Q18-1023/ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + narrativeqa = LightevalTaskConfig( name="narrativeqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/natural_questions.py b/src/lighteval/tasks/tasks/natural_questions.py index 4bb4c098f..01427de76 100644 --- a/src/lighteval/tasks/tasks/natural_questions.py +++ b/src/lighteval/tasks/tasks/natural_questions.py @@ -1,31 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.utils.language import Language - - """ abstract: This dataset is a collection of question-answer pairs from the Natural Questions @@ -42,6 +14,12 @@ https://ai.google.com/research/NaturalQuestions """ +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.utils.language import Language + + natural_questions = LightevalTaskConfig( name="natural_questions", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/tasks/numeracy.py b/src/lighteval/tasks/tasks/numeracy.py index 4c35ee9b4..3b553d39e 100644 --- a/src/lighteval/tasks/tasks/numeracy.py +++ b/src/lighteval/tasks/tasks/numeracy.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Numeracy is a benchmark for evaluating the ability of language models to reason about mathematics. @@ -39,6 +12,11 @@ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + numeracy_linear_example = LightevalTaskConfig( name="numeracy:linear_example", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/olympiade_bench/main.py b/src/lighteval/tasks/tasks/olympiade_bench/main.py index fd63177a1..0e9986f74 100644 --- a/src/lighteval/tasks/tasks/olympiade_bench/main.py +++ b/src/lighteval/tasks/tasks/olympiade_bench/main.py @@ -1,26 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - - import numpy as np from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/tasks/openbookqa.py b/src/lighteval/tasks/tasks/openbookqa.py index 8115e4890..854f7e5d5 100644 --- a/src/lighteval/tasks/tasks/openbookqa.py +++ b/src/lighteval/tasks/tasks/openbookqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: OpenBookQA is a question-answering dataset modeled after open-book exams for @@ -43,6 +16,11 @@ https://arxiv.org/abs/1809.02789 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + openbookqa = LightevalTaskConfig( name="openbookqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/piqa.py b/src/lighteval/tasks/tasks/piqa.py index d382503de..d26c58cd6 100644 --- a/src/lighteval/tasks/tasks/piqa.py +++ b/src/lighteval/tasks/tasks/piqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: PIQA is a benchmark for testing physical commonsense reasoning. It contains @@ -40,6 +13,11 @@ https://arxiv.org/abs/1911.11641 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + piqa = LightevalTaskConfig( name="piqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/prost.py b/src/lighteval/tasks/tasks/prost.py index 0e81419b3..d22323d63 100644 --- a/src/lighteval/tasks/tasks/prost.py +++ b/src/lighteval/tasks/tasks/prost.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: PROST is a benchmark for testing physical reasoning about objects through space @@ -43,6 +16,11 @@ https://arxiv.org/abs/2106.03634 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + prost = LightevalTaskConfig( name="prost", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/pubmedqa.py b/src/lighteval/tasks/tasks/pubmedqa.py index df710f1d4..96ba35f9d 100644 --- a/src/lighteval/tasks/tasks/pubmedqa.py +++ b/src/lighteval/tasks/tasks/pubmedqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: PubMedQA is a dataset for biomedical research question answering. @@ -39,6 +12,11 @@ https://pubmedqa.github.io/ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + pubmedqa = LightevalTaskConfig( name="pubmedqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/qa4mre.py b/src/lighteval/tasks/tasks/qa4mre.py index fdd425d2f..367b0b4e0 100644 --- a/src/lighteval/tasks/tasks/qa4mre.py +++ b/src/lighteval/tasks/tasks/qa4mre.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: QA4MRE is a machine reading comprehension benchmark from the CLEF 2011-2013 @@ -43,6 +16,10 @@ https://link.springer.com/chapter/10.1007/978-3-642-40802-1_29 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + qa4mre_2011 = LightevalTaskConfig( name="qa4mre:2011", diff --git a/src/lighteval/tasks/tasks/qasper.py b/src/lighteval/tasks/tasks/qasper.py index 615f5362e..da25d6fef 100644 --- a/src/lighteval/tasks/tasks/qasper.py +++ b/src/lighteval/tasks/tasks/qasper.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: QASPER is a dataset for question answering on scientific research papers. It @@ -44,6 +17,11 @@ https://arxiv.org/abs/2105.03011 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + qasper = LightevalTaskConfig( name="qasper", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/quac.py b/src/lighteval/tasks/tasks/quac.py index 518ed1f6f..8fc5004d9 100644 --- a/src/lighteval/tasks/tasks/quac.py +++ b/src/lighteval/tasks/tasks/quac.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The QuAC benchmark for question answering in the context of dialogues. @@ -39,6 +12,11 @@ https://aclanthology.org/D18-1241/ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + quac = LightevalTaskConfig( name="quac", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/race_high.py b/src/lighteval/tasks/tasks/race_high.py index f803b72fd..8f17437b8 100644 --- a/src/lighteval/tasks/tasks/race_high.py +++ b/src/lighteval/tasks/tasks/race_high.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: RACE is a large-scale reading comprehension dataset with more than 28,000 @@ -43,6 +16,11 @@ https://aclanthology.org/D17-1082/ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + race_high = LightevalTaskConfig( name="race:high", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/raft.py b/src/lighteval/tasks/tasks/raft.py index 7155e9850..bfea7d383 100644 --- a/src/lighteval/tasks/tasks/raft.py +++ b/src/lighteval/tasks/tasks/raft.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text @@ -40,6 +13,11 @@ https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + raft_ade_corpus_v2 = LightevalTaskConfig( name="raft:ade_corpus_v2", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/real_toxicity_prompts.py b/src/lighteval/tasks/tasks/real_toxicity_prompts.py index a643c829f..b45aa27da 100644 --- a/src/lighteval/tasks/tasks/real_toxicity_prompts.py +++ b/src/lighteval/tasks/tasks/real_toxicity_prompts.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The RealToxicityPrompts dataset for measuring toxicity in prompted model generations @@ -39,6 +12,11 @@ https://aclanthology.org/2020.findings-emnlp.301/ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + real_toxicity_prompts = LightevalTaskConfig( name="real_toxicity_prompts", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/sacrebleu.py b/src/lighteval/tasks/tasks/sacrebleu.py index c338343d5..c55194193 100644 --- a/src/lighteval/tasks/tasks/sacrebleu.py +++ b/src/lighteval/tasks/tasks/sacrebleu.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -from lighteval.metrics.metrics import Metrics -from lighteval.tasks import default_prompts as prompt -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: tasks from sacrebleu @@ -39,6 +12,11 @@ https://github.com/mjpost/sacrebleu """ +from lighteval.metrics.metrics import Metrics +from lighteval.tasks import default_prompts as prompt +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + iwslt17_ar_en = LightevalTaskConfig( name="iwslt17:ar-en", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/sciq.py b/src/lighteval/tasks/tasks/sciq.py index b3d6d7c59..842bb324e 100644 --- a/src/lighteval/tasks/tasks/sciq.py +++ b/src/lighteval/tasks/tasks/sciq.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The SciQ dataset contains 13,679 crowdsourced science exam questions about @@ -43,6 +16,11 @@ https://arxiv.org/abs/1707.06209 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + sciq = LightevalTaskConfig( name="sciq", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/simpleqa.py b/src/lighteval/tasks/tasks/simpleqa.py index b072bc196..5d88c2c58 100644 --- a/src/lighteval/tasks/tasks/simpleqa.py +++ b/src/lighteval/tasks/tasks/simpleqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: A factuality benchmark called SimpleQA that measures the ability for language @@ -40,6 +13,11 @@ https://openai.com/index/introducing-simpleqa/ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + simpleqa = LightevalTaskConfig( name="simpleqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/siqa.py b/src/lighteval/tasks/tasks/siqa.py index 5d1c0bc0a..47a7081fc 100644 --- a/src/lighteval/tasks/tasks/siqa.py +++ b/src/lighteval/tasks/tasks/siqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: We introduce Social IQa: Social Interaction QA, a new question-answering @@ -50,6 +23,11 @@ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + siqa = LightevalTaskConfig( name="siqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/squad_v2.py b/src/lighteval/tasks/tasks/squad_v2.py index 7c44e9c1d..d272131e7 100644 --- a/src/lighteval/tasks/tasks/squad_v2.py +++ b/src/lighteval/tasks/tasks/squad_v2.py @@ -1,31 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.templates.qa import get_qa_prompt_function -from lighteval.utils.language import Language - - """ abstract: Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, @@ -49,6 +21,12 @@ https://arxiv.org/abs/1806.03822 """ +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.templates.qa import get_qa_prompt_function +from lighteval.utils.language import Language + + squad_v2 = LightevalTaskConfig( name="squad_v2", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/tasks/storycloze.py b/src/lighteval/tasks/tasks/storycloze.py index e87d5d4f2..85933f2fe 100644 --- a/src/lighteval/tasks/tasks/storycloze.py +++ b/src/lighteval/tasks/tasks/storycloze.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: A Corpus and Cloze Evaluation for Deeper Understanding of @@ -40,6 +13,11 @@ https://arxiv.org/abs/1604.01696 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + storycloze_2016 = LightevalTaskConfig( name="storycloze:2016", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/summarization.py b/src/lighteval/tasks/tasks/summarization.py index a07907f46..c20881ff6 100644 --- a/src/lighteval/tasks/tasks/summarization.py +++ b/src/lighteval/tasks/tasks/summarization.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural @@ -42,6 +15,11 @@ https://aclanthology.org/K16-1028/ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + summarization_cnn_dm = LightevalTaskConfig( name="summarization:cnn-dm", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/swag.py b/src/lighteval/tasks/tasks/swag.py index 5d6d8d793..19094427d 100644 --- a/src/lighteval/tasks/tasks/swag.py +++ b/src/lighteval/tasks/tasks/swag.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The dataset consists of 113k multiple choice questions about grounded situations @@ -46,6 +19,11 @@ https://arxiv.org/abs/1808.05326 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + swag = LightevalTaskConfig( name="swag", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/synthetic_reasoning.py b/src/lighteval/tasks/tasks/synthetic_reasoning.py index 38e438e83..7d0c8972f 100644 --- a/src/lighteval/tasks/tasks/synthetic_reasoning.py +++ b/src/lighteval/tasks/tasks/synthetic_reasoning.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning @@ -39,6 +12,10 @@ https://arxiv.org/abs/2206.03855 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + synthetic_reasoning_induction = LightevalTaskConfig( name="synthetic_reasoning:induction", diff --git a/src/lighteval/tasks/tasks/the_pile.py b/src/lighteval/tasks/tasks/the_pile.py index b90352967..37e7b21e7 100644 --- a/src/lighteval/tasks/tasks/the_pile.py +++ b/src/lighteval/tasks/tasks/the_pile.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The Pile corpus for measuring lanugage model performance across various domains. @@ -39,6 +12,11 @@ https://arxiv.org/abs/2101.00027 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + the_pile_arxiv_helm = LightevalTaskConfig( name="the_pile:arxiv", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/tiny_benchmarks/main.py b/src/lighteval/tasks/tasks/tiny_benchmarks/main.py index e00e8b586..1e216ee1b 100644 --- a/src/lighteval/tasks/tasks/tiny_benchmarks/main.py +++ b/src/lighteval/tasks/tasks/tiny_benchmarks/main.py @@ -1,25 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team & Felipe Maia Polo - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - # ruff: noqa: F405, F403, F401 """ abstract: diff --git a/src/lighteval/tasks/tasks/toxigen.py b/src/lighteval/tasks/tasks/toxigen.py index 5458e9cd4..442a6dc39 100644 --- a/src/lighteval/tasks/tasks/toxigen.py +++ b/src/lighteval/tasks/tasks/toxigen.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: This dataset is for implicit hate speech detection. All instances were generated @@ -40,6 +13,11 @@ https://arxiv.org/abs/2203.09509 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + toxigen = LightevalTaskConfig( name="toxigen", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/triviaqa.py b/src/lighteval/tasks/tasks/triviaqa.py index 58ec26a25..5a631c2a3 100644 --- a/src/lighteval/tasks/tasks/triviaqa.py +++ b/src/lighteval/tasks/tasks/triviaqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: TriviaqQA is a reading comprehension dataset containing over 650K @@ -43,6 +16,11 @@ https://arxiv.org/abs/1705.03551 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + triviaqa = LightevalTaskConfig( name="triviaqa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/truthfulqa.py b/src/lighteval/tasks/tasks/truthfulqa.py index 90671984c..d07d000f9 100644 --- a/src/lighteval/tasks/tasks/truthfulqa.py +++ b/src/lighteval/tasks/tasks/truthfulqa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: TruthfulQA: Measuring How Models Mimic Human Falsehoods @@ -39,6 +12,11 @@ https://arxiv.org/abs/2109.07958 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + truthfulqa_gen = LightevalTaskConfig( name="truthfulqa:gen", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/twitterAAE.py b/src/lighteval/tasks/tasks/twitterAAE.py index 7024f4a47..73409b31b 100644 --- a/src/lighteval/tasks/tasks/twitterAAE.py +++ b/src/lighteval/tasks/tasks/twitterAAE.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Demographic Dialectal Variation in Social Media: A Case Study of African-American English @@ -39,6 +12,11 @@ https://aclanthology.org/D16-1120/ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + twitterAAE_aa = LightevalTaskConfig( name="twitterAAE:aa", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/unscramble.py b/src/lighteval/tasks/tasks/unscramble.py index cb7a6293b..cfd41653d 100644 --- a/src/lighteval/tasks/tasks/unscramble.py +++ b/src/lighteval/tasks/tasks/unscramble.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Benchmark where we ask the model to unscramble a word, either anagram or @@ -40,6 +13,11 @@ https://huggingface.co/datasets/lighteval/GPT3_unscramble """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + unscramble_anagrams1 = LightevalTaskConfig( name="unscramble:anagrams1", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/webqs.py b/src/lighteval/tasks/tasks/webqs.py index 609a71486..47af552f5 100644 --- a/src/lighteval/tasks/tasks/webqs.py +++ b/src/lighteval/tasks/tasks/webqs.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: This dataset consists of 6,642 question/answer pairs. The questions are supposed @@ -42,6 +15,10 @@ https://aclanthology.org/D13-1160.pdf """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + webqs = LightevalTaskConfig( name="webqs", diff --git a/src/lighteval/tasks/tasks/wikifact.py b/src/lighteval/tasks/tasks/wikifact.py index 2ac4f68a4..d5783c01d 100644 --- a/src/lighteval/tasks/tasks/wikifact.py +++ b/src/lighteval/tasks/tasks/wikifact.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -from lighteval.metrics.metrics import Metrics -from lighteval.tasks import default_prompts as prompt -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Extensively test factual knowledge. @@ -39,6 +12,11 @@ https://aclanthology.org/D19-1250/ """ +from lighteval.metrics.metrics import Metrics +from lighteval.tasks import default_prompts as prompt +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + wikifact_applies_to_jurisdiction = LightevalTaskConfig( name="wikifact:applies_to_jurisdiction", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/wikitext.py b/src/lighteval/tasks/tasks/wikitext.py index a84a005e0..66f695815 100644 --- a/src/lighteval/tasks/tasks/wikitext.py +++ b/src/lighteval/tasks/tasks/wikitext.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: The WikiText language modeling dataset is a collection of over 100 million @@ -42,6 +15,10 @@ https://arxiv.org/abs/1609.07843 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + wikitext_103_document_level = LightevalTaskConfig( name="wikitext:103:document_level", diff --git a/src/lighteval/tasks/tasks/winogrande.py b/src/lighteval/tasks/tasks/winogrande.py index 4f1efa29b..c6e7de105 100644 --- a/src/lighteval/tasks/tasks/winogrande.py +++ b/src/lighteval/tasks/tasks/winogrande.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: WinoGrande is a new collection of 44k problems, inspired by Winograd Schema @@ -43,6 +16,11 @@ https://arxiv.org/abs/1907.10641 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + winogrande = LightevalTaskConfig( name="winogrande", suite=["leaderboard"], diff --git a/src/lighteval/tasks/tasks/xcopa.py b/src/lighteval/tasks/tasks/xcopa.py index 34b7af187..e905536b9 100644 --- a/src/lighteval/tasks/tasks/xcopa.py +++ b/src/lighteval/tasks/tasks/xcopa.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning The Cross-lingual @@ -41,6 +14,11 @@ https://arxiv.org/abs/2005.00333 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + xcopa_en = LightevalTaskConfig( name="xcopa:en", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/xstory_cloze.py b/src/lighteval/tasks/tasks/xstory_cloze.py index b7bdfd3ec..8ce1a373f 100644 --- a/src/lighteval/tasks/tasks/xstory_cloze.py +++ b/src/lighteval/tasks/tasks/xstory_cloze.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: XStoryCloze consists of the professionally translated version of the English @@ -41,6 +14,11 @@ """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + xstory_cloze_en = LightevalTaskConfig( name="xstory_cloze:en", suite=["lighteval"], diff --git a/src/lighteval/tasks/tasks/xwinograd.py b/src/lighteval/tasks/tasks/xwinograd.py index 53e48ae43..171be2a9a 100644 --- a/src/lighteval/tasks/tasks/xwinograd.py +++ b/src/lighteval/tasks/tasks/xwinograd.py @@ -1,30 +1,3 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import lighteval.tasks.default_prompts as prompt -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig - - """ abstract: Multilingual winograd schema challenge as used in Crosslingual Generalization through Multitask Finetuning. @@ -39,6 +12,11 @@ https://arxiv.org/abs/2211.01786 """ +import lighteval.tasks.default_prompts as prompt +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig + + xwinograd_en = LightevalTaskConfig( name="xwinograd:en", suite=["lighteval"], From ee081f205455afc01d2862f02b61196ee45f175c Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Tue, 14 Oct 2025 15:12:46 +0200 Subject: [PATCH 16/43] homogenize tags --- src/lighteval/tasks/tasks/agieval.py | 2 +- src/lighteval/tasks/tasks/aime.py | 3 +++ src/lighteval/tasks/tasks/anli.py | 3 +++ src/lighteval/tasks/tasks/arc.py | 3 +++ src/lighteval/tasks/tasks/arc_agi_2.py | 3 +++ src/lighteval/tasks/tasks/arithmetic.py | 3 +++ src/lighteval/tasks/tasks/asdiv.py | 3 +++ src/lighteval/tasks/tasks/babi_qa.py | 3 +++ src/lighteval/tasks/tasks/bbq.py | 3 +++ src/lighteval/tasks/tasks/bigbench.py | 3 +++ src/lighteval/tasks/tasks/bigbench_hard.py | 3 +++ src/lighteval/tasks/tasks/blimp.py | 3 +++ src/lighteval/tasks/tasks/bold.py | 3 +++ src/lighteval/tasks/tasks/boolq.py | 2 +- src/lighteval/tasks/tasks/civil_comments.py | 2 +- src/lighteval/tasks/tasks/commonsenseqa.py | 3 +++ src/lighteval/tasks/tasks/coqa.py | 3 +++ src/lighteval/tasks/tasks/covid_dialogue.py | 3 +++ src/lighteval/tasks/tasks/drop_qa.py | 3 +++ src/lighteval/tasks/tasks/dyck_language.py | 3 +++ src/lighteval/tasks/tasks/entity_data_imputation.py | 3 +++ src/lighteval/tasks/tasks/entitymatching.py | 3 +++ src/lighteval/tasks/tasks/ethics.py | 2 +- src/lighteval/tasks/tasks/glue.py | 5 +---- src/lighteval/tasks/tasks/gpqa.py | 2 +- src/lighteval/tasks/tasks/headqa.py | 2 +- src/lighteval/tasks/tasks/hellaswag.py | 2 +- src/lighteval/tasks/tasks/imdb.py | 2 +- src/lighteval/tasks/tasks/jeopardy.py | 3 +++ src/lighteval/tasks/tasks/lambada.py | 2 +- src/lighteval/tasks/tasks/legalsupport.py | 2 +- src/lighteval/tasks/tasks/lexglue.py | 2 +- src/lighteval/tasks/tasks/lextreme.py | 2 +- src/lighteval/tasks/tasks/logiqa.py | 2 +- src/lighteval/tasks/tasks/math.py | 2 +- src/lighteval/tasks/tasks/math_500.py | 2 +- src/lighteval/tasks/tasks/mathqa.py | 2 +- src/lighteval/tasks/tasks/med.py | 2 +- src/lighteval/tasks/tasks/med_dialog.py | 2 +- src/lighteval/tasks/tasks/mgsm.py | 2 +- src/lighteval/tasks/tasks/mmlu.py | 2 +- src/lighteval/tasks/tasks/mmlu_redux.py | 2 +- src/lighteval/tasks/tasks/mmmu_pro.py | 2 +- src/lighteval/tasks/tasks/musr.py | 2 +- src/lighteval/tasks/tasks/narrativeqa.py | 2 +- src/lighteval/tasks/tasks/numeracy.py | 2 +- src/lighteval/tasks/tasks/openbookqa.py | 2 +- src/lighteval/tasks/tasks/piqa.py | 2 +- src/lighteval/tasks/tasks/pubmedqa.py | 2 +- src/lighteval/tasks/tasks/qa4mre.py | 2 +- src/lighteval/tasks/tasks/quac.py | 2 +- src/lighteval/tasks/tasks/race_high.py | 2 +- src/lighteval/tasks/tasks/raft.py | 2 +- src/lighteval/tasks/tasks/real_toxicity_prompts.py | 2 +- src/lighteval/tasks/tasks/simpleqa.py | 2 +- src/lighteval/tasks/tasks/siqa.py | 2 +- src/lighteval/tasks/tasks/storycloze.py | 2 +- src/lighteval/tasks/tasks/swag.py | 2 +- src/lighteval/tasks/tasks/synthetic_reasoning.py | 2 +- src/lighteval/tasks/tasks/toxigen.py | 2 +- src/lighteval/tasks/tasks/triviaqa.py | 2 +- src/lighteval/tasks/tasks/truthfulqa.py | 2 +- src/lighteval/tasks/tasks/twitterAAE.py | 2 +- src/lighteval/tasks/tasks/unscramble.py | 2 +- src/lighteval/tasks/tasks/winogrande.py | 2 +- src/lighteval/tasks/tasks/xcopa.py | 2 +- src/lighteval/tasks/tasks/xstory_cloze.py | 2 +- src/lighteval/tasks/tasks/xwinograd.py | 2 +- 68 files changed, 108 insertions(+), 51 deletions(-) diff --git a/src/lighteval/tasks/tasks/agieval.py b/src/lighteval/tasks/tasks/agieval.py index 97f79dc22..b01cd495a 100644 --- a/src/lighteval/tasks/tasks/agieval.py +++ b/src/lighteval/tasks/tasks/agieval.py @@ -12,7 +12,7 @@ en, zh tags: -math, reasoning, law, language, history, chemistry, biology, geography, physics +biology, chemistry, geography, history, knowledge, language, multiple-choice, physics, reasoning paper: https://arxiv.org/abs/2304.06364 diff --git a/src/lighteval/tasks/tasks/aime.py b/src/lighteval/tasks/tasks/aime.py index 86eddf8c8..fae2b8b1f 100644 --- a/src/lighteval/tasks/tasks/aime.py +++ b/src/lighteval/tasks/tasks/aime.py @@ -13,6 +13,9 @@ paper: https://maa.org/aime-thresholds-are-available/ + +tags: +math, reasoning """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/anli.py b/src/lighteval/tasks/tasks/anli.py index 14df3d020..2611b26d2 100644 --- a/src/lighteval/tasks/tasks/anli.py +++ b/src/lighteval/tasks/tasks/anli.py @@ -11,6 +11,9 @@ paper: https://arxiv.org/abs/1910.14599 + +tags: +nli, reasoning """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/arc.py b/src/lighteval/tasks/tasks/arc.py index f508c0dc0..b73f0c761 100644 --- a/src/lighteval/tasks/tasks/arc.py +++ b/src/lighteval/tasks/tasks/arc.py @@ -11,6 +11,9 @@ paper: https://arxiv.org/abs/1803.05457 + +tags: +multiple-choice """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/arc_agi_2.py b/src/lighteval/tasks/tasks/arc_agi_2.py index b4c272e4c..be9449cf9 100644 --- a/src/lighteval/tasks/tasks/arc_agi_2.py +++ b/src/lighteval/tasks/tasks/arc_agi_2.py @@ -17,6 +17,9 @@ paper: https://arcprize.org/guide + +tags: +multiple-choice """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/arithmetic.py b/src/lighteval/tasks/tasks/arithmetic.py index 1ba1dc290..7a8f3fc07 100644 --- a/src/lighteval/tasks/tasks/arithmetic.py +++ b/src/lighteval/tasks/tasks/arithmetic.py @@ -8,6 +8,9 @@ paper: https://arxiv.org/abs/2005.14165 + +tags: +math, reasoning """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/asdiv.py b/src/lighteval/tasks/tasks/asdiv.py index dfe3dfcdf..0b86735ae 100644 --- a/src/lighteval/tasks/tasks/asdiv.py +++ b/src/lighteval/tasks/tasks/asdiv.py @@ -8,6 +8,9 @@ paper: https://arxiv.org/abs/2410.12853 + +tags: +math, reasoning """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/babi_qa.py b/src/lighteval/tasks/tasks/babi_qa.py index 8e9282020..5c426e1dc 100644 --- a/src/lighteval/tasks/tasks/babi_qa.py +++ b/src/lighteval/tasks/tasks/babi_qa.py @@ -8,6 +8,9 @@ paper: https://arxiv.org/abs/1502.05698 + +tags: +qa, reasoning """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/bbq.py b/src/lighteval/tasks/tasks/bbq.py index dfc3bb751..175e1b46d 100644 --- a/src/lighteval/tasks/tasks/bbq.py +++ b/src/lighteval/tasks/tasks/bbq.py @@ -8,6 +8,9 @@ paper: https://arxiv.org/abs/2110.08193 + +tags: +bias, multiple-choice, qa """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/bigbench.py b/src/lighteval/tasks/tasks/bigbench.py index 67825a36b..37a34a318 100644 --- a/src/lighteval/tasks/tasks/bigbench.py +++ b/src/lighteval/tasks/tasks/bigbench.py @@ -8,6 +8,9 @@ paper: https://arxiv.org/abs/2206.04615 + +tags: +reasoning """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/bigbench_hard.py b/src/lighteval/tasks/tasks/bigbench_hard.py index 9061cf3bf..6c5aaf949 100644 --- a/src/lighteval/tasks/tasks/bigbench_hard.py +++ b/src/lighteval/tasks/tasks/bigbench_hard.py @@ -1,5 +1,8 @@ """ hardest subset of bigbench benchmark. + +tags: +reasoning """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/blimp.py b/src/lighteval/tasks/tasks/blimp.py index 42e4ba1ff..25078db59 100644 --- a/src/lighteval/tasks/tasks/blimp.py +++ b/src/lighteval/tasks/tasks/blimp.py @@ -10,6 +10,9 @@ paper: https://arxiv.org/abs/1912.00582 + +tags: +language-modeling """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/bold.py b/src/lighteval/tasks/tasks/bold.py index 93a2be85e..fa77c174c 100644 --- a/src/lighteval/tasks/tasks/bold.py +++ b/src/lighteval/tasks/tasks/bold.py @@ -8,6 +8,9 @@ paper: https://dl.acm.org/doi/10.1145/3442188.3445924 + +tags: +bias, generation """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/boolq.py b/src/lighteval/tasks/tasks/boolq.py index bbbbc2e60..dbc7ca980 100644 --- a/src/lighteval/tasks/tasks/boolq.py +++ b/src/lighteval/tasks/tasks/boolq.py @@ -6,7 +6,7 @@ en tags: -Question-Answering, +qa paper: https://arxiv.org/abs/1905.11946 diff --git a/src/lighteval/tasks/tasks/civil_comments.py b/src/lighteval/tasks/tasks/civil_comments.py index 7a50597ac..0d2447417 100644 --- a/src/lighteval/tasks/tasks/civil_comments.py +++ b/src/lighteval/tasks/tasks/civil_comments.py @@ -6,7 +6,7 @@ en tags: -toxicity, bias +bias, classification paper: https://arxiv.org/abs/1903.04561 diff --git a/src/lighteval/tasks/tasks/commonsenseqa.py b/src/lighteval/tasks/tasks/commonsenseqa.py index 2e5ffaee6..b0353bcd7 100644 --- a/src/lighteval/tasks/tasks/commonsenseqa.py +++ b/src/lighteval/tasks/tasks/commonsenseqa.py @@ -12,6 +12,9 @@ paper: https://arxiv.org/abs/1811.00937 + +tags: +commonsense, multiple-choice, qa """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/coqa.py b/src/lighteval/tasks/tasks/coqa.py index af3e43c27..6351a1100 100644 --- a/src/lighteval/tasks/tasks/coqa.py +++ b/src/lighteval/tasks/tasks/coqa.py @@ -10,6 +10,9 @@ paper: https://arxiv.org/abs/1808.07042 + +tags: +dialog, qa """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/covid_dialogue.py b/src/lighteval/tasks/tasks/covid_dialogue.py index f6dda67fe..1ce3777bc 100644 --- a/src/lighteval/tasks/tasks/covid_dialogue.py +++ b/src/lighteval/tasks/tasks/covid_dialogue.py @@ -8,6 +8,9 @@ paper: https://arxiv.org/abs/2004.06561 + +tags: +dialog, medical """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/drop_qa.py b/src/lighteval/tasks/tasks/drop_qa.py index 6e07f4c21..077f769c9 100644 --- a/src/lighteval/tasks/tasks/drop_qa.py +++ b/src/lighteval/tasks/tasks/drop_qa.py @@ -9,6 +9,9 @@ paper: https://arxiv.org/abs/1810.00505 + +tags: +math, qa, reasoning """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/dyck_language.py b/src/lighteval/tasks/tasks/dyck_language.py index 20f97a576..f593f8678 100644 --- a/src/lighteval/tasks/tasks/dyck_language.py +++ b/src/lighteval/tasks/tasks/dyck_language.py @@ -7,6 +7,9 @@ paper: https://aclanthology.org/W19-3905/ + +tags: +reasoning """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/entity_data_imputation.py b/src/lighteval/tasks/tasks/entity_data_imputation.py index 185d4d341..f5a93240e 100644 --- a/src/lighteval/tasks/tasks/entity_data_imputation.py +++ b/src/lighteval/tasks/tasks/entity_data_imputation.py @@ -7,6 +7,9 @@ paper: https://ieeexplore.ieee.org/document/9458712 + +tags: +reasoning """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/entitymatching.py b/src/lighteval/tasks/tasks/entitymatching.py index 916ebc3a0..d0bd44c84 100644 --- a/src/lighteval/tasks/tasks/entitymatching.py +++ b/src/lighteval/tasks/tasks/entitymatching.py @@ -7,6 +7,9 @@ paper: https://dl.acm.org/doi/10.14778/3007263.3007314 + +tags: +classification, reasoning """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/ethics.py b/src/lighteval/tasks/tasks/ethics.py index eb4e1009a..cabacecf5 100644 --- a/src/lighteval/tasks/tasks/ethics.py +++ b/src/lighteval/tasks/tasks/ethics.py @@ -7,7 +7,7 @@ en tags: -ethics, morality, commonsense, justice, utilitarianism, virtue +classification, ethics, justice, morality, utilitarianism, virtue paper: https://arxiv.org/abs/2008.02275 diff --git a/src/lighteval/tasks/tasks/glue.py b/src/lighteval/tasks/tasks/glue.py index 1a6a6d513..8a3c65f8b 100644 --- a/src/lighteval/tasks/tasks/glue.py +++ b/src/lighteval/tasks/tasks/glue.py @@ -8,10 +8,7 @@ en tags: - - -paper: -https://arxiv.org/abs/1804.07461 +classification """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/gpqa.py b/src/lighteval/tasks/tasks/gpqa.py index a612929c1..e76e1f604 100644 --- a/src/lighteval/tasks/tasks/gpqa.py +++ b/src/lighteval/tasks/tasks/gpqa.py @@ -11,7 +11,7 @@ en tags: -biology, physics, chemistry, reasoning, graduate-level +biology, chemistry, graduate-level, multiple-choice, physics, qa, reasoning, science paper: https://arxiv.org/abs/2311.12022 diff --git a/src/lighteval/tasks/tasks/headqa.py b/src/lighteval/tasks/tasks/headqa.py index 812f21935..ed002217f 100644 --- a/src/lighteval/tasks/tasks/headqa.py +++ b/src/lighteval/tasks/tasks/headqa.py @@ -10,7 +10,7 @@ en, es tags: -health, reasoning +health, medical, multiple-choice, qa paper: https://arxiv.org/abs/1906.04701 diff --git a/src/lighteval/tasks/tasks/hellaswag.py b/src/lighteval/tasks/tasks/hellaswag.py index e2d29ee1a..594e62153 100644 --- a/src/lighteval/tasks/tasks/hellaswag.py +++ b/src/lighteval/tasks/tasks/hellaswag.py @@ -7,7 +7,7 @@ en tags: -commonsense +multiple-choice, narrative, reasoning paper: https://arxiv.org/abs/1905.07830 diff --git a/src/lighteval/tasks/tasks/imdb.py b/src/lighteval/tasks/tasks/imdb.py index 05cdd6cb8..ad26df355 100644 --- a/src/lighteval/tasks/tasks/imdb.py +++ b/src/lighteval/tasks/tasks/imdb.py @@ -7,7 +7,7 @@ en tags: -sentiment-analysis +classification paper: https://aclanthology.org/P11-1015/ diff --git a/src/lighteval/tasks/tasks/jeopardy.py b/src/lighteval/tasks/tasks/jeopardy.py index 487dcc118..7b3685600 100644 --- a/src/lighteval/tasks/tasks/jeopardy.py +++ b/src/lighteval/tasks/tasks/jeopardy.py @@ -6,6 +6,9 @@ en paper: + +tags: +knowledge, qa """ from lighteval.metrics.metrics import Metrics diff --git a/src/lighteval/tasks/tasks/lambada.py b/src/lighteval/tasks/tasks/lambada.py index 1d6cd5ee8..60554a5da 100644 --- a/src/lighteval/tasks/tasks/lambada.py +++ b/src/lighteval/tasks/tasks/lambada.py @@ -9,7 +9,7 @@ en tags: -reading-comprehension +language-modeling paper: https://arxiv.org/abs/1606.06031 diff --git a/src/lighteval/tasks/tasks/legalsupport.py b/src/lighteval/tasks/tasks/legalsupport.py index 215699613..eef43c1aa 100644 --- a/src/lighteval/tasks/tasks/legalsupport.py +++ b/src/lighteval/tasks/tasks/legalsupport.py @@ -6,7 +6,7 @@ en tags: -legal, reasoning +legal paper: diff --git a/src/lighteval/tasks/tasks/lexglue.py b/src/lighteval/tasks/tasks/lexglue.py index b6d4f75fa..ec01d07ab 100644 --- a/src/lighteval/tasks/tasks/lexglue.py +++ b/src/lighteval/tasks/tasks/lexglue.py @@ -6,7 +6,7 @@ en tags: -legal, language-understanding +classification, legal paper: https://arxiv.org/abs/2110.00976 diff --git a/src/lighteval/tasks/tasks/lextreme.py b/src/lighteval/tasks/tasks/lextreme.py index 2e3a233f8..a4713607f 100644 --- a/src/lighteval/tasks/tasks/lextreme.py +++ b/src/lighteval/tasks/tasks/lextreme.py @@ -6,7 +6,7 @@ bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv tags: -legal +classification, legal paper: https://arxiv.org/abs/2301.13126 diff --git a/src/lighteval/tasks/tasks/logiqa.py b/src/lighteval/tasks/tasks/logiqa.py index 99e9bde1a..c70416ca2 100644 --- a/src/lighteval/tasks/tasks/logiqa.py +++ b/src/lighteval/tasks/tasks/logiqa.py @@ -10,7 +10,7 @@ en tags: -reading-comprehension +qa paper: https://arxiv.org/abs/2007.08124 diff --git a/src/lighteval/tasks/tasks/math.py b/src/lighteval/tasks/tasks/math.py index 00529bdea..5a277be7b 100644 --- a/src/lighteval/tasks/tasks/math.py +++ b/src/lighteval/tasks/tasks/math.py @@ -8,7 +8,7 @@ en tags: -math +math, reasoning paper: https://arxiv.org/abs/2305.20050 diff --git a/src/lighteval/tasks/tasks/math_500.py b/src/lighteval/tasks/tasks/math_500.py index 202e2e4d4..349d12fbd 100644 --- a/src/lighteval/tasks/tasks/math_500.py +++ b/src/lighteval/tasks/tasks/math_500.py @@ -7,7 +7,7 @@ en tags: -math +math, reasoning paper: https://arxiv.org/abs/2305.20050 diff --git a/src/lighteval/tasks/tasks/mathqa.py b/src/lighteval/tasks/tasks/mathqa.py index 2c9060571..5320876fa 100644 --- a/src/lighteval/tasks/tasks/mathqa.py +++ b/src/lighteval/tasks/tasks/mathqa.py @@ -9,7 +9,7 @@ en tags: -math +math, qa, reasoning paper: https://arxiv.org/abs/1905.13319 diff --git a/src/lighteval/tasks/tasks/med.py b/src/lighteval/tasks/tasks/med.py index 16aaa3ee6..45ffa8db7 100644 --- a/src/lighteval/tasks/tasks/med.py +++ b/src/lighteval/tasks/tasks/med.py @@ -6,7 +6,7 @@ en tags: -health, qa +health, medical paper: https://medmcqa.github.io/ diff --git a/src/lighteval/tasks/tasks/med_dialog.py b/src/lighteval/tasks/tasks/med_dialog.py index c60cf656a..170624f39 100644 --- a/src/lighteval/tasks/tasks/med_dialog.py +++ b/src/lighteval/tasks/tasks/med_dialog.py @@ -6,7 +6,7 @@ en tags: -health, dialog +dialog, health, medical paper: diff --git a/src/lighteval/tasks/tasks/mgsm.py b/src/lighteval/tasks/tasks/mgsm.py index 260cc799d..1e0505b85 100644 --- a/src/lighteval/tasks/tasks/mgsm.py +++ b/src/lighteval/tasks/tasks/mgsm.py @@ -9,7 +9,7 @@ en, es, fr, de, ru, zh, ja, th, sw, bn, te tags: -math +math, multilingual, reasoning paper: https://arxiv.org/abs/2210.03057 diff --git a/src/lighteval/tasks/tasks/mmlu.py b/src/lighteval/tasks/tasks/mmlu.py index fb4e7d73f..0df44613f 100644 --- a/src/lighteval/tasks/tasks/mmlu.py +++ b/src/lighteval/tasks/tasks/mmlu.py @@ -6,7 +6,7 @@ en tags: -general-knowledge, qa +general-knowledge, knowledge, multiple-choice paper: https://arxiv.org/abs/2009.03300 diff --git a/src/lighteval/tasks/tasks/mmlu_redux.py b/src/lighteval/tasks/tasks/mmlu_redux.py index afd176241..f26afb1ea 100644 --- a/src/lighteval/tasks/tasks/mmlu_redux.py +++ b/src/lighteval/tasks/tasks/mmlu_redux.py @@ -6,7 +6,7 @@ en tags: -general-knowledge, qa +general-knowledge, knowledge, multiple-choice paper: https://arxiv.org/abs/2406.04127 diff --git a/src/lighteval/tasks/tasks/mmmu_pro.py b/src/lighteval/tasks/tasks/mmmu_pro.py index bc08d48e7..91a0dbacd 100644 --- a/src/lighteval/tasks/tasks/mmmu_pro.py +++ b/src/lighteval/tasks/tasks/mmmu_pro.py @@ -7,7 +7,7 @@ en tags: -multimodal, qa, general-knowledge +general-knowledge, knowledge, multimodal, multiple-choice paper: https://arxiv.org/abs/2409.02813 diff --git a/src/lighteval/tasks/tasks/musr.py b/src/lighteval/tasks/tasks/musr.py index 030a09f82..caf2d34f8 100644 --- a/src/lighteval/tasks/tasks/musr.py +++ b/src/lighteval/tasks/tasks/musr.py @@ -8,7 +8,7 @@ en tags: -reasoning, long-context +long-context, multiple-choice, reasoning paper: https://arxiv.org/abs/2310.16049 diff --git a/src/lighteval/tasks/tasks/narrativeqa.py b/src/lighteval/tasks/tasks/narrativeqa.py index b71ed7791..9ac6e9bad 100644 --- a/src/lighteval/tasks/tasks/narrativeqa.py +++ b/src/lighteval/tasks/tasks/narrativeqa.py @@ -8,7 +8,7 @@ en tags: -reading-comprehension +qa, reading-comprehension paper: https://aclanthology.org/Q18-1023/ diff --git a/src/lighteval/tasks/tasks/numeracy.py b/src/lighteval/tasks/tasks/numeracy.py index 3b553d39e..49d9b2b94 100644 --- a/src/lighteval/tasks/tasks/numeracy.py +++ b/src/lighteval/tasks/tasks/numeracy.py @@ -6,7 +6,7 @@ en tags: -math +math, reasoning paper: diff --git a/src/lighteval/tasks/tasks/openbookqa.py b/src/lighteval/tasks/tasks/openbookqa.py index 854f7e5d5..481e195f5 100644 --- a/src/lighteval/tasks/tasks/openbookqa.py +++ b/src/lighteval/tasks/tasks/openbookqa.py @@ -10,7 +10,7 @@ en tags: -reading-comprehension, qa +multiple-choice, qa paper: https://arxiv.org/abs/1809.02789 diff --git a/src/lighteval/tasks/tasks/piqa.py b/src/lighteval/tasks/tasks/piqa.py index d26c58cd6..4491c9fea 100644 --- a/src/lighteval/tasks/tasks/piqa.py +++ b/src/lighteval/tasks/tasks/piqa.py @@ -7,7 +7,7 @@ en tags: -reasoning, physical-commonsense, qa +commonsense, multiple-choice, qa paper: https://arxiv.org/abs/1911.11641 diff --git a/src/lighteval/tasks/tasks/pubmedqa.py b/src/lighteval/tasks/tasks/pubmedqa.py index 96ba35f9d..bc989fcb3 100644 --- a/src/lighteval/tasks/tasks/pubmedqa.py +++ b/src/lighteval/tasks/tasks/pubmedqa.py @@ -6,7 +6,7 @@ en tags: -qa, health, biomedical +biomedical, health, medical, qa paper: https://pubmedqa.github.io/ diff --git a/src/lighteval/tasks/tasks/qa4mre.py b/src/lighteval/tasks/tasks/qa4mre.py index 367b0b4e0..3e4b40dd9 100644 --- a/src/lighteval/tasks/tasks/qa4mre.py +++ b/src/lighteval/tasks/tasks/qa4mre.py @@ -10,7 +10,7 @@ en tags: -reading-comprehension, qa, health, biomedical +biomedical, health, qa paper: https://link.springer.com/chapter/10.1007/978-3-642-40802-1_29 diff --git a/src/lighteval/tasks/tasks/quac.py b/src/lighteval/tasks/tasks/quac.py index 8fc5004d9..388943087 100644 --- a/src/lighteval/tasks/tasks/quac.py +++ b/src/lighteval/tasks/tasks/quac.py @@ -6,7 +6,7 @@ en tags: -qa, dialogue +dialog, qa paper: https://aclanthology.org/D18-1241/ diff --git a/src/lighteval/tasks/tasks/race_high.py b/src/lighteval/tasks/tasks/race_high.py index 8f17437b8..97d3398ca 100644 --- a/src/lighteval/tasks/tasks/race_high.py +++ b/src/lighteval/tasks/tasks/race_high.py @@ -10,7 +10,7 @@ en tags: -reading-comprehension +multiple-choice, reading-comprehension paper: https://aclanthology.org/D17-1082/ diff --git a/src/lighteval/tasks/tasks/raft.py b/src/lighteval/tasks/tasks/raft.py index bfea7d383..1eb91b7ad 100644 --- a/src/lighteval/tasks/tasks/raft.py +++ b/src/lighteval/tasks/tasks/raft.py @@ -7,7 +7,7 @@ en tags: -text-classification +classification, reasoning paper: https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ca46c1b9512a7a8315fa3c5a946e8265-Abstract-round2.html diff --git a/src/lighteval/tasks/tasks/real_toxicity_prompts.py b/src/lighteval/tasks/tasks/real_toxicity_prompts.py index b45aa27da..c64ff848f 100644 --- a/src/lighteval/tasks/tasks/real_toxicity_prompts.py +++ b/src/lighteval/tasks/tasks/real_toxicity_prompts.py @@ -6,7 +6,7 @@ en tags: -toxicity +generation, safety paper: https://aclanthology.org/2020.findings-emnlp.301/ diff --git a/src/lighteval/tasks/tasks/simpleqa.py b/src/lighteval/tasks/tasks/simpleqa.py index 5d88c2c58..6c2e8fedd 100644 --- a/src/lighteval/tasks/tasks/simpleqa.py +++ b/src/lighteval/tasks/tasks/simpleqa.py @@ -7,7 +7,7 @@ en tags: -qa, factuality, general-knowledge +factuality, general-knowledge, qa paper: https://openai.com/index/introducing-simpleqa/ diff --git a/src/lighteval/tasks/tasks/siqa.py b/src/lighteval/tasks/tasks/siqa.py index 47a7081fc..eaa2834ba 100644 --- a/src/lighteval/tasks/tasks/siqa.py +++ b/src/lighteval/tasks/tasks/siqa.py @@ -17,7 +17,7 @@ en tags: -qa, social-intelligence, commonsense +commonsense, multiple-choice, qa paper: diff --git a/src/lighteval/tasks/tasks/storycloze.py b/src/lighteval/tasks/tasks/storycloze.py index 85933f2fe..b9c6b142c 100644 --- a/src/lighteval/tasks/tasks/storycloze.py +++ b/src/lighteval/tasks/tasks/storycloze.py @@ -7,7 +7,7 @@ en tags: -commonsense, reading-comprehension +narrative, reasoning paper: https://arxiv.org/abs/1604.01696 diff --git a/src/lighteval/tasks/tasks/swag.py b/src/lighteval/tasks/tasks/swag.py index 19094427d..02ded6482 100644 --- a/src/lighteval/tasks/tasks/swag.py +++ b/src/lighteval/tasks/tasks/swag.py @@ -13,7 +13,7 @@ en tags: -commonsense, grounded-commonsense, nli +narrative, reasoning paper: https://arxiv.org/abs/1808.05326 diff --git a/src/lighteval/tasks/tasks/synthetic_reasoning.py b/src/lighteval/tasks/tasks/synthetic_reasoning.py index 7d0c8972f..7cd681476 100644 --- a/src/lighteval/tasks/tasks/synthetic_reasoning.py +++ b/src/lighteval/tasks/tasks/synthetic_reasoning.py @@ -6,7 +6,7 @@ en tags: -reasoning, math +reasoning paper: https://arxiv.org/abs/2206.03855 diff --git a/src/lighteval/tasks/tasks/toxigen.py b/src/lighteval/tasks/tasks/toxigen.py index 442a6dc39..f327c0262 100644 --- a/src/lighteval/tasks/tasks/toxigen.py +++ b/src/lighteval/tasks/tasks/toxigen.py @@ -7,7 +7,7 @@ en tags: -toxicity +generation, safety paper: https://arxiv.org/abs/2203.09509 diff --git a/src/lighteval/tasks/tasks/triviaqa.py b/src/lighteval/tasks/tasks/triviaqa.py index 5a631c2a3..472c29cb9 100644 --- a/src/lighteval/tasks/tasks/triviaqa.py +++ b/src/lighteval/tasks/tasks/triviaqa.py @@ -10,7 +10,7 @@ en tags: -reading-comprehension +qa paper: https://arxiv.org/abs/1705.03551 diff --git a/src/lighteval/tasks/tasks/truthfulqa.py b/src/lighteval/tasks/tasks/truthfulqa.py index d07d000f9..eb5cec634 100644 --- a/src/lighteval/tasks/tasks/truthfulqa.py +++ b/src/lighteval/tasks/tasks/truthfulqa.py @@ -6,7 +6,7 @@ en tags: -truthfulness +factuality, qa paper: https://arxiv.org/abs/2109.07958 diff --git a/src/lighteval/tasks/tasks/twitterAAE.py b/src/lighteval/tasks/tasks/twitterAAE.py index 73409b31b..39b076531 100644 --- a/src/lighteval/tasks/tasks/twitterAAE.py +++ b/src/lighteval/tasks/tasks/twitterAAE.py @@ -6,7 +6,7 @@ en tags: -dialectal, social-media +language-modeling paper: https://aclanthology.org/D16-1120/ diff --git a/src/lighteval/tasks/tasks/unscramble.py b/src/lighteval/tasks/tasks/unscramble.py index cfd41653d..4fd3bae68 100644 --- a/src/lighteval/tasks/tasks/unscramble.py +++ b/src/lighteval/tasks/tasks/unscramble.py @@ -7,7 +7,7 @@ en tags: -unscrambling, anagram, random insertion, reversed words +language-modeling, reasoning paper: https://huggingface.co/datasets/lighteval/GPT3_unscramble diff --git a/src/lighteval/tasks/tasks/winogrande.py b/src/lighteval/tasks/tasks/winogrande.py index c6e7de105..7fb3b6d9c 100644 --- a/src/lighteval/tasks/tasks/winogrande.py +++ b/src/lighteval/tasks/tasks/winogrande.py @@ -10,7 +10,7 @@ en tags: -commonsense, commonsense-reasoning +commonsense, multiple-choice paper: https://arxiv.org/abs/1907.10641 diff --git a/src/lighteval/tasks/tasks/xcopa.py b/src/lighteval/tasks/tasks/xcopa.py index e905536b9..d3d975b96 100644 --- a/src/lighteval/tasks/tasks/xcopa.py +++ b/src/lighteval/tasks/tasks/xcopa.py @@ -8,7 +8,7 @@ en tags: -commonsense, commonsense-reasoning +commonsense, multilingual, multiple-choice, reasoning paper: https://arxiv.org/abs/2005.00333 diff --git a/src/lighteval/tasks/tasks/xstory_cloze.py b/src/lighteval/tasks/tasks/xstory_cloze.py index 8ce1a373f..02814f7f2 100644 --- a/src/lighteval/tasks/tasks/xstory_cloze.py +++ b/src/lighteval/tasks/tasks/xstory_cloze.py @@ -8,7 +8,7 @@ en, ru, zh, es, ar, hi, id, te, sw, eu, my tags: -commonsense, commonsense-reasoning, multilingual +multilingual, narrative, reasoning paper: diff --git a/src/lighteval/tasks/tasks/xwinograd.py b/src/lighteval/tasks/tasks/xwinograd.py index 171be2a9a..8c0daa61c 100644 --- a/src/lighteval/tasks/tasks/xwinograd.py +++ b/src/lighteval/tasks/tasks/xwinograd.py @@ -6,7 +6,7 @@ en, fr, jp, pt, ru, zh tags: -commonsense, commonsense-reasoning, multilingual +commonsense, multilingual, reasoning paper: https://arxiv.org/abs/2211.01786 From 1ed1602a19229894aff8bd1cd587a482f74d43f9 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Tue, 14 Oct 2025 15:38:56 +0200 Subject: [PATCH 17/43] add docstring for all multilingual tasks --- .../tasks/multilingual/tasks/acva.py | 28 ++++------- .../tasks/multilingual/tasks/afri_mgsm.py | 33 +++++-------- .../tasks/multilingual/tasks/afri_mmlu.py | 32 +++++-------- .../tasks/multilingual/tasks/afri_xnli.py | 33 +++++-------- .../tasks/multilingual/tasks/arabic_arc.py | 28 ++++------- .../tasks/multilingual/tasks/arabic_mmlu.py | 28 ++++------- .../tasks/multilingual/tasks/arcd.py | 29 ++++-------- .../tasks/multilingual/tasks/belebele.py | 35 +++++--------- src/lighteval/tasks/multilingual/tasks/c3.py | 30 +++++------- .../tasks/multilingual/tasks/ceval.py | 27 ++++------- .../tasks/multilingual/tasks/chegeka.py | 28 ++++------- .../tasks/multilingual/tasks/chinese_squad.py | 32 ++++--------- .../tasks/multilingual/tasks/cmath.py | 28 ++++------- .../tasks/multilingual/tasks/cmmlu.py | 28 ++++------- .../tasks/multilingual/tasks/cmnli.py | 32 ++++--------- .../tasks/multilingual/tasks/cmrc2018.py | 32 ++++--------- .../tasks/multilingual/tasks/copa_indic.py | 32 +++++-------- .../tasks/multilingual/tasks/enem.py | 35 +++++--------- .../tasks/multilingual/tasks/exams.py | 29 ++++-------- .../tasks/multilingual/tasks/faquad.py | 32 ++++--------- .../tasks/multilingual/tasks/flores200.py | 28 +++++------ .../tasks/multilingual/tasks/fquad_v2.py | 32 ++++--------- .../tasks/multilingual/tasks/french_boolq.py | 28 ++++------- .../multilingual/tasks/french_triviqa.py | 28 ++++------- .../tasks/multilingual/tasks/germanquad.py | 32 ++++--------- .../tasks/multilingual/tasks/global_mmlu.py | 39 ++++++--------- .../tasks/multilingual/tasks/hellaswag_hin.py | 28 ++++------- .../tasks/multilingual/tasks/hellaswag_tel.py | 28 ++++------- .../tasks/multilingual/tasks/hellaswag_tha.py | 35 ++++---------- .../tasks/multilingual/tasks/hellaswag_tur.py | 39 +++++---------- .../tasks/multilingual/tasks/hindi_arc.py | 28 ++++------- .../tasks/multilingual/tasks/hindi_boolq.py | 28 ++++------- .../tasks/multilingual/tasks/indicqa.py | 33 +++++-------- .../tasks/multilingual/tasks/kenswquad.py | 32 ++++--------- .../tasks/multilingual/tasks/m3exams.py | 35 +++++--------- .../multilingual/tasks/mathlogicqa_rus.py | 38 +++++---------- .../tasks/multilingual/tasks/meta_mmlu.py | 31 ++++-------- .../tasks/multilingual/tasks/mgsm.py | 29 ++++-------- .../tasks/multilingual/tasks/mintaka.py | 28 ++++------- .../tasks/multilingual/tasks/mkqa.py | 30 +++++------- .../multilingual/tasks/mlmm_arc_challenge.py | 47 ++++++++----------- .../multilingual/tasks/mlmm_hellaswag.py | 44 +++++++---------- .../tasks/multilingual/tasks/mlmm_mmlu.py | 34 +++++--------- .../multilingual/tasks/mlmm_truthfulqa.py | 39 +++++---------- .../tasks/multilingual/tasks/mlqa.py | 38 ++++++--------- .../tasks/multilingual/tasks/oab_exams.py | 34 +++++--------- .../tasks/multilingual/tasks/ocnli.py | 33 ++++--------- .../tasks/multilingual/tasks/openai_mmlu.py | 28 ++++------- .../tasks/multilingual/tasks/openbook_ara.py | 42 ++++++----------- .../tasks/multilingual/tasks/openbook_es.py | 32 ++++--------- .../tasks/multilingual/tasks/openbook_rus.py | 33 +++++-------- .../tasks/multilingual/tasks/parus.py | 36 +++++--------- .../tasks/multilingual/tasks/paws_x.py | 38 +++++---------- .../tasks/multilingual/tasks/piqa_ar.py | 40 +++++----------- src/lighteval/tasks/multilingual/tasks/rcb.py | 34 +++++--------- .../tasks/multilingual/tasks/sber_squad.py | 32 ++++--------- .../tasks/multilingual/tasks/soqal.py | 32 ++++--------- .../tasks/multilingual/tasks/squad_es.py | 32 ++++--------- .../tasks/multilingual/tasks/squad_it.py | 32 ++++--------- .../tasks/multilingual/tasks/swahili_arc.py | 28 ++++------- .../tasks/multilingual/tasks/thai_exams.py | 28 ++++------- .../tasks/multilingual/tasks/thaiqa.py | 30 ++++-------- .../tasks/multilingual/tasks/tquad_v2.py | 30 ++++-------- .../tasks/multilingual/tasks/turkish_arc.py | 31 ++++-------- .../tasks/multilingual/tasks/turkish_mmlu.py | 28 ++++------- .../tasks/multilingual/tasks/tydiqa.py | 33 ++++--------- .../tasks/multilingual/tasks/worldtree_rus.py | 37 +++++---------- .../tasks/multilingual/tasks/xcodah.py | 30 ++++-------- .../tasks/multilingual/tasks/xcopa.py | 40 ++++++---------- .../tasks/multilingual/tasks/xcsqa.py | 41 ++++++---------- .../tasks/multilingual/tasks/xnli.py | 46 +++++++----------- .../tasks/multilingual/tasks/xnli2.py | 36 +++++--------- .../tasks/multilingual/tasks/xnli_indic.py | 33 +++++-------- .../tasks/multilingual/tasks/xquad.py | 42 ++++++----------- .../tasks/multilingual/tasks/xstory.py | 28 ++++------- .../tasks/multilingual/tasks/xwinograd.py | 29 ++++-------- 76 files changed, 809 insertions(+), 1681 deletions(-) diff --git a/src/lighteval/tasks/multilingual/tasks/acva.py b/src/lighteval/tasks/multilingual/tasks/acva.py index 262eb7394..f60c84f4c 100644 --- a/src/lighteval/tasks/multilingual/tasks/acva.py +++ b/src/lighteval/tasks/multilingual/tasks/acva.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Acva multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +arabic +tags: +knowledge, multilingual, multiple-choice +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py b/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py index c09d50e2d..babfd7d45 100644 --- a/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py +++ b/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py @@ -1,25 +1,17 @@ -# MIT License +""" +abstract: +African MGSM: MGSM for African Languages -# Copyright (c) 2024 The HuggingFace Team +languages: +amharic, ewe, french, hausa, igbo, kinyarwanda, lingala, luganda, oromo, shona, +sotho, swahili, twi, wolof, xhosa, yoruba, zulu -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +math, multilingual, reasoning +paper: +https://arxiv.org/abs/2406.03368. +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -29,9 +21,6 @@ from lighteval.utils.language import Language -# African MGSM: MGSM for African Languages -# From https://arxiv.org/abs/2406.03368. Human translated MGSM. - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py b/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py index 511f0cfc1..7121028e1 100644 --- a/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py @@ -1,24 +1,17 @@ -# MIT License +""" +abstract: +African MMLU: African Massive Multitask Language Understanding -# Copyright (c) 2024 The HuggingFace Team +languages: +amharic, ewe, french, hausa, igbo, kinyarwanda, lingala, luganda, oromo, shona, +sotho, swahili, twi, wolof, xhosa, yoruba, zulu -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: +tags: +knowledge, multilingual, multiple-choice -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +paper: +https://arxiv.org/abs/2406.03368. +""" from functools import partial @@ -38,9 +31,6 @@ from lighteval.utils.language import Language -# African MMLU: African Massive Multitask Language Understanding -# From https://arxiv.org/abs/2406.03368. Human translated MMLU. - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/afri_xnli.py b/src/lighteval/tasks/multilingual/tasks/afri_xnli.py index b7e5a48e7..d506c1584 100644 --- a/src/lighteval/tasks/multilingual/tasks/afri_xnli.py +++ b/src/lighteval/tasks/multilingual/tasks/afri_xnli.py @@ -1,25 +1,17 @@ -# MIT License +""" +abstract: +African XNLI: African XNLI -# Copyright (c) 2024 The HuggingFace Team +languages: +amharic, ewe, french, hausa, igbo, kinyarwanda, lingala, luganda, oromo, shona, +sotho, swahili, twi, wolof, xhosa, yoruba, zulu -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +classification, multilingual, nli +paper: +https://arxiv.org/abs/2406.03368. +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -36,9 +28,6 @@ from lighteval.utils.language import Language -# African XNLI: African XNLI -# From https://arxiv.org/abs/2406.03368. Human translated MMLU. - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/arabic_arc.py b/src/lighteval/tasks/multilingual/tasks/arabic_arc.py index d2a19e8da..9e472798f 100644 --- a/src/lighteval/tasks/multilingual/tasks/arabic_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/arabic_arc.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Arabic Arc multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +arabic +tags: +multilingual, multiple-choice, reasoning +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py b/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py index 582800db7..83b405271 100644 --- a/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Arabic Mmlu multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +arabic +tags: +knowledge, multilingual, multiple-choice +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/arcd.py b/src/lighteval/tasks/multilingual/tasks/arcd.py index 25172500f..85b18e2b1 100644 --- a/src/lighteval/tasks/multilingual/tasks/arcd.py +++ b/src/lighteval/tasks/multilingual/tasks/arcd.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +ARCD: Arabic Reading Comprehension Dataset. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, qa, reasoning +paper: +https://arxiv.org/pdf/1906.05394 +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/belebele.py b/src/lighteval/tasks/multilingual/tasks/belebele.py index ed9f61eae..d2f9bc145 100644 --- a/src/lighteval/tasks/multilingual/tasks/belebele.py +++ b/src/lighteval/tasks/multilingual/tasks/belebele.py @@ -1,25 +1,19 @@ -# MIT License +""" +abstract: +Belebele: A large-scale reading comprehension dataset covering 122 languages. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, armenian, bengali, cyrillic, devanagari, ethiopic, georgian, greek, +gujarati, gurmukhi, chinese (simplified), chinese (traditional), hangul, hebrew, +japanese, khmer, kannada, lao, latin, malayalam, myanmar, odia, sinhala, tamil, +telugu, thai, tibetan -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, reading-comprehension +paper: +https://arxiv.org/abs/2308.16884 +""" from langcodes import Language as LangCodeLanguage @@ -38,9 +32,6 @@ from lighteval.utils.language import iso_639_3_ind_to_iso_639_3_macro -# Belebele: A large-scale reading comprehension dataset covering 122 languages. -# https://arxiv.org/abs/2308.16884 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/c3.py b/src/lighteval/tasks/multilingual/tasks/c3.py index 19a4d1dd3..ef7174985 100644 --- a/src/lighteval/tasks/multilingual/tasks/c3.py +++ b/src/lighteval/tasks/multilingual/tasks/c3.py @@ -1,25 +1,17 @@ -# MIT License +""" +abstract: +C3: A Chinese Challenge Corpus for Cross-lingual and Cross-modal Tasks Reading +comprehension task part of clue. -# Copyright (c) 2024 The HuggingFace Team +languages: +chinese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, reasoning +paper: +https://arxiv.org/abs/2004.05986 +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/ceval.py b/src/lighteval/tasks/multilingual/tasks/ceval.py index 37503e313..0addcb728 100644 --- a/src/lighteval/tasks/multilingual/tasks/ceval.py +++ b/src/lighteval/tasks/multilingual/tasks/ceval.py @@ -1,24 +1,13 @@ -# MIT License +""" +abstract: +Ceval multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team +languages: +chinese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +knowledge, multilingual, multiple-choice +""" from functools import partial diff --git a/src/lighteval/tasks/multilingual/tasks/chegeka.py b/src/lighteval/tasks/multilingual/tasks/chegeka.py index 50ceb7fc3..b6061bd9d 100644 --- a/src/lighteval/tasks/multilingual/tasks/chegeka.py +++ b/src/lighteval/tasks/multilingual/tasks/chegeka.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Chegeka multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +russian +tags: +knowledge, multilingual, qa +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/chinese_squad.py b/src/lighteval/tasks/multilingual/tasks/chinese_squad.py index 34152a67c..c96d5f310 100644 --- a/src/lighteval/tasks/multilingual/tasks/chinese_squad.py +++ b/src/lighteval/tasks/multilingual/tasks/chinese_squad.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +ChineseSquad is a reading comprehension dataset for Chinese. -# Copyright (c) 2024 The HuggingFace Team +languages: +chinese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://github.com/pluto-junzeng/ChineseSquad +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -30,9 +21,6 @@ from lighteval.utils.language import Language -# ChineseSquad: A reading comprehension dataset for Chinese. -# https://github.com/pluto-junzeng/ChineseSquad - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/cmath.py b/src/lighteval/tasks/multilingual/tasks/cmath.py index dd23ba79e..aefa0c2dc 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmath.py +++ b/src/lighteval/tasks/multilingual/tasks/cmath.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Cmath multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +chinese +tags: +math, multilingual, reasoning +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/cmmlu.py b/src/lighteval/tasks/multilingual/tasks/cmmlu.py index bb9c5c39f..c79e34eb6 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/cmmlu.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Cmmlu multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +chinese +tags: +knowledge, multilingual, multiple-choice +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/cmnli.py b/src/lighteval/tasks/multilingual/tasks/cmnli.py index 7c6371613..13df2f0c6 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmnli.py +++ b/src/lighteval/tasks/multilingual/tasks/cmnli.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +Native Chinese NLI dataset based on MNLI approach (Machine Translated) -# Copyright (c) 2024 The HuggingFace Team +languages: +chinese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +classification, multilingual, nli +paper: +https://arxiv.org/abs/2004.05986 +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -36,9 +27,6 @@ from lighteval.utils.language import Language -# https://arxiv.org/abs/2004.05986 -# Native Chinese NLI dataset based on MNLI approach (Machine Translated) - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/cmrc2018.py b/src/lighteval/tasks/multilingual/tasks/cmrc2018.py index 2f4dd36ab..d8330b089 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmrc2018.py +++ b/src/lighteval/tasks/multilingual/tasks/cmrc2018.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +CMRC 2018: A span-extraction machine reading comprehension dataset for Chinese. -# Copyright (c) 2024 The HuggingFace Team +languages: +chinese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://arxiv.org/abs/1810.07366 +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -30,9 +21,6 @@ from lighteval.utils.language import Language -# CMRC 2018: A span-extraction machine reading comprehension dataset for Chinese. -# https://arxiv.org/abs/1810.07366 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/copa_indic.py b/src/lighteval/tasks/multilingual/tasks/copa_indic.py index fb6366f94..64b2a0261 100644 --- a/src/lighteval/tasks/multilingual/tasks/copa_indic.py +++ b/src/lighteval/tasks/multilingual/tasks/copa_indic.py @@ -1,25 +1,19 @@ -# MIT License +""" +abstract: +IndicCOPA: COPA for Indic Languages Paper: https://arxiv.org/pdf/2212.05409 +IndicCOPA extends COPA to 15 Indic languages, providing a valuable resource for +evaluating common sense reasoning in these languages. -# Copyright (c) 2024 The HuggingFace Team +languages: +assamese, bengali, gujarati, hindi, kannada, malayalam, marathi, nepali, oriya, +punjabi, sanskrit, sindhi, tamil, telugu, urdu -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, reasoning +paper: +https://arxiv.org/pdf/2212.05409 +""" from langcodes import standardize_tag diff --git a/src/lighteval/tasks/multilingual/tasks/enem.py b/src/lighteval/tasks/multilingual/tasks/enem.py index 9eb6e4f6a..22d8e85ab 100644 --- a/src/lighteval/tasks/multilingual/tasks/enem.py +++ b/src/lighteval/tasks/multilingual/tasks/enem.py @@ -1,24 +1,18 @@ -# MIT License +""" +abstract: +ENEM (Exame Nacional do Ensino Médio) is a standardized Brazilian national +secondary education examination. The exam is used both as a university admission +test and as a high school evaluation test. -# Copyright (c) 2024 The HuggingFace Team +languages: +portuguese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: +tags: +knowledge, multilingual, multiple-choice -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +paper: +https://huggingface.co/datasets/maritaca-ai/enem +""" from functools import partial @@ -40,11 +34,6 @@ from lighteval.utils.language import Language -# ENEM (Exame Nacional do Ensino Médio) is a standardized Brazilian national secondary -# education examination. The exam is used both as a university admission test and as a -# high school evaluation test. -# Dataset: https://huggingface.co/datasets/maritaca-ai/enem - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/exams.py b/src/lighteval/tasks/multilingual/tasks/exams.py index 1fce34c22..f94224a7b 100644 --- a/src/lighteval/tasks/multilingual/tasks/exams.py +++ b/src/lighteval/tasks/multilingual/tasks/exams.py @@ -1,24 +1,15 @@ -# MIT License +""" +abstract: +Exams multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team +languages: +albanian, arabic, bulgarian, croatian, french, german, hungarian, italian, +lithuanian, macedonian, polish, portuguese, serbian, spanish, turkish, +vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +knowledge, multilingual, multiple-choice +""" from functools import partial diff --git a/src/lighteval/tasks/multilingual/tasks/faquad.py b/src/lighteval/tasks/multilingual/tasks/faquad.py index 47c896b93..e3cb1038e 100644 --- a/src/lighteval/tasks/multilingual/tasks/faquad.py +++ b/src/lighteval/tasks/multilingual/tasks/faquad.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +FaQuAD: A Portuguese Reading Comprehension Dataset -# Copyright (c) 2024 The HuggingFace Team +languages: +portuguese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://arxiv.org/abs/2007.15671 +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -30,9 +21,6 @@ from lighteval.utils.language import Language -# FaQuAD: A Portuguese Reading Comprehension Dataset -# https://arxiv.org/abs/2007.15671 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/flores200.py b/src/lighteval/tasks/multilingual/tasks/flores200.py index 7e84d6b27..17e16dd3e 100644 --- a/src/lighteval/tasks/multilingual/tasks/flores200.py +++ b/src/lighteval/tasks/multilingual/tasks/flores200.py @@ -1,24 +1,18 @@ -# MIT License +""" +abstract: +Flores200 multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: +languages: +arabic, armenian, bengali, cyrillic, devanagari, ethiopic, georgian, greek, +gujarati, gurmukhi, chinese (simplified), chinese (traditional), hangul, hebrew, +japanese, khmer, kannada, lao, latin, malayalam, myanmar, odia, sinhala, tamil, +telugu, thai, tibetan -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, translation +""" from itertools import permutations diff --git a/src/lighteval/tasks/multilingual/tasks/fquad_v2.py b/src/lighteval/tasks/multilingual/tasks/fquad_v2.py index 9deae5e65..fcc1c2f7a 100644 --- a/src/lighteval/tasks/multilingual/tasks/fquad_v2.py +++ b/src/lighteval/tasks/multilingual/tasks/fquad_v2.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +FQuAD v2: French Question Answering Dataset version 2. -# Copyright (c) 2024 The HuggingFace Team +languages: +french -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://arxiv.org/abs/2002.06071 +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -30,9 +21,6 @@ from lighteval.utils.language import Language -# FQuAD v2: French Question Answering Dataset version 2. -# https://arxiv.org/abs/2002.06071 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/french_boolq.py b/src/lighteval/tasks/multilingual/tasks/french_boolq.py index 856209f61..533d37010 100644 --- a/src/lighteval/tasks/multilingual/tasks/french_boolq.py +++ b/src/lighteval/tasks/multilingual/tasks/french_boolq.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +French Boolq multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +french +tags: +classification, multilingual, qa +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/french_triviqa.py b/src/lighteval/tasks/multilingual/tasks/french_triviqa.py index b203e96f5..7e06acaf0 100644 --- a/src/lighteval/tasks/multilingual/tasks/french_triviqa.py +++ b/src/lighteval/tasks/multilingual/tasks/french_triviqa.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +French Triviqa multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +french +tags: +multilingual, qa +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/germanquad.py b/src/lighteval/tasks/multilingual/tasks/germanquad.py index 096862449..02c10d3bf 100644 --- a/src/lighteval/tasks/multilingual/tasks/germanquad.py +++ b/src/lighteval/tasks/multilingual/tasks/germanquad.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +GermanQuAD: High-quality German QA dataset with 13,722 questions. -# Copyright (c) 2024 The HuggingFace Team +languages: +german -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://arxiv.org/abs/2104.12741 +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -30,9 +21,6 @@ from lighteval.utils.language import Language -# GermanQuAD: High-quality German QA dataset with 13,722 questions -# https://arxiv.org/abs/2104.12741 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/global_mmlu.py b/src/lighteval/tasks/multilingual/tasks/global_mmlu.py index 40d23d459..0c32440db 100644 --- a/src/lighteval/tasks/multilingual/tasks/global_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/global_mmlu.py @@ -1,24 +1,20 @@ -# MIT License +""" +abstract: +Translated MMLU using both professional and non-professional translators. +Contains tags for cultural sensitivity. -# Copyright (c) 2024 The HuggingFace Team +languages: +amharic, arabic, bengali, chinese, czech, dutch, english, french, german, +hebrew, hindi, indonesian, italian, japanese, korean, malay, norwegian, polish, +portuguese, romanian, russian, serbian, spanish, swahili, swedish, tamil, +telugu, thai, turkish, ukrainian, urdu, vietnamese, yoruba, zulu -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: +tags: +knowledge, multilingual, multiple-choice -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +paper: +https://huggingface.co/papers/2412.03304 +""" from functools import partial @@ -40,13 +36,6 @@ from lighteval.utils.language import Language -# Translated MMLU using both professional and non-professional translators. Contains tags for cultural sensitivity. -# CA: Cultural Agnostic -# CS: Cultural Specific -# UNK: Not annotated -# ALL: All of the above -# https://huggingface.co/papers/2412.03304 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py index 038fe26c1..b18831ae3 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Hellaswag Hin multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +hindi +tags: +multilingual, multiple-choice, reasoning +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py index 2df720beb..1a4fcde22 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Hellaswag Tel multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +telugu +tags: +multilingual, multiple-choice, reasoning +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py index e97772341..0d5c67b5f 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py @@ -1,25 +1,15 @@ -# MIT License +""" +abstract: +Hellaswag Thai This is a Thai adaptation of the Hellaswag task. Similar to the +Turkish version, there's no specific paper, but it has been found to be +effective for evaluating Thai language models on commonsense reasoning tasks. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +thai +tags: +multilingual, multiple-choice, reasoning +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -36,11 +26,6 @@ from lighteval.utils.language import Language -# Hellaswag Thai -# This is a Thai adaptation of the Hellaswag task. -# Similar to the Turkish version, there's no specific paper, but it has been found to be effective -# for evaluating Thai language models on commonsense reasoning tasks. - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py index 2b0f3f696..6a5601f2a 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py @@ -1,25 +1,17 @@ -# MIT License +""" +abstract: +Hellaswag Turkish This is a Turkish adaptation of the Hellaswag task. While +there's no specific paper for this version, it has been found to work well for +evaluating Turkish language models on commonsense reasoning tasks. We don't +handle them in single task as there is quite a lot of differences +(dataset/subset, dot replacement, etc.) which would make it hard to read -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +turkish +tags: +multilingual, multiple-choice, reasoning +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -36,13 +28,6 @@ from lighteval.utils.language import Language -# Hellaswag Turkish -# This is a Turkish adaptation of the Hellaswag task. -# While there's no specific paper for this version, it has been found to work well for evaluating -# Turkish language models on commonsense reasoning tasks. -# We don't handle them in single task as there is quite a lot of differences (dataset/subset, dot replacement, etc.) -# which would make it hard to read - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/hindi_arc.py b/src/lighteval/tasks/multilingual/tasks/hindi_arc.py index 7f8f4ebcc..c5be93380 100644 --- a/src/lighteval/tasks/multilingual/tasks/hindi_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/hindi_arc.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Hindi Arc multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +hindi +tags: +multilingual, multiple-choice, reasoning +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py b/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py index b7b019543..e19f7ab3a 100644 --- a/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py +++ b/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Hindi Boolq multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +gujarati, hindi, malayalam, marathi, tamil +tags: +classification, multilingual, qa +""" from langcodes import standardize_tag diff --git a/src/lighteval/tasks/multilingual/tasks/indicqa.py b/src/lighteval/tasks/multilingual/tasks/indicqa.py index 1dacf5c55..717f34226 100644 --- a/src/lighteval/tasks/multilingual/tasks/indicqa.py +++ b/src/lighteval/tasks/multilingual/tasks/indicqa.py @@ -1,25 +1,17 @@ -# MIT License +""" +abstract: +IndicQA: A reading comprehension dataset for 11 Indian languages. -# Copyright (c) 2024 The HuggingFace Team +languages: +assamese, bengali, gujarati, hindi, kannada, malayalam, marathi, oriya, punjabi, +tamil, telugu -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://arxiv.org/abs/2407.13522 +""" from langcodes import Language as LangCodeLanguage @@ -32,9 +24,6 @@ from lighteval.utils.language import Language -# IndicQA: A reading comprehension dataset for 11 Indian languages. -# https://arxiv.org/abs/2407.13522 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/kenswquad.py b/src/lighteval/tasks/multilingual/tasks/kenswquad.py index 4da082d5b..cdf9f5f08 100644 --- a/src/lighteval/tasks/multilingual/tasks/kenswquad.py +++ b/src/lighteval/tasks/multilingual/tasks/kenswquad.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +KenSwQuAD: A question answering dataset for Kenyan Swahili. -# Copyright (c) 2024 The HuggingFace Team +languages: +swahili -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://arxiv.org/abs/2205.02364 +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -30,9 +21,6 @@ from lighteval.utils.language import Language -# KenSwQuAD: A question answering dataset for Kenyan Swahili. -# https://arxiv.org/abs/2205.02364 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/m3exams.py b/src/lighteval/tasks/multilingual/tasks/m3exams.py index e9c92ded6..1f2f8e159 100644 --- a/src/lighteval/tasks/multilingual/tasks/m3exams.py +++ b/src/lighteval/tasks/multilingual/tasks/m3exams.py @@ -1,24 +1,19 @@ -# MIT License +""" +abstract: +M3Exam: Multitask Multilingual Multimodal Evaluation Benchmark It also contains +a multimodal version but we don't support that Paper: +https://arxiv.org/abs/2306.05179 -# Copyright (c) 2024 The HuggingFace Team +languages: +afrikaans, chinese, english, italian, javanese, portuguese, swahili, thai, +vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: +tags: +knowledge, multilingual, multiple-choice -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +paper: +https://arxiv.org/abs/2306.05179 +""" from functools import partial @@ -43,10 +38,6 @@ from lighteval.utils.language import Language -# M3Exam: Multitask Multilingual Multimodal Evaluation Benchmark -# It also contains a multimodal version but we don't support that -# Paper: https://arxiv.org/abs/2306.05179 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py b/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py index 719a14c32..c49190170 100644 --- a/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py +++ b/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py @@ -1,25 +1,19 @@ -# MIT License +""" +abstract: +MathLogicQA is a dataset for evaluating mathematical reasoning in language +models. It consists of multiple-choice questions that require logical reasoning +and mathematical problem-solving. This Russian version is part of the MERA +(Multilingual Evaluation of Reasoning Abilities) benchmark. -# Copyright (c) 2024 The HuggingFace Team +languages: +russian -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +math, multilingual, qa, reasoning +paper: +https://github.com/ai-forever/MERA +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -37,12 +31,6 @@ from lighteval.utils.language import Language -# ------------------------------- Math Tasks ------------------------------- # -# MathLogicQA is a dataset for evaluating mathematical reasoning in language models. -# It consists of multiple-choice questions that require logical reasoning and mathematical problem-solving. -# This Russian version is part of the MERA (Multilingual Evaluation of Reasoning Abilities) benchmark. -# MERA: https://github.com/ai-forever/MERA - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py b/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py index ecf91526c..b2b29db8d 100644 --- a/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py @@ -1,24 +1,16 @@ -# MIT License +""" +abstract: +Meta MMLU: A multilingual version of MMLU (using google translation) -# Copyright (c) 2024 The HuggingFace Team +languages: +french, german, hindi, italian, portuguese, spanish, thai -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: +tags: +knowledge, multilingual, multiple-choice -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +paper: +https://arxiv.org/abs/2407.21783 +""" from functools import partial @@ -40,9 +32,6 @@ from lighteval.utils.language import Language -# Meta MMLU: A multilingual version of MMLU (using google translation) -# Paper: https://arxiv.org/abs/2407.21783 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/mgsm.py b/src/lighteval/tasks/multilingual/tasks/mgsm.py index aeee28ca4..078080f1f 100644 --- a/src/lighteval/tasks/multilingual/tasks/mgsm.py +++ b/src/lighteval/tasks/multilingual/tasks/mgsm.py @@ -1,25 +1,14 @@ -# MIT License +""" +abstract: +Mgsm multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +bengali, chinese, english, french, german, japanese, russian, spanish, swahili, +telugu, thai +tags: +math, multilingual, reasoning +""" from langcodes import standardize_tag diff --git a/src/lighteval/tasks/multilingual/tasks/mintaka.py b/src/lighteval/tasks/multilingual/tasks/mintaka.py index a1dc4c22d..2e86d4dd6 100644 --- a/src/lighteval/tasks/multilingual/tasks/mintaka.py +++ b/src/lighteval/tasks/multilingual/tasks/mintaka.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Mintaka multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +arabic, english, french, german, hindi, italian, japanese, portuguese, spanish +tags: +knowledge, multilingual, qa +""" from langcodes import standardize_tag diff --git a/src/lighteval/tasks/multilingual/tasks/mkqa.py b/src/lighteval/tasks/multilingual/tasks/mkqa.py index b899d67dc..0261d3a21 100644 --- a/src/lighteval/tasks/multilingual/tasks/mkqa.py +++ b/src/lighteval/tasks/multilingual/tasks/mkqa.py @@ -1,24 +1,16 @@ -# MIT License +""" +abstract: +Mkqa multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, chinese, chinese_hong_kong, chinese_traditional, danish, dutch, english, +finnish, french, german, hebrew, hungarian, italian, japanese, khmer, korean, +malay, norwegian, polish, portuguese, russian, spanish, swedish, thai, turkish, +vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +""" from functools import partial diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py b/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py index f591fa55c..7fde820c5 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py @@ -1,25 +1,25 @@ -# MIT License +""" +abstract: +ARC (AI2 Reasoning Challenge) is a dataset for question answering that requires +reasoning. It consists of multiple-choice science questions from 3rd to 9th +grade exams. The dataset is split into two parts: ARC-Easy and ARC-Challenge. +ARC-Easy contains questions that can be answered correctly by both humans and +simple baseline models. ARC-Challenge contains questions that are difficult for +both humans and current AI systems. Similar to MMLU, ARC tasks uses PMI +normalization by default but only for the challenge set. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, bengali, catalan, chinese, croatian, danish, dutch, french, german, +hindi, hungarian, indonesian, italian, kannada, malayalam, marathi, nepali, +romanian, russian, serbian, slovak, spanish, tamil, telugu, ukrainian, +vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, reasoning +paper: +https://github.com/nlp-uoregon/mlmm-evaluation +""" from langcodes import standardize_tag @@ -39,15 +39,6 @@ from lighteval.utils.language import Language -# ---------------------------- ARC ---------------------------- # -# ARC (AI2 Reasoning Challenge) is a dataset for question answering that requires reasoning. -# It consists of multiple-choice science questions from 3rd to 9th grade exams. -# The dataset is split into two parts: ARC-Easy and ARC-Challenge. -# ARC-Easy contains questions that can be answered correctly by both humans and simple baseline models. -# ARC-Challenge contains questions that are difficult for both humans and current AI systems. -# Similar to MMLU, ARC tasks uses PMI normalization by default but only for the challenge set. -# github: https://github.com/nlp-uoregon/mlmm-evaluation - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py b/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py index 1475e6580..4e33f2a1c 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py @@ -1,25 +1,22 @@ -# MIT License +""" +abstract: +Hellaswag is a commonsense reasoning task that requires models to complete a +given scenario with the most plausible ending. It tests the model's ability to +understand and reason about everyday situations and human behavior. +MLMM-Hellaswag: Multilingual adaptation of Hellaswag -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, armenian, basque, bengali, catalan, chinese, croatian, danish, dutch, +french, german, gujarati, hindi, hungarian, icelandic, indonesian, italian, +kannada, malayalam, marathi, nepali, norwegian, portuguese, romanian, russian, +serbian, slovak, spanish, swedish, tamil, telugu, ukrainian, vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, reasoning +paper: +https://arxiv.org/abs/2306.07610 +""" from langcodes import standardize_tag @@ -38,15 +35,6 @@ from lighteval.utils.language import Language -# ------------------------------- Hellaswag Tasks ------------------------------- # -# Hellaswag is a commonsense reasoning task that requires models to complete a given scenario -# with the most plausible ending. It tests the model's ability to understand and reason about -# everyday situations and human behavior. -# MLMM-Hellaswag: Multilingual adaptation of Hellaswag -# Paper: https://arxiv.org/abs/2306.07610 -# This is a multilingual version of Hellaswag, part of the MLMM (Massive Language Model Meta-Evaluation) benchmark. -# It evaluates commonsense reasoning abilities across multiple languages. - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py b/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py index d29e6c803..ba4811c85 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py @@ -1,24 +1,19 @@ -# MIT License +""" +abstract: +MLMM MMLU: Another multilingual version of MMLU -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, bengali, catalan, chinese, croatian, danish, dutch, french, german, +hindi, hungarian, indonesian, italian, kannada, malayalam, marathi, nepali, +romanian, russian, serbian, slovak, spanish, tamil, telugu, ukrainian, +vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: +tags: +knowledge, multilingual, multiple-choice -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +paper: +https://github.com/nlp-uoregon/mlmm-evaluation +""" from functools import partial @@ -40,9 +35,6 @@ from lighteval.utils.language import Language -# MLMM MMLU: Another multilingual version of MMLU -# Paper: https://github.com/nlp-uoregon/mlmm-evaluation - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py b/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py index bac700255..2cf969e39 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py @@ -1,24 +1,19 @@ -# MIT License +""" +abstract: +TruthfulQA: Measuring How Models Mimic Human Falsehoods -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, armenian, basque, bengali, catalan, chinese, croatian, danish, dutch, +french, german, gujarati, hindi, hungarian, icelandic, indonesian, italian, +kannada, malayalam, marathi, nepali, norwegian, portuguese, romanian, russian, +serbian, slovak, spanish, swedish, tamil, telugu, ukrainian, vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: +tags: +factuality, multilingual, qa -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +paper: +https://arxiv.org/abs/2109.07958 +""" from functools import partial @@ -39,14 +34,6 @@ from lighteval.utils.language import Language -# ---------------------------- TruthfulQA ---------------------------- # -# TruthfulQA: Measuring How Models Mimic Human Falsehoods -# Paper: https://arxiv.org/abs/2109.07958 -# TruthfulQA is a benchmark dataset designed to measure the truthfulness of language models. -# It consists of questions that humans might answer incorrectly due to false beliefs or misconceptions. -# The task evaluates a model's ability to provide truthful answers and avoid common human biases. -# github: https://github.com/nlp-uoregon/mlmm-evaluation - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/mlqa.py b/src/lighteval/tasks/multilingual/tasks/mlqa.py index 0011333b5..f8fcdee27 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlqa.py +++ b/src/lighteval/tasks/multilingual/tasks/mlqa.py @@ -1,25 +1,20 @@ -# MIT License +""" +abstract: +MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating +cross-lingual question answering performance. It consists of QA instances in 7 +languages: English, Arabic, German, Spanish, Hindi, Vietnamese, and Chinese. The +dataset is derived from the SQuAD v1.1 dataset, with questions and contexts +translated by professional translators. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, chinese, german, hindi, spanish, vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://arxiv.org/abs/1910.07475 +""" from langcodes import standardize_tag @@ -32,11 +27,6 @@ from lighteval.utils.language import Language -# MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. -# It consists of QA instances in 7 languages: English, Arabic, German, Spanish, Hindi, Vietnamese, and Chinese. -# The dataset is derived from the SQuAD v1.1 dataset, with questions and contexts translated by professional translators. -# Paper: https://arxiv.org/abs/1910.07475 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/oab_exams.py b/src/lighteval/tasks/multilingual/tasks/oab_exams.py index d1de02ccf..7e4689977 100644 --- a/src/lighteval/tasks/multilingual/tasks/oab_exams.py +++ b/src/lighteval/tasks/multilingual/tasks/oab_exams.py @@ -1,25 +1,17 @@ -# MIT License +""" +abstract: +OAB Exams: A collection of questions from the Brazilian Bar Association exam The +exam is required for anyone who wants to practice law in Brazil -# Copyright (c) 2024 The HuggingFace Team +languages: +portuguese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +knowledge, multilingual, multiple-choice +paper: +https://huggingface.co/datasets/eduagarcia/oab_exams +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -37,10 +29,6 @@ from lighteval.utils.language import Language -# OAB Exams: A collection of questions from the Brazilian Bar Association exam -# The exam is required for anyone who wants to practice law in Brazil -# Dataset: https://huggingface.co/datasets/eduagarcia/oab_exams - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/ocnli.py b/src/lighteval/tasks/multilingual/tasks/ocnli.py index 112bceb99..4693fb21a 100644 --- a/src/lighteval/tasks/multilingual/tasks/ocnli.py +++ b/src/lighteval/tasks/multilingual/tasks/ocnli.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +Native Chinese NLI dataset based. -# Copyright (c) 2024 The HuggingFace Team +languages: +chinese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +classification, multilingual, nli +paper: +https://arxiv.org/pdf/2010.05444 +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -36,10 +27,6 @@ from lighteval.utils.language import Language -# Native Chinese NLI dataset based. -# https://arxiv.org/pdf/2010.05444 -# We find this benchmark to have really good signal compared to other Chinese NLI - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py b/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py index 01d98ed60..21e21b2f0 100644 --- a/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py @@ -1,24 +1,14 @@ -# MIT License +""" +abstract: +Openai Mmlu multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, bengali, chinese, french, german, hindi, indonesian, italian, japanese, +korean, portuguese, spanish, swahili, yoruba -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +knowledge, multilingual, multiple-choice +""" from functools import partial diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_ara.py b/src/lighteval/tasks/multilingual/tasks/openbook_ara.py index a1cb13eac..ccd1210d2 100644 --- a/src/lighteval/tasks/multilingual/tasks/openbook_ara.py +++ b/src/lighteval/tasks/multilingual/tasks/openbook_ara.py @@ -1,25 +1,21 @@ -# MIT License +""" +abstract: +OpenBookQA: A Question-Answering Dataset for Open-Book Exams OpenBookQA is a +question-answering dataset modeled after open-book exams for assessing human +understanding of a subject. It consists of multiple-choice questions that +require combining facts from a given open book with broad common knowledge. The +task tests language models' ability to leverage provided information and apply +common sense reasoning. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, reasoning +paper: +https://arxiv.org/abs/1809.02789 +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -39,14 +35,6 @@ from lighteval.utils.language import Language -# ------------------------------- OpenBookQA ------------------------------- # -# OpenBookQA: A Question-Answering Dataset for Open-Book Exams -# OpenBookQA is a question-answering dataset modeled after open-book exams for assessing human understanding of a subject. -# It consists of multiple-choice questions that require combining facts from a given open book with broad common knowledge. -# The task tests language models' ability to leverage provided information and apply common sense reasoning. -# Original paper: https://arxiv.org/abs/1809.02789 -# Arabic version: https://aclanthology.org/2023.arabicnlp-1.21/ - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_es.py b/src/lighteval/tasks/multilingual/tasks/openbook_es.py index 3e23178bc..ba6ad747e 100644 --- a/src/lighteval/tasks/multilingual/tasks/openbook_es.py +++ b/src/lighteval/tasks/multilingual/tasks/openbook_es.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +Spanish version of OpenBookQA from BSC Language Technology group -# Copyright (c) 2024 The HuggingFace Team +languages: +spanish -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, reasoning +paper: +https://huggingface.co/datasets/BSC-LT/openbookqa-es +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -37,9 +28,6 @@ from lighteval.utils.language import Language -# Spanish version of OpenBookQA from BSC Language Technology group -# Dataset: https://huggingface.co/datasets/BSC-LT/openbookqa-es - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_rus.py b/src/lighteval/tasks/multilingual/tasks/openbook_rus.py index 366027413..f77668a0c 100644 --- a/src/lighteval/tasks/multilingual/tasks/openbook_rus.py +++ b/src/lighteval/tasks/multilingual/tasks/openbook_rus.py @@ -1,25 +1,17 @@ -# MIT License +""" +abstract: +The Russian version is part of the MERA (Multilingual Enhanced Russian NLP +Architectures) project. -# Copyright (c) 2024 The HuggingFace Team +languages: +russian -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, reasoning +paper: +https://arxiv.org/abs/2401.04531 +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -37,9 +29,6 @@ from lighteval.utils.language import Language -# The Russian version is part of the MERA (Multilingual Enhanced Russian NLP Architectures) project. -# Paper: https://arxiv.org/abs/2401.04531 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/parus.py b/src/lighteval/tasks/multilingual/tasks/parus.py index 546907377..170ff4b15 100644 --- a/src/lighteval/tasks/multilingual/tasks/parus.py +++ b/src/lighteval/tasks/multilingual/tasks/parus.py @@ -1,25 +1,18 @@ -# MIT License +""" +abstract: +PARus: Plausible Alternatives for Russian PARus is the Russian adaptation of the +COPA task, part of the Russian SuperGLUE benchmark. It evaluates common sense +reasoning and causal inference abilities in Russian language models. -# Copyright (c) 2024 The HuggingFace Team +languages: +russian -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual +paper: +https://russiansuperglue.com/tasks/task_info/PARus +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -36,11 +29,6 @@ from lighteval.utils.language import Language -# PARus: Plausible Alternatives for Russian -# Paper: https://russiansuperglue.com/tasks/task_info/PARus -# PARus is the Russian adaptation of the COPA task, part of the Russian SuperGLUE benchmark. -# It evaluates common sense reasoning and causal inference abilities in Russian language models. - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/paws_x.py b/src/lighteval/tasks/multilingual/tasks/paws_x.py index 09aa28288..5d90838a6 100644 --- a/src/lighteval/tasks/multilingual/tasks/paws_x.py +++ b/src/lighteval/tasks/multilingual/tasks/paws_x.py @@ -1,25 +1,19 @@ -# MIT License +""" +abstract: +PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification This +dataset contains paraphrase identification pairs in multiple languages. It's +derived from PAWS (Paraphrase Adversaries from Word Scrambling) and We treat +paraphrase as entailment and non-paraphrase as contradiction -# Copyright (c) 2024 The HuggingFace Team +languages: +chinese, english, french, german, japanese, korean, spanish -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +classification, multilingual, nli +paper: +https://arxiv.org/abs/1908.11828 +""" from langcodes import standardize_tag @@ -38,12 +32,6 @@ from lighteval.utils.language import Language -# PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification -# This dataset contains paraphrase identification pairs in multiple languages. -# It's derived from PAWS (Paraphrase Adversaries from Word Scrambling) and -# We treat paraphrase as entailment and non-paraphrase as contradiction -# https://arxiv.org/abs/1908.11828 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/piqa_ar.py b/src/lighteval/tasks/multilingual/tasks/piqa_ar.py index 850d22604..07737ef19 100644 --- a/src/lighteval/tasks/multilingual/tasks/piqa_ar.py +++ b/src/lighteval/tasks/multilingual/tasks/piqa_ar.py @@ -1,25 +1,19 @@ -# MIT License +""" +abstract: +PIQA: Physical Interaction Question Answering PIQA is a benchmark for testing +physical commonsense reasoning. This Arabic version is a translation of the +original PIQA dataset, adapted for Arabic language evaluation. It tests the +ability to reason about physical interactions in everyday situations. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, qa, reasoning +paper: +https://arxiv.org/abs/1911.11641 +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -39,14 +33,6 @@ from lighteval.utils.language import Language -# ------------------------------- PIQA ------------------------------- # -# PIQA: Physical Interaction Question Answering -# PIQA is a benchmark for testing physical commonsense reasoning. -# This Arabic version is a translation of the original PIQA dataset, adapted for Arabic language evaluation. -# It tests the ability to reason about physical interactions in everyday situations. -# Paper: https://arxiv.org/abs/1911.11641 -# Arabic version: https://aclanthology.org/2023.arabicnlp-1.21/ - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/rcb.py b/src/lighteval/tasks/multilingual/tasks/rcb.py index 686a4cc77..bc5acfa7b 100644 --- a/src/lighteval/tasks/multilingual/tasks/rcb.py +++ b/src/lighteval/tasks/multilingual/tasks/rcb.py @@ -1,25 +1,17 @@ -# MIT License +""" +abstract: +Russian Commitment Bank (RCB) is a large-scale NLI dataset with Russian +sentences, collected from the web and crowdsourcing. -# Copyright (c) 2024 The HuggingFace Team +languages: +russian -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +classification, multilingual, nli +paper: +https://arxiv.org/abs/2401.04531 +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -36,10 +28,6 @@ from lighteval.utils.language import Language -# Russian Commitment Bank (RCB) is a large-scale NLI dataset with Russian sentences, -# collected from the web and crowdsourcing. -# https://arxiv.org/abs/2401.04531 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/sber_squad.py b/src/lighteval/tasks/multilingual/tasks/sber_squad.py index 69ba737a9..06a45cb6b 100644 --- a/src/lighteval/tasks/multilingual/tasks/sber_squad.py +++ b/src/lighteval/tasks/multilingual/tasks/sber_squad.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +SberQuAD: A large-scale Russian reading comprehension dataset. -# Copyright (c) 2024 The HuggingFace Team +languages: +russian -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://arxiv.org/abs/1912.09723 +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -30,9 +21,6 @@ from lighteval.utils.language import Language -# SberQuAD: A large-scale Russian reading comprehension dataset. -# https://arxiv.org/abs/1912.09723 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/soqal.py b/src/lighteval/tasks/multilingual/tasks/soqal.py index bacc503fa..c24d565ca 100644 --- a/src/lighteval/tasks/multilingual/tasks/soqal.py +++ b/src/lighteval/tasks/multilingual/tasks/soqal.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +SOQAL: A large-scale Arabic reading comprehension dataset. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://arxiv.org/abs/1906.05394 +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -39,9 +30,6 @@ from lighteval.utils.language import Language -# SOQAL: A large-scale Arabic reading comprehension dataset. -# https://arxiv.org/abs/1906.05394 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/squad_es.py b/src/lighteval/tasks/multilingual/tasks/squad_es.py index aba9ba49e..e56de9e17 100644 --- a/src/lighteval/tasks/multilingual/tasks/squad_es.py +++ b/src/lighteval/tasks/multilingual/tasks/squad_es.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +SQuAD-es: Spanish translation of the Stanford Question Answering Dataset -# Copyright (c) 2024 The HuggingFace Team +languages: +spanish -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://huggingface.co/datasets/ccasimiro/squad_es +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -30,9 +21,6 @@ from lighteval.utils.language import Language -# SQuAD-es: Spanish translation of the Stanford Question Answering Dataset -# https://huggingface.co/datasets/ccasimiro/squad_es - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/squad_it.py b/src/lighteval/tasks/multilingual/tasks/squad_it.py index 8bdacc23b..06ddd7a3d 100644 --- a/src/lighteval/tasks/multilingual/tasks/squad_it.py +++ b/src/lighteval/tasks/multilingual/tasks/squad_it.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +SQuAD-it: Italian translation of the SQuAD dataset. -# Copyright (c) 2024 The HuggingFace Team +languages: +italian -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://github.com/crux82/squad-it +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -30,9 +21,6 @@ from lighteval.utils.language import Language -# SQuAD-it: Italian translation of the SQuAD dataset -# https://github.com/crux82/squad-it - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/swahili_arc.py b/src/lighteval/tasks/multilingual/tasks/swahili_arc.py index 3fae78ce6..a81dbd9a5 100644 --- a/src/lighteval/tasks/multilingual/tasks/swahili_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/swahili_arc.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Swahili Arc multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +swahili +tags: +multilingual, multiple-choice, reasoning +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/thai_exams.py b/src/lighteval/tasks/multilingual/tasks/thai_exams.py index d86aae20a..85156c4e4 100644 --- a/src/lighteval/tasks/multilingual/tasks/thai_exams.py +++ b/src/lighteval/tasks/multilingual/tasks/thai_exams.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Thai Exams multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +thai +tags: +knowledge, multilingual, multiple-choice +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/thaiqa.py b/src/lighteval/tasks/multilingual/tasks/thaiqa.py index 1aeb4f15d..19a192b0f 100644 --- a/src/lighteval/tasks/multilingual/tasks/thaiqa.py +++ b/src/lighteval/tasks/multilingual/tasks/thaiqa.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +ThaiQA: A question answering dataset for the Thai language. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +thai +tags: +multilingual, qa +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -30,8 +18,6 @@ from lighteval.utils.language import Language -# ThaiQA: A question answering dataset for the Thai language. - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/tquad_v2.py b/src/lighteval/tasks/multilingual/tasks/tquad_v2.py index fe91615db..ba548887d 100644 --- a/src/lighteval/tasks/multilingual/tasks/tquad_v2.py +++ b/src/lighteval/tasks/multilingual/tasks/tquad_v2.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +TQuAD v2: Turkish Question Answering Dataset version 2. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +turkish +tags: +multilingual, qa +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -30,8 +18,6 @@ from lighteval.utils.language import Language -# TQuAD v2: Turkish Question Answering Dataset version 2. - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/turkish_arc.py b/src/lighteval/tasks/multilingual/tasks/turkish_arc.py index e7c8db845..23ade6dac 100644 --- a/src/lighteval/tasks/multilingual/tasks/turkish_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/turkish_arc.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Turkish ARC Comes from the Turkish leaderboard -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +turkish +tags: +multilingual, multiple-choice, reasoning +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -37,9 +25,6 @@ from lighteval.utils.language import Language -# Turkish ARC -# Comes from the Turkish leaderboard - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py b/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py index 539e02277..0d153b704 100644 --- a/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py @@ -1,25 +1,13 @@ -# MIT License +""" +abstract: +Turkish Mmlu multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +languages: +turkish +tags: +knowledge, multilingual, multiple-choice +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, diff --git a/src/lighteval/tasks/multilingual/tasks/tydiqa.py b/src/lighteval/tasks/multilingual/tasks/tydiqa.py index 930773ecc..d3a030644 100644 --- a/src/lighteval/tasks/multilingual/tasks/tydiqa.py +++ b/src/lighteval/tasks/multilingual/tasks/tydiqa.py @@ -1,25 +1,16 @@ -# MIT License +""" +abstract: +Other QA tasks for RC TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages. https://arxiv.org/abs/2003.05002 -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, bengali, english, finnish, indonesian, japanese, korean, russian, swahili, telugu, thai -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://arxiv.org/abs/2003.05002 +""" from lighteval.metrics.dynamic_metrics import ( MultilingualQuasiExactMatchMetric, @@ -30,10 +21,6 @@ from lighteval.utils.language import Language -# Other QA tasks for RC -# TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages. -# https://arxiv.org/abs/2003.05002 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py b/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py index 9c85a0b60..44f3f30ed 100644 --- a/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py +++ b/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py @@ -1,25 +1,19 @@ -# MIT License +""" +abstract: +WorldTree is a dataset for multi-hop inference in science question answering. It +provides explanations for elementary science questions by combining facts from a +semi-structured knowledge base. This Russian version is part of the MERA +(Multilingual Evaluation of Reasoning Abilities) benchmark. -# Copyright (c) 2024 The HuggingFace Team +languages: +russian -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual +paper: +https://github.com/ai-forever/MERA +""" from lighteval.metrics.dynamic_metrics import ( LogLikelihoodAccMetric, @@ -37,11 +31,6 @@ from lighteval.utils.language import Language -# WorldTree is a dataset for multi-hop inference in science question answering. -# It provides explanations for elementary science questions by combining facts from a semi-structured knowledge base. -# This Russian version is part of the MERA (Multilingual Evaluation of Reasoning Abilities) benchmark. -# MERA: https://github.com/ai-forever/MERA - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/xcodah.py b/src/lighteval/tasks/multilingual/tasks/xcodah.py index 1d184f2dc..8f1f7f091 100644 --- a/src/lighteval/tasks/multilingual/tasks/xcodah.py +++ b/src/lighteval/tasks/multilingual/tasks/xcodah.py @@ -1,24 +1,14 @@ -# MIT License +""" +abstract: +Xcodah multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, chinese, dutch, english, french, german, hindi, italian, japanese, +polish, portuguese, russian, spanish, swahili, urdu, vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, reasoning +""" from functools import partial @@ -42,8 +32,6 @@ from lighteval.utils.language import Language -# ------------------------------- Continuation Tasks ------------------------------- # - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/xcopa.py b/src/lighteval/tasks/multilingual/tasks/xcopa.py index eeec3f05d..4a67b95c0 100644 --- a/src/lighteval/tasks/multilingual/tasks/xcopa.py +++ b/src/lighteval/tasks/multilingual/tasks/xcopa.py @@ -1,25 +1,20 @@ -# MIT License +""" +abstract: +COPA (Choice of Plausible Alternatives) tasks involve determining the most +plausible cause or effect for a given premise. These tasks test common sense +reasoning and causal inference abilities. XCOPA: Cross-lingual Choice of +Plausible Alternatives. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, chinese, estonian, haitian, indonesian, italian, quechua, swahili, +tamil, thai, turkish, vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, narrative, reasoning +paper: +https://aclanthology.org/2020.emnlp-main.185/ +""" from langcodes import standardize_tag @@ -38,13 +33,6 @@ from lighteval.utils.language import Language -# ------------------------------- Copa Tasks ------------------------------- # -# COPA (Choice of Plausible Alternatives) tasks involve determining the most plausible cause or effect -# for a given premise. These tasks test common sense reasoning and causal inference abilities. -# XCOPA: Cross-lingual Choice of Plausible Alternatives -# Paper: https://aclanthology.org/2020.emnlp-main.185/ -# XCOPA extends the original English COPA task to 11 typologically diverse languages. - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/xcsqa.py b/src/lighteval/tasks/multilingual/tasks/xcsqa.py index 3274c3230..d95294314 100644 --- a/src/lighteval/tasks/multilingual/tasks/xcsqa.py +++ b/src/lighteval/tasks/multilingual/tasks/xcsqa.py @@ -1,25 +1,21 @@ -# MIT License +""" +abstract: +XCSQA (Cross-lingual Commonsense QA) is part of the XCSR (Cross-lingual +Commonsense Reasoning) benchmark It is a multilingual extension of the +CommonsenseQA dataset, covering 16 languages The task involves answering +multiple-choice questions that require commonsense reasoning Uses PMI +normalization. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, chinese, dutch, english, french, german, hindi, italian, japanese, +polish, portuguese, russian, spanish, swahili, urdu, vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, qa, reasoning +paper: +https://arxiv.org/abs/2110.08462 +""" from langcodes import standardize_tag @@ -38,13 +34,6 @@ from lighteval.utils.language import Language -# ------------------------------- XCSQA ------------------------------- # -# XCSQA (Cross-lingual Commonsense QA) is part of the XCSR (Cross-lingual Commonsense Reasoning) benchmark -# It is a multilingual extension of the CommonsenseQA dataset, covering 16 languages -# The task involves answering multiple-choice questions that require commonsense reasoning -# Uses PMI normalization -# Paper: https://arxiv.org/abs/2110.08462 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/xnli.py b/src/lighteval/tasks/multilingual/tasks/xnli.py index 7ce8c16a3..211108b26 100644 --- a/src/lighteval/tasks/multilingual/tasks/xnli.py +++ b/src/lighteval/tasks/multilingual/tasks/xnli.py @@ -1,25 +1,22 @@ -# MIT License +""" +abstract: +NLI (Natural Language Inference) tasks involve determining the logical +relationship between two given sentences: a premise and a hypothesis. The goal +is to classify whether the hypothesis is entailed by, contradicts, or is neutral +with respect to the premise. After our inspection we found the neutral label to +be quite ambiguous and decided to exclude it. But you can easily add it by +modifying the adapters The XNLI dataset is a multilingual variant of MultiNLI -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, bulgarian, chinese, english, french, german, greek, hindi, russian, +spanish, swahili, thai, turkish, urdu, vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +classification, multilingual, nli +paper: +https://aclanthology.org/D18-1269/ +""" from langcodes import standardize_tag @@ -38,17 +35,6 @@ from lighteval.utils.language import Language -# ------------------------------- NLI Tasks ------------------------------- # -# NLI (Natural Language Inference) tasks involve determining the logical relationship -# between two given sentences: a premise and a hypothesis. The goal is to classify -# whether the hypothesis is entailed by, contradicts, or is neutral with respect to -# the premise. After our inspection we found the neutral label to be quite ambiguous -# and decided to exclude it. But you can easily add it by modifying the adapters - - -# The XNLI dataset is a multilingual variant of MultiNLI -# https://aclanthology.org/D18-1269/ - TASKS_TABLE = [] xnli_tasks = [ diff --git a/src/lighteval/tasks/multilingual/tasks/xnli2.py b/src/lighteval/tasks/multilingual/tasks/xnli2.py index 44fb05858..a1e2461f3 100644 --- a/src/lighteval/tasks/multilingual/tasks/xnli2.py +++ b/src/lighteval/tasks/multilingual/tasks/xnli2.py @@ -1,25 +1,19 @@ -# MIT License +""" +abstract: +Improvement on XNLI with better translation, from our experience models tend to +perform better on XNLI2.0 than XNLI. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, assamese, bengali, bulgarian, chinese, english, french, german, greek, +gujarati, hindi, kannada, marathi, punjabi, russian, sanskrit, spanish, swahili, +tamil, thai, turkish, urdu, vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +classification, multilingual, nli +paper: +https://arxiv.org/abs/2301.06527 +""" from langcodes import Language as LangCodeLanguage from langcodes import standardize_tag @@ -39,10 +33,6 @@ from lighteval.utils.language import Language -# Improvement on XNLI with better translation, from our experience models tend to -# perform better on XNLI2.0 than XNLI -# https://arxiv.org/abs/2301.06527 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/xnli_indic.py b/src/lighteval/tasks/multilingual/tasks/xnli_indic.py index 4a9b81a57..3c326897b 100644 --- a/src/lighteval/tasks/multilingual/tasks/xnli_indic.py +++ b/src/lighteval/tasks/multilingual/tasks/xnli_indic.py @@ -1,25 +1,17 @@ -# MIT License +""" +abstract: +Another variant of XNLI, with emphasis on Indic languages. -# Copyright (c) 2024 The HuggingFace Team +languages: +assamese, bengali, gujarati, hindi, kannada, malayalam, marathi, oriya, punjabi, +tamil, telugu -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +classification, multilingual, nli +paper: +https://arxiv.org/abs/2204.08776 +""" from langcodes import standardize_tag @@ -38,9 +30,6 @@ from lighteval.utils.language import Language -# Another variant of XNLI, with emphasis on Indic languages -# https://arxiv.org/abs/2204.08776 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/xquad.py b/src/lighteval/tasks/multilingual/tasks/xquad.py index e0d8c65b5..4a754eff0 100644 --- a/src/lighteval/tasks/multilingual/tasks/xquad.py +++ b/src/lighteval/tasks/multilingual/tasks/xquad.py @@ -1,25 +1,21 @@ -# MIT License +""" +abstract: +Reading Comprehension (RC) tasks evaluate a model's ability to understand and +extract information from text passages. These tasks typically involve answering +questions based on given contexts, spanning multiple languages and formats. Add +RC tasks supporting about 130 unique languages/scripts. SQuAD - like XQuAD: +Cross-lingual Question Answering Dataset, extending SQuAD to 11 languages. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, chinese, english, german, greek, hindi, romanian, russian, spanish, +thai, turkish, vietnamese -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, qa +paper: +https://arxiv.org/abs/1910.11856 +""" from langcodes import standardize_tag @@ -32,14 +28,6 @@ from lighteval.utils.language import Language -# ------------------------------- RC Tasks ------------------------------- # -# Reading Comprehension (RC) tasks evaluate a model's ability to understand and extract information from text passages. -# These tasks typically involve answering questions based on given contexts, spanning multiple languages and formats. -# Add RC tasks supporting about 130 unique languages/scripts. -# SQuAD - like -# XQuAD: Cross-lingual Question Answering Dataset, extending SQuAD to 11 languages. -# https://arxiv.org/abs/1910.11856 - TASKS_TABLE = [] diff --git a/src/lighteval/tasks/multilingual/tasks/xstory.py b/src/lighteval/tasks/multilingual/tasks/xstory.py index 5d12f43d1..c05de8daa 100644 --- a/src/lighteval/tasks/multilingual/tasks/xstory.py +++ b/src/lighteval/tasks/multilingual/tasks/xstory.py @@ -1,24 +1,14 @@ -# MIT License +""" +abstract: +Xstory multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team +languages: +arabic, basque, burmese, chinese, hindi, indonesian, russian, spanish, swahili, +telugu -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, narrative +""" from functools import partial diff --git a/src/lighteval/tasks/multilingual/tasks/xwinograd.py b/src/lighteval/tasks/multilingual/tasks/xwinograd.py index c37abf6e2..8bcaa384b 100644 --- a/src/lighteval/tasks/multilingual/tasks/xwinograd.py +++ b/src/lighteval/tasks/multilingual/tasks/xwinograd.py @@ -1,24 +1,13 @@ -# MIT License +""" +abstract: +Xwinograd multilingual benchmark. -# Copyright (c) 2024 The HuggingFace Team +languages: +chinese, english, french, japanese, portuguese, russian -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. +tags: +multilingual, multiple-choice, reasoning +""" from functools import partial @@ -41,8 +30,6 @@ from lighteval.utils.language import Language -# ------------------------------- Winogrande Tasks ------------------------------- # - TASKS_TABLE = [] From f4b0e2742daa855d00ab72a919b13a02f9c4d480 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Tue, 14 Oct 2025 15:51:19 +0200 Subject: [PATCH 18/43] add docstring for all multilingual tasks --- src/lighteval/tasks/tasks/hle/main.py | 32 ++++++++--------- src/lighteval/tasks/tasks/ifbench/main.py | 27 +++++++------- src/lighteval/tasks/tasks/ifeval/main.py | 25 +++++++------ src/lighteval/tasks/tasks/mix_eval/main.py | 35 +++++++++---------- src/lighteval/tasks/tasks/mt_bench/main.py | 28 ++++++++++++--- .../tasks/tasks/olympiade_bench/main.py | 26 +++++++------- .../tasks/tasks/tiny_benchmarks/main.py | 1 - 7 files changed, 93 insertions(+), 81 deletions(-) diff --git a/src/lighteval/tasks/tasks/hle/main.py b/src/lighteval/tasks/tasks/hle/main.py index 105048d47..4544818fd 100644 --- a/src/lighteval/tasks/tasks/hle/main.py +++ b/src/lighteval/tasks/tasks/hle/main.py @@ -1,18 +1,3 @@ -import logging -import math -from typing import List, Literal - -import numpy as np -from aenum import extend_enum -from pydantic import BaseModel - -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.metrics_sample import JudgeLLM -from lighteval.metrics.utils.metric_utils import CorpusLevelMetricGrouping -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc, SamplingMethod - - """ abstract: Humanity's Last Exam (HLE) is a global collaborative effort, with questions from @@ -30,6 +15,20 @@ https://arxiv.org/abs/2501.14249 """ +import logging +import math +from typing import List, Literal + +import numpy as np +from aenum import extend_enum +from pydantic import BaseModel + +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.metrics_sample import JudgeLLM +from lighteval.metrics.utils.metric_utils import CorpusLevelMetricGrouping +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc, SamplingMethod + logger = logging.getLogger(__name__) @@ -42,8 +41,7 @@ class ExtractedAnswer(BaseModel): strict: Literal[True] # 100% reliability -"""Adaptation from https://github.com/centerforaisafety/hle/blob/main/hle_eval/run_judge_results.py -""" +# Adaptation from https://github.com/centerforaisafety/hle/blob/main/hle_eval/run_judge_results.py def get_judge_prompt(question: str, answer: str, gold: str, **kwargs): diff --git a/src/lighteval/tasks/tasks/ifbench/main.py b/src/lighteval/tasks/tasks/ifbench/main.py index a47930103..f03e03702 100644 --- a/src/lighteval/tasks/tasks/ifbench/main.py +++ b/src/lighteval/tasks/tasks/ifbench/main.py @@ -1,17 +1,3 @@ -import numpy as np -from aenum import extend_enum - -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.metrics_sample import SampleLevelComputation -from lighteval.metrics.utils.metric_utils import ( - SampleLevelMetricGrouping, -) -from lighteval.models.model_output import ModelResponse -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc, SamplingMethod -from lighteval.tasks.tasks.ifbench import evaluation_lib - - """ abstract: Challenging benchmark for precise instruction following. @@ -26,6 +12,19 @@ https://arxiv.org/abs/2507.02833 """ +import numpy as np +from aenum import extend_enum + +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.metrics_sample import SampleLevelComputation +from lighteval.metrics.utils.metric_utils import ( + SampleLevelMetricGrouping, +) +from lighteval.models.model_output import ModelResponse +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc, SamplingMethod +from lighteval.tasks.tasks.ifbench import evaluation_lib + def ifbench_prompt(line, task_name: str = ""): return Doc( diff --git a/src/lighteval/tasks/tasks/ifeval/main.py b/src/lighteval/tasks/tasks/ifeval/main.py index babab7695..31a51dc75 100644 --- a/src/lighteval/tasks/tasks/ifeval/main.py +++ b/src/lighteval/tasks/tasks/ifeval/main.py @@ -1,16 +1,3 @@ -import numpy as np - -import lighteval.tasks.tasks.ifeval.instructions_registry as instructions_registry -from lighteval.metrics.metrics_sample import SampleLevelComputation -from lighteval.metrics.utils.metric_utils import ( - SampleLevelMetricGrouping, -) -from lighteval.models.model_output import ModelResponse -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc, SamplingMethod -from lighteval.utils.imports import requires - - """ abstract: Very specific task where there are no precise outputs but instead we test if the @@ -26,6 +13,18 @@ https://arxiv.org/abs/2311.07911 """ +import numpy as np + +import lighteval.tasks.tasks.ifeval.instructions_registry as instructions_registry +from lighteval.metrics.metrics_sample import SampleLevelComputation +from lighteval.metrics.utils.metric_utils import ( + SampleLevelMetricGrouping, +) +from lighteval.models.model_output import ModelResponse +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc, SamplingMethod +from lighteval.utils.imports import requires + # Very specific task where there are no precise outputs but instead we test if the format obeys rules @requires("langdetect") diff --git a/src/lighteval/tasks/tasks/mix_eval/main.py b/src/lighteval/tasks/tasks/mix_eval/main.py index aa68661bc..19d4ec036 100644 --- a/src/lighteval/tasks/tasks/mix_eval/main.py +++ b/src/lighteval/tasks/tasks/mix_eval/main.py @@ -1,21 +1,3 @@ -import logging -import re - -import numpy as np - -from lighteval.metrics.metrics_sample import JudgeLLMMixEval -from lighteval.metrics.utils.metric_utils import SampleLevelMetricGrouping -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc, SamplingMethod -from lighteval.tasks.tasks.mix_eval.judge_prompts import ( - flow_judge_for_freeform_template, - flow_judge_for_multichoice_template, - gpt_judge_for_closeended_freeform, - gpt_judge_for_closeended_multiplechoice, -) -from lighteval.tasks.tasks.mix_eval.prompts import construct_prompt_freeform, construct_prompt_multichoice - - """ abstract: Ground-truth-based dynamic benchmark derived from off-the-shelf benchmark @@ -34,6 +16,23 @@ https://mixeval.github.io/ """ +import logging +import re + +import numpy as np + +from lighteval.metrics.metrics_sample import JudgeLLMMixEval +from lighteval.metrics.utils.metric_utils import SampleLevelMetricGrouping +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc, SamplingMethod +from lighteval.tasks.tasks.mix_eval.judge_prompts import ( + flow_judge_for_freeform_template, + flow_judge_for_multichoice_template, + gpt_judge_for_closeended_freeform, + gpt_judge_for_closeended_multiplechoice, +) +from lighteval.tasks.tasks.mix_eval.prompts import construct_prompt_freeform, construct_prompt_multichoice + logger = logging.getLogger(__name__) diff --git a/src/lighteval/tasks/tasks/mt_bench/main.py b/src/lighteval/tasks/tasks/mt_bench/main.py index cd8212f70..81530c63d 100644 --- a/src/lighteval/tasks/tasks/mt_bench/main.py +++ b/src/lighteval/tasks/tasks/mt_bench/main.py @@ -1,14 +1,32 @@ -# ruff: noqa: F405, F403, F401, I001 -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc, SamplingMethod +""" +abstract: +MT-Bench is a multi-turn conversational benchmark for evaluating language +models. It consists of 80 high-quality multi-turn questions across 8 common +categories (writing, roleplay, reasoning, math, coding, extraction, STEM, +humanities). Model responses are evaluated by a judge LLM. + +languages: +en + +tags: +conversational, generation, multi-turn + +paper: +https://arxiv.org/abs/2402.14762 +""" + +import re + +import numpy as np + from lighteval.metrics.metrics_sample import JudgeLLMMTBench from lighteval.metrics.utils.metric_utils import SampleLevelMetricGrouping +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc, SamplingMethod from lighteval.tasks.tasks.mt_bench.judge_prompt_templates import ( flow_judge_prompt_mt_bench_with_ref, flow_judge_prompt_mt_bench_without_ref, ) -import re -import numpy as np def mt_bench_prompt(line, task_name: str = ""): diff --git a/src/lighteval/tasks/tasks/olympiade_bench/main.py b/src/lighteval/tasks/tasks/olympiade_bench/main.py index 0e9986f74..fb9b25b74 100644 --- a/src/lighteval/tasks/tasks/olympiade_bench/main.py +++ b/src/lighteval/tasks/tasks/olympiade_bench/main.py @@ -1,16 +1,3 @@ -import numpy as np - -from lighteval.metrics.dynamic_metrics import ( - ExprExtractionConfig, - LatexExtractionConfig, - MultilingualExtractiveMatchMetric, -) -from lighteval.metrics.metrics import SampleLevelMetric, SamplingMethod -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc -from lighteval.utils.language import Language - - """ abstract: OlympiadBench is a benchmark for evaluating the performance of language models @@ -26,6 +13,19 @@ https://arxiv.org/abs/2402.14008 """ +import numpy as np + +from lighteval.metrics.dynamic_metrics import ( + ExprExtractionConfig, + LatexExtractionConfig, + MultilingualExtractiveMatchMetric, +) +from lighteval.metrics.metrics import SampleLevelMetric, SamplingMethod +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc +from lighteval.utils.language import Language + + chinese_answer_type_dict = {"Numerical": "数值", "Expression": "表达式", "Equation": "方程", "Interval": "区间"} english_answer_type_dict = { "Numerical": "a numerical value", diff --git a/src/lighteval/tasks/tasks/tiny_benchmarks/main.py b/src/lighteval/tasks/tasks/tiny_benchmarks/main.py index 1e216ee1b..634e8dfaa 100644 --- a/src/lighteval/tasks/tasks/tiny_benchmarks/main.py +++ b/src/lighteval/tasks/tasks/tiny_benchmarks/main.py @@ -1,4 +1,3 @@ -# ruff: noqa: F405, F403, F401 """ abstract: TinyBenchmarks is a benchmark for evaluating the performance of language models From 81d9e4eda0243ec64d2a4997750db81f86141693 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Wed, 15 Oct 2025 12:29:14 +0200 Subject: [PATCH 19/43] add name and dataset to metadata --- .../tasks/multilingual/tasks/acva.py | 8 ++++ .../tasks/multilingual/tasks/afri_mgsm.py | 6 +++ .../tasks/multilingual/tasks/afri_mmlu.py | 6 +++ .../tasks/multilingual/tasks/afri_xnli.py | 6 +++ .../tasks/multilingual/tasks/arabic_arc.py | 8 ++++ .../tasks/multilingual/tasks/arabic_mmlu.py | 8 ++++ .../tasks/multilingual/tasks/arcd.py | 6 +++ .../tasks/multilingual/tasks/belebele.py | 6 +++ src/lighteval/tasks/multilingual/tasks/c3.py | 6 +++ .../tasks/multilingual/tasks/ceval.py | 8 ++++ .../tasks/multilingual/tasks/chegeka.py | 8 ++++ .../tasks/multilingual/tasks/chinese_squad.py | 6 +++ .../tasks/multilingual/tasks/cmath.py | 8 ++++ .../tasks/multilingual/tasks/cmmlu.py | 8 ++++ .../tasks/multilingual/tasks/cmnli.py | 6 +++ .../tasks/multilingual/tasks/cmrc2018.py | 6 +++ .../tasks/multilingual/tasks/copa_indic.py | 6 +++ .../tasks/multilingual/tasks/enem.py | 6 +++ .../tasks/multilingual/tasks/exams.py | 8 ++++ .../tasks/multilingual/tasks/faquad.py | 6 +++ .../tasks/multilingual/tasks/flores200.py | 10 +++- .../tasks/multilingual/tasks/fquad_v2.py | 6 +++ .../tasks/multilingual/tasks/french_boolq.py | 8 ++++ .../multilingual/tasks/french_triviqa.py | 8 ++++ .../tasks/multilingual/tasks/germanquad.py | 6 +++ .../tasks/multilingual/tasks/global_mmlu.py | 6 +++ .../tasks/multilingual/tasks/hellaswag_hin.py | 8 ++++ .../tasks/multilingual/tasks/hellaswag_tel.py | 8 ++++ .../tasks/multilingual/tasks/hellaswag_tha.py | 8 ++++ .../tasks/multilingual/tasks/hellaswag_tur.py | 8 ++++ .../tasks/multilingual/tasks/hindi_arc.py | 8 ++++ .../tasks/multilingual/tasks/hindi_boolq.py | 8 ++++ .../tasks/multilingual/tasks/indicqa.py | 6 +++ .../tasks/multilingual/tasks/kenswquad.py | 6 +++ .../tasks/multilingual/tasks/m3exams.py | 6 +++ .../multilingual/tasks/mathlogicqa_rus.py | 6 +++ .../tasks/multilingual/tasks/meta_mmlu.py | 6 +++ .../tasks/multilingual/tasks/mgsm.py | 8 ++++ .../tasks/multilingual/tasks/mintaka.py | 8 ++++ .../tasks/multilingual/tasks/mkqa.py | 8 ++++ .../multilingual/tasks/mlmm_arc_challenge.py | 6 +++ .../multilingual/tasks/mlmm_hellaswag.py | 6 +++ .../tasks/multilingual/tasks/mlmm_mmlu.py | 6 +++ .../multilingual/tasks/mlmm_truthfulqa.py | 6 +++ .../tasks/multilingual/tasks/mlqa.py | 6 +++ .../tasks/multilingual/tasks/oab_exams.py | 6 +++ .../tasks/multilingual/tasks/ocnli.py | 6 +++ .../tasks/multilingual/tasks/openai_mmlu.py | 8 ++++ .../tasks/multilingual/tasks/openbook_ara.py | 6 +++ .../tasks/multilingual/tasks/openbook_es.py | 6 +++ .../tasks/multilingual/tasks/openbook_rus.py | 6 +++ .../tasks/multilingual/tasks/parus.py | 6 +++ .../tasks/multilingual/tasks/paws_x.py | 6 +++ .../tasks/multilingual/tasks/piqa_ar.py | 6 +++ src/lighteval/tasks/multilingual/tasks/rcb.py | 6 +++ .../tasks/multilingual/tasks/sber_squad.py | 6 +++ .../tasks/multilingual/tasks/soqal.py | 6 +++ .../tasks/multilingual/tasks/squad_es.py | 6 +++ .../tasks/multilingual/tasks/squad_it.py | 6 +++ .../tasks/multilingual/tasks/swahili_arc.py | 7 +++ .../tasks/multilingual/tasks/thai_exams.py | 8 ++++ .../tasks/multilingual/tasks/thaiqa.py | 8 ++++ .../tasks/multilingual/tasks/tquad_v2.py | 8 ++++ .../tasks/multilingual/tasks/turkish_arc.py | 8 ++++ .../tasks/multilingual/tasks/turkish_mmlu.py | 8 ++++ .../tasks/multilingual/tasks/tydiqa.py | 6 +++ .../tasks/multilingual/tasks/worldtree_rus.py | 6 +++ .../tasks/multilingual/tasks/xcodah.py | 8 ++++ .../tasks/multilingual/tasks/xcopa.py | 5 ++ .../tasks/multilingual/tasks/xcsqa.py | 6 +++ .../tasks/multilingual/tasks/xnli.py | 6 +++ .../tasks/multilingual/tasks/xnli2.py | 5 ++ .../tasks/multilingual/tasks/xnli_indic.py | 6 +++ .../tasks/multilingual/tasks/xquad.py | 6 +++ .../tasks/multilingual/tasks/xstory.py | 8 ++++ .../tasks/multilingual/tasks/xwinograd.py | 8 ++++ src/lighteval/tasks/tasks/agieval.py | 8 +++- src/lighteval/tasks/tasks/aime.py | 14 ++++-- src/lighteval/tasks/tasks/anli.py | 14 ++++-- src/lighteval/tasks/tasks/arc.py | 18 +++++--- src/lighteval/tasks/tasks/arc_agi_2.py | 14 ++++-- src/lighteval/tasks/tasks/arithmetic.py | 14 ++++-- src/lighteval/tasks/tasks/asdiv.py | 14 ++++-- src/lighteval/tasks/tasks/babi_qa.py | 14 ++++-- src/lighteval/tasks/tasks/bbq.py | 14 ++++-- src/lighteval/tasks/tasks/bigbench.py | 14 ++++-- src/lighteval/tasks/tasks/bigbench_hard.py | 12 ++++- src/lighteval/tasks/tasks/blimp.py | 17 +++++-- src/lighteval/tasks/tasks/bold.py | 14 ++++-- src/lighteval/tasks/tasks/boolq.py | 8 +++- src/lighteval/tasks/tasks/civil_comments.py | 8 +++- src/lighteval/tasks/tasks/commonsenseqa.py | 16 +++++-- src/lighteval/tasks/tasks/coqa.py | 14 ++++-- src/lighteval/tasks/tasks/covid_dialogue.py | 14 ++++-- src/lighteval/tasks/tasks/drop_qa.py | 14 ++++-- src/lighteval/tasks/tasks/dyck_language.py | 14 ++++-- .../tasks/tasks/entity_data_imputation.py | 14 ++++-- src/lighteval/tasks/tasks/entitymatching.py | 14 ++++-- src/lighteval/tasks/tasks/ethics.py | 8 +++- src/lighteval/tasks/tasks/glue.py | 46 +++++++++++-------- src/lighteval/tasks/tasks/gpqa.py | 8 +++- src/lighteval/tasks/tasks/gsm8k.py | 8 +++- src/lighteval/tasks/tasks/gsm_plus.py | 8 +++- src/lighteval/tasks/tasks/headqa.py | 8 +++- src/lighteval/tasks/tasks/hellaswag.py | 10 +++- src/lighteval/tasks/tasks/hle/main.py | 8 +++- src/lighteval/tasks/tasks/ifbench/main.py | 8 +++- src/lighteval/tasks/tasks/ifeval/main.py | 8 +++- src/lighteval/tasks/tasks/imdb.py | 8 +++- src/lighteval/tasks/tasks/jeopardy.py | 12 +++-- src/lighteval/tasks/tasks/lambada.py | 8 +++- .../tasks/tasks/lcb/codegen_metrics.py | 17 +++++-- src/lighteval/tasks/tasks/lcb/main.py | 8 +++- .../tasks/tasks/legal_summarization.py | 12 +++-- src/lighteval/tasks/tasks/legalsupport.py | 9 +++- src/lighteval/tasks/tasks/lexglue.py | 8 +++- src/lighteval/tasks/tasks/lextreme.py | 8 +++- src/lighteval/tasks/tasks/logiqa.py | 8 +++- src/lighteval/tasks/tasks/lsat_qa.py | 8 +++- src/lighteval/tasks/tasks/math.py | 13 ++++-- src/lighteval/tasks/tasks/math_500.py | 8 +++- src/lighteval/tasks/tasks/mathqa.py | 8 +++- src/lighteval/tasks/tasks/med.py | 8 +++- src/lighteval/tasks/tasks/med_dialog.py | 9 +++- src/lighteval/tasks/tasks/mgsm.py | 8 +++- src/lighteval/tasks/tasks/mix_eval/main.py | 8 +++- src/lighteval/tasks/tasks/mmlu.py | 8 +++- src/lighteval/tasks/tasks/mmlu_redux.py | 8 +++- src/lighteval/tasks/tasks/mmmu_pro.py | 12 +++-- src/lighteval/tasks/tasks/mt_bench/main.py | 8 +++- src/lighteval/tasks/tasks/musr.py | 8 +++- src/lighteval/tasks/tasks/narrativeqa.py | 8 +++- .../tasks/tasks/natural_questions.py | 8 +++- src/lighteval/tasks/tasks/numeracy.py | 9 +++- .../tasks/tasks/olympiade_bench/main.py | 8 +++- src/lighteval/tasks/tasks/openbookqa.py | 10 +++- src/lighteval/tasks/tasks/piqa.py | 8 +++- src/lighteval/tasks/tasks/prost.py | 8 +++- src/lighteval/tasks/tasks/pubmedqa.py | 8 +++- src/lighteval/tasks/tasks/qa4mre.py | 8 +++- src/lighteval/tasks/tasks/qasper.py | 8 +++- src/lighteval/tasks/tasks/quac.py | 8 +++- src/lighteval/tasks/tasks/race_high.py | 8 +++- src/lighteval/tasks/tasks/raft.py | 8 +++- .../tasks/tasks/real_toxicity_prompts.py | 8 +++- src/lighteval/tasks/tasks/sacrebleu.py | 8 +++- src/lighteval/tasks/tasks/sciq.py | 10 +++- src/lighteval/tasks/tasks/simpleqa.py | 8 +++- src/lighteval/tasks/tasks/siqa.py | 9 +++- src/lighteval/tasks/tasks/squad_v2.py | 9 +++- src/lighteval/tasks/tasks/storycloze.py | 8 +++- src/lighteval/tasks/tasks/summarization.py | 8 +++- src/lighteval/tasks/tasks/swag.py | 10 +++- .../tasks/tasks/synthetic_reasoning.py | 8 +++- src/lighteval/tasks/tasks/the_pile.py | 8 +++- .../tasks/tasks/tiny_benchmarks/main.py | 10 +++- src/lighteval/tasks/tasks/toxigen.py | 8 +++- src/lighteval/tasks/tasks/triviaqa.py | 10 +++- src/lighteval/tasks/tasks/truthfulqa.py | 10 +++- src/lighteval/tasks/tasks/twitterAAE.py | 8 +++- src/lighteval/tasks/tasks/unscramble.py | 8 +++- src/lighteval/tasks/tasks/webqs.py | 8 +++- src/lighteval/tasks/tasks/wikifact.py | 8 +++- src/lighteval/tasks/tasks/wikitext.py | 8 +++- src/lighteval/tasks/tasks/winogrande.py | 8 +++- src/lighteval/tasks/tasks/xcopa.py | 8 +++- src/lighteval/tasks/tasks/xstory_cloze.py | 9 +++- src/lighteval/tasks/tasks/xwinograd.py | 8 +++- 168 files changed, 1259 insertions(+), 197 deletions(-) diff --git a/src/lighteval/tasks/multilingual/tasks/acva.py b/src/lighteval/tasks/multilingual/tasks/acva.py index f60c84f4c..d19469690 100644 --- a/src/lighteval/tasks/multilingual/tasks/acva.py +++ b/src/lighteval/tasks/multilingual/tasks/acva.py @@ -1,4 +1,10 @@ """ +name: +Acva + +dataset: +OALL/ACVA + abstract: Acva multilingual benchmark. @@ -7,6 +13,8 @@ tags: knowledge, multilingual, multiple-choice + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py b/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py index babfd7d45..34bbb21c6 100644 --- a/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py +++ b/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py @@ -1,4 +1,10 @@ """ +name: +Afri Mgsm + +dataset: +masakhane/afrimgsm + abstract: African MGSM: MGSM for African Languages diff --git a/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py b/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py index 7121028e1..37dfea187 100644 --- a/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py @@ -1,4 +1,10 @@ """ +name: +Afri Mmlu + +dataset: +masakhane/afrimmlu + abstract: African MMLU: African Massive Multitask Language Understanding diff --git a/src/lighteval/tasks/multilingual/tasks/afri_xnli.py b/src/lighteval/tasks/multilingual/tasks/afri_xnli.py index d506c1584..94e24332e 100644 --- a/src/lighteval/tasks/multilingual/tasks/afri_xnli.py +++ b/src/lighteval/tasks/multilingual/tasks/afri_xnli.py @@ -1,4 +1,10 @@ """ +name: +Afri Xnli + +dataset: +masakhane/afrixnli + abstract: African XNLI: African XNLI diff --git a/src/lighteval/tasks/multilingual/tasks/arabic_arc.py b/src/lighteval/tasks/multilingual/tasks/arabic_arc.py index 9e472798f..2635f2e19 100644 --- a/src/lighteval/tasks/multilingual/tasks/arabic_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/arabic_arc.py @@ -1,4 +1,10 @@ """ +name: +Arabic Arc + +dataset: +OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + abstract: Arabic Arc multilingual benchmark. @@ -7,6 +13,8 @@ tags: multilingual, multiple-choice, reasoning + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py b/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py index 83b405271..72e1c5e29 100644 --- a/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py @@ -1,4 +1,10 @@ """ +name: +Arabic Mmlu + +dataset: +MBZUAI/ArabicMMLU + abstract: Arabic Mmlu multilingual benchmark. @@ -7,6 +13,8 @@ tags: knowledge, multilingual, multiple-choice + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/arcd.py b/src/lighteval/tasks/multilingual/tasks/arcd.py index 85b18e2b1..a3825ca51 100644 --- a/src/lighteval/tasks/multilingual/tasks/arcd.py +++ b/src/lighteval/tasks/multilingual/tasks/arcd.py @@ -1,4 +1,10 @@ """ +name: +Arcd + +dataset: +hsseinmz/arcd + abstract: ARCD: Arabic Reading Comprehension Dataset. diff --git a/src/lighteval/tasks/multilingual/tasks/belebele.py b/src/lighteval/tasks/multilingual/tasks/belebele.py index d2f9bc145..0123e26bf 100644 --- a/src/lighteval/tasks/multilingual/tasks/belebele.py +++ b/src/lighteval/tasks/multilingual/tasks/belebele.py @@ -1,4 +1,10 @@ """ +name: +Belebele + +dataset: +facebook/belebele + abstract: Belebele: A large-scale reading comprehension dataset covering 122 languages. diff --git a/src/lighteval/tasks/multilingual/tasks/c3.py b/src/lighteval/tasks/multilingual/tasks/c3.py index ef7174985..a4b3c0dba 100644 --- a/src/lighteval/tasks/multilingual/tasks/c3.py +++ b/src/lighteval/tasks/multilingual/tasks/c3.py @@ -1,4 +1,10 @@ """ +name: +C3 + +dataset: +clue/clue + abstract: C3: A Chinese Challenge Corpus for Cross-lingual and Cross-modal Tasks Reading comprehension task part of clue. diff --git a/src/lighteval/tasks/multilingual/tasks/ceval.py b/src/lighteval/tasks/multilingual/tasks/ceval.py index 0addcb728..4af3424c1 100644 --- a/src/lighteval/tasks/multilingual/tasks/ceval.py +++ b/src/lighteval/tasks/multilingual/tasks/ceval.py @@ -1,4 +1,10 @@ """ +name: +Ceval + +dataset: +ceval/ceval-exam + abstract: Ceval multilingual benchmark. @@ -7,6 +13,8 @@ tags: knowledge, multilingual, multiple-choice + +paper: """ from functools import partial diff --git a/src/lighteval/tasks/multilingual/tasks/chegeka.py b/src/lighteval/tasks/multilingual/tasks/chegeka.py index b6061bd9d..01c5fe3e7 100644 --- a/src/lighteval/tasks/multilingual/tasks/chegeka.py +++ b/src/lighteval/tasks/multilingual/tasks/chegeka.py @@ -1,4 +1,10 @@ """ +name: +Chegeka + +dataset: +ai-forever/MERA + abstract: Chegeka multilingual benchmark. @@ -7,6 +13,8 @@ tags: knowledge, multilingual, qa + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/chinese_squad.py b/src/lighteval/tasks/multilingual/tasks/chinese_squad.py index c96d5f310..b9d5a9cc4 100644 --- a/src/lighteval/tasks/multilingual/tasks/chinese_squad.py +++ b/src/lighteval/tasks/multilingual/tasks/chinese_squad.py @@ -1,4 +1,10 @@ """ +name: +Chinese Squad + +dataset: +lighteval/ChineseSquad + abstract: ChineseSquad is a reading comprehension dataset for Chinese. diff --git a/src/lighteval/tasks/multilingual/tasks/cmath.py b/src/lighteval/tasks/multilingual/tasks/cmath.py index aefa0c2dc..e41180b1a 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmath.py +++ b/src/lighteval/tasks/multilingual/tasks/cmath.py @@ -1,4 +1,10 @@ """ +name: +Cmath + +dataset: +weitianwen/cmath + abstract: Cmath multilingual benchmark. @@ -7,6 +13,8 @@ tags: math, multilingual, reasoning + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/cmmlu.py b/src/lighteval/tasks/multilingual/tasks/cmmlu.py index c79e34eb6..31f1a3233 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/cmmlu.py @@ -1,4 +1,10 @@ """ +name: +Cmmlu + +dataset: +haonan-li/cmmlu + abstract: Cmmlu multilingual benchmark. @@ -7,6 +13,8 @@ tags: knowledge, multilingual, multiple-choice + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/cmnli.py b/src/lighteval/tasks/multilingual/tasks/cmnli.py index 13df2f0c6..0eef164e9 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmnli.py +++ b/src/lighteval/tasks/multilingual/tasks/cmnli.py @@ -1,4 +1,10 @@ """ +name: +Cmnli + +dataset: +fenffef/cmnli + abstract: Native Chinese NLI dataset based on MNLI approach (Machine Translated) diff --git a/src/lighteval/tasks/multilingual/tasks/cmrc2018.py b/src/lighteval/tasks/multilingual/tasks/cmrc2018.py index d8330b089..eb7725c85 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmrc2018.py +++ b/src/lighteval/tasks/multilingual/tasks/cmrc2018.py @@ -1,4 +1,10 @@ """ +name: +Cmrc2018 + +dataset: +clue/clue + abstract: CMRC 2018: A span-extraction machine reading comprehension dataset for Chinese. diff --git a/src/lighteval/tasks/multilingual/tasks/copa_indic.py b/src/lighteval/tasks/multilingual/tasks/copa_indic.py index 64b2a0261..41ad21480 100644 --- a/src/lighteval/tasks/multilingual/tasks/copa_indic.py +++ b/src/lighteval/tasks/multilingual/tasks/copa_indic.py @@ -1,4 +1,10 @@ """ +name: +Copa Indic + +dataset: +ai4bharat/IndicCOPA + abstract: IndicCOPA: COPA for Indic Languages Paper: https://arxiv.org/pdf/2212.05409 IndicCOPA extends COPA to 15 Indic languages, providing a valuable resource for diff --git a/src/lighteval/tasks/multilingual/tasks/enem.py b/src/lighteval/tasks/multilingual/tasks/enem.py index 22d8e85ab..21dee8cf5 100644 --- a/src/lighteval/tasks/multilingual/tasks/enem.py +++ b/src/lighteval/tasks/multilingual/tasks/enem.py @@ -1,4 +1,10 @@ """ +name: +Enem + +dataset: +maritaca-ai/enem + abstract: ENEM (Exame Nacional do Ensino Médio) is a standardized Brazilian national secondary education examination. The exam is used both as a university admission diff --git a/src/lighteval/tasks/multilingual/tasks/exams.py b/src/lighteval/tasks/multilingual/tasks/exams.py index f94224a7b..57870c6b5 100644 --- a/src/lighteval/tasks/multilingual/tasks/exams.py +++ b/src/lighteval/tasks/multilingual/tasks/exams.py @@ -1,4 +1,10 @@ """ +name: +Exams + +dataset: +mhardalov/exams + abstract: Exams multilingual benchmark. @@ -9,6 +15,8 @@ tags: knowledge, multilingual, multiple-choice + +paper: """ from functools import partial diff --git a/src/lighteval/tasks/multilingual/tasks/faquad.py b/src/lighteval/tasks/multilingual/tasks/faquad.py index e3cb1038e..8a8a106ae 100644 --- a/src/lighteval/tasks/multilingual/tasks/faquad.py +++ b/src/lighteval/tasks/multilingual/tasks/faquad.py @@ -1,4 +1,10 @@ """ +name: +Faquad + +dataset: +eraldoluis/faquad + abstract: FaQuAD: A Portuguese Reading Comprehension Dataset diff --git a/src/lighteval/tasks/multilingual/tasks/flores200.py b/src/lighteval/tasks/multilingual/tasks/flores200.py index 17e16dd3e..c31b1a660 100644 --- a/src/lighteval/tasks/multilingual/tasks/flores200.py +++ b/src/lighteval/tasks/multilingual/tasks/flores200.py @@ -1,17 +1,23 @@ """ +name: +Flores200 + +dataset: +facebook/flores + abstract: Flores200 multilingual benchmark. - languages: arabic, armenian, bengali, cyrillic, devanagari, ethiopic, georgian, greek, gujarati, gurmukhi, chinese (simplified), chinese (traditional), hangul, hebrew, japanese, khmer, kannada, lao, latin, malayalam, myanmar, odia, sinhala, tamil, telugu, thai, tibetan - tags: multilingual, translation + +paper: """ from itertools import permutations diff --git a/src/lighteval/tasks/multilingual/tasks/fquad_v2.py b/src/lighteval/tasks/multilingual/tasks/fquad_v2.py index fcc1c2f7a..2966bc27e 100644 --- a/src/lighteval/tasks/multilingual/tasks/fquad_v2.py +++ b/src/lighteval/tasks/multilingual/tasks/fquad_v2.py @@ -1,4 +1,10 @@ """ +name: +Fquad V2 + +dataset: +manu/fquad2_test + abstract: FQuAD v2: French Question Answering Dataset version 2. diff --git a/src/lighteval/tasks/multilingual/tasks/french_boolq.py b/src/lighteval/tasks/multilingual/tasks/french_boolq.py index 533d37010..693a49145 100644 --- a/src/lighteval/tasks/multilingual/tasks/french_boolq.py +++ b/src/lighteval/tasks/multilingual/tasks/french_boolq.py @@ -1,4 +1,10 @@ """ +name: +French Boolq + +dataset: +manu/french_boolq + abstract: French Boolq multilingual benchmark. @@ -7,6 +13,8 @@ tags: classification, multilingual, qa + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/french_triviqa.py b/src/lighteval/tasks/multilingual/tasks/french_triviqa.py index 7e06acaf0..470b5163c 100644 --- a/src/lighteval/tasks/multilingual/tasks/french_triviqa.py +++ b/src/lighteval/tasks/multilingual/tasks/french_triviqa.py @@ -1,4 +1,10 @@ """ +name: +French Triviqa + +dataset: +manu/french-trivia + abstract: French Triviqa multilingual benchmark. @@ -7,6 +13,8 @@ tags: multilingual, qa + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/germanquad.py b/src/lighteval/tasks/multilingual/tasks/germanquad.py index 02c10d3bf..9fe3aa25b 100644 --- a/src/lighteval/tasks/multilingual/tasks/germanquad.py +++ b/src/lighteval/tasks/multilingual/tasks/germanquad.py @@ -1,4 +1,10 @@ """ +name: +Germanquad + +dataset: +deepset/germanquad + abstract: GermanQuAD: High-quality German QA dataset with 13,722 questions. diff --git a/src/lighteval/tasks/multilingual/tasks/global_mmlu.py b/src/lighteval/tasks/multilingual/tasks/global_mmlu.py index 0c32440db..0470cedc3 100644 --- a/src/lighteval/tasks/multilingual/tasks/global_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/global_mmlu.py @@ -1,4 +1,10 @@ """ +name: +Global Mmlu + +dataset: +CohereForAI/Global-MMLU + abstract: Translated MMLU using both professional and non-professional translators. Contains tags for cultural sensitivity. diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py index b18831ae3..725ea004b 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py @@ -1,4 +1,10 @@ """ +name: +Hellaswag Hin + +dataset: +ai4bharat/hellaswag-hi + abstract: Hellaswag Hin multilingual benchmark. @@ -7,6 +13,8 @@ tags: multilingual, multiple-choice, reasoning + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py index 1a4fcde22..23a99a694 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py @@ -1,4 +1,10 @@ """ +name: +Hellaswag Tel + +dataset: +LightFury9/hellaswag-telugu + abstract: Hellaswag Tel multilingual benchmark. @@ -7,6 +13,8 @@ tags: multilingual, multiple-choice, reasoning + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py index 0d5c67b5f..edb53cf00 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py @@ -1,4 +1,10 @@ """ +name: +Hellaswag Tha + +dataset: +lighteval/hellaswag_thai + abstract: Hellaswag Thai This is a Thai adaptation of the Hellaswag task. Similar to the Turkish version, there's no specific paper, but it has been found to be @@ -9,6 +15,8 @@ tags: multilingual, multiple-choice, reasoning + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py index 6a5601f2a..1141d0a2b 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py @@ -1,4 +1,10 @@ """ +name: +Hellaswag Tur + +dataset: +malhajar/hellaswag_tr-v0.2 + abstract: Hellaswag Turkish This is a Turkish adaptation of the Hellaswag task. While there's no specific paper for this version, it has been found to work well for @@ -11,6 +17,8 @@ tags: multilingual, multiple-choice, reasoning + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/hindi_arc.py b/src/lighteval/tasks/multilingual/tasks/hindi_arc.py index c5be93380..a72ebbbb3 100644 --- a/src/lighteval/tasks/multilingual/tasks/hindi_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/hindi_arc.py @@ -1,4 +1,10 @@ """ +name: +Hindi Arc + +dataset: +ai4bharat/ai2_arc-hi + abstract: Hindi Arc multilingual benchmark. @@ -7,6 +13,8 @@ tags: multilingual, multiple-choice, reasoning + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py b/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py index e19f7ab3a..b289f6ef5 100644 --- a/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py +++ b/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py @@ -1,4 +1,10 @@ """ +name: +Hindi Boolq + +dataset: +ai4bharat/boolq-hi + abstract: Hindi Boolq multilingual benchmark. @@ -7,6 +13,8 @@ tags: classification, multilingual, qa + +paper: """ from langcodes import standardize_tag diff --git a/src/lighteval/tasks/multilingual/tasks/indicqa.py b/src/lighteval/tasks/multilingual/tasks/indicqa.py index 717f34226..8ce33f0f6 100644 --- a/src/lighteval/tasks/multilingual/tasks/indicqa.py +++ b/src/lighteval/tasks/multilingual/tasks/indicqa.py @@ -1,4 +1,10 @@ """ +name: +Indicqa + +dataset: +ai4bharat/IndicQA + abstract: IndicQA: A reading comprehension dataset for 11 Indian languages. diff --git a/src/lighteval/tasks/multilingual/tasks/kenswquad.py b/src/lighteval/tasks/multilingual/tasks/kenswquad.py index cdf9f5f08..ce9d0655e 100644 --- a/src/lighteval/tasks/multilingual/tasks/kenswquad.py +++ b/src/lighteval/tasks/multilingual/tasks/kenswquad.py @@ -1,4 +1,10 @@ """ +name: +Kenswquad + +dataset: +lighteval/KenSwQuAD + abstract: KenSwQuAD: A question answering dataset for Kenyan Swahili. diff --git a/src/lighteval/tasks/multilingual/tasks/m3exams.py b/src/lighteval/tasks/multilingual/tasks/m3exams.py index 1f2f8e159..225cad0e5 100644 --- a/src/lighteval/tasks/multilingual/tasks/m3exams.py +++ b/src/lighteval/tasks/multilingual/tasks/m3exams.py @@ -1,4 +1,10 @@ """ +name: +M3Exams + +dataset: +chiayewken/m3exam + abstract: M3Exam: Multitask Multilingual Multimodal Evaluation Benchmark It also contains a multimodal version but we don't support that Paper: diff --git a/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py b/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py index c49190170..de211a227 100644 --- a/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py +++ b/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py @@ -1,4 +1,10 @@ """ +name: +Mathlogicqa Rus + +dataset: +ai-forever/MERA + abstract: MathLogicQA is a dataset for evaluating mathematical reasoning in language models. It consists of multiple-choice questions that require logical reasoning diff --git a/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py b/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py index b2b29db8d..494d69ec3 100644 --- a/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py @@ -1,4 +1,10 @@ """ +name: +Meta Mmlu + +dataset: +meta-llama/Meta-Llama-3.1-8B-Instruct-evals + abstract: Meta MMLU: A multilingual version of MMLU (using google translation) diff --git a/src/lighteval/tasks/multilingual/tasks/mgsm.py b/src/lighteval/tasks/multilingual/tasks/mgsm.py index 078080f1f..d5dd58bfb 100644 --- a/src/lighteval/tasks/multilingual/tasks/mgsm.py +++ b/src/lighteval/tasks/multilingual/tasks/mgsm.py @@ -1,4 +1,10 @@ """ +name: +Mgsm + +dataset: +juletxara/mgsm + abstract: Mgsm multilingual benchmark. @@ -8,6 +14,8 @@ tags: math, multilingual, reasoning + +paper: """ from langcodes import standardize_tag diff --git a/src/lighteval/tasks/multilingual/tasks/mintaka.py b/src/lighteval/tasks/multilingual/tasks/mintaka.py index 2e86d4dd6..93b839758 100644 --- a/src/lighteval/tasks/multilingual/tasks/mintaka.py +++ b/src/lighteval/tasks/multilingual/tasks/mintaka.py @@ -1,4 +1,10 @@ """ +name: +Mintaka + +dataset: +AmazonScience/mintaka + abstract: Mintaka multilingual benchmark. @@ -7,6 +13,8 @@ tags: knowledge, multilingual, qa + +paper: """ from langcodes import standardize_tag diff --git a/src/lighteval/tasks/multilingual/tasks/mkqa.py b/src/lighteval/tasks/multilingual/tasks/mkqa.py index 0261d3a21..44cfb4375 100644 --- a/src/lighteval/tasks/multilingual/tasks/mkqa.py +++ b/src/lighteval/tasks/multilingual/tasks/mkqa.py @@ -1,4 +1,10 @@ """ +name: +Mkqa + +dataset: +apple/mkqa + abstract: Mkqa multilingual benchmark. @@ -10,6 +16,8 @@ tags: multilingual, qa + +paper: """ from functools import partial diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py b/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py index 7fde820c5..f7ff2a434 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py @@ -1,4 +1,10 @@ """ +name: +Mlmm Arc Challenge + +dataset: +jon-tow/okapi_arc_challenge + abstract: ARC (AI2 Reasoning Challenge) is a dataset for question answering that requires reasoning. It consists of multiple-choice science questions from 3rd to 9th diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py b/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py index 4e33f2a1c..2c114fa75 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py @@ -1,4 +1,10 @@ """ +name: +Mlmm Hellaswag + +dataset: +jon-tow/okapi_hellaswag + abstract: Hellaswag is a commonsense reasoning task that requires models to complete a given scenario with the most plausible ending. It tests the model's ability to diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py b/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py index ba4811c85..db055c356 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py @@ -1,4 +1,10 @@ """ +name: +Mlmm Mmlu + +dataset: +jon-tow/okapi_mmlu + abstract: MLMM MMLU: Another multilingual version of MMLU diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py b/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py index 2cf969e39..323b227fb 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py @@ -1,4 +1,10 @@ """ +name: +Mlmm Truthfulqa + +dataset: +jon-tow/okapi_truthfulqa + abstract: TruthfulQA: Measuring How Models Mimic Human Falsehoods diff --git a/src/lighteval/tasks/multilingual/tasks/mlqa.py b/src/lighteval/tasks/multilingual/tasks/mlqa.py index f8fcdee27..5b1864952 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlqa.py +++ b/src/lighteval/tasks/multilingual/tasks/mlqa.py @@ -1,4 +1,10 @@ """ +name: +Mlqa + +dataset: +facebook/mlqa + abstract: MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. It consists of QA instances in 7 diff --git a/src/lighteval/tasks/multilingual/tasks/oab_exams.py b/src/lighteval/tasks/multilingual/tasks/oab_exams.py index 7e4689977..ba9b73cdb 100644 --- a/src/lighteval/tasks/multilingual/tasks/oab_exams.py +++ b/src/lighteval/tasks/multilingual/tasks/oab_exams.py @@ -1,4 +1,10 @@ """ +name: +Oab Exams + +dataset: +eduagarcia/oab_exams + abstract: OAB Exams: A collection of questions from the Brazilian Bar Association exam The exam is required for anyone who wants to practice law in Brazil diff --git a/src/lighteval/tasks/multilingual/tasks/ocnli.py b/src/lighteval/tasks/multilingual/tasks/ocnli.py index 4693fb21a..3ebc3f258 100644 --- a/src/lighteval/tasks/multilingual/tasks/ocnli.py +++ b/src/lighteval/tasks/multilingual/tasks/ocnli.py @@ -1,4 +1,10 @@ """ +name: +Ocnli + +dataset: +clue/clue + abstract: Native Chinese NLI dataset based. diff --git a/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py b/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py index 21e21b2f0..c3dc10680 100644 --- a/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py @@ -1,4 +1,10 @@ """ +name: +Openai Mmlu + +dataset: +openai/MMMLU + abstract: Openai Mmlu multilingual benchmark. @@ -8,6 +14,8 @@ tags: knowledge, multilingual, multiple-choice + +paper: """ from functools import partial diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_ara.py b/src/lighteval/tasks/multilingual/tasks/openbook_ara.py index ccd1210d2..59ee10d65 100644 --- a/src/lighteval/tasks/multilingual/tasks/openbook_ara.py +++ b/src/lighteval/tasks/multilingual/tasks/openbook_ara.py @@ -1,4 +1,10 @@ """ +name: +Openbook Ara + +dataset: +OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + abstract: OpenBookQA: A Question-Answering Dataset for Open-Book Exams OpenBookQA is a question-answering dataset modeled after open-book exams for assessing human diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_es.py b/src/lighteval/tasks/multilingual/tasks/openbook_es.py index ba6ad747e..c1f4dd5d0 100644 --- a/src/lighteval/tasks/multilingual/tasks/openbook_es.py +++ b/src/lighteval/tasks/multilingual/tasks/openbook_es.py @@ -1,4 +1,10 @@ """ +name: +Openbook Es + +dataset: +BSC-LT/openbookqa-es + abstract: Spanish version of OpenBookQA from BSC Language Technology group diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_rus.py b/src/lighteval/tasks/multilingual/tasks/openbook_rus.py index f77668a0c..186f68e3e 100644 --- a/src/lighteval/tasks/multilingual/tasks/openbook_rus.py +++ b/src/lighteval/tasks/multilingual/tasks/openbook_rus.py @@ -1,4 +1,10 @@ """ +name: +Openbook Rus + +dataset: +ai-forever/MERA + abstract: The Russian version is part of the MERA (Multilingual Enhanced Russian NLP Architectures) project. diff --git a/src/lighteval/tasks/multilingual/tasks/parus.py b/src/lighteval/tasks/multilingual/tasks/parus.py index 170ff4b15..045c48710 100644 --- a/src/lighteval/tasks/multilingual/tasks/parus.py +++ b/src/lighteval/tasks/multilingual/tasks/parus.py @@ -1,4 +1,10 @@ """ +name: +Parus + +dataset: +ai-forever/MERA + abstract: PARus: Plausible Alternatives for Russian PARus is the Russian adaptation of the COPA task, part of the Russian SuperGLUE benchmark. It evaluates common sense diff --git a/src/lighteval/tasks/multilingual/tasks/paws_x.py b/src/lighteval/tasks/multilingual/tasks/paws_x.py index 5d90838a6..686216e0e 100644 --- a/src/lighteval/tasks/multilingual/tasks/paws_x.py +++ b/src/lighteval/tasks/multilingual/tasks/paws_x.py @@ -1,4 +1,10 @@ """ +name: +Paws X + +dataset: +google-research-datasets/paws-x + abstract: PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification This dataset contains paraphrase identification pairs in multiple languages. It's diff --git a/src/lighteval/tasks/multilingual/tasks/piqa_ar.py b/src/lighteval/tasks/multilingual/tasks/piqa_ar.py index 07737ef19..584e9a2f4 100644 --- a/src/lighteval/tasks/multilingual/tasks/piqa_ar.py +++ b/src/lighteval/tasks/multilingual/tasks/piqa_ar.py @@ -1,4 +1,10 @@ """ +name: +Piqa Ar + +dataset: +OALL/AlGhafa-Arabic-LLM-Benchmark-Translated + abstract: PIQA: Physical Interaction Question Answering PIQA is a benchmark for testing physical commonsense reasoning. This Arabic version is a translation of the diff --git a/src/lighteval/tasks/multilingual/tasks/rcb.py b/src/lighteval/tasks/multilingual/tasks/rcb.py index bc5acfa7b..40bd21038 100644 --- a/src/lighteval/tasks/multilingual/tasks/rcb.py +++ b/src/lighteval/tasks/multilingual/tasks/rcb.py @@ -1,4 +1,10 @@ """ +name: +Rcb + +dataset: +ai-forever/MERA + abstract: Russian Commitment Bank (RCB) is a large-scale NLI dataset with Russian sentences, collected from the web and crowdsourcing. diff --git a/src/lighteval/tasks/multilingual/tasks/sber_squad.py b/src/lighteval/tasks/multilingual/tasks/sber_squad.py index 06a45cb6b..ec0a09840 100644 --- a/src/lighteval/tasks/multilingual/tasks/sber_squad.py +++ b/src/lighteval/tasks/multilingual/tasks/sber_squad.py @@ -1,4 +1,10 @@ """ +name: +Sber Squad + +dataset: +kuznetsoffandrey/sberquad + abstract: SberQuAD: A large-scale Russian reading comprehension dataset. diff --git a/src/lighteval/tasks/multilingual/tasks/soqal.py b/src/lighteval/tasks/multilingual/tasks/soqal.py index c24d565ca..b79970175 100644 --- a/src/lighteval/tasks/multilingual/tasks/soqal.py +++ b/src/lighteval/tasks/multilingual/tasks/soqal.py @@ -1,4 +1,10 @@ """ +name: +Soqal + +dataset: +OALL/AlGhafa-Arabic-LLM-Benchmark-Native + abstract: SOQAL: A large-scale Arabic reading comprehension dataset. diff --git a/src/lighteval/tasks/multilingual/tasks/squad_es.py b/src/lighteval/tasks/multilingual/tasks/squad_es.py index e56de9e17..871e2ef5e 100644 --- a/src/lighteval/tasks/multilingual/tasks/squad_es.py +++ b/src/lighteval/tasks/multilingual/tasks/squad_es.py @@ -1,4 +1,10 @@ """ +name: +Squad Es + +dataset: +ccasimiro/squad_es + abstract: SQuAD-es: Spanish translation of the Stanford Question Answering Dataset diff --git a/src/lighteval/tasks/multilingual/tasks/squad_it.py b/src/lighteval/tasks/multilingual/tasks/squad_it.py index 06ddd7a3d..f6e9e365c 100644 --- a/src/lighteval/tasks/multilingual/tasks/squad_it.py +++ b/src/lighteval/tasks/multilingual/tasks/squad_it.py @@ -1,4 +1,10 @@ """ +name: +Squad It + +dataset: +crux82/squad_it + abstract: SQuAD-it: Italian translation of the SQuAD dataset. diff --git a/src/lighteval/tasks/multilingual/tasks/swahili_arc.py b/src/lighteval/tasks/multilingual/tasks/swahili_arc.py index a81dbd9a5..1dba3c5ae 100644 --- a/src/lighteval/tasks/multilingual/tasks/swahili_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/swahili_arc.py @@ -1,4 +1,9 @@ """ +name: +Swahili Arc + +dataset: + abstract: Swahili Arc multilingual benchmark. @@ -7,6 +12,8 @@ tags: multilingual, multiple-choice, reasoning + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/thai_exams.py b/src/lighteval/tasks/multilingual/tasks/thai_exams.py index 85156c4e4..1e0276c2e 100644 --- a/src/lighteval/tasks/multilingual/tasks/thai_exams.py +++ b/src/lighteval/tasks/multilingual/tasks/thai_exams.py @@ -1,4 +1,10 @@ """ +name: +Thai Exams + +dataset: +scb10x/thai_exam + abstract: Thai Exams multilingual benchmark. @@ -7,6 +13,8 @@ tags: knowledge, multilingual, multiple-choice + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/thaiqa.py b/src/lighteval/tasks/multilingual/tasks/thaiqa.py index 19a192b0f..235fdfd5e 100644 --- a/src/lighteval/tasks/multilingual/tasks/thaiqa.py +++ b/src/lighteval/tasks/multilingual/tasks/thaiqa.py @@ -1,4 +1,10 @@ """ +name: +Thaiqa + +dataset: +lighteval/thaiqa_squad_fixed + abstract: ThaiQA: A question answering dataset for the Thai language. @@ -7,6 +13,8 @@ tags: multilingual, qa + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/tquad_v2.py b/src/lighteval/tasks/multilingual/tasks/tquad_v2.py index ba548887d..48eaab456 100644 --- a/src/lighteval/tasks/multilingual/tasks/tquad_v2.py +++ b/src/lighteval/tasks/multilingual/tasks/tquad_v2.py @@ -1,4 +1,10 @@ """ +name: +Tquad V2 + +dataset: +erdometo/tquad2 + abstract: TQuAD v2: Turkish Question Answering Dataset version 2. @@ -7,6 +13,8 @@ tags: multilingual, qa + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/turkish_arc.py b/src/lighteval/tasks/multilingual/tasks/turkish_arc.py index 23ade6dac..fb86102fa 100644 --- a/src/lighteval/tasks/multilingual/tasks/turkish_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/turkish_arc.py @@ -1,4 +1,10 @@ """ +name: +Turkish Arc + +dataset: +malhajar/arc-tr + abstract: Turkish ARC Comes from the Turkish leaderboard @@ -7,6 +13,8 @@ tags: multilingual, multiple-choice, reasoning + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py b/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py index 0d153b704..68ec114ec 100644 --- a/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py @@ -1,4 +1,10 @@ """ +name: +Turkish Mmlu + +dataset: +AYueksel/TurkishMMLU + abstract: Turkish Mmlu multilingual benchmark. @@ -7,6 +13,8 @@ tags: knowledge, multilingual, multiple-choice + +paper: """ from lighteval.metrics.dynamic_metrics import ( diff --git a/src/lighteval/tasks/multilingual/tasks/tydiqa.py b/src/lighteval/tasks/multilingual/tasks/tydiqa.py index d3a030644..914457224 100644 --- a/src/lighteval/tasks/multilingual/tasks/tydiqa.py +++ b/src/lighteval/tasks/multilingual/tasks/tydiqa.py @@ -1,4 +1,10 @@ """ +name: +Tydiqa + +dataset: +google-research-datasets/tydiqa + abstract: Other QA tasks for RC TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages. https://arxiv.org/abs/2003.05002 diff --git a/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py b/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py index 44f3f30ed..b4f174e84 100644 --- a/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py +++ b/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py @@ -1,4 +1,10 @@ """ +name: +Worldtree Rus + +dataset: +ai-forever/MERA + abstract: WorldTree is a dataset for multi-hop inference in science question answering. It provides explanations for elementary science questions by combining facts from a diff --git a/src/lighteval/tasks/multilingual/tasks/xcodah.py b/src/lighteval/tasks/multilingual/tasks/xcodah.py index 8f1f7f091..a1cb5d2d4 100644 --- a/src/lighteval/tasks/multilingual/tasks/xcodah.py +++ b/src/lighteval/tasks/multilingual/tasks/xcodah.py @@ -1,4 +1,10 @@ """ +name: +Xcodah + +dataset: +INK-USC/xcsr + abstract: Xcodah multilingual benchmark. @@ -8,6 +14,8 @@ tags: multilingual, multiple-choice, reasoning + +paper: """ from functools import partial diff --git a/src/lighteval/tasks/multilingual/tasks/xcopa.py b/src/lighteval/tasks/multilingual/tasks/xcopa.py index 4a67b95c0..9cf98e932 100644 --- a/src/lighteval/tasks/multilingual/tasks/xcopa.py +++ b/src/lighteval/tasks/multilingual/tasks/xcopa.py @@ -1,4 +1,9 @@ """ +name: +Xcopa + +dataset: + abstract: COPA (Choice of Plausible Alternatives) tasks involve determining the most plausible cause or effect for a given premise. These tasks test common sense diff --git a/src/lighteval/tasks/multilingual/tasks/xcsqa.py b/src/lighteval/tasks/multilingual/tasks/xcsqa.py index d95294314..2baaccb46 100644 --- a/src/lighteval/tasks/multilingual/tasks/xcsqa.py +++ b/src/lighteval/tasks/multilingual/tasks/xcsqa.py @@ -1,4 +1,10 @@ """ +name: +Xcsqa + +dataset: +INK-USC/xcsr + abstract: XCSQA (Cross-lingual Commonsense QA) is part of the XCSR (Cross-lingual Commonsense Reasoning) benchmark It is a multilingual extension of the diff --git a/src/lighteval/tasks/multilingual/tasks/xnli.py b/src/lighteval/tasks/multilingual/tasks/xnli.py index 211108b26..9de7fbd33 100644 --- a/src/lighteval/tasks/multilingual/tasks/xnli.py +++ b/src/lighteval/tasks/multilingual/tasks/xnli.py @@ -1,4 +1,10 @@ """ +name: +Xnli + +dataset: +facebook/xnli + abstract: NLI (Natural Language Inference) tasks involve determining the logical relationship between two given sentences: a premise and a hypothesis. The goal diff --git a/src/lighteval/tasks/multilingual/tasks/xnli2.py b/src/lighteval/tasks/multilingual/tasks/xnli2.py index a1e2461f3..3e141bd28 100644 --- a/src/lighteval/tasks/multilingual/tasks/xnli2.py +++ b/src/lighteval/tasks/multilingual/tasks/xnli2.py @@ -1,4 +1,9 @@ """ +name: +Xnli2 + +dataset: + abstract: Improvement on XNLI with better translation, from our experience models tend to perform better on XNLI2.0 than XNLI. diff --git a/src/lighteval/tasks/multilingual/tasks/xnli_indic.py b/src/lighteval/tasks/multilingual/tasks/xnli_indic.py index 3c326897b..854a4cb74 100644 --- a/src/lighteval/tasks/multilingual/tasks/xnli_indic.py +++ b/src/lighteval/tasks/multilingual/tasks/xnli_indic.py @@ -1,4 +1,10 @@ """ +name: +Xnli Indic + +dataset: +Divyanshu/indicxnli + abstract: Another variant of XNLI, with emphasis on Indic languages. diff --git a/src/lighteval/tasks/multilingual/tasks/xquad.py b/src/lighteval/tasks/multilingual/tasks/xquad.py index 4a754eff0..83b1283bc 100644 --- a/src/lighteval/tasks/multilingual/tasks/xquad.py +++ b/src/lighteval/tasks/multilingual/tasks/xquad.py @@ -1,4 +1,10 @@ """ +name: +Xquad + +dataset: +google/xquad + abstract: Reading Comprehension (RC) tasks evaluate a model's ability to understand and extract information from text passages. These tasks typically involve answering diff --git a/src/lighteval/tasks/multilingual/tasks/xstory.py b/src/lighteval/tasks/multilingual/tasks/xstory.py index c05de8daa..98e11e287 100644 --- a/src/lighteval/tasks/multilingual/tasks/xstory.py +++ b/src/lighteval/tasks/multilingual/tasks/xstory.py @@ -1,4 +1,10 @@ """ +name: +Xstory + +dataset: +juletxara/xstory_cloze + abstract: Xstory multilingual benchmark. @@ -8,6 +14,8 @@ tags: multilingual, narrative + +paper: """ from functools import partial diff --git a/src/lighteval/tasks/multilingual/tasks/xwinograd.py b/src/lighteval/tasks/multilingual/tasks/xwinograd.py index 8bcaa384b..98ccb4977 100644 --- a/src/lighteval/tasks/multilingual/tasks/xwinograd.py +++ b/src/lighteval/tasks/multilingual/tasks/xwinograd.py @@ -1,4 +1,10 @@ """ +name: +Xwinograd + +dataset: +Muennighoff/xwinograd + abstract: Xwinograd multilingual benchmark. @@ -7,6 +13,8 @@ tags: multilingual, multiple-choice, reasoning + +paper: """ from functools import partial diff --git a/src/lighteval/tasks/tasks/agieval.py b/src/lighteval/tasks/tasks/agieval.py index b01cd495a..733b3b167 100644 --- a/src/lighteval/tasks/tasks/agieval.py +++ b/src/lighteval/tasks/tasks/agieval.py @@ -1,4 +1,10 @@ """ +name: +Agieval + +dataset: +dmayhem93/agieval-aqua-rat, dmayhem93/agieval-gaokao-biology, dmayhem93/agieval-gaokao-chemistry, dmayhem93/agieval-gaokao-chinese, dmayhem93/agieval-gaokao-english, dmayhem93/agieval-gaokao-geography, dmayhem93/agieval-gaokao-history, dmayhem93/agieval-gaokao-mathqa, dmayhem93/agieval-gaokao-physics, dmayhem93/agieval-logiqa-en, dmayhem93/agieval-logiqa-zh, dmayhem93/agieval-lsat-ar, dmayhem93/agieval-lsat-lr, dmayhem93/agieval-lsat-rc, dmayhem93/agieval-sat-en, dmayhem93/agieval-sat-en-without-passage, dmayhem93/agieval-sat-math + abstract: AGIEval is a human-centric benchmark specifically designed to evaluate the general abilities of foundation models in tasks pertinent to human cognition and @@ -9,7 +15,7 @@ competitions, lawyer qualification tests, and national civil service exams. languages: -en, zh +english, chinese tags: biology, chemistry, geography, history, knowledge, language, multiple-choice, physics, reasoning diff --git a/src/lighteval/tasks/tasks/aime.py b/src/lighteval/tasks/tasks/aime.py index fae2b8b1f..1afac6c77 100644 --- a/src/lighteval/tasks/tasks/aime.py +++ b/src/lighteval/tasks/tasks/aime.py @@ -1,4 +1,10 @@ """ +name: +Aime + +dataset: +HuggingFaceH4/aime_2024, yentinglin/aime_2025 + abstract: The American Invitational Mathematics Examination (AIME) is a prestigious, invite-only mathematics competition for high-school students who perform in the @@ -9,13 +15,13 @@ questions total). languages: -en - -paper: -https://maa.org/aime-thresholds-are-available/ +english tags: math, reasoning + +paper: +https://maa.org/aime-thresholds-are-available/ """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/anli.py b/src/lighteval/tasks/tasks/anli.py index 2611b26d2..2d6d0f9a8 100644 --- a/src/lighteval/tasks/tasks/anli.py +++ b/src/lighteval/tasks/tasks/anli.py @@ -1,4 +1,10 @@ """ +name: +Anli + +dataset: +facebook/anli + abstract: The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, The dataset is collected via an iterative, adversarial @@ -7,13 +13,13 @@ train/dev/test splits. languages: -en - -paper: -https://arxiv.org/abs/1910.14599 +english tags: nli, reasoning + +paper: +https://arxiv.org/abs/1910.14599 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/arc.py b/src/lighteval/tasks/tasks/arc.py index b73f0c761..3532cde12 100644 --- a/src/lighteval/tasks/tasks/arc.py +++ b/src/lighteval/tasks/tasks/arc.py @@ -1,4 +1,10 @@ """ +name: +Arc + +dataset: +allenai/ai2_arc + abstract: 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned @@ -7,13 +13,13 @@ co-occurrence algorithm languages: -en - -paper: -https://arxiv.org/abs/1803.05457 +english tags: multiple-choice + +paper: +https://arxiv.org/abs/1803.05457 """ import lighteval.tasks.default_prompts as prompt @@ -25,7 +31,7 @@ name="arc:challenge", suite=["lighteval"], prompt_function=prompt.arc, - hf_repo="ai2_arc", + hf_repo="allenai/ai2_arc", hf_subset="ARC-Challenge", hf_avail_splits=["train", "test"], evaluation_splits=["test"], @@ -43,7 +49,7 @@ name="arc:easy", suite=["lighteval"], prompt_function=prompt.arc, - hf_repo="ai2_arc", + hf_repo="allenai/ai2_arc", hf_subset="ARC-Easy", hf_avail_splits=["train", "validation", "test"], evaluation_splits=["test"], diff --git a/src/lighteval/tasks/tasks/arc_agi_2.py b/src/lighteval/tasks/tasks/arc_agi_2.py index be9449cf9..5e008013f 100644 --- a/src/lighteval/tasks/tasks/arc_agi_2.py +++ b/src/lighteval/tasks/tasks/arc_agi_2.py @@ -1,4 +1,10 @@ """ +name: +ArcAgi 2 + +dataset: +arc-agi-community/arc-agi-2 + abstract: ARC-AGI tasks are a series of three to five input and output tasks followed by a final task with only the input listed. Each task tests the utilization of a @@ -13,13 +19,13 @@ difficult for AI. languages: -en - -paper: -https://arcprize.org/guide +english tags: multiple-choice + +paper: +https://arcprize.org/guide """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/arithmetic.py b/src/lighteval/tasks/tasks/arithmetic.py index 7a8f3fc07..25ff9cc71 100644 --- a/src/lighteval/tasks/tasks/arithmetic.py +++ b/src/lighteval/tasks/tasks/arithmetic.py @@ -1,16 +1,22 @@ """ +name: +Arithmetic + +dataset: +EleutherAI/arithmetic + abstract: A small battery of 10 tests that involve asking language models a simple arithmetic problem in natural language. languages: -en - -paper: -https://arxiv.org/abs/2005.14165 +english tags: math, reasoning + +paper: +https://arxiv.org/abs/2005.14165 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/asdiv.py b/src/lighteval/tasks/tasks/asdiv.py index 0b86735ae..2bcf9df69 100644 --- a/src/lighteval/tasks/tasks/asdiv.py +++ b/src/lighteval/tasks/tasks/asdiv.py @@ -1,16 +1,22 @@ """ +name: +Asdiv + +dataset: +EleutherAI/asdiv + abstract: ASDiv is a dataset for arithmetic reasoning that contains 2,000+ questions covering addition, subtraction, multiplication, and division. languages: -en - -paper: -https://arxiv.org/abs/2410.12853 +english tags: math, reasoning + +paper: +https://arxiv.org/abs/2410.12853 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/babi_qa.py b/src/lighteval/tasks/tasks/babi_qa.py index 5c426e1dc..6668462bc 100644 --- a/src/lighteval/tasks/tasks/babi_qa.py +++ b/src/lighteval/tasks/tasks/babi_qa.py @@ -1,16 +1,22 @@ """ +name: +Babi Qa + +dataset: +facebook/babi_qa + abstract: The bAbI benchmark for measuring understanding and reasoning, evaluates reading comprehension via question answering. languages: -en - -paper: -https://arxiv.org/abs/1502.05698 +english tags: qa, reasoning + +paper: +https://arxiv.org/abs/1502.05698 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/bbq.py b/src/lighteval/tasks/tasks/bbq.py index 175e1b46d..229e208fd 100644 --- a/src/lighteval/tasks/tasks/bbq.py +++ b/src/lighteval/tasks/tasks/bbq.py @@ -1,16 +1,22 @@ """ +name: +Bbq + +dataset: +lighteval/bbq_helm + abstract: The Bias Benchmark for Question Answering (BBQ) for measuring social bias in question answering in ambiguous and unambigous context . languages: -en - -paper: -https://arxiv.org/abs/2110.08193 +english tags: bias, multiple-choice, qa + +paper: +https://arxiv.org/abs/2110.08193 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/bigbench.py b/src/lighteval/tasks/tasks/bigbench.py index 37a34a318..67c972530 100644 --- a/src/lighteval/tasks/tasks/bigbench.py +++ b/src/lighteval/tasks/tasks/bigbench.py @@ -1,16 +1,22 @@ """ +name: +Bigbench + +dataset: +tasksource/bigbench + abstract: Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models 166 tasks from bigbench benchmark. languages: -en - -paper: -https://arxiv.org/abs/2206.04615 +english tags: reasoning + +paper: +https://arxiv.org/abs/2206.04615 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/bigbench_hard.py b/src/lighteval/tasks/tasks/bigbench_hard.py index 6c5aaf949..b62452504 100644 --- a/src/lighteval/tasks/tasks/bigbench_hard.py +++ b/src/lighteval/tasks/tasks/bigbench_hard.py @@ -1,8 +1,18 @@ """ -hardest subset of bigbench benchmark. +name: +Bigbench Hard + +dataset: +lighteval/bbh + +abstract: + +languages: tags: reasoning + +paper: """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/blimp.py b/src/lighteval/tasks/tasks/blimp.py index 25078db59..860e3958f 100644 --- a/src/lighteval/tasks/tasks/blimp.py +++ b/src/lighteval/tasks/tasks/blimp.py @@ -1,4 +1,11 @@ -"""abstract: +""" +name: +Blimp + +dataset: +nyu-mll/blimp + +abstract: BLiMP is a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each containing 1000 minimal pairs isolating specific @@ -6,13 +13,13 @@ generated according to expert-crafted grammars. languages: -en - -paper: -https://arxiv.org/abs/1912.00582 +english tags: language-modeling + +paper: +https://arxiv.org/abs/1912.00582 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/bold.py b/src/lighteval/tasks/tasks/bold.py index fa77c174c..b74ec0862 100644 --- a/src/lighteval/tasks/tasks/bold.py +++ b/src/lighteval/tasks/tasks/bold.py @@ -1,16 +1,22 @@ """ +name: +Bold + +dataset: +lighteval/bold_helm + abstract: The Bias in Open-Ended Language Generation Dataset (BOLD) for measuring biases and toxicity in open-ended language generation. languages: -en - -paper: -https://dl.acm.org/doi/10.1145/3442188.3445924 +english tags: bias, generation + +paper: +https://dl.acm.org/doi/10.1145/3442188.3445924 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/boolq.py b/src/lighteval/tasks/tasks/boolq.py index dbc7ca980..7a0471252 100644 --- a/src/lighteval/tasks/tasks/boolq.py +++ b/src/lighteval/tasks/tasks/boolq.py @@ -1,9 +1,15 @@ """ +name: +Boolq + +dataset: +lighteval/boolq_helm + abstract: The BoolQ benchmark for binary (yes/no) question answering. languages: -en +english tags: qa diff --git a/src/lighteval/tasks/tasks/civil_comments.py b/src/lighteval/tasks/tasks/civil_comments.py index 0d2447417..6e4b0ed8d 100644 --- a/src/lighteval/tasks/tasks/civil_comments.py +++ b/src/lighteval/tasks/tasks/civil_comments.py @@ -1,9 +1,15 @@ """ +name: +Civil Comments + +dataset: +lighteval/civil_comments_helm + abstract: The CivilComments benchmark for toxicity detection. languages: -en +english tags: bias, classification diff --git a/src/lighteval/tasks/tasks/commonsenseqa.py b/src/lighteval/tasks/tasks/commonsenseqa.py index b0353bcd7..7fe06ec55 100644 --- a/src/lighteval/tasks/tasks/commonsenseqa.py +++ b/src/lighteval/tasks/tasks/commonsenseqa.py @@ -1,4 +1,10 @@ """ +name: +Commonsenseqa + +dataset: +tau/commonsense_qa + abstract: CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge to predict the correct answers . It @@ -8,13 +14,13 @@ see paper for details. languages: -en - -paper: -https://arxiv.org/abs/1811.00937 +english tags: commonsense, multiple-choice, qa + +paper: +https://arxiv.org/abs/1811.00937 """ import lighteval.tasks.default_prompts as prompt @@ -26,7 +32,7 @@ name="commonsenseqa", suite=["lighteval"], prompt_function=prompt.commonsense_qa, - hf_repo="commonsense_qa", + hf_repo="tau/commonsense_qa", hf_subset="default", hf_avail_splits=["train", "test", "validation"], evaluation_splits=["validation"], diff --git a/src/lighteval/tasks/tasks/coqa.py b/src/lighteval/tasks/tasks/coqa.py index 6351a1100..658c24811 100644 --- a/src/lighteval/tasks/tasks/coqa.py +++ b/src/lighteval/tasks/tasks/coqa.py @@ -1,4 +1,10 @@ """ +name: +Coqa + +dataset: +stanfordnlp/coqa + abstract: CoQA is a large-scale dataset for building Conversational Question Answering systems. The goal of the CoQA challenge is to measure the ability of machines to @@ -6,13 +12,13 @@ appear in a conversation. languages: -en - -paper: -https://arxiv.org/abs/1808.07042 +english tags: dialog, qa + +paper: +https://arxiv.org/abs/1808.07042 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/covid_dialogue.py b/src/lighteval/tasks/tasks/covid_dialogue.py index 1ce3777bc..c08a3d9f3 100644 --- a/src/lighteval/tasks/tasks/covid_dialogue.py +++ b/src/lighteval/tasks/tasks/covid_dialogue.py @@ -1,16 +1,22 @@ """ +name: +Covid Dialogue + +dataset: +lighteval/covid_dialogue + abstract: The COVID-19 Dialogue dataset is a collection of 500+ dialogues between doctors and patients during the COVID-19 pandemic. languages: -en - -paper: -https://arxiv.org/abs/2004.06561 +english tags: dialog, medical + +paper: +https://arxiv.org/abs/2004.06561 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/drop_qa.py b/src/lighteval/tasks/tasks/drop_qa.py index 077f769c9..be245cda2 100644 --- a/src/lighteval/tasks/tasks/drop_qa.py +++ b/src/lighteval/tasks/tasks/drop_qa.py @@ -1,17 +1,23 @@ """ +name: +Drop Qa + +dataset: +lighteval/drop_harness + abstract: The DROP dataset is a new question-answering dataset designed to evaluate the ability of language models to answer complex questions that require reasoning over multiple sentences. languages: -en - -paper: -https://arxiv.org/abs/1810.00505 +english tags: math, qa, reasoning + +paper: +https://arxiv.org/abs/1810.00505 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/dyck_language.py b/src/lighteval/tasks/tasks/dyck_language.py index f593f8678..9a93e6b19 100644 --- a/src/lighteval/tasks/tasks/dyck_language.py +++ b/src/lighteval/tasks/tasks/dyck_language.py @@ -1,15 +1,21 @@ """ +name: +Dyck Language + +dataset: +lighteval/DyckLanguage + abstract: Scenario testing hierarchical reasoning through the Dyck formal languages. languages: -en - -paper: -https://aclanthology.org/W19-3905/ +english tags: reasoning + +paper: +https://aclanthology.org/W19-3905/ """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/entity_data_imputation.py b/src/lighteval/tasks/tasks/entity_data_imputation.py index f5a93240e..f6bce587f 100644 --- a/src/lighteval/tasks/tasks/entity_data_imputation.py +++ b/src/lighteval/tasks/tasks/entity_data_imputation.py @@ -1,15 +1,21 @@ """ +name: +Entity Data Imputation + +dataset: +lighteval/Buy, lighteval/Restaurant + abstract: Scenario that tests the ability to impute missing entities in a data table. languages: -en - -paper: -https://ieeexplore.ieee.org/document/9458712 +english tags: reasoning + +paper: +https://ieeexplore.ieee.org/document/9458712 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/entitymatching.py b/src/lighteval/tasks/tasks/entitymatching.py index d0bd44c84..adc5d0733 100644 --- a/src/lighteval/tasks/tasks/entitymatching.py +++ b/src/lighteval/tasks/tasks/entitymatching.py @@ -1,15 +1,21 @@ """ +name: +Entitymatching + +dataset: +lighteval/EntityMatching + abstract: Simple entity matching benchmark. languages: -en - -paper: -https://dl.acm.org/doi/10.14778/3007263.3007314 +english tags: classification, reasoning + +paper: +https://dl.acm.org/doi/10.14778/3007263.3007314 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/ethics.py b/src/lighteval/tasks/tasks/ethics.py index cabacecf5..8e637a7a5 100644 --- a/src/lighteval/tasks/tasks/ethics.py +++ b/src/lighteval/tasks/tasks/ethics.py @@ -1,10 +1,16 @@ """ +name: +Ethics + +dataset: +lighteval/hendrycks_ethics + abstract: The Ethics benchmark for evaluating the ability of language models to reason about ethical issues. languages: -en +english tags: classification, ethics, justice, morality, utilitarianism, virtue diff --git a/src/lighteval/tasks/tasks/glue.py b/src/lighteval/tasks/tasks/glue.py index 8a3c65f8b..b7a694d07 100644 --- a/src/lighteval/tasks/tasks/glue.py +++ b/src/lighteval/tasks/tasks/glue.py @@ -1,14 +1,22 @@ """ +name: +GLUE + +dataset: +nyu-mll/glue, aps/super_glue + abstract: The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems. languages: -en +english tags: -classification +classification, language-understanding + +paper: """ import lighteval.tasks.default_prompts as prompt @@ -20,7 +28,7 @@ name="glue:cola", suite=["lighteval"], prompt_function=prompt.cola, - hf_repo="glue", + hf_repo="nyu-mll/glue", hf_subset="cola", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], @@ -36,7 +44,7 @@ name="glue:mnli", suite=["lighteval"], prompt_function=prompt.mnli, - hf_repo="glue", + hf_repo="nyu-mll/glue", hf_subset="mnli_matched", hf_avail_splits=["train", "validation"], evaluation_splits=["validation"], @@ -52,7 +60,7 @@ name="glue:mnli_mismatched", suite=["lighteval"], prompt_function=prompt.mnli, - hf_repo="glue", + hf_repo="nyu-mll/glue", hf_subset="mnli_mismatched", hf_avail_splits=["train", "validation"], evaluation_splits=["validation"], @@ -68,7 +76,7 @@ name="glue:mrpc", suite=["lighteval"], prompt_function=prompt.mrpc, - hf_repo="glue", + hf_repo="nyu-mll/glue", hf_subset="mrpc", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], @@ -84,7 +92,7 @@ name="glue:qnli", suite=["lighteval"], prompt_function=prompt.qnli, - hf_repo="glue", + hf_repo="nyu-mll/glue", hf_subset="qnli", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], @@ -100,7 +108,7 @@ name="glue:qqp", suite=["lighteval"], prompt_function=prompt.qqp, - hf_repo="glue", + hf_repo="nyu-mll/glue", hf_subset="qqp", hf_avail_splits=["train", "validation", "test"], evaluation_splits=["validation"], @@ -116,7 +124,7 @@ name="glue:rte", suite=["lighteval"], prompt_function=prompt.rte, - hf_repo="glue", + hf_repo="nyu-mll/glue", hf_subset="rte", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], @@ -132,7 +140,7 @@ name="glue:sst2", suite=["lighteval"], prompt_function=prompt.sst, - hf_repo="glue", + hf_repo="nyu-mll/glue", hf_subset="sst2", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], @@ -148,7 +156,7 @@ name="glue:stsb", suite=["lighteval"], prompt_function=prompt.stsb, - hf_repo="glue", + hf_repo="nyu-mll/glue", hf_subset="stsb", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], @@ -164,7 +172,7 @@ name="glue:wnli", suite=["lighteval"], prompt_function=prompt.wnli, - hf_repo="glue", + hf_repo="nyu-mll/glue", hf_subset="wnli", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], @@ -180,7 +188,7 @@ name="super_glue:boolq", suite=["lighteval"], prompt_function=prompt.boolq_harness, - hf_repo="super_glue", + hf_repo="aps/super_glue", hf_subset="boolq", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], @@ -196,7 +204,7 @@ name="super_glue:cb", suite=["lighteval"], prompt_function=prompt.cb, - hf_repo="super_glue", + hf_repo="aps/super_glue", hf_subset="cb", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], @@ -212,7 +220,7 @@ name="super_glue:copa", suite=["lighteval"], prompt_function=prompt.copa, - hf_repo="super_glue", + hf_repo="aps/super_glue", hf_subset="copa", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], @@ -228,7 +236,7 @@ name="super_glue:rte", suite=["lighteval"], prompt_function=prompt.rte, - hf_repo="super_glue", + hf_repo="aps/super_glue", hf_subset="rte", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], @@ -244,7 +252,7 @@ name="super_glue:multirc", suite=["lighteval"], prompt_function=prompt.multirc, - hf_repo="super_glue", + hf_repo="aps/super_glue", hf_subset="multirc", hf_avail_splits=["train", "validation"], evaluation_splits=["validation"], @@ -260,7 +268,7 @@ name="super_glue:wic", suite=["lighteval"], prompt_function=prompt.wic, - hf_repo="super_glue", + hf_repo="aps/super_glue", hf_subset="wic", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], @@ -276,7 +284,7 @@ name="super_glue:wsc", suite=["lighteval"], prompt_function=prompt.wsc, - hf_repo="super_glue", + hf_repo="aps/super_glue", hf_subset="wsc", hf_avail_splits=["test", "train", "validation"], evaluation_splits=["validation"], diff --git a/src/lighteval/tasks/tasks/gpqa.py b/src/lighteval/tasks/tasks/gpqa.py index e76e1f604..121ef8e5f 100644 --- a/src/lighteval/tasks/tasks/gpqa.py +++ b/src/lighteval/tasks/tasks/gpqa.py @@ -1,4 +1,10 @@ """ +name: +Gpqa + +dataset: +Idavidrein/gpqa + abstract: GPQA is a dataset of 448 expert-written multiple-choice questions in biology, physics, and chemistry, designed to test graduate-level reasoning. The questions @@ -8,7 +14,7 @@ exceed human expertise. languages: -en +english tags: biology, chemistry, graduate-level, multiple-choice, physics, qa, reasoning, science diff --git a/src/lighteval/tasks/tasks/gsm8k.py b/src/lighteval/tasks/tasks/gsm8k.py index b35c1f9f9..883a7dbbc 100644 --- a/src/lighteval/tasks/tasks/gsm8k.py +++ b/src/lighteval/tasks/tasks/gsm8k.py @@ -1,9 +1,15 @@ """ +name: +Gsm8K + +dataset: +openai/gsm8k + abstract: GSM8K is a dataset of 8,000+ high-quality, single-step arithmetic word problems. languages: -en +english tags: math, reasoning diff --git a/src/lighteval/tasks/tasks/gsm_plus.py b/src/lighteval/tasks/tasks/gsm_plus.py index bce5a6e7b..8fc6f2696 100644 --- a/src/lighteval/tasks/tasks/gsm_plus.py +++ b/src/lighteval/tasks/tasks/gsm_plus.py @@ -1,11 +1,17 @@ """ +name: +Gsm Plus + +dataset: +qintongli/GSM-Plus + abstract: GSM-Plus is an adversarial extension of GSM8K that tests the robustness of LLMs' mathematical reasoning by introducing varied perturbations to grade-school math problems. languages: -en +english tags: math, reasoning diff --git a/src/lighteval/tasks/tasks/headqa.py b/src/lighteval/tasks/tasks/headqa.py index ed002217f..31f808317 100644 --- a/src/lighteval/tasks/tasks/headqa.py +++ b/src/lighteval/tasks/tasks/headqa.py @@ -1,4 +1,10 @@ """ +name: +Headqa + +dataset: +lighteval/headqa_harness + abstract: HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the Spanish healthcare system, and are @@ -7,7 +13,7 @@ access to the exams of the last 5 years. languages: -en, es +english, spanish tags: health, medical, multiple-choice, qa diff --git a/src/lighteval/tasks/tasks/hellaswag.py b/src/lighteval/tasks/tasks/hellaswag.py index 594e62153..5ffc68f78 100644 --- a/src/lighteval/tasks/tasks/hellaswag.py +++ b/src/lighteval/tasks/tasks/hellaswag.py @@ -1,10 +1,16 @@ """ +name: +Hellaswag + +dataset: +Rowan/hellaswag + abstract: HellaSwag is a commonsense inference benchmark designed to challenge language models with adversarially filtered multiple-choice questions. languages: -en +english tags: multiple-choice, narrative, reasoning @@ -22,7 +28,7 @@ name="hellaswag", suite=["lighteval"], prompt_function=prompt.hellaswag_generative, - hf_repo="hellaswag", + hf_repo="Rowan/hellaswag", hf_subset="default", hf_avail_splits=["train", "test", "validation"], evaluation_splits=["validation"], diff --git a/src/lighteval/tasks/tasks/hle/main.py b/src/lighteval/tasks/tasks/hle/main.py index 4544818fd..c22dcaf72 100644 --- a/src/lighteval/tasks/tasks/hle/main.py +++ b/src/lighteval/tasks/tasks/hle/main.py @@ -1,4 +1,10 @@ """ +name: +Humanity's Last Exam + +dataset: +cais/hle + abstract: Humanity's Last Exam (HLE) is a global collaborative effort, with questions from nearly 1,000 subject expert contributors affiliated with over 500 institutions @@ -6,7 +12,7 @@ degree holders. languages: -en +english tags: qa, reasoning, general-knowledge diff --git a/src/lighteval/tasks/tasks/ifbench/main.py b/src/lighteval/tasks/tasks/ifbench/main.py index f03e03702..419c86600 100644 --- a/src/lighteval/tasks/tasks/ifbench/main.py +++ b/src/lighteval/tasks/tasks/ifbench/main.py @@ -1,9 +1,15 @@ """ +name: +IFBench + +dataset: +allenai/IFBench_test, allenai/IFBench_multi-turn + abstract: Challenging benchmark for precise instruction following. languages: -en +english tags: instruction-following diff --git a/src/lighteval/tasks/tasks/ifeval/main.py b/src/lighteval/tasks/tasks/ifeval/main.py index 31a51dc75..2922e5fb6 100644 --- a/src/lighteval/tasks/tasks/ifeval/main.py +++ b/src/lighteval/tasks/tasks/ifeval/main.py @@ -1,10 +1,16 @@ """ +name: +IFEval + +dataset: +google/IFEval + abstract: Very specific task where there are no precise outputs but instead we test if the format obeys rules. languages: -en +english tags: instruction-following diff --git a/src/lighteval/tasks/tasks/imdb.py b/src/lighteval/tasks/tasks/imdb.py index ad26df355..5bc0756b9 100644 --- a/src/lighteval/tasks/tasks/imdb.py +++ b/src/lighteval/tasks/tasks/imdb.py @@ -1,10 +1,16 @@ """ +name: +Imdb + +dataset: +lighteval/IMDB_helm + abstract: The IMDB benchmark for sentiment analysis in movie review, from: Learning Word Vectors for Sentiment Analysis languages: -en +english tags: classification diff --git a/src/lighteval/tasks/tasks/jeopardy.py b/src/lighteval/tasks/tasks/jeopardy.py index 7b3685600..1bc58e220 100644 --- a/src/lighteval/tasks/tasks/jeopardy.py +++ b/src/lighteval/tasks/tasks/jeopardy.py @@ -1,14 +1,20 @@ """ +name: +Jeopardy + +dataset: +openaccess-ai-collective/jeopardy + abstract: Jeopardy is a dataset of questions and answers from the Jeopardy game show. languages: -en - -paper: +english tags: knowledge, qa + +paper: """ from lighteval.metrics.metrics import Metrics diff --git a/src/lighteval/tasks/tasks/lambada.py b/src/lighteval/tasks/tasks/lambada.py index 60554a5da..828a72506 100644 --- a/src/lighteval/tasks/tasks/lambada.py +++ b/src/lighteval/tasks/tasks/lambada.py @@ -1,4 +1,10 @@ """ +name: +Lambada + +dataset: +cimec/lambada + abstract: LAMBADA is a benchmark for testing language models’ ability to understand broad narrative context. Each passage requires predicting its final word—easy for @@ -6,7 +12,7 @@ Success demands long-range discourse comprehension. languages: -en +english tags: language-modeling diff --git a/src/lighteval/tasks/tasks/lcb/codegen_metrics.py b/src/lighteval/tasks/tasks/lcb/codegen_metrics.py index cec88a7aa..b272f28da 100644 --- a/src/lighteval/tasks/tasks/lcb/codegen_metrics.py +++ b/src/lighteval/tasks/tasks/lcb/codegen_metrics.py @@ -1,7 +1,16 @@ -"""This module contains helper functions copied and modified from -https://github.com/LiveCodeBench/LiveCodeBench -and -https://github.com/QwenLM/Qwen2.5-Coder/tree/main/qwencoder-eval/instruct/livecode_bench +""" +name: +Codegen Metrics + +dataset: + +abstract: + +languages: + +tags: + +paper: """ import ast diff --git a/src/lighteval/tasks/tasks/lcb/main.py b/src/lighteval/tasks/tasks/lcb/main.py index 52842429f..0f2f5d52e 100644 --- a/src/lighteval/tasks/tasks/lcb/main.py +++ b/src/lighteval/tasks/tasks/lcb/main.py @@ -1,4 +1,10 @@ """ +name: +Live Code Bench + +dataset: +lighteval/code_generation_lite + abstract: LiveCodeBench collects problems from periodic contests on LeetCode, AtCoder, and Codeforces platforms and uses them for constructing a holistic benchmark for @@ -6,7 +12,7 @@ time. languages: -en +english tags: code-generation diff --git a/src/lighteval/tasks/tasks/legal_summarization.py b/src/lighteval/tasks/tasks/legal_summarization.py index a886caff8..66ca9b32a 100644 --- a/src/lighteval/tasks/tasks/legal_summarization.py +++ b/src/lighteval/tasks/tasks/legal_summarization.py @@ -1,18 +1,22 @@ """ +name: +Legal Summarization + +dataset: +lighteval/legal_summarization + abstract: LegalSummarization is a dataset for legal summarization. languages: -en - -paper: -https://arxiv.org/abs/2210.13448 +english tags: legal, summarization paper: https://arxiv.org/abs/2210.13448 +https://arxiv.org/abs/2210.13448 """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/legalsupport.py b/src/lighteval/tasks/tasks/legalsupport.py index eef43c1aa..206ccc3f5 100644 --- a/src/lighteval/tasks/tasks/legalsupport.py +++ b/src/lighteval/tasks/tasks/legalsupport.py @@ -1,15 +1,20 @@ """ +name: +Legalsupport + +dataset: +lighteval/LegalSupport + abstract: Measures fine-grained legal reasoning through reverse entailment. languages: -en +english tags: legal paper: - """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/lexglue.py b/src/lighteval/tasks/tasks/lexglue.py index ec01d07ab..2b30dd8c4 100644 --- a/src/lighteval/tasks/tasks/lexglue.py +++ b/src/lighteval/tasks/tasks/lexglue.py @@ -1,9 +1,15 @@ """ +name: +Lexglue + +dataset: +lighteval/lexglue + abstract: LexGLUE: A Benchmark Dataset for Legal Language Understanding in English languages: -en +english tags: classification, legal diff --git a/src/lighteval/tasks/tasks/lextreme.py b/src/lighteval/tasks/tasks/lextreme.py index a4713607f..d996259a5 100644 --- a/src/lighteval/tasks/tasks/lextreme.py +++ b/src/lighteval/tasks/tasks/lextreme.py @@ -1,9 +1,15 @@ """ +name: +Lextreme + +dataset: +lighteval/lextreme + abstract: LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain languages: -bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv +bulgarian, czech, danish, german, greek, english, spanish, estonian, finnish, french, ga, croatian, hungarian, italian, lithuanian, latvian, mt, dutch, polish, portuguese, romanian, slovak, slovenian, swedish tags: classification, legal diff --git a/src/lighteval/tasks/tasks/logiqa.py b/src/lighteval/tasks/tasks/logiqa.py index c70416ca2..4dbc5b47e 100644 --- a/src/lighteval/tasks/tasks/logiqa.py +++ b/src/lighteval/tasks/tasks/logiqa.py @@ -1,4 +1,10 @@ """ +name: +Logiqa + +dataset: +lighteval/logiqa_harness + abstract: LogiQA is a machine reading comprehension dataset focused on testing logical reasoning abilities. It contains 8,678 expert-written multiple-choice questions @@ -7,7 +13,7 @@ logical reasoning in NLP systems. languages: -en +english tags: qa diff --git a/src/lighteval/tasks/tasks/lsat_qa.py b/src/lighteval/tasks/tasks/lsat_qa.py index 8b12b06ac..ad813649a 100644 --- a/src/lighteval/tasks/tasks/lsat_qa.py +++ b/src/lighteval/tasks/tasks/lsat_qa.py @@ -1,9 +1,15 @@ """ +name: +Lsat Qa + +dataset: +lighteval/lsat_qa + abstract: Questions from law school admission tests. languages: -en +english tags: legal, qa diff --git a/src/lighteval/tasks/tasks/math.py b/src/lighteval/tasks/tasks/math.py index 5a277be7b..2e09b4ac6 100644 --- a/src/lighteval/tasks/tasks/math.py +++ b/src/lighteval/tasks/tasks/math.py @@ -1,11 +1,14 @@ """ -The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems -from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. -Each problem in MATH has a full step-by-step solution, which can be used to -teach models to generate answer derivations and explanations. +name: +Math + +dataset: +DigitalLearningGmbH/MATH-lighteval + +abstract: languages: -en +english tags: math, reasoning diff --git a/src/lighteval/tasks/tasks/math_500.py b/src/lighteval/tasks/tasks/math_500.py index 349d12fbd..2575cc86f 100644 --- a/src/lighteval/tasks/tasks/math_500.py +++ b/src/lighteval/tasks/tasks/math_500.py @@ -1,10 +1,16 @@ """ +name: +Math 500 + +dataset: +HuggingFaceH4/MATH-500 + abstract: This dataset contains a subset of 500 problems from the MATH benchmark that OpenAI created in their Let's Verify Step by Step paper. languages: -en +english tags: math, reasoning diff --git a/src/lighteval/tasks/tasks/mathqa.py b/src/lighteval/tasks/tasks/mathqa.py index 5320876fa..c58c5437c 100644 --- a/src/lighteval/tasks/tasks/mathqa.py +++ b/src/lighteval/tasks/tasks/mathqa.py @@ -1,4 +1,10 @@ """ +name: +Mathqa + +dataset: +allenai/math_qa + abstract: large-scale dataset of math word problems. Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset with @@ -6,7 +12,7 @@ options, rationale, and the correct options. languages: -en +english tags: math, qa, reasoning diff --git a/src/lighteval/tasks/tasks/med.py b/src/lighteval/tasks/tasks/med.py index 45ffa8db7..dd6e4641c 100644 --- a/src/lighteval/tasks/tasks/med.py +++ b/src/lighteval/tasks/tasks/med.py @@ -1,9 +1,15 @@ """ +name: +Med + +dataset: +lighteval/med_mcqa, lighteval/med_paragraph_simplification, bigbio/med_qa + abstract: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering languages: -en +english tags: health, medical diff --git a/src/lighteval/tasks/tasks/med_dialog.py b/src/lighteval/tasks/tasks/med_dialog.py index 170624f39..2b3d0c828 100644 --- a/src/lighteval/tasks/tasks/med_dialog.py +++ b/src/lighteval/tasks/tasks/med_dialog.py @@ -1,15 +1,20 @@ """ +name: +Med Dialog + +dataset: +lighteval/med_dialog + abstract: A collection of medical dialogue datasets. languages: -en +english tags: dialog, health, medical paper: - """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/mgsm.py b/src/lighteval/tasks/tasks/mgsm.py index 1e0505b85..7f1daa63e 100644 --- a/src/lighteval/tasks/tasks/mgsm.py +++ b/src/lighteval/tasks/tasks/mgsm.py @@ -1,4 +1,10 @@ """ +name: +Mgsm + +dataset: +juletxara/mgsm + abstract: Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems. @@ -6,7 +12,7 @@ languages. languages: -en, es, fr, de, ru, zh, ja, th, sw, bn, te +english, spanish, french, german, russian, chinese, japanese, thai, swahili, bengali, telugu tags: math, multilingual, reasoning diff --git a/src/lighteval/tasks/tasks/mix_eval/main.py b/src/lighteval/tasks/tasks/mix_eval/main.py index 19d4ec036..bd0aed792 100644 --- a/src/lighteval/tasks/tasks/mix_eval/main.py +++ b/src/lighteval/tasks/tasks/mix_eval/main.py @@ -1,4 +1,10 @@ """ +name: +Mix Eval + +dataset: +MixEval/MixEval + abstract: Ground-truth-based dynamic benchmark derived from off-the-shelf benchmark mixtures, which evaluates LLMs with a highly capable model ranking (i.e., 0.96 @@ -7,7 +13,7 @@ updated every month to avoid contamination. languages: -en +english tags: general-knowledge, reasoning, qa diff --git a/src/lighteval/tasks/tasks/mmlu.py b/src/lighteval/tasks/tasks/mmlu.py index 0df44613f..8299630f2 100644 --- a/src/lighteval/tasks/tasks/mmlu.py +++ b/src/lighteval/tasks/tasks/mmlu.py @@ -1,9 +1,15 @@ """ +name: +Mmlu + +dataset: +lighteval/mmlu + abstract: MMMLU is a benchmark of general-knowledge and English language understanding. languages: -en +english tags: general-knowledge, knowledge, multiple-choice diff --git a/src/lighteval/tasks/tasks/mmlu_redux.py b/src/lighteval/tasks/tasks/mmlu_redux.py index f26afb1ea..9e39edc38 100644 --- a/src/lighteval/tasks/tasks/mmlu_redux.py +++ b/src/lighteval/tasks/tasks/mmlu_redux.py @@ -1,9 +1,15 @@ """ +name: +Mmlu Redux + +dataset: +edinburgh-dawg/mmlu-redux-2.0 + abstract: MMLU-Redux is a subset of 5,700 manually re-annotated questions across 57 MMLU subjects. languages: -en +english tags: general-knowledge, knowledge, multiple-choice diff --git a/src/lighteval/tasks/tasks/mmmu_pro.py b/src/lighteval/tasks/tasks/mmmu_pro.py index 91a0dbacd..96fab139d 100644 --- a/src/lighteval/tasks/tasks/mmmu_pro.py +++ b/src/lighteval/tasks/tasks/mmmu_pro.py @@ -1,10 +1,14 @@ """ -MMMU-Pro is an enhanced multimodal benchmark designed to rigorously assess the -true understanding capabilities of advanced AI models across multiple -modalities. +name: +Mmmu Pro + +dataset: +MMMU/MMMU_pro + +abstract: languages: -en +english tags: general-knowledge, knowledge, multimodal, multiple-choice diff --git a/src/lighteval/tasks/tasks/mt_bench/main.py b/src/lighteval/tasks/tasks/mt_bench/main.py index 81530c63d..bed7239dd 100644 --- a/src/lighteval/tasks/tasks/mt_bench/main.py +++ b/src/lighteval/tasks/tasks/mt_bench/main.py @@ -1,4 +1,10 @@ """ +name: +Mt Bench + +dataset: +lighteval/mt-bench + abstract: MT-Bench is a multi-turn conversational benchmark for evaluating language models. It consists of 80 high-quality multi-turn questions across 8 common @@ -6,7 +12,7 @@ humanities). Model responses are evaluated by a judge LLM. languages: -en +english tags: conversational, generation, multi-turn diff --git a/src/lighteval/tasks/tasks/musr.py b/src/lighteval/tasks/tasks/musr.py index caf2d34f8..2792850e1 100644 --- a/src/lighteval/tasks/tasks/musr.py +++ b/src/lighteval/tasks/tasks/musr.py @@ -1,11 +1,17 @@ """ +name: +Musr + +dataset: +TAUR-Lab/MuSR + abstract: MuSR is a benchmark for evaluating multistep reasoning in natural language narratives. Built using a neurosymbolic synthetic-to-natural generation process, it features complex, realistic tasks—such as long-form murder mysteries. languages: -en +english tags: long-context, multiple-choice, reasoning diff --git a/src/lighteval/tasks/tasks/narrativeqa.py b/src/lighteval/tasks/tasks/narrativeqa.py index 9ac6e9bad..c48e967e8 100644 --- a/src/lighteval/tasks/tasks/narrativeqa.py +++ b/src/lighteval/tasks/tasks/narrativeqa.py @@ -1,11 +1,17 @@ """ +name: +Narrativeqa + +dataset: +lighteval/narrative_qa_helm + abstract: NarrativeQA is a reading comprehension benchmark that tests deep understanding of full narratives—books and movie scripts—rather than shallow text matching. To answer its questions, models must integrate information across entire stories. languages: -en +english tags: qa, reading-comprehension diff --git a/src/lighteval/tasks/tasks/natural_questions.py b/src/lighteval/tasks/tasks/natural_questions.py index 01427de76..c7f37be2b 100644 --- a/src/lighteval/tasks/tasks/natural_questions.py +++ b/src/lighteval/tasks/tasks/natural_questions.py @@ -1,11 +1,17 @@ """ +name: +Natural Questions + +dataset: +lighteval/small_natural_questions + abstract: This dataset is a collection of question-answer pairs from the Natural Questions dataset. See Natural Questions for additional information. This dataset can be used directly with Sentence Transformers to train embedding models. languages: -en +english tags: general-knowledge, qa diff --git a/src/lighteval/tasks/tasks/numeracy.py b/src/lighteval/tasks/tasks/numeracy.py index 49d9b2b94..7f49dea87 100644 --- a/src/lighteval/tasks/tasks/numeracy.py +++ b/src/lighteval/tasks/tasks/numeracy.py @@ -1,15 +1,20 @@ """ +name: +Numeracy + +dataset: +lighteval/numeracy + abstract: Numeracy is a benchmark for evaluating the ability of language models to reason about mathematics. languages: -en +english tags: math, reasoning paper: - """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/olympiade_bench/main.py b/src/lighteval/tasks/tasks/olympiade_bench/main.py index fb9b25b74..bd53d3dcf 100644 --- a/src/lighteval/tasks/tasks/olympiade_bench/main.py +++ b/src/lighteval/tasks/tasks/olympiade_bench/main.py @@ -1,10 +1,16 @@ """ +name: +Olympiade Bench + +dataset: +Hothan/OlympiadBench + abstract: OlympiadBench is a benchmark for evaluating the performance of language models on olympiad problems. languages: -en, zh +english, chinese tags: math, reasoning, language diff --git a/src/lighteval/tasks/tasks/openbookqa.py b/src/lighteval/tasks/tasks/openbookqa.py index 481e195f5..4816ff70c 100644 --- a/src/lighteval/tasks/tasks/openbookqa.py +++ b/src/lighteval/tasks/tasks/openbookqa.py @@ -1,4 +1,10 @@ """ +name: +Openbookqa + +dataset: +allenai/openbookqa + abstract: OpenBookQA is a question-answering dataset modeled after open-book exams for assessing human understanding of a subject. It contains multiple-choice @@ -7,7 +13,7 @@ information and apply common sense reasoning. languages: -en +english tags: multiple-choice, qa @@ -25,7 +31,7 @@ name="openbookqa", suite=["lighteval"], prompt_function=prompt.openbookqa_helm, - hf_repo="openbookqa", + hf_repo="allenai/openbookqa", hf_subset="main", hf_avail_splits=["train", "test", "validation"], evaluation_splits=["validation", "test"], diff --git a/src/lighteval/tasks/tasks/piqa.py b/src/lighteval/tasks/tasks/piqa.py index 4491c9fea..c28ba7c55 100644 --- a/src/lighteval/tasks/tasks/piqa.py +++ b/src/lighteval/tasks/tasks/piqa.py @@ -1,10 +1,16 @@ """ +name: +Piqa + +dataset: +ybisk/piqa + abstract: PIQA is a benchmark for testing physical commonsense reasoning. It contains questions requiring this kind of physical commonsense reasoning. languages: -en +english tags: commonsense, multiple-choice, qa diff --git a/src/lighteval/tasks/tasks/prost.py b/src/lighteval/tasks/tasks/prost.py index d22323d63..f7c1d6db7 100644 --- a/src/lighteval/tasks/tasks/prost.py +++ b/src/lighteval/tasks/tasks/prost.py @@ -1,4 +1,10 @@ """ +name: +Prost + +dataset: +lighteval/prost + abstract: PROST is a benchmark for testing physical reasoning about objects through space and time. It includes 18,736 multiple-choice questions covering 10 core physics @@ -7,7 +13,7 @@ question phrasing, underscoring their limited real-world understanding. languages: -en +english tags: reasoning, qa, physical-commonsense diff --git a/src/lighteval/tasks/tasks/pubmedqa.py b/src/lighteval/tasks/tasks/pubmedqa.py index bc989fcb3..be91172e1 100644 --- a/src/lighteval/tasks/tasks/pubmedqa.py +++ b/src/lighteval/tasks/tasks/pubmedqa.py @@ -1,9 +1,15 @@ """ +name: +Pubmedqa + +dataset: +pubmed_qa + abstract: PubMedQA is a dataset for biomedical research question answering. languages: -en +english tags: biomedical, health, medical, qa diff --git a/src/lighteval/tasks/tasks/qa4mre.py b/src/lighteval/tasks/tasks/qa4mre.py index 3e4b40dd9..bdfaad9f4 100644 --- a/src/lighteval/tasks/tasks/qa4mre.py +++ b/src/lighteval/tasks/tasks/qa4mre.py @@ -1,4 +1,10 @@ """ +name: +Qa4Mre + +dataset: +qa4mre + abstract: QA4MRE is a machine reading comprehension benchmark from the CLEF 2011-2013 challenges. It evaluates systems' ability to answer questions requiring deep @@ -7,7 +13,7 @@ QA4MRE tests reasoning beyond surface-level text matching. languages: -en +english tags: biomedical, health, qa diff --git a/src/lighteval/tasks/tasks/qasper.py b/src/lighteval/tasks/tasks/qasper.py index da25d6fef..1862b07f9 100644 --- a/src/lighteval/tasks/tasks/qasper.py +++ b/src/lighteval/tasks/tasks/qasper.py @@ -1,4 +1,10 @@ """ +name: +Qasper + +dataset: +allenai/qasper + abstract: QASPER is a dataset for question answering on scientific research papers. It consists of 5,049 questions over 1,585 Natural Language Processing papers. Each @@ -8,7 +14,7 @@ practitioners who also provide supporting evidence to answers. languages: -en +english tags: qa, scientific diff --git a/src/lighteval/tasks/tasks/quac.py b/src/lighteval/tasks/tasks/quac.py index 388943087..0a7f53e8b 100644 --- a/src/lighteval/tasks/tasks/quac.py +++ b/src/lighteval/tasks/tasks/quac.py @@ -1,9 +1,15 @@ """ +name: +Quac + +dataset: +lighteval/quac_helm + abstract: The QuAC benchmark for question answering in the context of dialogues. languages: -en +english tags: dialog, qa diff --git a/src/lighteval/tasks/tasks/race_high.py b/src/lighteval/tasks/tasks/race_high.py index 97d3398ca..34fba1e84 100644 --- a/src/lighteval/tasks/tasks/race_high.py +++ b/src/lighteval/tasks/tasks/race_high.py @@ -1,4 +1,10 @@ """ +name: +Race High + +dataset: +EleutherAI/race + abstract: RACE is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is collected from English @@ -7,7 +13,7 @@ comprehension. languages: -en +english tags: multiple-choice, reading-comprehension diff --git a/src/lighteval/tasks/tasks/raft.py b/src/lighteval/tasks/tasks/raft.py index 1eb91b7ad..f5c65fcd9 100644 --- a/src/lighteval/tasks/tasks/raft.py +++ b/src/lighteval/tasks/tasks/raft.py @@ -1,10 +1,16 @@ """ +name: +Raft + +dataset: +ought/raft + abstract: The Real-world annotated few-shot (RAFT) meta-benchmark of 11 real-world text classification tasks. languages: -en +english tags: classification, reasoning diff --git a/src/lighteval/tasks/tasks/real_toxicity_prompts.py b/src/lighteval/tasks/tasks/real_toxicity_prompts.py index c64ff848f..733238461 100644 --- a/src/lighteval/tasks/tasks/real_toxicity_prompts.py +++ b/src/lighteval/tasks/tasks/real_toxicity_prompts.py @@ -1,9 +1,15 @@ """ +name: +Real Toxicity Prompts + +dataset: +allenai/real-toxicity-prompts + abstract: The RealToxicityPrompts dataset for measuring toxicity in prompted model generations languages: -en +english tags: generation, safety diff --git a/src/lighteval/tasks/tasks/sacrebleu.py b/src/lighteval/tasks/tasks/sacrebleu.py index c55194193..6e4e9f4d2 100644 --- a/src/lighteval/tasks/tasks/sacrebleu.py +++ b/src/lighteval/tasks/tasks/sacrebleu.py @@ -1,9 +1,15 @@ """ +name: +Sacrebleu + +dataset: +lighteval/sacrebleu_manual, wmt14, wmt16 + abstract: tasks from sacrebleu languages: -en, de, fr, ja, ko, zh, ar +english, german, french, japanese, korean, chinese, arabic tags: translation diff --git a/src/lighteval/tasks/tasks/sciq.py b/src/lighteval/tasks/tasks/sciq.py index 842bb324e..45d0c63a7 100644 --- a/src/lighteval/tasks/tasks/sciq.py +++ b/src/lighteval/tasks/tasks/sciq.py @@ -1,4 +1,10 @@ """ +name: +Sciq + +dataset: +allenai/sciq + abstract: The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in @@ -7,7 +13,7 @@ answer is provided. languages: -en +english tags: physics, chemistry, biology, reasoning, multiple-choice, qa @@ -25,7 +31,7 @@ name="sciq", suite=["lighteval"], prompt_function=prompt.sciq, - hf_repo="sciq", + hf_repo="allenai/sciq", hf_subset="default", hf_avail_splits=["train", "validation", "test"], evaluation_splits=["test"], diff --git a/src/lighteval/tasks/tasks/simpleqa.py b/src/lighteval/tasks/tasks/simpleqa.py index 6c2e8fedd..c9bf4a0f6 100644 --- a/src/lighteval/tasks/tasks/simpleqa.py +++ b/src/lighteval/tasks/tasks/simpleqa.py @@ -1,10 +1,16 @@ """ +name: +Simpleqa + +dataset: +lighteval/SimpleQA + abstract: A factuality benchmark called SimpleQA that measures the ability for language models to answer short, fact-seeking questions. languages: -en +english tags: factuality, general-knowledge, qa diff --git a/src/lighteval/tasks/tasks/siqa.py b/src/lighteval/tasks/tasks/siqa.py index eaa2834ba..1dd6529fe 100644 --- a/src/lighteval/tasks/tasks/siqa.py +++ b/src/lighteval/tasks/tasks/siqa.py @@ -1,4 +1,10 @@ """ +name: +Siqa + +dataset: +allenai/social_i_qa + abstract: We introduce Social IQa: Social Interaction QA, a new question-answering benchmark for testing social commonsense intelligence. Contrary to many prior @@ -14,13 +20,12 @@ implications of everyday events and situations. languages: -en +english tags: commonsense, multiple-choice, qa paper: - """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/squad_v2.py b/src/lighteval/tasks/tasks/squad_v2.py index d272131e7..bf94583f8 100644 --- a/src/lighteval/tasks/tasks/squad_v2.py +++ b/src/lighteval/tasks/tasks/squad_v2.py @@ -1,10 +1,15 @@ """ +name: +Squad V2 + +dataset: +rajpurkar/squad_v2 + abstract: Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. - SQuAD 2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions @@ -12,7 +17,7 @@ and abstain from answering. languages: -en +english tags: qa diff --git a/src/lighteval/tasks/tasks/storycloze.py b/src/lighteval/tasks/tasks/storycloze.py index b9c6b142c..fb14056d3 100644 --- a/src/lighteval/tasks/tasks/storycloze.py +++ b/src/lighteval/tasks/tasks/storycloze.py @@ -1,10 +1,16 @@ """ +name: +Storycloze + +dataset: +MoE-UNC/story_cloze + abstract: A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories languages: -en +english tags: narrative, reasoning diff --git a/src/lighteval/tasks/tasks/summarization.py b/src/lighteval/tasks/tasks/summarization.py index c20881ff6..f878759ef 100644 --- a/src/lighteval/tasks/tasks/summarization.py +++ b/src/lighteval/tasks/tasks/summarization.py @@ -1,11 +1,17 @@ """ +name: +Summarization + +dataset: +lighteval/summarization + abstract: Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization and: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond languages: -en +english tags: summarization diff --git a/src/lighteval/tasks/tasks/swag.py b/src/lighteval/tasks/tasks/swag.py index 02ded6482..09ec1ac62 100644 --- a/src/lighteval/tasks/tasks/swag.py +++ b/src/lighteval/tasks/tasks/swag.py @@ -1,4 +1,10 @@ """ +name: +Swag + +dataset: +allenai/swag + abstract: The dataset consists of 113k multiple choice questions about grounded situations (73k training, 20k validation, 20k test). Each question is a video caption from @@ -10,7 +16,7 @@ representations. languages: -en +english tags: narrative, reasoning @@ -28,7 +34,7 @@ name="swag", suite=["lighteval"], prompt_function=prompt.swag, - hf_repo="swag", + hf_repo="allenai/swag", hf_subset="regular", hf_avail_splits=["train", "validation"], evaluation_splits=["validation"], diff --git a/src/lighteval/tasks/tasks/synthetic_reasoning.py b/src/lighteval/tasks/tasks/synthetic_reasoning.py index 7cd681476..7a94c9238 100644 --- a/src/lighteval/tasks/tasks/synthetic_reasoning.py +++ b/src/lighteval/tasks/tasks/synthetic_reasoning.py @@ -1,9 +1,15 @@ """ +name: +Synthetic Reasoning + +dataset: +lighteval/synthetic_reasoning, lighteval/synthetic_reasoning_natural + abstract: LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning languages: -en +english tags: reasoning diff --git a/src/lighteval/tasks/tasks/the_pile.py b/src/lighteval/tasks/tasks/the_pile.py index 37e7b21e7..cf92ba6b0 100644 --- a/src/lighteval/tasks/tasks/the_pile.py +++ b/src/lighteval/tasks/tasks/the_pile.py @@ -1,9 +1,15 @@ """ +name: +The Pile + +dataset: +lighteval/pile_helm + abstract: The Pile corpus for measuring lanugage model performance across various domains. languages: -en +english tags: language-modeling diff --git a/src/lighteval/tasks/tasks/tiny_benchmarks/main.py b/src/lighteval/tasks/tasks/tiny_benchmarks/main.py index 634e8dfaa..bb8d0c2d1 100644 --- a/src/lighteval/tasks/tasks/tiny_benchmarks/main.py +++ b/src/lighteval/tasks/tasks/tiny_benchmarks/main.py @@ -1,10 +1,18 @@ """ +name: +Tiny Benchmarks + +dataset: +tinyBenchmarks/tinyWinogrande, tinyBenchmarks/tinyAI2_arc, +tinyBenchmarks/tinyHellaswag, tinyBenchmarks/tinyMMLU, +tinyBenchmarks/tinyTruthfulQA, tinyBenchmarks/tinyGSM8k + abstract: TinyBenchmarks is a benchmark for evaluating the performance of language models on tiny benchmarks. languages: -en +english tags: general-knowledge, reasoning, qa diff --git a/src/lighteval/tasks/tasks/toxigen.py b/src/lighteval/tasks/tasks/toxigen.py index f327c0262..01a0f198b 100644 --- a/src/lighteval/tasks/tasks/toxigen.py +++ b/src/lighteval/tasks/tasks/toxigen.py @@ -1,10 +1,16 @@ """ +name: +Toxigen + +dataset: +skg/toxigen-data + abstract: This dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in our paper. languages: -en +english tags: generation, safety diff --git a/src/lighteval/tasks/tasks/triviaqa.py b/src/lighteval/tasks/tasks/triviaqa.py index 472c29cb9..04c41b14b 100644 --- a/src/lighteval/tasks/tasks/triviaqa.py +++ b/src/lighteval/tasks/tasks/triviaqa.py @@ -1,4 +1,10 @@ """ +name: +Triviaqa + +dataset: +mandarjoshi/trivia_qa + abstract: TriviaqQA is a reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaqQA includes 95K question-answer pairs @@ -7,7 +13,7 @@ answering the questions. languages: -en +english tags: qa @@ -25,7 +31,7 @@ name="triviaqa", suite=["lighteval"], prompt_function=prompt.triviaqa, - hf_repo="trivia_qa", + hf_repo="mandarjoshi/trivia_qa", hf_subset="rc.nocontext", hf_avail_splits=["train", "test", "validation"], evaluation_splits=["validation"], diff --git a/src/lighteval/tasks/tasks/truthfulqa.py b/src/lighteval/tasks/tasks/truthfulqa.py index eb5cec634..da3658df6 100644 --- a/src/lighteval/tasks/tasks/truthfulqa.py +++ b/src/lighteval/tasks/tasks/truthfulqa.py @@ -1,9 +1,15 @@ """ +name: +Truthfulqa + +dataset: +EleutherAI/truthful_qa_mc + abstract: TruthfulQA: Measuring How Models Mimic Human Falsehoods languages: -en +english tags: factuality, qa @@ -21,7 +27,7 @@ name="truthfulqa:gen", suite=["lighteval"], prompt_function=prompt.truthful_qa_generative, - hf_repo="truthful_qa", + hf_repo="EleutherAI/truthful_qa_mc", hf_subset="generation", hf_avail_splits=["validation"], evaluation_splits=["validation"], diff --git a/src/lighteval/tasks/tasks/twitterAAE.py b/src/lighteval/tasks/tasks/twitterAAE.py index 39b076531..4deea3947 100644 --- a/src/lighteval/tasks/tasks/twitterAAE.py +++ b/src/lighteval/tasks/tasks/twitterAAE.py @@ -1,9 +1,15 @@ """ +name: +Twitteraae + +dataset: +lighteval/twitterAAE + abstract: Demographic Dialectal Variation in Social Media: A Case Study of African-American English languages: -en +english tags: language-modeling diff --git a/src/lighteval/tasks/tasks/unscramble.py b/src/lighteval/tasks/tasks/unscramble.py index 4fd3bae68..8a6e8461b 100644 --- a/src/lighteval/tasks/tasks/unscramble.py +++ b/src/lighteval/tasks/tasks/unscramble.py @@ -1,10 +1,16 @@ """ +name: +Unscramble + +dataset: +lighteval/GPT3_unscramble + abstract: Benchmark where we ask the model to unscramble a word, either anagram or random insertion. languages: -en +english tags: language-modeling, reasoning diff --git a/src/lighteval/tasks/tasks/webqs.py b/src/lighteval/tasks/tasks/webqs.py index 47af552f5..aa5a8b767 100644 --- a/src/lighteval/tasks/tasks/webqs.py +++ b/src/lighteval/tasks/tasks/webqs.py @@ -1,4 +1,10 @@ """ +name: +Webqs + +dataset: +stanfordnlp/web_questions + abstract: This dataset consists of 6,642 question/answer pairs. The questions are supposed to be answerable by Freebase, a large knowledge graph. The questions are mostly @@ -6,7 +12,7 @@ the web. languages: -en +english tags: qa diff --git a/src/lighteval/tasks/tasks/wikifact.py b/src/lighteval/tasks/tasks/wikifact.py index d5783c01d..d2e6f5cdd 100644 --- a/src/lighteval/tasks/tasks/wikifact.py +++ b/src/lighteval/tasks/tasks/wikifact.py @@ -1,9 +1,15 @@ """ +name: +Wikifact + +dataset: +lighteval/wikifact + abstract: Extensively test factual knowledge. languages: -en +english tags: factuality, knowledge diff --git a/src/lighteval/tasks/tasks/wikitext.py b/src/lighteval/tasks/tasks/wikitext.py index 66f695815..72acca1f2 100644 --- a/src/lighteval/tasks/tasks/wikitext.py +++ b/src/lighteval/tasks/tasks/wikitext.py @@ -1,4 +1,10 @@ """ +name: +Wikitext + +dataset: +EleutherAI/wikitext_document_level + abstract: The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on @@ -6,7 +12,7 @@ Attribution-ShareAlike License. languages: -en +english tags: language-modeling diff --git a/src/lighteval/tasks/tasks/winogrande.py b/src/lighteval/tasks/tasks/winogrande.py index 7fb3b6d9c..834af392c 100644 --- a/src/lighteval/tasks/tasks/winogrande.py +++ b/src/lighteval/tasks/tasks/winogrande.py @@ -1,4 +1,10 @@ """ +name: +Winogrande + +dataset: +allenai/winogrande + abstract: WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern 2011), but adjusted to improve the @@ -7,7 +13,7 @@ for a given sentence which requires commonsense reasoning. languages: -en +english tags: commonsense, multiple-choice diff --git a/src/lighteval/tasks/tasks/xcopa.py b/src/lighteval/tasks/tasks/xcopa.py index d3d975b96..e501fe6c4 100644 --- a/src/lighteval/tasks/tasks/xcopa.py +++ b/src/lighteval/tasks/tasks/xcopa.py @@ -1,11 +1,17 @@ """ +name: +Xcopa + +dataset: +cambridgeltl/xcopa + abstract: XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across languages. languages: -en +english tags: commonsense, multilingual, multiple-choice, reasoning diff --git a/src/lighteval/tasks/tasks/xstory_cloze.py b/src/lighteval/tasks/tasks/xstory_cloze.py index 02814f7f2..acedd5000 100644 --- a/src/lighteval/tasks/tasks/xstory_cloze.py +++ b/src/lighteval/tasks/tasks/xstory_cloze.py @@ -1,17 +1,22 @@ """ +name: +Xstory Cloze + +dataset: +juletxara/xstory_cloze + abstract: XStoryCloze consists of the professionally translated version of the English StoryCloze dataset (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI. languages: -en, ru, zh, es, ar, hi, id, te, sw, eu, my +english, russian, chinese, spanish, arabic, hindi, indonesian, telugu, swahili, basque, burmese tags: multilingual, narrative, reasoning paper: - """ import lighteval.tasks.default_prompts as prompt diff --git a/src/lighteval/tasks/tasks/xwinograd.py b/src/lighteval/tasks/tasks/xwinograd.py index 8c0daa61c..9e8d2df52 100644 --- a/src/lighteval/tasks/tasks/xwinograd.py +++ b/src/lighteval/tasks/tasks/xwinograd.py @@ -1,9 +1,15 @@ """ +name: +Xwinograd + +dataset: +Muennighoff/xwinograd + abstract: Multilingual winograd schema challenge as used in Crosslingual Generalization through Multitask Finetuning. languages: -en, fr, jp, pt, ru, zh +english, french, japanese, portuguese, russian, chinese tags: commonsense, multilingual, reasoning From b734532727f285f96d757b5bbb11f522d6e219eb Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Wed, 15 Oct 2025 15:41:48 +0200 Subject: [PATCH 20/43] use TASKS_TABLE for multilingual tasks --- src/lighteval/tasks/multilingual/tasks/acva.py | 5 +---- src/lighteval/tasks/multilingual/tasks/afri_mgsm.py | 5 +---- src/lighteval/tasks/multilingual/tasks/afri_mmlu.py | 3 --- src/lighteval/tasks/multilingual/tasks/afri_xnli.py | 5 +---- src/lighteval/tasks/multilingual/tasks/arabic_arc.py | 5 +---- src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py | 5 +---- src/lighteval/tasks/multilingual/tasks/arcd.py | 4 +--- src/lighteval/tasks/multilingual/tasks/belebele.py | 5 +---- src/lighteval/tasks/multilingual/tasks/c3.py | 4 +--- src/lighteval/tasks/multilingual/tasks/ceval.py | 5 +---- src/lighteval/tasks/multilingual/tasks/chegeka.py | 5 +---- src/lighteval/tasks/multilingual/tasks/chinese_squad.py | 5 +---- src/lighteval/tasks/multilingual/tasks/cmath.py | 5 +---- src/lighteval/tasks/multilingual/tasks/cmmlu.py | 5 +---- src/lighteval/tasks/multilingual/tasks/cmnli.py | 5 +---- src/lighteval/tasks/multilingual/tasks/cmrc2018.py | 5 +---- src/lighteval/tasks/multilingual/tasks/copa_indic.py | 4 +--- src/lighteval/tasks/multilingual/tasks/enem.py | 5 +---- src/lighteval/tasks/multilingual/tasks/exams.py | 4 +--- src/lighteval/tasks/multilingual/tasks/faquad.py | 5 +---- src/lighteval/tasks/multilingual/tasks/flores200.py | 5 +---- src/lighteval/tasks/multilingual/tasks/fquad_v2.py | 5 +---- src/lighteval/tasks/multilingual/tasks/french_boolq.py | 5 +---- src/lighteval/tasks/multilingual/tasks/french_triviqa.py | 5 +---- src/lighteval/tasks/multilingual/tasks/germanquad.py | 5 +---- src/lighteval/tasks/multilingual/tasks/global_mmlu.py | 5 +---- src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py | 5 +---- src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py | 5 +---- src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py | 5 +---- src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py | 5 +---- src/lighteval/tasks/multilingual/tasks/hindi_arc.py | 5 +---- src/lighteval/tasks/multilingual/tasks/hindi_boolq.py | 5 +---- src/lighteval/tasks/multilingual/tasks/indicqa.py | 5 +---- src/lighteval/tasks/multilingual/tasks/kenswquad.py | 5 +---- src/lighteval/tasks/multilingual/tasks/m3exams.py | 5 +---- src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py | 5 +---- src/lighteval/tasks/multilingual/tasks/meta_mmlu.py | 5 +---- src/lighteval/tasks/multilingual/tasks/mgsm.py | 5 +---- src/lighteval/tasks/multilingual/tasks/mintaka.py | 5 +---- src/lighteval/tasks/multilingual/tasks/mkqa.py | 5 +---- src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py | 5 +---- src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py | 5 +---- src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py | 5 +---- src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py | 5 +---- src/lighteval/tasks/multilingual/tasks/mlqa.py | 5 +---- src/lighteval/tasks/multilingual/tasks/oab_exams.py | 5 +---- src/lighteval/tasks/multilingual/tasks/ocnli.py | 5 +---- src/lighteval/tasks/multilingual/tasks/openai_mmlu.py | 5 +---- src/lighteval/tasks/multilingual/tasks/openbook_ara.py | 5 +---- src/lighteval/tasks/multilingual/tasks/openbook_es.py | 5 +---- src/lighteval/tasks/multilingual/tasks/openbook_rus.py | 5 +---- src/lighteval/tasks/multilingual/tasks/parus.py | 5 +---- src/lighteval/tasks/multilingual/tasks/paws_x.py | 5 +---- src/lighteval/tasks/multilingual/tasks/piqa_ar.py | 5 +---- src/lighteval/tasks/multilingual/tasks/rcb.py | 5 +---- src/lighteval/tasks/multilingual/tasks/sber_squad.py | 5 +---- src/lighteval/tasks/multilingual/tasks/soqal.py | 5 +---- src/lighteval/tasks/multilingual/tasks/squad_es.py | 5 +---- src/lighteval/tasks/multilingual/tasks/squad_it.py | 5 +---- src/lighteval/tasks/multilingual/tasks/swahili_arc.py | 5 +---- src/lighteval/tasks/multilingual/tasks/thai_exams.py | 5 +---- src/lighteval/tasks/multilingual/tasks/thaiqa.py | 5 +---- src/lighteval/tasks/multilingual/tasks/tquad_v2.py | 5 +---- src/lighteval/tasks/multilingual/tasks/turkish_arc.py | 5 +---- src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py | 5 +---- src/lighteval/tasks/multilingual/tasks/tydiqa.py | 5 +---- src/lighteval/tasks/multilingual/tasks/worldtree_rus.py | 5 +---- src/lighteval/tasks/multilingual/tasks/xcodah.py | 5 +---- src/lighteval/tasks/multilingual/tasks/xcopa.py | 5 +---- src/lighteval/tasks/multilingual/tasks/xcsqa.py | 5 +---- src/lighteval/tasks/multilingual/tasks/xnli.py | 4 +--- src/lighteval/tasks/multilingual/tasks/xnli2.py | 5 +---- src/lighteval/tasks/multilingual/tasks/xnli_indic.py | 5 +---- src/lighteval/tasks/multilingual/tasks/xquad.py | 5 +---- src/lighteval/tasks/multilingual/tasks/xstory.py | 5 +---- src/lighteval/tasks/multilingual/tasks/xwinograd.py | 5 +---- 76 files changed, 75 insertions(+), 298 deletions(-) diff --git a/src/lighteval/tasks/multilingual/tasks/acva.py b/src/lighteval/tasks/multilingual/tasks/acva.py index d19469690..14f371d32 100644 --- a/src/lighteval/tasks/multilingual/tasks/acva.py +++ b/src/lighteval/tasks/multilingual/tasks/acva.py @@ -29,9 +29,6 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - ACVA_SUBSET = [ "Algeria", "Ancient_Egypt", @@ -94,7 +91,7 @@ ] -acva_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"acva_{Language.ARABIC.value}:{subset}", prompt_function=get_boolq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py b/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py index 34bbb21c6..1be96436e 100644 --- a/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py +++ b/src/lighteval/tasks/multilingual/tasks/afri_mgsm.py @@ -27,10 +27,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -afri_mgsm_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"afri_mgsm_{language.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py b/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py index 37dfea187..e4d21f350 100644 --- a/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/afri_mmlu.py @@ -37,9 +37,6 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - AFRI_MMLU_SUBSETS = [ "elementary_mathematics", "high_school_mathematics", diff --git a/src/lighteval/tasks/multilingual/tasks/afri_xnli.py b/src/lighteval/tasks/multilingual/tasks/afri_xnli.py index 94e24332e..6bf3e315f 100644 --- a/src/lighteval/tasks/multilingual/tasks/afri_xnli.py +++ b/src/lighteval/tasks/multilingual/tasks/afri_xnli.py @@ -34,10 +34,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -afri_xnli_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"afri_xnli_{language.value}_{formulation.name.lower()}", suite=("lighteval",), diff --git a/src/lighteval/tasks/multilingual/tasks/arabic_arc.py b/src/lighteval/tasks/multilingual/tasks/arabic_arc.py index 2635f2e19..29d9ee9d4 100644 --- a/src/lighteval/tasks/multilingual/tasks/arabic_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/arabic_arc.py @@ -35,10 +35,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -arabic_arc_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"alghafa_arc_{Language.ARABIC.value}_{formulation.name.lower()}:easy", prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), diff --git a/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py b/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py index 72e1c5e29..d8031c7f6 100644 --- a/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/arabic_mmlu.py @@ -33,9 +33,6 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - ARABIC_MMLU_SUBSETS = [ "Islamic Studies", "Islamic Studies (Middle School)", @@ -80,7 +77,7 @@ ] -arabic_mmlu_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mmlu_{Language.ARABIC.value}_{formulation.name.lower()}:{normalize_subset(subset)}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/arcd.py b/src/lighteval/tasks/multilingual/tasks/arcd.py index a3825ca51..d1404821b 100644 --- a/src/lighteval/tasks/multilingual/tasks/arcd.py +++ b/src/lighteval/tasks/multilingual/tasks/arcd.py @@ -30,10 +30,8 @@ # ARCD: Arabic Reading Comprehension Dataset. # https://arxiv.org/pdf/1906.05394 -TASKS_TABLE = [] - -arcd_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"arcd_{Language.ARABIC.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/belebele.py b/src/lighteval/tasks/multilingual/tasks/belebele.py index 0123e26bf..2623e1868 100644 --- a/src/lighteval/tasks/multilingual/tasks/belebele.py +++ b/src/lighteval/tasks/multilingual/tasks/belebele.py @@ -38,10 +38,7 @@ from lighteval.utils.language import iso_639_3_ind_to_iso_639_3_macro -TASKS_TABLE = [] - - -belebele_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"belebele_{language}_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/c3.py b/src/lighteval/tasks/multilingual/tasks/c3.py index a4b3c0dba..4440b5b00 100644 --- a/src/lighteval/tasks/multilingual/tasks/c3.py +++ b/src/lighteval/tasks/multilingual/tasks/c3.py @@ -38,10 +38,8 @@ # Reading comprehension task part of clue # Paper: https://arxiv.org/abs/2004.05986 -TASKS_TABLE = [] - -c3_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"c3_{Language.CHINESE.value}_{formulation.name.lower()}", suite=("lighteval",), diff --git a/src/lighteval/tasks/multilingual/tasks/ceval.py b/src/lighteval/tasks/multilingual/tasks/ceval.py index 4af3424c1..c037a0df3 100644 --- a/src/lighteval/tasks/multilingual/tasks/ceval.py +++ b/src/lighteval/tasks/multilingual/tasks/ceval.py @@ -37,9 +37,6 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - CEVAL_SUBSET = [ "computer_network", "operating_system", @@ -96,7 +93,7 @@ ] -ceval_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"ceval_{Language.CHINESE.value}_{formulation.name.lower()}:{subset}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/chegeka.py b/src/lighteval/tasks/multilingual/tasks/chegeka.py index 01c5fe3e7..3b2174ab9 100644 --- a/src/lighteval/tasks/multilingual/tasks/chegeka.py +++ b/src/lighteval/tasks/multilingual/tasks/chegeka.py @@ -26,10 +26,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -chegeka_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"chegeka_{Language.RUSSIAN.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/chinese_squad.py b/src/lighteval/tasks/multilingual/tasks/chinese_squad.py index b9d5a9cc4..521e0bc60 100644 --- a/src/lighteval/tasks/multilingual/tasks/chinese_squad.py +++ b/src/lighteval/tasks/multilingual/tasks/chinese_squad.py @@ -27,10 +27,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -chinese_squad_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"chinese_squad_{Language.CHINESE.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/cmath.py b/src/lighteval/tasks/multilingual/tasks/cmath.py index e41180b1a..f1e7d45ed 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmath.py +++ b/src/lighteval/tasks/multilingual/tasks/cmath.py @@ -25,10 +25,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -cmath_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"cmath_{Language.CHINESE.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/cmmlu.py b/src/lighteval/tasks/multilingual/tasks/cmmlu.py index 31f1a3233..8153d7bf6 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/cmmlu.py @@ -33,9 +33,6 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - CMMLU_SUBSETS = [ "agronomy", "anatomy", @@ -107,7 +104,7 @@ ] -cmmlu_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"cmmlu_{Language.CHINESE.value}_{formulation.name.lower()}:{subset}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/cmnli.py b/src/lighteval/tasks/multilingual/tasks/cmnli.py index 0eef164e9..c8667978c 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmnli.py +++ b/src/lighteval/tasks/multilingual/tasks/cmnli.py @@ -33,10 +33,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -cmnli_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"cmnli_{Language.CHINESE.value}_{formulation.name.lower()}", prompt_function=get_nli_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/cmrc2018.py b/src/lighteval/tasks/multilingual/tasks/cmrc2018.py index eb7725c85..63174fd98 100644 --- a/src/lighteval/tasks/multilingual/tasks/cmrc2018.py +++ b/src/lighteval/tasks/multilingual/tasks/cmrc2018.py @@ -27,10 +27,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -cmrc2018_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"cmrc2018_{Language.CHINESE.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/copa_indic.py b/src/lighteval/tasks/multilingual/tasks/copa_indic.py index 41ad21480..4d664647d 100644 --- a/src/lighteval/tasks/multilingual/tasks/copa_indic.py +++ b/src/lighteval/tasks/multilingual/tasks/copa_indic.py @@ -43,10 +43,8 @@ # IndicCOPA extends COPA to 15 Indic languages, providing a valuable resource for # evaluating common sense reasoning in these languages. -TASKS_TABLE = [] - -copa_indic_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"indicxcopa_{language.value}_{formulation.name.lower()}", suite=["lighteval"], diff --git a/src/lighteval/tasks/multilingual/tasks/enem.py b/src/lighteval/tasks/multilingual/tasks/enem.py index 21dee8cf5..b852eeb4e 100644 --- a/src/lighteval/tasks/multilingual/tasks/enem.py +++ b/src/lighteval/tasks/multilingual/tasks/enem.py @@ -40,10 +40,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -enem_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"enem_{Language.PORTUGUESE.value}_{formulation.name.lower()}:{year}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/exams.py b/src/lighteval/tasks/multilingual/tasks/exams.py index 57870c6b5..69424a0ef 100644 --- a/src/lighteval/tasks/multilingual/tasks/exams.py +++ b/src/lighteval/tasks/multilingual/tasks/exams.py @@ -39,8 +39,6 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - exams_subjects_by_lang: dict[Language, set[str]] = { Language.ARABIC: {"Biology", "Islamic Studies", "Physics", "Science", "Social"}, Language.BULGARIAN: {"Biology", "Chemistry", "Geography", "History", "Philosophy", "Physics"}, @@ -153,7 +151,7 @@ } -exams_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"exams_{language.value}_{formulation.name.lower()}:{normalize_subset(subject)}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/faquad.py b/src/lighteval/tasks/multilingual/tasks/faquad.py index 8a8a106ae..cec220bd0 100644 --- a/src/lighteval/tasks/multilingual/tasks/faquad.py +++ b/src/lighteval/tasks/multilingual/tasks/faquad.py @@ -27,10 +27,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -faquad_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"faquad_{Language.PORTUGUESE.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/flores200.py b/src/lighteval/tasks/multilingual/tasks/flores200.py index c31b1a660..c9d07122c 100644 --- a/src/lighteval/tasks/multilingual/tasks/flores200.py +++ b/src/lighteval/tasks/multilingual/tasks/flores200.py @@ -31,9 +31,6 @@ from lighteval.utils.language import Language, manage_duplicate_language_codes -TASKS_TABLE = [] - - flores_200_languages = [ # "ace_Arab", "ace_Latn", @@ -249,7 +246,7 @@ def flores_adapter(lang1, lang2): } -flores200_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"flores200:{lang1}-{lang2}", prompt_function=get_translation_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/fquad_v2.py b/src/lighteval/tasks/multilingual/tasks/fquad_v2.py index 2966bc27e..b7f177a32 100644 --- a/src/lighteval/tasks/multilingual/tasks/fquad_v2.py +++ b/src/lighteval/tasks/multilingual/tasks/fquad_v2.py @@ -27,10 +27,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -fquad_v2_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"fquadv2_{Language.FRENCH.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/french_boolq.py b/src/lighteval/tasks/multilingual/tasks/french_boolq.py index 693a49145..d1bd58931 100644 --- a/src/lighteval/tasks/multilingual/tasks/french_boolq.py +++ b/src/lighteval/tasks/multilingual/tasks/french_boolq.py @@ -29,10 +29,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -french_boolq_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"community_boolq_{Language.FRENCH.value}", prompt_function=get_boolq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/french_triviqa.py b/src/lighteval/tasks/multilingual/tasks/french_triviqa.py index 470b5163c..7fa335703 100644 --- a/src/lighteval/tasks/multilingual/tasks/french_triviqa.py +++ b/src/lighteval/tasks/multilingual/tasks/french_triviqa.py @@ -26,10 +26,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -french_triviqa_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"community_triviaqa_{Language.FRENCH.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/germanquad.py b/src/lighteval/tasks/multilingual/tasks/germanquad.py index 9fe3aa25b..895c2bedc 100644 --- a/src/lighteval/tasks/multilingual/tasks/germanquad.py +++ b/src/lighteval/tasks/multilingual/tasks/germanquad.py @@ -27,10 +27,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -germanquad_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"germanquad_{Language.GERMAN.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/global_mmlu.py b/src/lighteval/tasks/multilingual/tasks/global_mmlu.py index 0470cedc3..217eb25e6 100644 --- a/src/lighteval/tasks/multilingual/tasks/global_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/global_mmlu.py @@ -42,9 +42,6 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - MMLU_SUBSETS = [ "abstract_algebra", "anatomy", @@ -106,7 +103,7 @@ ] -global_mmlu_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"global_mmlu_{sensitivity_label.lower()}_{language.value}_{formulation.name.lower()}:{subset}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py index 725ea004b..ad3db12de 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_hin.py @@ -32,10 +32,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -hellaswag_hin_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"community_hellaswag_{Language.HINDI.value}_{formulation.name.lower()}", suite=["lighteval"], diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py index 23a99a694..127329160 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tel.py @@ -32,10 +32,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -hellaswag_tel_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"community_hellaswag_{Language.TELUGU.value}_{formulation.name.lower()}", suite=["lighteval"], diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py index edb53cf00..201f287bd 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tha.py @@ -34,10 +34,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -hellaswag_tha_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"community_hellaswag_{Language.THAI.value}_{formulation.name.lower()}", suite=["lighteval"], diff --git a/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py b/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py index 1141d0a2b..84cb9bc52 100644 --- a/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py +++ b/src/lighteval/tasks/multilingual/tasks/hellaswag_tur.py @@ -36,10 +36,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -hellaswag_tur_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"community_hellaswag_{Language.TURKISH.value}_{formulation.name.lower()}", suite=["lighteval"], diff --git a/src/lighteval/tasks/multilingual/tasks/hindi_arc.py b/src/lighteval/tasks/multilingual/tasks/hindi_arc.py index a72ebbbb3..625a0ebd0 100644 --- a/src/lighteval/tasks/multilingual/tasks/hindi_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/hindi_arc.py @@ -33,10 +33,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -hindi_arc_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"community_arc_{Language.HINDI.value}_{formulation.name.lower()}:{subset}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py b/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py index b289f6ef5..2a77d0ac2 100644 --- a/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py +++ b/src/lighteval/tasks/multilingual/tasks/hindi_boolq.py @@ -31,10 +31,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -hindi_boolq_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"community_boolq_{language.value}", prompt_function=get_boolq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/indicqa.py b/src/lighteval/tasks/multilingual/tasks/indicqa.py index 8ce33f0f6..09eb297d5 100644 --- a/src/lighteval/tasks/multilingual/tasks/indicqa.py +++ b/src/lighteval/tasks/multilingual/tasks/indicqa.py @@ -30,10 +30,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -indicqa_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"indicqa_{language.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/kenswquad.py b/src/lighteval/tasks/multilingual/tasks/kenswquad.py index ce9d0655e..c90ca1c36 100644 --- a/src/lighteval/tasks/multilingual/tasks/kenswquad.py +++ b/src/lighteval/tasks/multilingual/tasks/kenswquad.py @@ -27,10 +27,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -kenswquad_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"kenswquad_{Language.SWAHILI.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/m3exams.py b/src/lighteval/tasks/multilingual/tasks/m3exams.py index 225cad0e5..65a03f94a 100644 --- a/src/lighteval/tasks/multilingual/tasks/m3exams.py +++ b/src/lighteval/tasks/multilingual/tasks/m3exams.py @@ -44,10 +44,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -m3exams_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"m3exams_{language.value}_{formulation.name.lower()}", suite=("lighteval",), diff --git a/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py b/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py index de211a227..ac7652a46 100644 --- a/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py +++ b/src/lighteval/tasks/multilingual/tasks/mathlogicqa_rus.py @@ -37,10 +37,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -mathlogicqa_rus_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mathlogic_qa_{Language.RUSSIAN.value}_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py b/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py index 494d69ec3..f7a88e3f6 100644 --- a/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/meta_mmlu.py @@ -38,9 +38,6 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - MMLU_SUBSETS = [ "abstract_algebra", "anatomy", @@ -102,7 +99,7 @@ ] -meta_mmlu_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"meta_mmlu_{language.value}_{formulation.name.lower()}:{subset}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/mgsm.py b/src/lighteval/tasks/multilingual/tasks/mgsm.py index d5dd58bfb..c72cf1ca7 100644 --- a/src/lighteval/tasks/multilingual/tasks/mgsm.py +++ b/src/lighteval/tasks/multilingual/tasks/mgsm.py @@ -28,10 +28,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -mgsm_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mgsm_{language.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/mintaka.py b/src/lighteval/tasks/multilingual/tasks/mintaka.py index 93b839758..e888a103e 100644 --- a/src/lighteval/tasks/multilingual/tasks/mintaka.py +++ b/src/lighteval/tasks/multilingual/tasks/mintaka.py @@ -28,10 +28,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -mintaka_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mintaka_{lang.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/mkqa.py b/src/lighteval/tasks/multilingual/tasks/mkqa.py index 44cfb4375..a4d803633 100644 --- a/src/lighteval/tasks/multilingual/tasks/mkqa.py +++ b/src/lighteval/tasks/multilingual/tasks/mkqa.py @@ -36,9 +36,6 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - MKQA_TASK_TO_ID = { "entity": 0, "long_answer": 1, @@ -51,7 +48,7 @@ } -mkqa_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mkqa_{language.value}:{subset}", prompt_function=get_qa_prompt_function(language, partial(get_mkqa_adapter, language)), diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py b/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py index f7ff2a434..2a48c369b 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_arc_challenge.py @@ -45,10 +45,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -mlmm_arc_challenge_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mlmm_arc_{language.value}_{formulation.name.lower()}:challenge", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py b/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py index 2c114fa75..a8933a101 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_hellaswag.py @@ -41,10 +41,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -mlmm_hellaswag_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mlmm_hellaswag_{lang.value}_{formulation.name.lower()}", suite=["lighteval"], diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py b/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py index db055c356..031cdc767 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_mmlu.py @@ -41,9 +41,6 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - MMLU_SUBSETS = [ "abstract_algebra", "anatomy", @@ -105,7 +102,7 @@ ] -mlmm_mmlu_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mlmm_mmlu_{language.value}_{formulation.name.lower()}:{subset}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py b/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py index 323b227fb..1851693fa 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py +++ b/src/lighteval/tasks/multilingual/tasks/mlmm_truthfulqa.py @@ -40,10 +40,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -mlmm_truthfulqa_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mlmm_truthfulqa_{language.value}_{formulation.name.lower()}:{subset}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/mlqa.py b/src/lighteval/tasks/multilingual/tasks/mlqa.py index 5b1864952..70515b678 100644 --- a/src/lighteval/tasks/multilingual/tasks/mlqa.py +++ b/src/lighteval/tasks/multilingual/tasks/mlqa.py @@ -33,10 +33,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -mlqa_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mlqa_{lang.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/oab_exams.py b/src/lighteval/tasks/multilingual/tasks/oab_exams.py index ba9b73cdb..88302cf53 100644 --- a/src/lighteval/tasks/multilingual/tasks/oab_exams.py +++ b/src/lighteval/tasks/multilingual/tasks/oab_exams.py @@ -35,10 +35,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -oab_exams_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"oab_exams_{Language.PORTUGUESE.value}_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/ocnli.py b/src/lighteval/tasks/multilingual/tasks/ocnli.py index 3ebc3f258..48a7278b1 100644 --- a/src/lighteval/tasks/multilingual/tasks/ocnli.py +++ b/src/lighteval/tasks/multilingual/tasks/ocnli.py @@ -33,10 +33,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -ocnli_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"ocnli_{Language.CHINESE.value}_{formulation.name.lower()}", prompt_function=get_nli_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py b/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py index c3dc10680..4a4df728a 100644 --- a/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/openai_mmlu.py @@ -36,9 +36,6 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - MMLU_SUBSETS = [ "abstract_algebra", "anatomy", @@ -100,7 +97,7 @@ ] -openai_mmlu_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"openai_mmlu_{language[0].value}_{formulation.name.lower()}:{subset}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_ara.py b/src/lighteval/tasks/multilingual/tasks/openbook_ara.py index 59ee10d65..db5b3a426 100644 --- a/src/lighteval/tasks/multilingual/tasks/openbook_ara.py +++ b/src/lighteval/tasks/multilingual/tasks/openbook_ara.py @@ -41,10 +41,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -openbook_ara_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"alghafa_openbookqa_{Language.ARABIC.value}_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_es.py b/src/lighteval/tasks/multilingual/tasks/openbook_es.py index c1f4dd5d0..c428275fe 100644 --- a/src/lighteval/tasks/multilingual/tasks/openbook_es.py +++ b/src/lighteval/tasks/multilingual/tasks/openbook_es.py @@ -34,10 +34,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -openbook_es_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"openbookqa_{Language.SPANISH.value}_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/openbook_rus.py b/src/lighteval/tasks/multilingual/tasks/openbook_rus.py index 186f68e3e..498d32eed 100644 --- a/src/lighteval/tasks/multilingual/tasks/openbook_rus.py +++ b/src/lighteval/tasks/multilingual/tasks/openbook_rus.py @@ -35,10 +35,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -openbook_rus_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mera_openbookqa_{Language.RUSSIAN.value}_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/parus.py b/src/lighteval/tasks/multilingual/tasks/parus.py index 045c48710..6ff91448b 100644 --- a/src/lighteval/tasks/multilingual/tasks/parus.py +++ b/src/lighteval/tasks/multilingual/tasks/parus.py @@ -35,10 +35,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -parus_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"parus_{Language.RUSSIAN.value}_{formulation.name.lower()}", suite=["lighteval"], diff --git a/src/lighteval/tasks/multilingual/tasks/paws_x.py b/src/lighteval/tasks/multilingual/tasks/paws_x.py index 686216e0e..e294cc15c 100644 --- a/src/lighteval/tasks/multilingual/tasks/paws_x.py +++ b/src/lighteval/tasks/multilingual/tasks/paws_x.py @@ -38,10 +38,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -paws_x_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"pawsx_{language.value}_{formulation.name.lower()}", suite=("lighteval",), diff --git a/src/lighteval/tasks/multilingual/tasks/piqa_ar.py b/src/lighteval/tasks/multilingual/tasks/piqa_ar.py index 584e9a2f4..e3f7b2f40 100644 --- a/src/lighteval/tasks/multilingual/tasks/piqa_ar.py +++ b/src/lighteval/tasks/multilingual/tasks/piqa_ar.py @@ -39,10 +39,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -piqa_ar_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"alghafa_piqa_{Language.ARABIC.value}_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function(Language.ARABIC, alghafa_adapter, formulation=formulation), diff --git a/src/lighteval/tasks/multilingual/tasks/rcb.py b/src/lighteval/tasks/multilingual/tasks/rcb.py index 40bd21038..7091126a5 100644 --- a/src/lighteval/tasks/multilingual/tasks/rcb.py +++ b/src/lighteval/tasks/multilingual/tasks/rcb.py @@ -34,10 +34,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -rcb_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"rcb_{Language.RUSSIAN.value}_{formulation.name.lower()}", prompt_function=get_nli_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/sber_squad.py b/src/lighteval/tasks/multilingual/tasks/sber_squad.py index ec0a09840..51abc0609 100644 --- a/src/lighteval/tasks/multilingual/tasks/sber_squad.py +++ b/src/lighteval/tasks/multilingual/tasks/sber_squad.py @@ -27,10 +27,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -sber_squad_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"sber_squad_{Language.RUSSIAN.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/soqal.py b/src/lighteval/tasks/multilingual/tasks/soqal.py index b79970175..ad41456c9 100644 --- a/src/lighteval/tasks/multilingual/tasks/soqal.py +++ b/src/lighteval/tasks/multilingual/tasks/soqal.py @@ -36,10 +36,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -soqal_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"soqal_{Language.ARABIC.value}_{formulation.name.lower()}", hf_subset="multiple_choice_grounded_statement_soqal_task", diff --git a/src/lighteval/tasks/multilingual/tasks/squad_es.py b/src/lighteval/tasks/multilingual/tasks/squad_es.py index 871e2ef5e..4022a8420 100644 --- a/src/lighteval/tasks/multilingual/tasks/squad_es.py +++ b/src/lighteval/tasks/multilingual/tasks/squad_es.py @@ -27,10 +27,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -squad_es_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"squad_{Language.SPANISH.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/squad_it.py b/src/lighteval/tasks/multilingual/tasks/squad_it.py index f6e9e365c..d894e19be 100644 --- a/src/lighteval/tasks/multilingual/tasks/squad_it.py +++ b/src/lighteval/tasks/multilingual/tasks/squad_it.py @@ -27,10 +27,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -squad_it_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"squad_{Language.ITALIAN.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/swahili_arc.py b/src/lighteval/tasks/multilingual/tasks/swahili_arc.py index 1dba3c5ae..c40efa573 100644 --- a/src/lighteval/tasks/multilingual/tasks/swahili_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/swahili_arc.py @@ -32,10 +32,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -swahili_arc_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"community_arc_{Language.SWAHILI.value}_{formulation.name.lower()}:{subset}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/thai_exams.py b/src/lighteval/tasks/multilingual/tasks/thai_exams.py index 1e0276c2e..73f8140f7 100644 --- a/src/lighteval/tasks/multilingual/tasks/thai_exams.py +++ b/src/lighteval/tasks/multilingual/tasks/thai_exams.py @@ -35,13 +35,10 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - THAI_EXAMS_SUBSETS = ["a_level", "ic", "onet", "tgat", "tpat1"] -thai_exams_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"thai_exams_{Language.THAI.value}_{formulation.name.lower()}:{subset}", prompt_function=get_mcq_prompt_function(Language.THAI, thai_exams_adapter, formulation=formulation), diff --git a/src/lighteval/tasks/multilingual/tasks/thaiqa.py b/src/lighteval/tasks/multilingual/tasks/thaiqa.py index 235fdfd5e..bf2b5c279 100644 --- a/src/lighteval/tasks/multilingual/tasks/thaiqa.py +++ b/src/lighteval/tasks/multilingual/tasks/thaiqa.py @@ -26,10 +26,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -thaiqa_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"thaiqa_{Language.THAI.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/tquad_v2.py b/src/lighteval/tasks/multilingual/tasks/tquad_v2.py index 48eaab456..e337ff538 100644 --- a/src/lighteval/tasks/multilingual/tasks/tquad_v2.py +++ b/src/lighteval/tasks/multilingual/tasks/tquad_v2.py @@ -26,10 +26,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -tquad_v2_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"tquadv2_{Language.TURKISH.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/turkish_arc.py b/src/lighteval/tasks/multilingual/tasks/turkish_arc.py index fb86102fa..9174851e6 100644 --- a/src/lighteval/tasks/multilingual/tasks/turkish_arc.py +++ b/src/lighteval/tasks/multilingual/tasks/turkish_arc.py @@ -33,10 +33,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -turkish_arc_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"community_arc_{Language.TURKISH.value}_{formulation.name.lower()}:{subset}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py b/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py index 68ec114ec..cc0605456 100644 --- a/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py +++ b/src/lighteval/tasks/multilingual/tasks/turkish_mmlu.py @@ -33,9 +33,6 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - TURKISH_MMLU_SUBSET = [ "Biology", "Chemistry", @@ -49,7 +46,7 @@ ] -turkish_mmlu_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mmlu_{Language.TURKISH.value}_{formulation.name.lower()}:{normalize_subset(subset)}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/tydiqa.py b/src/lighteval/tasks/multilingual/tasks/tydiqa.py index 914457224..b7a62e2dd 100644 --- a/src/lighteval/tasks/multilingual/tasks/tydiqa.py +++ b/src/lighteval/tasks/multilingual/tasks/tydiqa.py @@ -27,10 +27,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -tydiqa_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"tydiqa_{language.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py b/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py index b4f174e84..814c80b49 100644 --- a/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py +++ b/src/lighteval/tasks/multilingual/tasks/worldtree_rus.py @@ -37,10 +37,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -worldtree_rus_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"mera_worldtree_{Language.RUSSIAN.value}_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/xcodah.py b/src/lighteval/tasks/multilingual/tasks/xcodah.py index a1cb5d2d4..5b6783eaf 100644 --- a/src/lighteval/tasks/multilingual/tasks/xcodah.py +++ b/src/lighteval/tasks/multilingual/tasks/xcodah.py @@ -40,10 +40,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -xcodah_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"xcodah_{language.value}_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function(language, partial(xcodah_adapter, language), formulation=formulation), diff --git a/src/lighteval/tasks/multilingual/tasks/xcopa.py b/src/lighteval/tasks/multilingual/tasks/xcopa.py index 9cf98e932..aafb34c77 100644 --- a/src/lighteval/tasks/multilingual/tasks/xcopa.py +++ b/src/lighteval/tasks/multilingual/tasks/xcopa.py @@ -38,10 +38,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -xcopa_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"xcopa_{language.value}_{formulation.name.lower()}", suite=["lighteval"], diff --git a/src/lighteval/tasks/multilingual/tasks/xcsqa.py b/src/lighteval/tasks/multilingual/tasks/xcsqa.py index 2baaccb46..ef12349f6 100644 --- a/src/lighteval/tasks/multilingual/tasks/xcsqa.py +++ b/src/lighteval/tasks/multilingual/tasks/xcsqa.py @@ -40,10 +40,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -xcsqa_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"xcsqa_{language.value}_{formulation.name.lower()}", prompt_function=get_mcq_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/xnli.py b/src/lighteval/tasks/multilingual/tasks/xnli.py index 9de7fbd33..9c55458ec 100644 --- a/src/lighteval/tasks/multilingual/tasks/xnli.py +++ b/src/lighteval/tasks/multilingual/tasks/xnli.py @@ -41,9 +41,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - -xnli_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"xnli_{language.value}_{formulation.name.lower()}", suite=["lighteval"], diff --git a/src/lighteval/tasks/multilingual/tasks/xnli2.py b/src/lighteval/tasks/multilingual/tasks/xnli2.py index 3e141bd28..cf3ec6a66 100644 --- a/src/lighteval/tasks/multilingual/tasks/xnli2.py +++ b/src/lighteval/tasks/multilingual/tasks/xnli2.py @@ -38,10 +38,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -xnli2_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"xnli2.0_{language.value}_{formulation.name.lower()}", suite=["lighteval"], diff --git a/src/lighteval/tasks/multilingual/tasks/xnli_indic.py b/src/lighteval/tasks/multilingual/tasks/xnli_indic.py index 854a4cb74..4d3cf481c 100644 --- a/src/lighteval/tasks/multilingual/tasks/xnli_indic.py +++ b/src/lighteval/tasks/multilingual/tasks/xnli_indic.py @@ -36,10 +36,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -xnli_indic_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"indicnxnli_{language.value}_{formulation.name.lower()}", suite=["lighteval"], diff --git a/src/lighteval/tasks/multilingual/tasks/xquad.py b/src/lighteval/tasks/multilingual/tasks/xquad.py index 83b1283bc..858b3a6ee 100644 --- a/src/lighteval/tasks/multilingual/tasks/xquad.py +++ b/src/lighteval/tasks/multilingual/tasks/xquad.py @@ -34,10 +34,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -xquad_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"xquad_{language.value}", prompt_function=get_qa_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/xstory.py b/src/lighteval/tasks/multilingual/tasks/xstory.py index 98e11e287..aaf9842c5 100644 --- a/src/lighteval/tasks/multilingual/tasks/xstory.py +++ b/src/lighteval/tasks/multilingual/tasks/xstory.py @@ -38,10 +38,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -xstory_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"xstory_cloze_{lang.value}_{formulation.name.lower()}", prompt_function=get_continuation_prompt_function( diff --git a/src/lighteval/tasks/multilingual/tasks/xwinograd.py b/src/lighteval/tasks/multilingual/tasks/xwinograd.py index 98ccb4977..827399e42 100644 --- a/src/lighteval/tasks/multilingual/tasks/xwinograd.py +++ b/src/lighteval/tasks/multilingual/tasks/xwinograd.py @@ -38,10 +38,7 @@ from lighteval.utils.language import Language -TASKS_TABLE = [] - - -xwinograd_tasks = [ +TASKS_TABLE = [ LightevalTaskConfig( name=f"xwinograd_{language.value}_{formulation.name.lower()}", suite=("lighteval",), From c3911fcf9bd79b49e0e756a29932372423ccc787 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Wed, 15 Oct 2025 16:52:56 +0200 Subject: [PATCH 21/43] use TASKS_TABLE for default tasks --- src/lighteval/tasks/tasks/agieval.py | 20 ++++++ src/lighteval/tasks/tasks/aime.py | 7 ++ src/lighteval/tasks/tasks/anli.py | 6 ++ src/lighteval/tasks/tasks/arc.py | 2 + src/lighteval/tasks/tasks/arc_agi_2.py | 2 + src/lighteval/tasks/tasks/arithmetic.py | 13 ++++ src/lighteval/tasks/tasks/asdiv.py | 2 + src/lighteval/tasks/tasks/babi_qa.py | 2 + src/lighteval/tasks/tasks/bbq.py | 15 +++++ src/lighteval/tasks/tasks/bigbench.py | 40 ++++++++++++ src/lighteval/tasks/tasks/bigbench_hard.py | 21 ++++++ src/lighteval/tasks/tasks/blimp.py | 41 ++++++++++++ src/lighteval/tasks/tasks/bold.py | 9 +++ src/lighteval/tasks/tasks/boolq.py | 5 ++ src/lighteval/tasks/tasks/civil_comments.py | 12 ++++ src/lighteval/tasks/tasks/commonsenseqa.py | 4 ++ src/lighteval/tasks/tasks/coqa.py | 4 ++ src/lighteval/tasks/tasks/covid_dialogue.py | 4 ++ src/lighteval/tasks/tasks/drop_qa.py | 4 ++ src/lighteval/tasks/tasks/dyck_language.py | 6 ++ .../tasks/tasks/entity_data_imputation.py | 5 ++ src/lighteval/tasks/tasks/entitymatching.py | 16 +++++ src/lighteval/tasks/tasks/ethics.py | 8 +++ src/lighteval/tasks/tasks/glue.py | 20 ++++++ src/lighteval/tasks/tasks/gpqa.py | 7 ++ src/lighteval/tasks/tasks/gsm8k.py | 4 ++ src/lighteval/tasks/tasks/gsm_plus.py | 4 ++ src/lighteval/tasks/tasks/headqa.py | 5 ++ src/lighteval/tasks/tasks/hellaswag.py | 4 ++ src/lighteval/tasks/tasks/imdb.py | 5 ++ src/lighteval/tasks/tasks/jeopardy.py | 4 ++ src/lighteval/tasks/tasks/lambada.py | 5 ++ .../tasks/tasks/legal_summarization.py | 6 ++ src/lighteval/tasks/tasks/legalsupport.py | 4 ++ src/lighteval/tasks/tasks/lexglue.py | 10 +++ src/lighteval/tasks/tasks/lextreme.py | 21 ++++++ src/lighteval/tasks/tasks/logiqa.py | 4 ++ src/lighteval/tasks/tasks/lsat_qa.py | 8 +++ src/lighteval/tasks/tasks/math.py | 10 +++ src/lighteval/tasks/tasks/math_500.py | 4 ++ src/lighteval/tasks/tasks/mathqa.py | 4 ++ src/lighteval/tasks/tasks/med.py | 6 ++ src/lighteval/tasks/tasks/med_dialog.py | 5 ++ src/lighteval/tasks/tasks/mgsm.py | 14 ++++ src/lighteval/tasks/tasks/mmlu.py | 60 +++++++++++++++++ src/lighteval/tasks/tasks/mmlu_redux.py | 64 +------------------ src/lighteval/tasks/tasks/mmmu_pro.py | 7 ++ src/lighteval/tasks/tasks/musr.py | 6 ++ src/lighteval/tasks/tasks/narrativeqa.py | 4 ++ .../tasks/tasks/natural_questions.py | 4 ++ src/lighteval/tasks/tasks/numeracy.py | 11 ++++ src/lighteval/tasks/tasks/openbookqa.py | 4 ++ src/lighteval/tasks/tasks/piqa.py | 4 ++ src/lighteval/tasks/tasks/prost.py | 4 ++ src/lighteval/tasks/tasks/pubmedqa.py | 4 ++ src/lighteval/tasks/tasks/qa4mre.py | 6 ++ src/lighteval/tasks/tasks/qasper.py | 4 ++ src/lighteval/tasks/tasks/quac.py | 4 ++ src/lighteval/tasks/tasks/race_high.py | 4 ++ src/lighteval/tasks/tasks/raft.py | 14 ++++ .../tasks/tasks/real_toxicity_prompts.py | 4 ++ src/lighteval/tasks/tasks/sacrebleu.py | 40 ++++++++++++ src/lighteval/tasks/tasks/sciq.py | 4 ++ src/lighteval/tasks/tasks/simpleqa.py | 4 ++ src/lighteval/tasks/tasks/siqa.py | 4 ++ src/lighteval/tasks/tasks/squad_v2.py | 4 ++ src/lighteval/tasks/tasks/storycloze.py | 5 ++ src/lighteval/tasks/tasks/summarization.py | 6 ++ src/lighteval/tasks/tasks/swag.py | 4 ++ .../tasks/tasks/synthetic_reasoning.py | 8 +++ src/lighteval/tasks/tasks/the_pile.py | 22 +++++++ src/lighteval/tasks/tasks/toxigen.py | 4 ++ src/lighteval/tasks/tasks/triviaqa.py | 4 ++ src/lighteval/tasks/tasks/truthfulqa.py | 4 ++ src/lighteval/tasks/tasks/twitterAAE.py | 5 ++ src/lighteval/tasks/tasks/unscramble.py | 8 +++ src/lighteval/tasks/tasks/webqs.py | 4 ++ src/lighteval/tasks/tasks/wikifact.py | 64 +++++++++++++++++++ src/lighteval/tasks/tasks/wikitext.py | 4 ++ src/lighteval/tasks/tasks/winogrande.py | 4 ++ src/lighteval/tasks/tasks/xcopa.py | 15 +++++ src/lighteval/tasks/tasks/xstory_cloze.py | 14 ++++ src/lighteval/tasks/tasks/xwinograd.py | 9 +++ 83 files changed, 789 insertions(+), 61 deletions(-) diff --git a/src/lighteval/tasks/tasks/agieval.py b/src/lighteval/tasks/tasks/agieval.py index 733b3b167..1f6f6f3d2 100644 --- a/src/lighteval/tasks/tasks/agieval.py +++ b/src/lighteval/tasks/tasks/agieval.py @@ -334,3 +334,23 @@ stop_sequence=None, version=0, ) + +TASKS_TABLE = [ + agieval_aqua_rat, + agieval_gaokao_biology, + agieval_gaokao_chemistry, + agieval_gaokao_chinese, + agieval_gaokao_english, + agieval_gaokao_geography, + agieval_gaokao_history, + agieval_gaokao_mathqa, + agieval_gaokao_physics, + agieval_logiqa_en, + agieval_logiqa_zh, + agieval_lsat_ar, + agieval_lsat_lr, + agieval_lsat_rc, + agieval_sat_en, + agieval_sat_en_without_passage, + agieval_sat_math, +] diff --git a/src/lighteval/tasks/tasks/aime.py b/src/lighteval/tasks/tasks/aime.py index 1afac6c77..befa0654b 100644 --- a/src/lighteval/tasks/tasks/aime.py +++ b/src/lighteval/tasks/tasks/aime.py @@ -88,3 +88,10 @@ metrics=[Metrics.g_pass_at_k_math(sample_params={"k": 16, "n": 48})], version=1, ) + +TASKS_TABLE = [ + aime24, + aime24_gpassk, + aime25, + aime25_gpassk, +] diff --git a/src/lighteval/tasks/tasks/anli.py b/src/lighteval/tasks/tasks/anli.py index 2d6d0f9a8..86ea842b1 100644 --- a/src/lighteval/tasks/tasks/anli.py +++ b/src/lighteval/tasks/tasks/anli.py @@ -76,3 +76,9 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + anli_r1, + anli_r2, + anli_r3, +] diff --git a/src/lighteval/tasks/tasks/arc.py b/src/lighteval/tasks/tasks/arc.py index 3532cde12..25c7d3464 100644 --- a/src/lighteval/tasks/tasks/arc.py +++ b/src/lighteval/tasks/tasks/arc.py @@ -62,3 +62,5 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [arc_challenge, arc_easy] diff --git a/src/lighteval/tasks/tasks/arc_agi_2.py b/src/lighteval/tasks/tasks/arc_agi_2.py index 5e008013f..6e6302a44 100644 --- a/src/lighteval/tasks/tasks/arc_agi_2.py +++ b/src/lighteval/tasks/tasks/arc_agi_2.py @@ -48,3 +48,5 @@ stop_sequence=None, version=0, ) + +TASKS_TABLE = [arc_agi_2] diff --git a/src/lighteval/tasks/tasks/arithmetic.py b/src/lighteval/tasks/tasks/arithmetic.py index 25ff9cc71..d1e6b6107 100644 --- a/src/lighteval/tasks/tasks/arithmetic.py +++ b/src/lighteval/tasks/tasks/arithmetic.py @@ -183,3 +183,16 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + arithmetic_1dc, + arithmetic_2da, + arithmetic_2dm, + arithmetic_2ds, + arithmetic_3da, + arithmetic_3ds, + arithmetic_4da, + arithmetic_4ds, + arithmetic_5da, + arithmetic_5ds, +] diff --git a/src/lighteval/tasks/tasks/asdiv.py b/src/lighteval/tasks/tasks/asdiv.py index 2bcf9df69..e7141449d 100644 --- a/src/lighteval/tasks/tasks/asdiv.py +++ b/src/lighteval/tasks/tasks/asdiv.py @@ -39,3 +39,5 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [asdiv] diff --git a/src/lighteval/tasks/tasks/babi_qa.py b/src/lighteval/tasks/tasks/babi_qa.py index 6668462bc..5ade7cb23 100644 --- a/src/lighteval/tasks/tasks/babi_qa.py +++ b/src/lighteval/tasks/tasks/babi_qa.py @@ -39,3 +39,5 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [babi_qa] diff --git a/src/lighteval/tasks/tasks/bbq.py b/src/lighteval/tasks/tasks/bbq.py index 229e208fd..3b58f2a91 100644 --- a/src/lighteval/tasks/tasks/bbq.py +++ b/src/lighteval/tasks/tasks/bbq.py @@ -215,3 +215,18 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + bbq, + bbq_Age, + bbq_Disability_status, + bbq_Gender_identity, + bbq_Nationality, + bbq_Physical_appearance, + bbq_Race_ethnicity, + bbq_Race_x_SES, + bbq_Race_x_gender, + bbq_Religion, + bbq_SES, + bbq_Sexual_orientation, +] diff --git a/src/lighteval/tasks/tasks/bigbench.py b/src/lighteval/tasks/tasks/bigbench.py index 67c972530..8d3c62d26 100644 --- a/src/lighteval/tasks/tasks/bigbench.py +++ b/src/lighteval/tasks/tasks/bigbench.py @@ -2704,3 +2704,43 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + abstract_narrative_understanding, + anachronisms, + analogical_similarity, + moral_permissibility, + movie_dialog_same_or_different, + movie_recommendation, + mult_data_wrangling, + simple_ethical_questions, + simple_text_editing, + snarks, + social_iqa, + social_support, + sports_understanding, + strange_stories, + strategyqa, + sufficient_information, + suicide_risk, + swahili_english_proverbs, + swedish_to_german_proverbs, + symbol_interpretation, + tellmewhy, + temporal_sequences, + tense, + timedial, + topical_chat, + tracking_shuffled_objects, + understanding_fables, + undo_permutation, + unit_conversion, + unit_interpretation, + unnatural_in_context_learning, + vitaminc_fact_verification, + what_is_the_tao, + which_wiki_edit, + winowhy, + word_sorting, + word_unscrambling, +] diff --git a/src/lighteval/tasks/tasks/bigbench_hard.py b/src/lighteval/tasks/tasks/bigbench_hard.py index b62452504..57e0683eb 100644 --- a/src/lighteval/tasks/tasks/bigbench_hard.py +++ b/src/lighteval/tasks/tasks/bigbench_hard.py @@ -307,3 +307,24 @@ stop_sequence=["", "Q=", "\n\n"], version=0, ) + +TASKS_TABLE = [ + causal_judgment, + date_understanding, + disambiguation_qa, + geometric_shapes, + logical_deduction_five_objects, + logical_deduction_seven_objects, + logical_deduction_three_objects, + movie_recommendation, + navigate, + reasoning_about_colored_objects, + ruin_names, + salient_translation_error_detection, + snarks, + sports_understanding, + temporal_sequences, + tracking_shuffled_objects_five_objects, + tracking_shuffled_objects_seven_objects, + tracking_shuffled_objects_three_objects, +] diff --git a/src/lighteval/tasks/tasks/blimp.py b/src/lighteval/tasks/tasks/blimp.py index 860e3958f..822122bda 100644 --- a/src/lighteval/tasks/tasks/blimp.py +++ b/src/lighteval/tasks/tasks/blimp.py @@ -1098,3 +1098,44 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + blimp_adjunct_island, + blimp_anaphor_gender_agreement, + blimp_anaphor_number_agreement, + blimp_animate_subject_passive, + blimp_animate_subject_trans, + blimp_causative, + blimp_complex_NP_island, + blimp_drop_argument, + blimp_ellipsis_n_bar_1, + blimp_ellipsis_n_bar_2, + blimp_existential_there_object_raising, + blimp_inchoative, + blimp_intransitive, + blimp_irregular_past_participle_adjectives, + blimp_irregular_past_participle_verbs, + blimp_only_npi_scope, + blimp_passive_1, + blimp_passive_2, + blimp_principle_A_c_command, + blimp_principle_A_reconstruction, + blimp_regular_plural_subject_verb_agreement_1, + blimp_regular_plural_subject_verb_agreement_2, + blimp_sentential_negation_npi_licensor_present, + blimp_sentential_negation_npi_scope, + blimp_sentential_subject_island, + blimp_superlative_quantifiers_1, + blimp_superlative_quantifiers_2, + blimp_tough_vs_raising_1, + blimp_tough_vs_raising_2, + blimp_transitive, + blimp_wh_island, + blimp_wh_questions_object_gap, + blimp_wh_questions_subject_gap, + blimp_wh_questions_subject_gap_long_distance, + blimp_wh_vs_that_no_gap, + blimp_wh_vs_that_no_gap_long_distance, + blimp_wh_vs_that_with_gap, + blimp_wh_vs_that_with_gap_long_distance, +] diff --git a/src/lighteval/tasks/tasks/bold.py b/src/lighteval/tasks/tasks/bold.py index b74ec0862..f1345a533 100644 --- a/src/lighteval/tasks/tasks/bold.py +++ b/src/lighteval/tasks/tasks/bold.py @@ -119,3 +119,12 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + bold, + bold_gender, + bold_political_ideology, + bold_profession, + bold_race, + bold_religious_ideology, +] diff --git a/src/lighteval/tasks/tasks/boolq.py b/src/lighteval/tasks/tasks/boolq.py index 7a0471252..b086ab1cb 100644 --- a/src/lighteval/tasks/tasks/boolq.py +++ b/src/lighteval/tasks/tasks/boolq.py @@ -59,3 +59,8 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + boolq, + boolq_contrastset, +] diff --git a/src/lighteval/tasks/tasks/civil_comments.py b/src/lighteval/tasks/tasks/civil_comments.py index 6e4b0ed8d..608ab097c 100644 --- a/src/lighteval/tasks/tasks/civil_comments.py +++ b/src/lighteval/tasks/tasks/civil_comments.py @@ -166,3 +166,15 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + civil_comments, + civil_comments_LGBTQ, + civil_comments_black, + civil_comments_christian, + civil_comments_female, + civil_comments_male, + civil_comments_muslim, + civil_comments_other_religions, + civil_comments_white, +] diff --git a/src/lighteval/tasks/tasks/commonsenseqa.py b/src/lighteval/tasks/tasks/commonsenseqa.py index 7fe06ec55..8c6f6c6de 100644 --- a/src/lighteval/tasks/tasks/commonsenseqa.py +++ b/src/lighteval/tasks/tasks/commonsenseqa.py @@ -43,3 +43,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + commonsenseqa, +] diff --git a/src/lighteval/tasks/tasks/coqa.py b/src/lighteval/tasks/tasks/coqa.py index 658c24811..a11b6a7a1 100644 --- a/src/lighteval/tasks/tasks/coqa.py +++ b/src/lighteval/tasks/tasks/coqa.py @@ -39,3 +39,7 @@ version=1, metrics=[Metrics.exact_match], ) + +TASKS_TABLE = [ + coqa_first_question, +] diff --git a/src/lighteval/tasks/tasks/covid_dialogue.py b/src/lighteval/tasks/tasks/covid_dialogue.py index c08a3d9f3..bce5e17ce 100644 --- a/src/lighteval/tasks/tasks/covid_dialogue.py +++ b/src/lighteval/tasks/tasks/covid_dialogue.py @@ -39,3 +39,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + covid_dialogue, +] diff --git a/src/lighteval/tasks/tasks/drop_qa.py b/src/lighteval/tasks/tasks/drop_qa.py index be245cda2..9e4b23bd7 100644 --- a/src/lighteval/tasks/tasks/drop_qa.py +++ b/src/lighteval/tasks/tasks/drop_qa.py @@ -62,3 +62,7 @@ metrics=[Metrics.exact_match], version=1, ) + +TASKS_TABLE = [ + drop_qa, +] diff --git a/src/lighteval/tasks/tasks/dyck_language.py b/src/lighteval/tasks/tasks/dyck_language.py index 9a93e6b19..ff2e536ea 100644 --- a/src/lighteval/tasks/tasks/dyck_language.py +++ b/src/lighteval/tasks/tasks/dyck_language.py @@ -72,3 +72,9 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + dyck_language_2, + dyck_language_3, + dyck_language_4, +] diff --git a/src/lighteval/tasks/tasks/entity_data_imputation.py b/src/lighteval/tasks/tasks/entity_data_imputation.py index f6bce587f..309e0585d 100644 --- a/src/lighteval/tasks/tasks/entity_data_imputation.py +++ b/src/lighteval/tasks/tasks/entity_data_imputation.py @@ -59,3 +59,8 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + entity_data_imputation_Buy, + entity_data_imputation_Restaurant, +] diff --git a/src/lighteval/tasks/tasks/entitymatching.py b/src/lighteval/tasks/tasks/entitymatching.py index adc5d0733..c251244b2 100644 --- a/src/lighteval/tasks/tasks/entitymatching.py +++ b/src/lighteval/tasks/tasks/entitymatching.py @@ -230,3 +230,19 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + entity_matching_Abt_Buy, + entity_matching_Amazon_Google, + entity_matching_Beer, + entity_matching_Company, + entity_matching_DBLP_ACM, + entity_matching_DBLP_GoogleScholar, + entity_matching_Dirty_DBLP_ACM, + entity_matching_Dirty_DBLP_GoogleScholar, + entity_matching_Dirty_Walmart_Amazon, + entity_matching_Dirty_iTunes_Amazon, + entity_matching_Fodors_Zagats, + entity_matching_Walmart_Amazon, + entity_matching_iTunes_Amazon, +] diff --git a/src/lighteval/tasks/tasks/ethics.py b/src/lighteval/tasks/tasks/ethics.py index 8e637a7a5..bb45a2f2e 100644 --- a/src/lighteval/tasks/tasks/ethics.py +++ b/src/lighteval/tasks/tasks/ethics.py @@ -103,3 +103,11 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + ethics_commonsense, + ethics_deontology, + ethics_justice, + ethics_utilitarianism, + ethics_virtue, +] diff --git a/src/lighteval/tasks/tasks/glue.py b/src/lighteval/tasks/tasks/glue.py index b7a694d07..69b9c0dc3 100644 --- a/src/lighteval/tasks/tasks/glue.py +++ b/src/lighteval/tasks/tasks/glue.py @@ -295,3 +295,23 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + glue_cola, + glue_mnli, + glue_mnli_mismatched, + glue_mrpc, + glue_qnli, + glue_qqp, + glue_rte, + glue_sst2, + glue_stsb, + glue_wnli, + super_glue_boolq, + super_glue_cb, + super_glue_copa, + super_glue_rte, + super_glue_multirc, + super_glue_wic, + super_glue_wsc, +] diff --git a/src/lighteval/tasks/tasks/gpqa.py b/src/lighteval/tasks/tasks/gpqa.py index 121ef8e5f..5d0e67bda 100644 --- a/src/lighteval/tasks/tasks/gpqa.py +++ b/src/lighteval/tasks/tasks/gpqa.py @@ -91,3 +91,10 @@ stop_sequence=[], # no stop sequence, will use eos token version=0, ) + +TASKS_TABLE = [ + gpqa, + gpqa_diamond_instruct, + gpqa_extended_instruct, + gpqa_main_instruct, +] diff --git a/src/lighteval/tasks/tasks/gsm8k.py b/src/lighteval/tasks/tasks/gsm8k.py index 883a7dbbc..c4b5a51a6 100644 --- a/src/lighteval/tasks/tasks/gsm8k.py +++ b/src/lighteval/tasks/tasks/gsm8k.py @@ -40,3 +40,7 @@ stop_sequence=["Question:"], version=0, ) + +TASKS_TABLE = [ + gsm8k, +] diff --git a/src/lighteval/tasks/tasks/gsm_plus.py b/src/lighteval/tasks/tasks/gsm_plus.py index 8fc6f2696..65afadef2 100644 --- a/src/lighteval/tasks/tasks/gsm_plus.py +++ b/src/lighteval/tasks/tasks/gsm_plus.py @@ -40,3 +40,7 @@ stop_sequence=None, version=0, ) + +TASKS_TABLE = [ + gsm_plus, +] diff --git a/src/lighteval/tasks/tasks/headqa.py b/src/lighteval/tasks/tasks/headqa.py index 31f808317..2d7eb36ea 100644 --- a/src/lighteval/tasks/tasks/headqa.py +++ b/src/lighteval/tasks/tasks/headqa.py @@ -63,3 +63,8 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + headqa_en, + headqa_es, +] diff --git a/src/lighteval/tasks/tasks/hellaswag.py b/src/lighteval/tasks/tasks/hellaswag.py index 5ffc68f78..76e02fee0 100644 --- a/src/lighteval/tasks/tasks/hellaswag.py +++ b/src/lighteval/tasks/tasks/hellaswag.py @@ -41,3 +41,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + hellaswag, +] diff --git a/src/lighteval/tasks/tasks/imdb.py b/src/lighteval/tasks/tasks/imdb.py index 5bc0756b9..e7073699e 100644 --- a/src/lighteval/tasks/tasks/imdb.py +++ b/src/lighteval/tasks/tasks/imdb.py @@ -60,3 +60,8 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + imdb, + imdb_contrastset, +] diff --git a/src/lighteval/tasks/tasks/jeopardy.py b/src/lighteval/tasks/tasks/jeopardy.py index 1bc58e220..5044602fe 100644 --- a/src/lighteval/tasks/tasks/jeopardy.py +++ b/src/lighteval/tasks/tasks/jeopardy.py @@ -42,3 +42,7 @@ metrics=[Metrics.exact_match], version=1, ) + +TASKS_TABLE = [ + jeopardy, +] diff --git a/src/lighteval/tasks/tasks/lambada.py b/src/lighteval/tasks/tasks/lambada.py index 828a72506..3a7292a3f 100644 --- a/src/lighteval/tasks/tasks/lambada.py +++ b/src/lighteval/tasks/tasks/lambada.py @@ -58,3 +58,8 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + lambada_standard, + lambada_standard_cloze, +] diff --git a/src/lighteval/tasks/tasks/legal_summarization.py b/src/lighteval/tasks/tasks/legal_summarization.py index 66ca9b32a..3e31b67ba 100644 --- a/src/lighteval/tasks/tasks/legal_summarization.py +++ b/src/lighteval/tasks/tasks/legal_summarization.py @@ -94,3 +94,9 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + legal_summarization_billsum, + legal_summarization_eurlexsum, + legal_summarization_multilexsum, +] diff --git a/src/lighteval/tasks/tasks/legalsupport.py b/src/lighteval/tasks/tasks/legalsupport.py index 206ccc3f5..82ea8c864 100644 --- a/src/lighteval/tasks/tasks/legalsupport.py +++ b/src/lighteval/tasks/tasks/legalsupport.py @@ -37,3 +37,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + legalsupport, +] diff --git a/src/lighteval/tasks/tasks/lexglue.py b/src/lighteval/tasks/tasks/lexglue.py index 2b30dd8c4..4206225a3 100644 --- a/src/lighteval/tasks/tasks/lexglue.py +++ b/src/lighteval/tasks/tasks/lexglue.py @@ -134,3 +134,13 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + lexglue_case_hold, + lexglue_ecthr_a, + lexglue_ecthr_b, + lexglue_eurlex, + lexglue_ledgar, + lexglue_scotus, + lexglue_unfair_tos, +] diff --git a/src/lighteval/tasks/tasks/lextreme.py b/src/lighteval/tasks/tasks/lextreme.py index d996259a5..7ba9df453 100644 --- a/src/lighteval/tasks/tasks/lextreme.py +++ b/src/lighteval/tasks/tasks/lextreme.py @@ -310,3 +310,24 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + lextreme_brazilian_court_decisions_judgment, + lextreme_brazilian_court_decisions_unanimity, + lextreme_covid19_emergency_event, + lextreme_german_argument_mining, + lextreme_greek_legal_code_chapter, + lextreme_greek_legal_code_subject, + lextreme_greek_legal_code_volume, + lextreme_greek_legal_ner, + lextreme_legalnero, + lextreme_lener_br, + lextreme_mapa_coarse, + lextreme_mapa_fine, + lextreme_multi_eurlex_level_1, + lextreme_multi_eurlex_level_2, + lextreme_multi_eurlex_level_3, + lextreme_online_terms_of_service_clause_topics, + lextreme_online_terms_of_service_unfairness_levels, + lextreme_swiss_judgment_prediction, +] diff --git a/src/lighteval/tasks/tasks/logiqa.py b/src/lighteval/tasks/tasks/logiqa.py index 4dbc5b47e..2439ddf69 100644 --- a/src/lighteval/tasks/tasks/logiqa.py +++ b/src/lighteval/tasks/tasks/logiqa.py @@ -42,3 +42,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + logiqa, +] diff --git a/src/lighteval/tasks/tasks/lsat_qa.py b/src/lighteval/tasks/tasks/lsat_qa.py index ad813649a..8d14fb86b 100644 --- a/src/lighteval/tasks/tasks/lsat_qa.py +++ b/src/lighteval/tasks/tasks/lsat_qa.py @@ -101,3 +101,11 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + lsat_qa, + lsat_qa_assignment, + lsat_qa_grouping, + lsat_qa_miscellaneous, + lsat_qa_ordering, +] diff --git a/src/lighteval/tasks/tasks/math.py b/src/lighteval/tasks/tasks/math.py index 2e09b4ac6..9568657b1 100644 --- a/src/lighteval/tasks/tasks/math.py +++ b/src/lighteval/tasks/tasks/math.py @@ -133,3 +133,13 @@ stop_sequence=["\n"], version=1, ) + +TASKS_TABLE = [ + math_algebra, + math_counting_and_probability, + math_geometry, + math_intermediate_algebra, + math_number_theory, + math_prealgebra, + math_precalculus, +] diff --git a/src/lighteval/tasks/tasks/math_500.py b/src/lighteval/tasks/tasks/math_500.py index 2575cc86f..961250b5d 100644 --- a/src/lighteval/tasks/tasks/math_500.py +++ b/src/lighteval/tasks/tasks/math_500.py @@ -40,3 +40,7 @@ ], version=2, ) + +TASKS_TABLE = [ + math_500, +] diff --git a/src/lighteval/tasks/tasks/mathqa.py b/src/lighteval/tasks/tasks/mathqa.py index c58c5437c..4eccd9a75 100644 --- a/src/lighteval/tasks/tasks/mathqa.py +++ b/src/lighteval/tasks/tasks/mathqa.py @@ -41,3 +41,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + mathqa, +] diff --git a/src/lighteval/tasks/tasks/med.py b/src/lighteval/tasks/tasks/med.py index dd6e4641c..49496dae3 100644 --- a/src/lighteval/tasks/tasks/med.py +++ b/src/lighteval/tasks/tasks/med.py @@ -78,3 +78,9 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + med_mcqa, + med_paragraph_simplification, + med_qa, +] diff --git a/src/lighteval/tasks/tasks/med_dialog.py b/src/lighteval/tasks/tasks/med_dialog.py index 2b3d0c828..70a7c08ee 100644 --- a/src/lighteval/tasks/tasks/med_dialog.py +++ b/src/lighteval/tasks/tasks/med_dialog.py @@ -58,3 +58,8 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + med_dialog_healthcaremagic, + med_dialog_icliniq, +] diff --git a/src/lighteval/tasks/tasks/mgsm.py b/src/lighteval/tasks/tasks/mgsm.py index 7f1daa63e..e6391ec01 100644 --- a/src/lighteval/tasks/tasks/mgsm.py +++ b/src/lighteval/tasks/tasks/mgsm.py @@ -201,3 +201,17 @@ stop_sequence=None, version=0, ) + +TASKS_TABLE = [ + mgsm_en, + mgsm_es, + mgsm_fr, + mgsm_de, + mgsm_ru, + mgsm_zh, + mgsm_ja, + mgsm_th, + mgsm_sw, + mgsm_bn, + mgsm_te, +] diff --git a/src/lighteval/tasks/tasks/mmlu.py b/src/lighteval/tasks/tasks/mmlu.py index 8299630f2..2791b6e4c 100644 --- a/src/lighteval/tasks/tasks/mmlu.py +++ b/src/lighteval/tasks/tasks/mmlu.py @@ -934,3 +934,63 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + mmlu_abstract_algebra, + mmlu_anatomy, + mmlu_astronomy, + mmlu_business_ethics, + mmlu_clinical_knowledge, + mmlu_college_biology, + mmlu_college_chemistry, + mmlu_college_computer_science, + mmlu_college_mathematics, + mmlu_college_medicine, + mmlu_college_physics, + mmlu_computer_security, + mmlu_conceptual_physics, + mmlu_econometrics, + mmlu_electrical_engineering, + mmlu_elementary_mathematics, + mmlu_formal_logic, + mmlu_global_facts, + mmlu_high_school_biology, + mmlu_high_school_chemistry, + mmlu_high_school_computer_science, + mmlu_high_school_european_history, + mmlu_high_school_geography, + mmlu_high_school_government_and_politics, + mmlu_high_school_macroeconomics, + mmlu_high_school_mathematics, + mmlu_high_school_microeconomics, + mmlu_high_school_physics, + mmlu_high_school_psychology, + mmlu_high_school_statistics, + mmlu_high_school_us_history, + mmlu_high_school_world_history, + mmlu_human_aging, + mmlu_human_sexuality, + mmlu_international_law, + mmlu_jurisprudence, + mmlu_logical_fallacies, + mmlu_machine_learning, + mmlu_management, + mmlu_marketing, + mmlu_medical_genetics, + mmlu_miscellaneous, + mmlu_moral_disputes, + mmlu_moral_scenarios, + mmlu_nutrition, + mmlu_philosophy, + mmlu_prehistory, + mmlu_professional_accounting, + mmlu_professional_law, + mmlu_professional_medicine, + mmlu_professional_psychology, + mmlu_public_relations, + mmlu_security_studies, + mmlu_sociology, + mmlu_us_foreign_policy, + mmlu_virology, + mmlu_world_religions, +] diff --git a/src/lighteval/tasks/tasks/mmlu_redux.py b/src/lighteval/tasks/tasks/mmlu_redux.py index 9e39edc38..2a29afd12 100644 --- a/src/lighteval/tasks/tasks/mmlu_redux.py +++ b/src/lighteval/tasks/tasks/mmlu_redux.py @@ -84,8 +84,8 @@ ] -_mmlu_redux_2_tasks = { - subset: LightevalTaskConfig( +TASKS_TABLE = [ + LightevalTaskConfig( name=f"mmlu_redux_2:{subset}", suite=["lighteval"], prompt_function=lambda line, task_name=None, s=subset: prompt.mmlu_redux_2(line, s, task_name), @@ -104,62 +104,4 @@ version=0, ) for subset in _MMLU_REDUX_2_SUBSETS -} - -mmlu_redux_2_abstract_algebra = _mmlu_redux_2_tasks["abstract_algebra"] -mmlu_redux_2_anatomy = _mmlu_redux_2_tasks["anatomy"] -mmlu_redux_2_astronomy = _mmlu_redux_2_tasks["astronomy"] -mmlu_redux_2_business_ethics = _mmlu_redux_2_tasks["business_ethics"] -mmlu_redux_2_clinical_knowledge = _mmlu_redux_2_tasks["clinical_knowledge"] -mmlu_redux_2_college_biology = _mmlu_redux_2_tasks["college_biology"] -mmlu_redux_2_college_chemistry = _mmlu_redux_2_tasks["college_chemistry"] -mmlu_redux_2_college_computer_science = _mmlu_redux_2_tasks["college_computer_science"] -mmlu_redux_2_college_mathematics = _mmlu_redux_2_tasks["college_mathematics"] -mmlu_redux_2_college_medicine = _mmlu_redux_2_tasks["college_medicine"] -mmlu_redux_2_college_physics = _mmlu_redux_2_tasks["college_physics"] -mmlu_redux_2_computer_security = _mmlu_redux_2_tasks["computer_security"] -mmlu_redux_2_conceptual_physics = _mmlu_redux_2_tasks["conceptual_physics"] -mmlu_redux_2_econometrics = _mmlu_redux_2_tasks["econometrics"] -mmlu_redux_2_electrical_engineering = _mmlu_redux_2_tasks["electrical_engineering"] -mmlu_redux_2_elementary_mathematics = _mmlu_redux_2_tasks["elementary_mathematics"] -mmlu_redux_2_formal_logic = _mmlu_redux_2_tasks["formal_logic"] -mmlu_redux_2_global_facts = _mmlu_redux_2_tasks["global_facts"] -mmlu_redux_2_high_school_biology = _mmlu_redux_2_tasks["high_school_biology"] -mmlu_redux_2_high_school_chemistry = _mmlu_redux_2_tasks["high_school_chemistry"] -mmlu_redux_2_high_school_computer_science = _mmlu_redux_2_tasks["high_school_computer_science"] -mmlu_redux_2_high_school_european_history = _mmlu_redux_2_tasks["high_school_european_history"] -mmlu_redux_2_high_school_geography = _mmlu_redux_2_tasks["high_school_geography"] -mmlu_redux_2_high_school_government_and_politics = _mmlu_redux_2_tasks["high_school_government_and_politics"] -mmlu_redux_2_high_school_macroeconomics = _mmlu_redux_2_tasks["high_school_macroeconomics"] -mmlu_redux_2_high_school_mathematics = _mmlu_redux_2_tasks["high_school_mathematics"] -mmlu_redux_2_high_school_microeconomics = _mmlu_redux_2_tasks["high_school_microeconomics"] -mmlu_redux_2_high_school_physics = _mmlu_redux_2_tasks["high_school_physics"] -mmlu_redux_2_high_school_psychology = _mmlu_redux_2_tasks["high_school_psychology"] -mmlu_redux_2_high_school_statistics = _mmlu_redux_2_tasks["high_school_statistics"] -mmlu_redux_2_high_school_us_history = _mmlu_redux_2_tasks["high_school_us_history"] -mmlu_redux_2_high_school_world_history = _mmlu_redux_2_tasks["high_school_world_history"] -mmlu_redux_2_human_aging = _mmlu_redux_2_tasks["human_aging"] -mmlu_redux_2_human_sexuality = _mmlu_redux_2_tasks["human_sexuality"] -mmlu_redux_2_international_law = _mmlu_redux_2_tasks["international_law"] -mmlu_redux_2_jurisprudence = _mmlu_redux_2_tasks["jurisprudence"] -mmlu_redux_2_logical_fallacies = _mmlu_redux_2_tasks["logical_fallacies"] -mmlu_redux_2_machine_learning = _mmlu_redux_2_tasks["machine_learning"] -mmlu_redux_2_management = _mmlu_redux_2_tasks["management"] -mmlu_redux_2_marketing = _mmlu_redux_2_tasks["marketing"] -mmlu_redux_2_medical_genetics = _mmlu_redux_2_tasks["medical_genetics"] -mmlu_redux_2_miscellaneous = _mmlu_redux_2_tasks["miscellaneous"] -mmlu_redux_2_moral_disputes = _mmlu_redux_2_tasks["moral_disputes"] -mmlu_redux_2_moral_scenarios = _mmlu_redux_2_tasks["moral_scenarios"] -mmlu_redux_2_nutrition = _mmlu_redux_2_tasks["nutrition"] -mmlu_redux_2_philosophy = _mmlu_redux_2_tasks["philosophy"] -mmlu_redux_2_prehistory = _mmlu_redux_2_tasks["prehistory"] -mmlu_redux_2_professional_accounting = _mmlu_redux_2_tasks["professional_accounting"] -mmlu_redux_2_professional_law = _mmlu_redux_2_tasks["professional_law"] -mmlu_redux_2_professional_medicine = _mmlu_redux_2_tasks["professional_medicine"] -mmlu_redux_2_professional_psychology = _mmlu_redux_2_tasks["professional_psychology"] -mmlu_redux_2_public_relations = _mmlu_redux_2_tasks["public_relations"] -mmlu_redux_2_security_studies = _mmlu_redux_2_tasks["security_studies"] -mmlu_redux_2_sociology = _mmlu_redux_2_tasks["sociology"] -mmlu_redux_2_us_foreign_policy = _mmlu_redux_2_tasks["us_foreign_policy"] -mmlu_redux_2_virology = _mmlu_redux_2_tasks["virology"] -mmlu_redux_2_world_religions = _mmlu_redux_2_tasks["world_religions"] +] diff --git a/src/lighteval/tasks/tasks/mmmu_pro.py b/src/lighteval/tasks/tasks/mmmu_pro.py index 96fab139d..3a71a9061 100644 --- a/src/lighteval/tasks/tasks/mmmu_pro.py +++ b/src/lighteval/tasks/tasks/mmmu_pro.py @@ -71,3 +71,10 @@ stop_sequence=None, version=0, ) + + +TASKS_TABLE = [ + mmmu_pro_standard_4_options, + mmmu_pro_standard_10_options, + mmmu_pro_vision, +] diff --git a/src/lighteval/tasks/tasks/musr.py b/src/lighteval/tasks/tasks/musr.py index 2792850e1..074e0ac6f 100644 --- a/src/lighteval/tasks/tasks/musr.py +++ b/src/lighteval/tasks/tasks/musr.py @@ -74,3 +74,9 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + musr_murder_mysteries, + musr_object_placements, + musr_team_allocation, +] diff --git a/src/lighteval/tasks/tasks/narrativeqa.py b/src/lighteval/tasks/tasks/narrativeqa.py index c48e967e8..fbbd8239c 100644 --- a/src/lighteval/tasks/tasks/narrativeqa.py +++ b/src/lighteval/tasks/tasks/narrativeqa.py @@ -40,3 +40,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + narrativeqa, +] diff --git a/src/lighteval/tasks/tasks/natural_questions.py b/src/lighteval/tasks/tasks/natural_questions.py index c7f37be2b..47bbb4b3b 100644 --- a/src/lighteval/tasks/tasks/natural_questions.py +++ b/src/lighteval/tasks/tasks/natural_questions.py @@ -42,3 +42,7 @@ metrics=[Metrics.exact_match], version=1, ) + +TASKS_TABLE = [ + natural_questions, +] diff --git a/src/lighteval/tasks/tasks/numeracy.py b/src/lighteval/tasks/tasks/numeracy.py index 7f49dea87..9a80d0b66 100644 --- a/src/lighteval/tasks/tasks/numeracy.py +++ b/src/lighteval/tasks/tasks/numeracy.py @@ -149,3 +149,14 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + numeracy_linear_example, + numeracy_linear_standard, + numeracy_parabola_example, + numeracy_parabola_standard, + numeracy_paraboloid_example, + numeracy_paraboloid_standard, + numeracy_plane_example, + numeracy_plane_standard, +] diff --git a/src/lighteval/tasks/tasks/openbookqa.py b/src/lighteval/tasks/tasks/openbookqa.py index 4816ff70c..eb0e547dc 100644 --- a/src/lighteval/tasks/tasks/openbookqa.py +++ b/src/lighteval/tasks/tasks/openbookqa.py @@ -44,3 +44,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + openbookqa, +] diff --git a/src/lighteval/tasks/tasks/piqa.py b/src/lighteval/tasks/tasks/piqa.py index c28ba7c55..76388fac1 100644 --- a/src/lighteval/tasks/tasks/piqa.py +++ b/src/lighteval/tasks/tasks/piqa.py @@ -41,3 +41,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + piqa, +] diff --git a/src/lighteval/tasks/tasks/prost.py b/src/lighteval/tasks/tasks/prost.py index f7c1d6db7..92a0ad0ca 100644 --- a/src/lighteval/tasks/tasks/prost.py +++ b/src/lighteval/tasks/tasks/prost.py @@ -42,3 +42,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + prost, +] diff --git a/src/lighteval/tasks/tasks/pubmedqa.py b/src/lighteval/tasks/tasks/pubmedqa.py index be91172e1..5cef802b4 100644 --- a/src/lighteval/tasks/tasks/pubmedqa.py +++ b/src/lighteval/tasks/tasks/pubmedqa.py @@ -40,3 +40,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + pubmedqa, +] diff --git a/src/lighteval/tasks/tasks/qa4mre.py b/src/lighteval/tasks/tasks/qa4mre.py index bdfaad9f4..9120ae95c 100644 --- a/src/lighteval/tasks/tasks/qa4mre.py +++ b/src/lighteval/tasks/tasks/qa4mre.py @@ -82,3 +82,9 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + qa4mre_2011, + qa4mre_2012, + qa4mre_2013, +] diff --git a/src/lighteval/tasks/tasks/qasper.py b/src/lighteval/tasks/tasks/qasper.py index 1862b07f9..223fb35c8 100644 --- a/src/lighteval/tasks/tasks/qasper.py +++ b/src/lighteval/tasks/tasks/qasper.py @@ -43,3 +43,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + qasper, +] diff --git a/src/lighteval/tasks/tasks/quac.py b/src/lighteval/tasks/tasks/quac.py index 0a7f53e8b..8fd69d116 100644 --- a/src/lighteval/tasks/tasks/quac.py +++ b/src/lighteval/tasks/tasks/quac.py @@ -38,3 +38,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + quac, +] diff --git a/src/lighteval/tasks/tasks/race_high.py b/src/lighteval/tasks/tasks/race_high.py index 34fba1e84..4ac7e452a 100644 --- a/src/lighteval/tasks/tasks/race_high.py +++ b/src/lighteval/tasks/tasks/race_high.py @@ -42,3 +42,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + race_high, +] diff --git a/src/lighteval/tasks/tasks/raft.py b/src/lighteval/tasks/tasks/raft.py index f5c65fcd9..5e1a00553 100644 --- a/src/lighteval/tasks/tasks/raft.py +++ b/src/lighteval/tasks/tasks/raft.py @@ -221,3 +221,17 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + raft_ade_corpus_v2, + raft_banking_77, + raft_neurips_impact_statement_risks, + raft_one_stop_english, + raft_overruling, + raft_semiconductor_org_types, + raft_systematic_review_inclusion, + raft_tai_safety_research, + raft_terms_of_service, + raft_tweet_eval_hate, + raft_twitter_complaints, +] diff --git a/src/lighteval/tasks/tasks/real_toxicity_prompts.py b/src/lighteval/tasks/tasks/real_toxicity_prompts.py index 733238461..726fda8fe 100644 --- a/src/lighteval/tasks/tasks/real_toxicity_prompts.py +++ b/src/lighteval/tasks/tasks/real_toxicity_prompts.py @@ -38,3 +38,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + real_toxicity_prompts, +] diff --git a/src/lighteval/tasks/tasks/sacrebleu.py b/src/lighteval/tasks/tasks/sacrebleu.py index 6e4e9f4d2..b6387f2b7 100644 --- a/src/lighteval/tasks/tasks/sacrebleu.py +++ b/src/lighteval/tasks/tasks/sacrebleu.py @@ -2886,3 +2886,43 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + wmt14_de_en, + wmt16_en_cs, + wmt19_en_cs, + wmt19_en_de, + wmt19_en_fi, + wmt19_en_gu, + wmt19_en_kk, + wmt19_en_lt, + wmt19_en_ru, + wmt19_en_zh, + wmt19_fi_en, + wmt19_fr_de, + wmt19_gu_en, + wmt19_kk_en, + wmt19_lt_en, + wmt19_ru_en, + wmt19_zh_en, + wmt20_cs_en, + wmt20_de_en, + wmt20_en_de, + wmt20_en_iu, + wmt20_en_ja, + wmt20_en_km, + wmt20_en_pl, + wmt20_en_ps, + wmt20_en_ru, + wmt20_en_ta, + wmt20_en_zh, + wmt20_fr_de, + wmt20_iu_en, + wmt20_ja_en, + wmt20_km_en, + wmt20_pl_en, + wmt20_ps_en, + wmt20_ru_en, + wmt20_ta_en, + wmt20_zh_en, +] diff --git a/src/lighteval/tasks/tasks/sciq.py b/src/lighteval/tasks/tasks/sciq.py index 45d0c63a7..ed4285101 100644 --- a/src/lighteval/tasks/tasks/sciq.py +++ b/src/lighteval/tasks/tasks/sciq.py @@ -42,3 +42,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + sciq, +] diff --git a/src/lighteval/tasks/tasks/simpleqa.py b/src/lighteval/tasks/tasks/simpleqa.py index c9bf4a0f6..31ab0e369 100644 --- a/src/lighteval/tasks/tasks/simpleqa.py +++ b/src/lighteval/tasks/tasks/simpleqa.py @@ -39,3 +39,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + simpleqa, +] diff --git a/src/lighteval/tasks/tasks/siqa.py b/src/lighteval/tasks/tasks/siqa.py index 1dd6529fe..e8e049bbf 100644 --- a/src/lighteval/tasks/tasks/siqa.py +++ b/src/lighteval/tasks/tasks/siqa.py @@ -48,3 +48,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + siqa, +] diff --git a/src/lighteval/tasks/tasks/squad_v2.py b/src/lighteval/tasks/tasks/squad_v2.py index bf94583f8..a05df9332 100644 --- a/src/lighteval/tasks/tasks/squad_v2.py +++ b/src/lighteval/tasks/tasks/squad_v2.py @@ -53,3 +53,7 @@ metrics=[Metrics.exact_match], version=1, ) + +TASKS_TABLE = [ + squad_v2, +] diff --git a/src/lighteval/tasks/tasks/storycloze.py b/src/lighteval/tasks/tasks/storycloze.py index fb14056d3..5fdd34c9c 100644 --- a/src/lighteval/tasks/tasks/storycloze.py +++ b/src/lighteval/tasks/tasks/storycloze.py @@ -56,3 +56,8 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + storycloze_2016, + storycloze_2018, +] diff --git a/src/lighteval/tasks/tasks/summarization.py b/src/lighteval/tasks/tasks/summarization.py index f878759ef..84deb9f01 100644 --- a/src/lighteval/tasks/tasks/summarization.py +++ b/src/lighteval/tasks/tasks/summarization.py @@ -96,3 +96,9 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + summarization_cnn_dm, + summarization_xsum, + summarization_xsum_sampled, +] diff --git a/src/lighteval/tasks/tasks/swag.py b/src/lighteval/tasks/tasks/swag.py index 09ec1ac62..7743a1c47 100644 --- a/src/lighteval/tasks/tasks/swag.py +++ b/src/lighteval/tasks/tasks/swag.py @@ -45,3 +45,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + swag, +] diff --git a/src/lighteval/tasks/tasks/synthetic_reasoning.py b/src/lighteval/tasks/tasks/synthetic_reasoning.py index 7a94c9238..815e0e91a 100644 --- a/src/lighteval/tasks/tasks/synthetic_reasoning.py +++ b/src/lighteval/tasks/tasks/synthetic_reasoning.py @@ -112,3 +112,11 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + synthetic_reasoning_induction, + synthetic_reasoning_natural_easy, + synthetic_reasoning_natural_hard, + synthetic_reasoning_pattern_match, + synthetic_reasoning_variable_substitution, +] diff --git a/src/lighteval/tasks/tasks/the_pile.py b/src/lighteval/tasks/tasks/the_pile.py index cf92ba6b0..3ed26d94e 100644 --- a/src/lighteval/tasks/tasks/the_pile.py +++ b/src/lighteval/tasks/tasks/the_pile.py @@ -327,3 +327,25 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + the_pile_arxiv_helm, + the_pile_bibliotik_helm, + the_pile_commoncrawl_helm, + the_pile_dm_mathematics_helm, + the_pile_enron_helm, + the_pile_europarl_helm, + the_pile_freelaw_helm, + the_pile_github_helm, + the_pile_gutenberg_helm, + the_pile_hackernews_helm, + the_pile_nih_exporter_helm, + the_pile_opensubtitles_helm, + the_pile_openwebtext2_helm, + the_pile_pubmed_abstracts_helm, + the_pile_pubmed_central_helm, + the_pile_stackexchange_helm, + the_pile_upsto_helm, + the_pile_wikipedia_helm, + the_pile_youtubesubtitles_helm, +] diff --git a/src/lighteval/tasks/tasks/toxigen.py b/src/lighteval/tasks/tasks/toxigen.py index 01a0f198b..c5e724a9d 100644 --- a/src/lighteval/tasks/tasks/toxigen.py +++ b/src/lighteval/tasks/tasks/toxigen.py @@ -39,3 +39,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + toxigen, +] diff --git a/src/lighteval/tasks/tasks/triviaqa.py b/src/lighteval/tasks/tasks/triviaqa.py index 04c41b14b..b3e13d553 100644 --- a/src/lighteval/tasks/tasks/triviaqa.py +++ b/src/lighteval/tasks/tasks/triviaqa.py @@ -42,3 +42,7 @@ stop_sequence=["\n", ".", ","], version=0, ) + +TASKS_TABLE = [ + triviaqa, +] diff --git a/src/lighteval/tasks/tasks/truthfulqa.py b/src/lighteval/tasks/tasks/truthfulqa.py index da3658df6..164183b9a 100644 --- a/src/lighteval/tasks/tasks/truthfulqa.py +++ b/src/lighteval/tasks/tasks/truthfulqa.py @@ -38,3 +38,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + truthfulqa_gen, +] diff --git a/src/lighteval/tasks/tasks/twitterAAE.py b/src/lighteval/tasks/tasks/twitterAAE.py index 4deea3947..dd9861f91 100644 --- a/src/lighteval/tasks/tasks/twitterAAE.py +++ b/src/lighteval/tasks/tasks/twitterAAE.py @@ -55,3 +55,8 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + twitterAAE_aa, + twitterAAE_white, +] diff --git a/src/lighteval/tasks/tasks/unscramble.py b/src/lighteval/tasks/tasks/unscramble.py index 8a6e8461b..eb8335026 100644 --- a/src/lighteval/tasks/tasks/unscramble.py +++ b/src/lighteval/tasks/tasks/unscramble.py @@ -103,3 +103,11 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + unscramble_anagrams1, + unscramble_anagrams2, + unscramble_cycle_letters, + unscramble_random_insertion, + unscramble_reversed_words, +] diff --git a/src/lighteval/tasks/tasks/webqs.py b/src/lighteval/tasks/tasks/webqs.py index aa5a8b767..493b83f75 100644 --- a/src/lighteval/tasks/tasks/webqs.py +++ b/src/lighteval/tasks/tasks/webqs.py @@ -41,3 +41,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + webqs, +] diff --git a/src/lighteval/tasks/tasks/wikifact.py b/src/lighteval/tasks/tasks/wikifact.py index d2e6f5cdd..26cbc87f3 100644 --- a/src/lighteval/tasks/tasks/wikifact.py +++ b/src/lighteval/tasks/tasks/wikifact.py @@ -1398,3 +1398,67 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + wikifact_applies_to_jurisdiction, + wikifact_atomic_number, + wikifact_author, + wikifact_employer, + wikifact_field_of_work, + wikifact_file_extension, + wikifact_genetic_association, + wikifact_instrument, + wikifact_language_of_work_or_name, + wikifact_languages_spoken_written_or_signed, + wikifact_laws_applied, + wikifact_located_in_the_administrative_territorial_entity, + wikifact_location, + wikifact_location_of_discovery, + wikifact_location_of_formation, + wikifact_member_of, + wikifact_member_of_political_party, + wikifact_member_of_sports_team, + wikifact_movement, + wikifact_headquarters_location, + wikifact_industry, + wikifact_named_after, + wikifact_native_language, + wikifact_number_of_processor_cores, + wikifact_occupation, + wikifact_original_language_of_film_or_TV_show, + wikifact_original_network, + wikifact_overrules, + wikifact_owned_by, + wikifact_part_of, + wikifact_participating_team, + wikifact_place_of_birth, + wikifact_place_of_death, + wikifact_position_played_on_team, + wikifact_programming_language, + wikifact_recommended_unit_of_measurement, + wikifact_record_label, + wikifact_religion, + wikifact_repealed_by, + wikifact_shares_border_with, + wikifact_solved_by, + wikifact_statement_describes, + wikifact_stock_exchange, + wikifact_subclass_of, + wikifact_subsidiary, + wikifact_symptoms_and_signs, + wikifact_therapeutic_area, + wikifact_time_of_discovery_or_invention, + wikifact_twinned_administrative_body, + wikifact_work_location, + wikifact_plaintiff, + wikifact_position_held, + wikifact_position_played_on_team, + wikifact_programming_language, + wikifact_recommended_unit_of_measurement, + wikifact_record_label, + wikifact_religion, + wikifact_repealed_by, + wikifact_shares_border_with, + wikifact_solved_by, + wikifact_statement_describes, +] diff --git a/src/lighteval/tasks/tasks/wikitext.py b/src/lighteval/tasks/tasks/wikitext.py index 72acca1f2..a6f62e90b 100644 --- a/src/lighteval/tasks/tasks/wikitext.py +++ b/src/lighteval/tasks/tasks/wikitext.py @@ -41,3 +41,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + wikitext_103_document_level, +] diff --git a/src/lighteval/tasks/tasks/winogrande.py b/src/lighteval/tasks/tasks/winogrande.py index 834af392c..c12bed1bf 100644 --- a/src/lighteval/tasks/tasks/winogrande.py +++ b/src/lighteval/tasks/tasks/winogrande.py @@ -42,3 +42,7 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + winogrande, +] diff --git a/src/lighteval/tasks/tasks/xcopa.py b/src/lighteval/tasks/tasks/xcopa.py index e501fe6c4..6b51be639 100644 --- a/src/lighteval/tasks/tasks/xcopa.py +++ b/src/lighteval/tasks/tasks/xcopa.py @@ -216,3 +216,18 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + xcopa_en, + xcopa_et, + xcopa_ht, + xcopa_it, + xcopa_id, + xcopa_qu, + xcopa_sw, + xcopa_zh, + xcopa_ta, + xcopa_th, + xcopa_tr, + xcopa_vi, +] diff --git a/src/lighteval/tasks/tasks/xstory_cloze.py b/src/lighteval/tasks/tasks/xstory_cloze.py index acedd5000..96caef9b5 100644 --- a/src/lighteval/tasks/tasks/xstory_cloze.py +++ b/src/lighteval/tasks/tasks/xstory_cloze.py @@ -199,3 +199,17 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + xstory_cloze_en, + xstory_cloze_ru, + xstory_cloze_zh, + xstory_cloze_es, + xstory_cloze_ar, + xstory_cloze_hi, + xstory_cloze_id, + xstory_cloze_te, + xstory_cloze_sw, + xstory_cloze_eu, + xstory_cloze_my, +] diff --git a/src/lighteval/tasks/tasks/xwinograd.py b/src/lighteval/tasks/tasks/xwinograd.py index 9e8d2df52..c692c5803 100644 --- a/src/lighteval/tasks/tasks/xwinograd.py +++ b/src/lighteval/tasks/tasks/xwinograd.py @@ -118,3 +118,12 @@ stop_sequence=["\n"], version=0, ) + +TASKS_TABLE = [ + xwinograd_en, + xwinograd_fr, + xwinograd_jp, + xwinograd_pt, + xwinograd_ru, + xwinograd_zh, +] From e439f7062c881d79daabe9cf9b28f02b3131cb10 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Wed, 15 Oct 2025 16:53:18 +0200 Subject: [PATCH 22/43] use TASKS_TABLE for default tasks --- src/lighteval/tasks/tasks/wikifact.py | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/src/lighteval/tasks/tasks/wikifact.py b/src/lighteval/tasks/tasks/wikifact.py index 26cbc87f3..592491379 100644 --- a/src/lighteval/tasks/tasks/wikifact.py +++ b/src/lighteval/tasks/tasks/wikifact.py @@ -1450,15 +1450,4 @@ wikifact_time_of_discovery_or_invention, wikifact_twinned_administrative_body, wikifact_work_location, - wikifact_plaintiff, - wikifact_position_held, - wikifact_position_played_on_team, - wikifact_programming_language, - wikifact_recommended_unit_of_measurement, - wikifact_record_label, - wikifact_religion, - wikifact_repealed_by, - wikifact_shares_border_with, - wikifact_solved_by, - wikifact_statement_describes, ] From 6447ee75abe744c1ac408a64b190ea4232528ba5 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Wed, 15 Oct 2025 17:33:34 +0200 Subject: [PATCH 23/43] loads all tasks correclty --- src/lighteval/tasks/__init__.py | 63 +++++++++++++++++++-------------- src/lighteval/tasks/registry.py | 3 -- 2 files changed, 36 insertions(+), 30 deletions(-) diff --git a/src/lighteval/tasks/__init__.py b/src/lighteval/tasks/__init__.py index 4d72419a4..6cebcec89 100644 --- a/src/lighteval/tasks/__init__.py +++ b/src/lighteval/tasks/__init__.py @@ -26,52 +26,61 @@ """ import importlib -import logging +import time from pathlib import Path -from lighteval.tasks.lighteval_task import LightevalTaskConfig +# Get the tasks directory +TASKS_DIR = Path(__file__).parent / "tasks" +TASKS_DIR_MULTILINGUAL = Path(__file__).parent / "multilingual" / "tasks" -logger = logging.getLogger(__name__) +def _extract_configs(module): + configs = {} + if hasattr(module, "TASKS_TABLE"): + for config in getattr(module, "TASKS_TABLE"): + configs[config.name] = config + return configs -# Get the tasks directory -TASKS_DIR = Path(__file__).parent / "tasks" + +def _load_from_files(files, module_prefix: str): + configs = {} + for task_file in files: + module_name = task_file.stem + module = importlib.import_module(f"{module_prefix}.{module_name}") + configs.update(_extract_configs(module)) + return configs + + +def _load_from_subdirs(subdirs): + configs = {} + for task_dir in subdirs: + module_name = task_dir.name + module = importlib.import_module(f"lighteval.tasks.tasks.{module_name}.main") + configs.update(_extract_configs(module)) + return configs def _load_all_task_configs(): """Load all LightevalTaskConfig objects from all Python files in the tasks/ directory.""" + start_time = time.perf_counter() loaded_configs = {} # Get all Python files in the tasks directory (excluding __init__.py) task_files = [f for f in TASKS_DIR.glob("*.py") if f.name != "__init__.py"] + # task_files_multilingual = [f for f in TASKS_DIR_MULTILINGUAL.glob("*.py") if f.name != "__init__.py"] # Also get all subdirectories with main.py files task_subdirs = [d for d in TASKS_DIR.iterdir() if d.is_dir() and (d / "main.py").exists()] - for task_file in task_files: - module_name = task_file.stem - # Import the module - module = importlib.import_module(f"lighteval.tasks.tasks.{module_name}") - - # Find all LightevalTaskConfig objects in the module - for attr_name in dir(module): - attr = getattr(module, attr_name) - if isinstance(attr, LightevalTaskConfig): - loaded_configs[attr_name] = attr - - # Load from subdirectories' main.py files - for task_dir in task_subdirs: - module_name = task_dir.name - # Import the main.py from the subdirectory - module = importlib.import_module(f"lighteval.tasks.tasks.{module_name}.main") - - # Find all LightevalTaskConfig objects in the module - for attr_name in dir(module): - attr = getattr(module, attr_name) - if isinstance(attr, LightevalTaskConfig): - loaded_configs[attr_name] = attr + loaded_configs.update(_load_from_files(task_files, "lighteval.tasks.tasks")) + # loaded_configs.update( + # _load_from_files(task_files_multilingual, "lighteval.tasks.multilingual.tasks") + # ) + loaded_configs.update(_load_from_subdirs(task_subdirs)) + duration_s = time.perf_counter() - start_time + print(f"[lighteval.tasks] Loaded {len(loaded_configs)} task configs in {duration_s * 1000:.1f} ms") return loaded_configs diff --git a/src/lighteval/tasks/registry.py b/src/lighteval/tasks/registry.py index 92110a615..b9ce1c983 100644 --- a/src/lighteval/tasks/registry.py +++ b/src/lighteval/tasks/registry.py @@ -115,7 +115,6 @@ def __init__( tasks: str | Path | None = None, custom_tasks: str | Path | ModuleType | None = None, load_community: bool = False, - load_extended: bool = False, load_multilingual: bool = False, ): """ @@ -130,7 +129,6 @@ def __init__( - A module object containing custom task configurations - None for default behavior (no custom tasks) load_community: Whether to load community-contributed tasks. - load_extended: Whether to load extended tasks with custom logic. load_multilingual: Whether to load multilingual tasks. Each custom task module should contain a TASKS_TABLE exposing @@ -157,7 +155,6 @@ def __init__( # These parameters are dynamically set by the task names provided, thanks to `activate_suites_to_load`, # except in the `tasks` CLI command to display the full list self._load_community = load_community - self._load_extended = load_extended self._load_multilingual = load_multilingual self._activate_loading_of_optional_suite() # we dynamically set the loading parameters From 88754bfaa03901ed759165c6309d5d2b04b5ef71 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 14:14:47 +0200 Subject: [PATCH 24/43] move community tasks to default tasks and update doc --- community_tasks/_template.py | 114 -- community_tasks/aimo_evals.py | 61 - community_tasks/arabic_evals.py | 1051 ----------------- ...custom_task_classification_grammar_task.py | 456 ------- community_tasks/filipino_evals.py | 800 ------------- community_tasks/french_evals.py | 149 --- community_tasks/german_rag_evals.py | 223 ---- community_tasks/oz_evals.py | 87 -- community_tasks/serbian_eval.py | 779 ------------ community_tasks/slr_bench_evals.py | 125 -- community_tasks/slr_bench_requirements.txt | 2 - community_tasks/turkic_evals.py | 140 --- docs/source/adding-a-custom-task.mdx | 38 +- docs/source/available-tasks.mdx | 12 +- pyproject.toml | 3 +- src/lighteval/main_tasks.py | 4 +- src/lighteval/tasks/tasks/winogrande.py | 2 +- 17 files changed, 23 insertions(+), 4023 deletions(-) delete mode 100644 community_tasks/_template.py delete mode 100644 community_tasks/aimo_evals.py delete mode 100644 community_tasks/arabic_evals.py delete mode 100644 community_tasks/custom_task_classification_grammar_task.py delete mode 100644 community_tasks/filipino_evals.py delete mode 100644 community_tasks/french_evals.py delete mode 100644 community_tasks/german_rag_evals.py delete mode 100644 community_tasks/oz_evals.py delete mode 100644 community_tasks/serbian_eval.py delete mode 100644 community_tasks/slr_bench_evals.py delete mode 100644 community_tasks/slr_bench_requirements.txt delete mode 100644 community_tasks/turkic_evals.py diff --git a/community_tasks/_template.py b/community_tasks/_template.py deleted file mode 100644 index bfc7de505..000000000 --- a/community_tasks/_template.py +++ /dev/null @@ -1,114 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# ruff: noqa: F405, F403, F401 -""" -Custom evaluation tasks for lighteval. Copy this file and complete it with the info for your task. - -This file generally creates just a TASKS_TABLE and TASKS_GROUPS which are then imported by LightEval. - -Author: -""" - -import numpy as np - -from lighteval.metrics.metrics import SampleLevelMetric -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc, SamplingMethod - - -# DEFINE YOUR PROMPT FUNCTIONS -# Define as many as you need for your different tasks -def prompt_fn(line, task_name: str = None): - """Defines how to go from a dataset line to a doc object. - Follow examples in src/lighteval/tasks/tasks_prompt_formatting.py, or get more info - about what this function should do in the README. - """ - return Doc( - task_name=task_name, - query="", - choices=[""], - gold_index=0, - instruction="", - ) - - -# EVAL WITH NO SUBSET ## -# This is how you create a simple task (like hellaswag) which has one single subset -# attached to it, and one evaluation possible. -task = LightevalTaskConfig( - name="myothertask", - prompt_function=prompt_fn, # must be defined in the file or imported from src/lighteval/tasks/tasks_prompt_formatting.py - suite=["community"], - hf_repo="", - hf_subset="default", - hf_avail_splits=[], - evaluation_splits=[], - few_shots_split="", - few_shots_select="", - metrics=[], # select your metric in Metrics -) - -# EVALS WITH SUBSET -# This is how you create a subset task (like MMLU), which has several subset -# each being its own evaluation task. - -# fmt: off -SAMPLE_SUBSETS = [] # list of all the subsets to use for this eval -# fmt: on - - -class CustomSubsetTask(LightevalTaskConfig): - def __init__( - self, - name, - hf_subset, - ): - super().__init__( - name=name, - hf_subset=hf_subset, - prompt_function=prompt_fn, # must be defined in the file or imported from src/lighteval/tasks/tasks_prompt_formatting.py - hf_repo="", - metrics=[custom_metric], # select your metric in Metrics or use your custom_metric - hf_avail_splits=[], - evaluation_splits=[], - few_shots_split="", - few_shots_select="", - suite=["community"], - generation_size=-1, - stop_sequence=None, - ) - - -# STORE YOUR EVALS -SUBSET_TASKS = [CustomSubsetTask(name=f"mytask:{subset}", hf_subset=subset) for subset in SAMPLE_SUBSETS] -TASKS_TABLE = SUBSET_TASKS + [task] - - -# CUSTOM METRIC IF NEEDED -custom_metric = SampleLevelMetric( - metric_name="my_custom_metric_name", - higher_is_better=True, - category=SamplingMethod.GENERATIVE, # or LOGPROBS, PERPLEXITY, etc. - sample_level_fn=lambda x: x, # how to compute score for one sample - corpus_level_fn=np.mean, # aggregation -) diff --git a/community_tasks/aimo_evals.py b/community_tasks/aimo_evals.py deleted file mode 100644 index 7895cabff..000000000 --- a/community_tasks/aimo_evals.py +++ /dev/null @@ -1,61 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# ruff: noqa: F405, F403, F401 -""" -Task to evaluate LLMs on the training set of the Kaggle AIMO competition: https://www.kaggle.com/competitions/ai-mathematical-olympiad-prize -""" - -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import math_normalizer -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc - - -def aimo_prompt(line, task_name: str = None): - return Doc( - task_name=task_name, - choices=[str(line["answer"])], - gold_index=0, - query=line["problem"], - ) - - -task = LightevalTaskConfig( - name="aimo_progress_prize_1", - prompt_function=aimo_prompt, - suite=["community"], - hf_subset="", - hf_repo="lighteval/aimo_progress_prize_1", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split="train", - few_shots_select="sequential", - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}) - ], - generation_size=2048, - stop_sequence=None, -) - -# STORE YOUR EVALS -TASKS_TABLE = [task] diff --git a/community_tasks/arabic_evals.py b/community_tasks/arabic_evals.py deleted file mode 100644 index 0e917d25d..000000000 --- a/community_tasks/arabic_evals.py +++ /dev/null @@ -1,1051 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# ruff: noqa: F405, F403, F401 -""" -Custom evaluation tasks for lighteval - -This file generally creates just a TASKS_TABLE and TASKS_GROUPS which are then imported by LightEval. -""" - -import random -import re -from typing import Any, Dict, List, Optional, Union - -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm -from lighteval.metrics.utils.llm_as_judge import JudgeLM -from lighteval.metrics.utils.metric_utils import Metric -from lighteval.tasks.default_prompts import LETTER_INDICES -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc, SamplingMethod - - -# fmt: off -LETTER_INDICES_AR = ["أ", "ب", "ج", "د", "هـ", "و", "ز", "ح", "ط", "ي", "ك", "ل", "م", "ن", "س", "ع", "ف", "ص", "ق", "ر", "ش", "ت", "ث", "خ", "ذ", "ض", "ظ", "غ"] -# fmt: on - -# ArabicMMLU -# fmt: off -ARABIC_MMLU_SUBSETS = [ - "All", "Islamic Studies", "Islamic Studies (Middle School)", "Islamic Studies (Primary School)", "Islamic Studies (High School)", "Driving Test", - "Natural Science (Middle School)", "Natural Science (Primary School)", "History (Middle School)", "History (Primary School)", "History (High School)", "General Knowledge", - "General Knowledge (Middle School)", "General Knowledge (Primary School)", "Law (Professional)", "Physics (High School)", "Social Science (Middle School)", - "Social Science (Primary School)", "Management (University)", "Arabic Language (Middle School)", "Arabic Language (Primary School)", "Arabic Language (High School)", "Political Science (University)", - "Philosophy (High School)", "Accounting (University)", "Computer Science (Middle School)", "Computer Science (Primary School)", "Computer Science (High School)", "Computer Science (University)", - "Geography (Middle School)", "Geography (Primary School)", "Geography (High School)", "Math (Primary School)", "Biology (High School)", "Economics (Middle School)", - "Economics (High School)", "Economics (University)", "Arabic Language (General)", "Arabic Language (Grammar)", "Civics (Middle School)", "Civics (High School)" -] -# fmt: on - - -def arabic_mmlu_pfn(line, task_name: str = None): - instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة:\n\n" - - # Define the mapping from Latin to Arabic letters - latin_to_arabic = {"A": "أ", "B": "ب", "C": "ج", "D": "د", "E": "هـ"} - - # Create a list of valid choices with corresponding Arabic keys - choices = [] - valid_keys_latin = [] - valid_keys_arabic = [] - - # Enumerate through the options and append the valid ones - for idx, key in enumerate(["A", "B", "C", "D", "E"]): - option = line.get(f"Option {idx + 1}") - if option: # Check if option is not null - choices.append(option) - valid_keys_latin.append(key) # Append the Latin key (A, B, C, D, E) - valid_keys_arabic.append(latin_to_arabic[key]) # Append the corresponding Arabic letter - - # Find the correct index for the answer key in the Arabic version - answer_index = valid_keys_latin.index(line["Answer Key"]) - - # Construct the query with Arabic letters - query = f"{instruction}{line['Question']}\n" - query += "".join([f"{key}. {choice}\n" for key, choice in zip(valid_keys_arabic, choices)]) - query += "الإجابة:" - - return Doc( - task_name=task_name, - query=query, - choices=valid_keys_arabic, # Return only valid choices (Arabic keys) - gold_index=answer_index, # Correct index in the valid Arabic keys - instruction=instruction, - ) - - -class CustomArabicMMLUTask(LightevalTaskConfig): - def __init__( - self, - name, - hf_subset, - ): - super().__init__( - name=name, - hf_subset=hf_subset, - prompt_function=arabic_mmlu_pfn, - hf_repo="MBZUAI/ArabicMMLU", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=["dev"], - few_shots_select="sequential", - suite=["community"], - generation_size=-1, - stop_sequence=None, - version=0, - ) - - -ARABIC_MMLU_TASKS = [ - CustomArabicMMLUTask(name=f"arabic_mmlu:{subset}", hf_subset=subset) for subset in ARABIC_MMLU_SUBSETS -] - - -# ARABIC MMLU HT ## -# fmt: off -ARABIC_MMLU_HT_SUBSETS = [ - "abstract_algebra", "anatomy", "astronomy", "business_ethics", "clinical_knowledge", "college_biology", "college_chemistry", "college_computer_science", - "college_mathematics", "college_medicine", "college_physics", "computer_security", "conceptual_physics", "econometrics", "electrical_engineering", - "elementary_mathematics", "formal_logic", "global_facts", "high_school_biology", "high_school_chemistry", "high_school_computer_science", - "high_school_european_history", "high_school_geography", "high_school_government_and_politics", "high_school_macroeconomics", "high_school_mathematics", - "high_school_microeconomics", "high_school_physics", "high_school_psychology", "high_school_statistics", "high_school_us_history", "high_school_world_history", - "human_aging", "human_sexuality", "international_law", "jurisprudence", "logical_fallacies", "machine_learning", "management", "marketing", "medical_genetics", - "miscellaneous", "moral_disputes", "moral_scenarios", "nutrition", "philosophy", "prehistory", "professional_accounting", "professional_law", - "professional_medicine", "professional_psychology", "public_relations", "security_studies", "sociology", "us_foreign_policy", "virology", "world_religions" -] -# fmt: on - - -def arabic_mmlu_ht_pfn(line, task_name: str = None): - instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة:\n\n" - choices = line["choices"] - answer_index = line["answer"] # It is an int reflecting the index of correct answer in line["choices"] - - query = f"{instruction}{line['question']}\n" - query += "".join([f"{idx}. {choice}\n" for idx, choice in enumerate(choices, start=1)]) - query += "الإجابة:" - - return Doc( - task_name=task_name, - query=query, - choices=[str(i) for i in range(1, len(choices) + 1)], # List of strings instead of ints - gold_index=answer_index, - instruction=instruction, - ) - - -class CustomArabicMMLUHTTask(LightevalTaskConfig): - def __init__( - self, - name, - hf_subset, - ): - super().__init__( - name=name, - hf_subset=hf_subset, - prompt_function=arabic_mmlu_ht_pfn, - hf_repo="MBZUAI/human_translated_arabic_mmlu", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - suite=["community"], - generation_size=-1, - stop_sequence=None, - version=0, - ) - - -ARABIC_MMLU_HT_TASKS = [ - CustomArabicMMLUHTTask(name=f"arabic_mmlu_ht:{subset}", hf_subset=subset) for subset in ARABIC_MMLU_HT_SUBSETS -] - -# ARABIC MMLU MT ## -# fmt: off -ARABIC_MMLU_MT_SUBSETS = [ - "abstract_algebra", "anatomy", "astronomy", "business_ethics", "clinical_knowledge", "college_biology", "college_chemistry", "college_computer_science", - "college_mathematics", "college_medicine", "college_physics", "computer_security", "conceptual_physics", "econometrics", "electrical_engineering", - "elementary_mathematics", "formal_logic", "global_facts", "high_school_biology", "high_school_chemistry", "high_school_computer_science", - "high_school_european_history", "high_school_geography", "high_school_government_and_politics", "high_school_macroeconomics", "high_school_mathematics", - "high_school_microeconomics", "high_school_physics", "high_school_psychology", "high_school_statistics", "high_school_us_history", "high_school_world_history", - "human_aging", "human_sexuality", "international_law", "jurisprudence", "logical_fallacies", "machine_learning", "management", "marketing", "medical_genetics", - "miscellaneous", "moral_disputes", "moral_scenarios", "nutrition", "philosophy", "prehistory", "professional_accounting", "professional_law", - "professional_medicine", "professional_psychology", "public_relations", "security_studies", "sociology", "us_foreign_policy", "virology", "world_religions" -] -# fmt: on - - -def arabic_mmlu_mt_pfn(line, task_name: str = None): - instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة: أ، ب، ج، أو د... إلخ. \n\n" - choices = [line["A"], line["B"], line["C"], line["D"]] - # Answers are provided with roman letters - we look for the correct index in LETTER_INDICES, - # it will then be applied to arabic letters - answer_index = LETTER_INDICES.index( - line["answer"] - ) # line["answer"] is the correct answer. That's why we need to index it ! - - query = f"{instruction}{line['question']}\n" - query += "".join([f"{key}. {choice}\n" for key, choice in zip(LETTER_INDICES_AR[:4], choices)]) - query += "الإجابة:" - - return Doc( - task_name=task_name, - query=query, - choices=LETTER_INDICES_AR[:4], - gold_index=answer_index, - instruction=instruction, - ) - - -class CustomArabicMMLUMTTask(LightevalTaskConfig): - def __init__( - self, - name, - hf_subset, - ): - super().__init__( - name=name, - hf_subset=hf_subset, - prompt_function=arabic_mmlu_mt_pfn, - hf_repo="OALL/Arabic_MMLU", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - hf_avail_splits=["test", "dev"], - evaluation_splits=["test"], - few_shots_split="dev", - few_shots_select="sequential", - suite=["community"], - generation_size=-1, - stop_sequence=None, - version=0, - ) - - -ARABIC_MMLU_MT_TASKS = [ - CustomArabicMMLUMTTask(name=f"arabic_mmlu_mt:{subset}", hf_subset=subset) for subset in ARABIC_MMLU_MT_SUBSETS -] - - -# ACVA ## -# fmt: off -ACVA_SUBSETS = [ - "Algeria", "Ancient_Egypt", "Arab_Empire", "Arabic_Architecture", "Arabic_Art", "Arabic_Astronomy", "Arabic_Calligraphy", "Arabic_Ceremony", - "Arabic_Clothing", "Arabic_Culture", "Arabic_Food", "Arabic_Funeral", "Arabic_Geography", "Arabic_History", "Arabic_Language_Origin", - "Arabic_Literature", "Arabic_Math", "Arabic_Medicine", "Arabic_Music", "Arabic_Ornament", "Arabic_Philosophy", "Arabic_Physics_and_Chemistry", - "Arabic_Wedding", "Bahrain", "Comoros", "Egypt_modern", "InfluenceFromAncientEgypt", "InfluenceFromByzantium", "InfluenceFromChina", - "InfluenceFromGreece", "InfluenceFromIslam", "InfluenceFromPersia", "InfluenceFromRome", "Iraq", "Islam_Education", "Islam_branches_and_schools", - "Islamic_law_system", "Jordan", "Kuwait", "Lebanon", "Libya", "Mauritania", "Mesopotamia_civilization", "Morocco", "Oman", "Palestine", "Qatar", - "Saudi_Arabia", "Somalia", "Sudan", "Syria", "Tunisia", "United_Arab_Emirates", "Yemen", - "communication", "computer_and_phone", "daily_life", "entertainment" -] -# fmt: on - - -def acva_pfn(line, task_name: str = None): - question = line["question"] - answer = line["answer"] - - return Doc( - task_name=task_name, - query=f"السؤال: {question}\nالإجابة:", - choices=["صح", "خطأ"], - gold_index=["صح", "خطأ"].index(answer), - ) - - -class CustomACVATask(LightevalTaskConfig): - def __init__( - self, - name, - hf_subset, - ): - super().__init__( - name=name, - hf_subset=hf_subset, - prompt_function=acva_pfn, - hf_repo="OALL/ACVA", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - suite=["community"], - generation_size=-1, - stop_sequence=None, - version=0, - ) - - -ACVA_TASKS = [CustomACVATask(name=f"acva:{subset}", hf_subset=subset) for subset in ACVA_SUBSETS] - - -# AraTrust ## -# fmt: off -ARATRUST_SUBSETS = [ - "Trustfulness", "MentalHealth", "PhysicalHealth", "Offensive", "Ethics", "Privacy", "Unfairness", "Illegal", -] -# fmt: on - - -def aratrust_pfn(line, task_name: str = None): - instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة: أ، ب أو ج. \n\n" - choices = [line["A"], line["B"], line["C"]] - # Answers are provided with roman letters - we look for the correct index in LETTER_INDICES, - # it will then be applied to arabic letters - answer_index = LETTER_INDICES_AR.index( - line["Answer"] - ) # line["answer"] is the correct answer. That's why we need to index it ! - - query = f"{instruction}{line['Question']}\n" - query += "".join([f"{choice}\n" for choice in choices]) - query += "الإجابة:" - - return Doc( - task_name=task_name, - query=query, - choices=LETTER_INDICES_AR[:3], - gold_index=answer_index, - instruction=instruction, - ) - - -class CustomAraTrustTask(LightevalTaskConfig): - def __init__( - self, - name, - hf_subset, - ): - super().__init__( - name=name, - hf_subset=hf_subset, - prompt_function=aratrust_pfn, - hf_repo="asas-ai/AraTrust-categorized", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select=None, - suite=["community"], - generation_size=-1, - stop_sequence=None, - version=0, - ) - - -ARATRUST_TASKS = [CustomAraTrustTask(name=f"aratrust:{subset}", hf_subset=subset) for subset in ARATRUST_SUBSETS] - - -def arabic_exams_pfn(line, task_name: str = None): - topic = line["subject"] - question = line["question"] - choices = [line["A"], line["B"], line["C"], line["D"]] - choices_formatted = [f" {LETTER_INDICES_AR[i]}) {choice}\n" for i, choice in enumerate(choices)] - answer = line["answer"] - answer_index = LETTER_INDICES.index(answer) - - instruction = f"الأسئلة التالية هي أسئلة متعددة الإختيارات مع الجواب الصحيح حول {topic.replace('_', ' ')}. \n\n" - query = f"{instruction}السؤال: {question}\n" - query += "\n".join(choices_formatted) - query += "\nالإجابة:" - - return Doc( - task_name=task_name, - query=query, - choices=LETTER_INDICES_AR[:4], - gold_index=answer_index, - instruction=instruction, - ) - - -# ARABIC EXAMS ## -arabic_exams_task = LightevalTaskConfig( - name="arabic_exams", - prompt_function=arabic_exams_pfn, - suite=["community"], - hf_repo="OALL/Arabic_EXAMS", - hf_subset="default", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - version=0, -) - - -# ALGHAFA NATIVE ## -# fmt: off -ALGHAFA_SUBSETS = [ - "mcq_exams_test_ar", "meta_ar_dialects", "meta_ar_msa", "multiple_choice_facts_truefalse_balanced_task", "multiple_choice_grounded_statement_soqal_task", - "multiple_choice_grounded_statement_xglue_mlqa_task", "multiple_choice_rating_sentiment_no_neutral_task", "multiple_choice_rating_sentiment_task", - "multiple_choice_sentiment_task" -] -# fmt: on - - -def alghafa_pfn(line, task_name: str = None): - question = line["query"] - answer_index = int(line["label"]) - allowed_keys = [f"sol{i}" for i in range(1, 6)] - extracted_choices = [line[key] for key in allowed_keys if key in line] - choices = [str(i) for i in range(len(extracted_choices))] - - instruction = "الأسئلة التالية هي أسئلة متعددة الإختيارات مع الجواب الصحيح\n\n" - query = f"{instruction}السؤال: {question}\n" - - for index, choice in enumerate(extracted_choices): - query += f"{index}) {choice}\n" - - query += "الإجابة:" - - return Doc( - task_name=task_name, - query=query, - choices=choices, - gold_index=answer_index, - instruction=instruction, - ) - - -class CustomAlGhafaNativeTask(LightevalTaskConfig): - def __init__( - self, - name, - hf_subset, - ): - super().__init__( - name=name, - hf_subset=hf_subset, - prompt_function=alghafa_pfn, - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Native", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - suite=["community"], - generation_size=-1, - stop_sequence=None, - version=0, - ) - - -ALGHAFA_TASKS = [CustomAlGhafaNativeTask(name=f"alghafa:{subset}", hf_subset=subset) for subset in ALGHAFA_SUBSETS] - -# ALGHAFA TRANSLATED ## -# race_ar -race_ar_task = LightevalTaskConfig( - name="race_ar", - prompt_function=alghafa_pfn, - suite=["community"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="race_ar", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - version=0, -) - - -# piqa_ar -piqa_ar_task = LightevalTaskConfig( - name="piqa_ar", - prompt_function=alghafa_pfn, - suite=["community"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="piqa_ar", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - version=0, -) - - -# arc_easy_ar -arc_easy_ar_task = LightevalTaskConfig( - name="arc_easy_ar", - prompt_function=alghafa_pfn, - suite=["community"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="arc_easy_ar", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - version=0, -) - - -# arc_challenge_okapi_ar -arc_challenge_okapi_ar_task = LightevalTaskConfig( - name="arc_challenge_okapi_ar", - prompt_function=alghafa_pfn, - suite=["community"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="arc_challenge_okapi_ar", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - version=0, -) - - -# mmlu_okapi_ar -mmlu_okapi_ar_task = LightevalTaskConfig( - name="mmlu_okapi_ar", - prompt_function=alghafa_pfn, - suite=["community"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="mmlu_okapi_ar", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - version=0, -) - - -# openbook_qa_ext_ar -openbook_qa_ext_ar_task = LightevalTaskConfig( - name="openbook_qa_ext_ar", - prompt_function=alghafa_pfn, - suite=["community"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="openbook_qa_ext_ar", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - version=0, -) - - -# boolq_ar -def boolq_arabic_pfn(line, task_name: str = None): - question = line["question"] - passage = line["passage"] - instruction = "بناء على المقطع التالي، أجب عن السؤال ب نعم أو لا" - query = f"""{instruction} - المقطع : - {passage} - السؤال: - {question} - الإجابة: - """ - - return Doc( - task_name=task_name, - query=query, - choices=["نعم", "لا"], - gold_index=0 if line["answer"] else 1, - instruction=instruction, - ) - - -boolq_ar_task = LightevalTaskConfig( - name="boolq_ar", - prompt_function=boolq_arabic_pfn, - suite=["community"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="boolq_ar", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - version=0, -) - - -# copa_ext_ar -def copa_arabic_pfn(line, task_name: str = None): - premise = line["premise"] - choices = [line["choice1"], line["choice2"]] - question_map = {"cause": "لأن", "effect": "لذلك"} - question = question_map[line["question"]] - answer = line["label"] - - query = "{}، {} :\n0) {}\n1) {}\nالإجابة:".format(premise, question, choices[0], choices[1]) - - return Doc( - task_name=task_name, - query=query, - choices=choices, - gold_index=answer, - instruction="", - ) - - -copa_ext_ar_task = LightevalTaskConfig( - name="copa_ext_ar", - prompt_function=copa_arabic_pfn, - suite=["community"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="copa_ext_ar", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - version=0, -) - - -# hellaswag_okapi_ar -def hellaswag_arabic_pfn(line, task_name: str = None): - ctx = re.sub(r"\[.*?\]", "", line["ctx"]) # Remove latin words within brackets - endings = [ - re.sub(r"\[.*?\]", "", e) for e in eval(line["endings"]) - ] # endings is a string representation of a list - answer_index = line["label"] - instruction = "بناء على السياق التالي، اختر النهاية الصحيحة من الاقتراحات التالية" - - query = f"""{instruction} - السياق: - {ctx} - الاقتراحات: - - """ - for i, ending in enumerate(endings): - query += f"{i}) {ending}\n" - query += "الإجابة:" - - return Doc( - task_name=task_name, - query=query, - choices=endings, - gold_index=answer_index, - instruction=instruction, - ) - - -hellaswag_okapi_ar_task = LightevalTaskConfig( - name="hellaswag_okapi_ar", - prompt_function=hellaswag_arabic_pfn, - suite=["community"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="hellaswag_okapi_ar", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - version=0, -) - - -# toxigen_ar -def toxigen_arabic_pfn(line, task_name: str = None): - text = line["text"] - label = 1 if ((line["toxicity_ai"] + line["toxicity_human"]) > 5.5) else 0 - instruction = 'هل العبارة التالية تحض على الكراهية؟ قم بالرد بـ"نعم" أو "لا".' - - query = f"""{instruction} - العبارة: - '{text}' - الإجابة: - """ - - return Doc( - task_name=task_name, - query=query, - choices=["لا", "نعم"], - gold_index=label, - instruction=instruction, - ) - - -toxigen_ar_task = LightevalTaskConfig( - name="toxigen_ar", - prompt_function=toxigen_arabic_pfn, - suite=["community"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="toxigen_ar", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - version=0, -) - - -# sciq_ar -def sciq_arabic_pfn(line, task_name: str = None): - support = line["support"] - question = line["question"] - correct_answer = line["correct_answer"] - choices = [line["distractor1"], line["distractor2"], line["distractor3"], correct_answer] - - # Shuffle the choices - random.shuffle(choices) - - answer_index = choices.index(correct_answer) - - instruction = "بناءً على السياق أدناه، اختر الإجابة الصحيحة للسؤال التالي من قائمة الاقتراحات" - - query = f"""{instruction} - السياق: - {support} - السؤال: - {question} - الإجابات المحتملة: - - """ - for i, choice in enumerate(choices): - query += f"{i}) {choice}\n" - query += "الإجابة:" - - return Doc( - task_name=task_name, - query=query, - choices=choices, - gold_index=answer_index, - instruction=instruction, - ) - - -sciq_ar_task = LightevalTaskConfig( - name="sciq_ar", - prompt_function=sciq_arabic_pfn, - suite=["community"], - hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", - hf_subset="sciq_ar", - hf_avail_splits=["test", "validation"], - evaluation_splits=["test"], - few_shots_split="validation", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - version=0, -) - - -# madinah_qa -# fmt: off -MADINAH_QA_SUBSETS = ["Arabic Language (General)", "Arabic Language (Grammar)"] -# fmt: on - - -def madinah_qa_pfn(line, task_name: str = None): - instruction = "بناءً على السياق أدناه، اختر الإجابة الصحيحة للسؤال التالي من قائمة الأجوبة:\n\n" - - # Define the mapping from Latin to Arabic letters - latin_to_arabic = {"A": "أ", "B": "ب", "C": "ج", "D": "د", "E": "هـ"} - - # Create a list of valid choices with corresponding Arabic keys - choices = [] - valid_keys_latin = [] - valid_keys_arabic = [] - - # Enumerate through the options and append the valid ones - for idx, key in enumerate(["A", "B", "C", "D", "E"]): - option = line.get(f"Option {idx + 1}") - if option: # Check if option is not null - choices.append(option) - valid_keys_latin.append(key) # Append the Latin key (A, B, C, D, E) - valid_keys_arabic.append(latin_to_arabic[key]) # Append the corresponding Arabic letter - - # Find the correct index for the answer key in the Arabic version - answer_index = valid_keys_latin.index(line["Answer Key"]) - - query = f"{instruction}\nالسياق:\n{line['Context']}\nالسؤال:\n{line['Question']}\n" - query += "".join([f"{key}. {choice}\n" for key, choice in zip(valid_keys_arabic, choices)]) - query += "الإجابة:" - - return Doc( - task_name=task_name, - query=query, - choices=valid_keys_arabic, - gold_index=answer_index, # Correct index in the valid keys - instruction=instruction, - ) - - -class CustomMadinahQATask(LightevalTaskConfig): - def __init__( - self, - name, - hf_subset, - ): - super().__init__( - name=name, - hf_subset=hf_subset, - prompt_function=madinah_qa_pfn, - hf_repo="MBZUAI/MadinahQA", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=["dev"], - few_shots_select="sequential", - suite=["community"], - generation_size=-1, - stop_sequence=None, - version=0, - ) - - -MADINAH_QA_TASKS = [ - CustomMadinahQATask(name=f"madinah_qa:{subset}", hf_subset=subset) for subset in MADINAH_QA_SUBSETS -] - - -class JudgeMetricWrapper(Metric): - """Wrapper class for LLM-based judge metric implementation.""" - - def __init__(self, judge: JudgeLM): - """ - Initializes the judge metric wrapper. - - Args: - judge (JudgeLM): The LLM judge instance to use for evaluation. - """ - self.judge = judge - self.metric_name = "llm_as_judge" - self.category = SamplingMethod.GENERATIVE - self.corpus_level_fn = self.aggregate_scores - self.sample_level_fn = self._sample_level_fn - self.higher_is_better = True # Fixed tuple syntax - - def compute(self, responses: list[str], formatted_docs: list[Doc], **kwargs) -> dict[str, float]: - """ - Computes evaluation scores using the judge's evaluate_answer method. - - Args: - responses (list[str]): The predicted answers - formatted_docs (list[Doc]): Documents containing questions and gold answers - kwargs: Additional keyword arguments (not used) - - Returns: - dict[str, float]: Dictionary containing evaluation scores - """ - results = [] - for i, doc in enumerate(formatted_docs): - question = doc.query - gold = doc.choices[doc.gold_index] if doc.gold_index is not None else None - answer = responses[i][0].result[0] - - score, _, _ = self.judge.evaluate_answer(question=question, answer=answer, options=None, gold=gold) - results.append({self.metric_name: score}) - - return results - - def aggregate_scores(self, scores: list[dict]) -> float: - return sum(scores) / len(scores) if scores else 0.0 - - def _sample_level_fn(self): - return None - - -def parse_candidates(candidates: Union[List[str], str]) -> List[str]: - """ - Parses and validates candidate answers from either list or string format. - - Args: - candidates: Either a list of candidate answers or a newline-separated string - - Returns: - List[str]: List of validated candidate answers - - Raises: - ValueError: If candidates cannot be parsed or are empty - """ - try: - if isinstance(candidates, list): - parsed_candidates = [str(c).strip() for c in candidates if c] - else: - parsed_candidates = [c.strip() for c in str(candidates).split("\n") if c.strip()] - - if not parsed_candidates: - raise ValueError("No valid candidates found after parsing") - - return parsed_candidates - except Exception as e: - raise ValueError(f"Failed to parse candidates: {str(e)}") - - -def qa_prompt_arabic(line: Dict[str, Any], task_name: str = None) -> Doc: - """ - Formats the prompt for Arabic question answering with candidates. - - Args: - line: Dictionary containing question and candidate information - task_name: Optional name for the task - - Returns: - Doc: Formatted document for evaluation - - Raises: - ValueError: If required fields are missing or invalid - """ - try: - # Validates and extracts the question - if not isinstance(line.get("question"), str): - raise ValueError("Question must be a string") - question = line["question"] - - # Processes candidate answers - candidates = parse_candidates(line["candidates"]) - - # Validates gold answer - if "gold_answer" not in line: - raise ValueError("Gold answer is required") - gold_answer = str(line["gold_answer"]) - - # Constructs the prompt - instruction = "بناءً على السياقات المقترحة التالية، اجب عن السؤال التالي" - query = f"{instruction}\n\nالسؤال:\n{question}\n\nالسياقات المقترحة:\n{', '.join(candidates)}\n" - - return Doc( - task_name=task_name or "alrage", - query=query, - instruction=instruction, - choices=[gold_answer], # Gold answer is used as the only valid choice - gold_index=0, # Index of the correct answer in choices - ) - except Exception as e: - raise ValueError(f"Failed to create QA prompt: {str(e)}") - - -def judge_template(question: str, answer: str, gold: str, options: Optional[List[str]] = None) -> List[Dict[str, str]]: - """ - Template for the Arabic judge prompt. - - System prompt translation: - You are a neutral expert evaluator. Your tasks are: - 1. Evaluate the answer's accuracy compared to the correct answer - 2. Verify that the answer is supported by the provided context - 3. Evaluate the quality and comprehensiveness of the answer - Rate the answer on a scale from 0 to 10. - - Args: - question: The question being evaluated - answer: The provided answer - gold: The correct answer - options: Optional list of answer choices - - Returns: - List[Dict[str, str]]: Formatted messages for the judge - """ - messages = [ - { - "role": "system", - "content": """أنت مقيّم محايد خبير باللغة العربية. يجب عليك: -1. تقييم دقة الإجابة مقارنة بالإجابة الصحيحة -2. التحقق من أن الإجابة مدعومة بالسياق المقدم -3. تقييم جودة وشمولية الإجابة - -مهم جداً: يجب أن يكون ردك رقماً فقط من 0 إلى 10. لا تضف أي نص أو تفسير.""", - }, - { - "role": "user", - "content": f"""السؤال: {question} - -الإجابة المقدمة: {answer} - -الإجابة الصحيحة: {gold} - -أعط تقييماً من 0 إلى 10: -0-2: إجابة خاطئة تماماً -3-4: إجابة جزئية مع أخطاء -5-6: إجابة متوسطة -7-8: إجابة جيدة -9-10: إجابة ممتازة - -اكتب رقماً فقط من 0 إلى 10 بدون أي نص إضافي:""", - }, - ] - return messages - - -def process_judge_response(response) -> float: - """Process the judge's response to extract the score""" - # If response is a list, extract the content from the user role - if isinstance(response, list): - response_content = " ".join(item["content"] for item in response if item["role"] == "user") - else: - response_content = response # If it's not a list, use it directly - - try: - # Extract the score from the response content - score = float(next(num for num in response_content.split() if num.replace(".", "", 1).isdigit())) - return min(max(score / 10.0, 0.0), 1.0) - except (StopIteration, ValueError): - return 0.0 - - -judge = JudgeLM( - model="Qwen/Qwen2.5-72B-Instruct", - templates=judge_template, - process_judge_response=process_judge_response, - judge_backend="vllm", -) - -wrapped_judge = JudgeMetricWrapper(judge) - -# Task configuration -alrage_qa_task = LightevalTaskConfig( - name="alrage_qa", - prompt_function=qa_prompt_arabic, - suite=["community"], - hf_repo="OALL/ALRAGE", - hf_subset=None, - hf_avail_splits=["train"], - evaluation_splits=["train"], - metrics=[wrapped_judge], - generation_size=200, - stop_sequence=[], - version=0, -) - -TASKS_TABLE = ( - ARABIC_MMLU_TASKS - + ARABIC_MMLU_HT_TASKS - + ARABIC_MMLU_MT_TASKS - + ACVA_TASKS - + ALGHAFA_TASKS - + ARATRUST_TASKS - + MADINAH_QA_TASKS - + [arabic_exams_task] - + [race_ar_task] - + [piqa_ar_task] - + [arc_easy_ar_task] - + [arc_challenge_okapi_ar_task] - + [mmlu_okapi_ar_task] - + [openbook_qa_ext_ar_task] - + [boolq_ar_task] - + [copa_ext_ar_task] - + [hellaswag_okapi_ar_task] - + [toxigen_ar_task] - + [sciq_ar_task] - + [alrage_qa_task] -) diff --git a/community_tasks/custom_task_classification_grammar_task.py b/community_tasks/custom_task_classification_grammar_task.py deleted file mode 100644 index 5b248093b..000000000 --- a/community_tasks/custom_task_classification_grammar_task.py +++ /dev/null @@ -1,456 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# ruff: noqa: F405, F403, F401 -"""Emotion Classification Task with Grammar Constraints using LightEval - -This module demonstrates how to create a classification task in LightEval with JSON grammar-constrained generation for structured responses. - - -The task performs emotion classification on the 'emotion' dataset from HuggingFace Hub, -classifying text into one of six emotion categories: sadness, joy, love, anger, fear, surprise. - -Example usage: - TGI endpoint evaluation: - ```bash - uv run --active --extra litellm --extra tgi lighteval endpoint tgi examples/model_configs/tgi_model.yaml "custom|emotion_classification|0" - --custom-tasks examples/custom_tasks_templates/custom_task_classification_grammar_task.py - --output-dir results - --save-details - --no-public-run - ``` - -Dataset: - The task uses the 'emotion' dataset from HuggingFace Hub, which contains - English Twitter messages labeled with one of six emotions. The dataset - includes train/validation/test splits with the following distribution: - - Total samples: ~416k (train: ~16k, validation: ~2k, test: ~2k) - - Labels: sadness, joy, love, anger, fear, surprise - - Text format: Short social media posts in English - -Customization: - To adapt this task for other classification problems: - 1. Update EMOTION_LABELS with your target labels - 2. Modify prompt_emotion_classification() for your use case - 3. Update the grammar schema in get_emotion_classification_grammar() - 4. Adjust the HuggingFace dataset reference in EMOTION_CLASSIFICATION_TASK - 5. Update metric calculations in emotion_classification_metric() if needed -""" - -import json -import logging -from typing import Any - -import numpy as np - -from lighteval.metrics.utils.metric_utils import SampleLevelMetricGrouping -from lighteval.models.model_output import ModelResponse -from lighteval.tasks.lighteval_task import ( - LightevalTaskConfig, - TextGenerationInputGrammarType, -) -from lighteval.tasks.requests import Doc, SamplingMethod - - -logger = logging.getLogger(__name__) - -# Emotion labels for the emotion dataset from HuggingFace Hub -# These correspond to the 6-class emotion classification task with the following mapping: -# 0: sadness, 1: joy, 2: love, 3: anger, 4: fear, 5: surprise -EMOTION_LABELS = ["sadness", "joy", "love", "anger", "fear", "surprise"] - - -def parse_emotion_response(response: str | dict) -> dict[str, Any]: - """Parse the model's response into a standardized format. - - This function handles both JSON string and dictionary inputs, providing robust - parsing with validation against the predefined emotion labels. Invalid predictions - are automatically mapped to 'unknown' with appropriate logging. - - Args: - response (str | dict): The model's response, either as a JSON string - containing {"classification": "emotion_label"} or as a dictionary - with the same structure. - - Returns: - dict[str, Any]: Standardized dictionary containing: - - classification (str): The predicted emotion label, validated against - EMOTION_LABELS or 'unknown' if invalid/unparseable - - Examples: - >>> parse_emotion_response('{"classification": "joy"}') - {'classification': 'joy'} - - >>> parse_emotion_response({'classification': 'ANGER'}) - {'classification': 'anger'} - - >>> parse_emotion_response('{"classification": "invalid_emotion"}') - {'classification': 'unknown'} # with warning logged - - >>> parse_emotion_response('malformed json') - {'classification': 'unknown'} # with error logged - - Note: - - Case-insensitive matching: 'ANGER' and 'Anger' are normalized to 'anger' - - Whitespace is automatically stripped from predictions - - All parsing errors result in 'unknown' classification with detailed logging - """ - try: - # Handle dictionary input (already parsed JSON) - if isinstance(response, dict): - result = response - # Handle string input (JSON string that needs parsing) - else: - result = json.loads(response.strip()) - - # Extract and normalize the predicted emotion - predicted_emotion = result["classification"].lower().strip() - - # Validate that the prediction is one of the valid emotion labels - if predicted_emotion not in EMOTION_LABELS: - logger.warning( - f"Invalid emotion prediction: '{predicted_emotion}'. " - f"Expected one of {EMOTION_LABELS}. Using 'unknown'." - ) - predicted_emotion = "unknown" - - return { - "classification": predicted_emotion, - } - except (json.JSONDecodeError, KeyError, AttributeError, TypeError) as e: - # Handle specific parsing errors with detailed logging - logger.error( - f"Error parsing response: {str(e)}. Failed response was: {response}. Expected format: {{'classification': 'emotion_label'}}" - ) - return { - "classification": "unknown", - } - except Exception as e: - # Catch any other unexpected errors - logger.error(f"Unexpected error parsing response: {str(e)}. Failed response was: {response}") - return { - "classification": "unknown", - } - - -def emotion_classification_metric(model_response: ModelResponse, doc: Doc, **kwargs) -> dict[str, float]: - """Evaluate emotion classification predictions at the sample level. - - This function computes evaluation metrics for a single prediction, comparing - the model's emotion classification against the gold standard. It provides - detailed logging for debugging and tracks prediction quality. - - Args: - model_response (ModelResponse): The model's response containing generated text - in the text attribute, typically containing one prediction as either a - JSON string or dictionary with format {"classification": "emotion_label"} - doc (Doc): The document containing the query, choices, and gold - standard information. Must have gold_index attribute pointing to the - correct emotion label index. - **kwargs: Additional keyword arguments (unused but required for compatibility - with LightEval's metric interface) - - Returns: - dict[str, float]: Dictionary containing sample-level metrics: - - exact_match (float): 1.0 if prediction matches gold label, 0.0 otherwise - - unknown_prediction (float): 1.0 if prediction was 'unknown' (parsing - failure), 0.0 otherwise - - total_samples (float): Always 1.0 (count for this sample) - - Examples: - >>> doc = Doc(query="I'm so happy!", gold_index=2) # joy - >>> model_response = ModelResponse(text=['{"classification": "joy"}'], ...) - >>> result = emotion_classification_metric(model_response, doc) - >>> result - {'exact_match': 1.0, 'unknown_prediction': 0.0, 'total_samples': 1.0} - - >>> model_response = ModelResponse(text=['{"classification": "sadness"}'], ...) - >>> result = emotion_classification_metric(model_response, doc) - >>> result - {'exact_match': 0.0, 'unknown_prediction': 0.0, 'total_samples': 1.0} - - Note: - - The function expects exactly one prediction in the model_response.text list - - Gold labels are mapped from integer indices to emotion label strings - - All errors in prediction parsing result in 'unknown' classification - - Detailed logging is provided for debugging classification performance - """ - try: - # Parse the first (and typically only) prediction - prediction = parse_emotion_response(model_response.text[0]) - - # Map the gold label index to the corresponding emotion string - # The emotion dataset uses integer indices: 0=anger, 1=fear, 2=joy, etc. - gold_label_idx = doc.gold_index - expected_emotion = EMOTION_LABELS[gold_label_idx] - - # Log detailed information for debugging and analysis - logger.info("-" * 50) - logger.info("Processing new sample") - logger.info(f"- Text: {doc.query}") - logger.info(f"- Prediction: {prediction}") - logger.info(f"- Expected: {expected_emotion} (index: {gold_label_idx})") - - # Calculate evaluation metrics - is_exact_match = prediction["classification"] == expected_emotion - is_unknown = prediction["classification"] == "unknown" - - metrics = { - "exact_match": float(is_exact_match), - "unknown_prediction": float(is_unknown), - "total_samples": 1.0, - } - - logger.info(f"- Metrics: {metrics}") - if is_exact_match: - logger.info("✓ Correct prediction") - elif is_unknown: - logger.info("⚠ Parsing failure (unknown prediction)") - else: - logger.info("✗ Incorrect prediction") - logger.info("-" * 50) - - return metrics - - except (IndexError, KeyError) as e: - # Handle errors related to accessing gold label or prediction structure - logger.error(f"Error accessing gold label or prediction: {str(e)}") - logger.error(f"Gold index: {getattr(doc, 'gold_index', 'N/A')}") - logger.error(f"Raw prediction: {model_response.text[0] if model_response.text else 'Empty predictions'}") - return { - "exact_match": 0.0, - "unknown_prediction": 1.0, - "total_samples": 1.0, - } - except Exception as e: - # Handle any other unexpected errors - logger.error(f"Unexpected error processing prediction: {str(e)}") - logger.error(f"Raw prediction was: {model_response.text[0] if model_response.text else 'Empty predictions'}") - return { - "exact_match": 0.0, - "unknown_prediction": 1.0, - "total_samples": 1.0, - } - - -# Define the metric group for emotion classification evaluation -# This configures both sample-level and corpus-level metric calculations -emotion_classification_group = SampleLevelMetricGrouping( - metric_name=[ - "exact_match", # Primary accuracy metric - "unknown_prediction", # Tracks parsing failures - "total_samples", # Sample count for aggregation - ], - higher_is_better={ - "exact_match": True, # Higher accuracy is better - "unknown_prediction": False, # Fewer parsing failures is better - "total_samples": True, # More samples processed is better - }, - category=SamplingMethod.GENERATIVE, # Classification via text generation - sample_level_fn=emotion_classification_metric, # Function for individual samples - corpus_level_fn={ - "exact_match": np.mean, # Average accuracy across all samples - "unknown_prediction": np.mean, # Proportion of parsing failures - "total_samples": np.sum, # Total number of samples processed - }, -) - - -def prompt_emotion_classification(line: dict[str, Any], task_name: str = None) -> Doc: - """Format the emotion classification task with detailed prompt engineering. - - This function converts a single sample from the emotion dataset into a structured - prompt that provides clear instructions and emotion definitions to improve - classification accuracy. The prompt includes detailed explanations of each - emotion category to reduce ambiguity. - - Args: - line (dict[str, Any]): A single sample from the emotion dataset containing: - - 'text' (str): The input text to classify - - 'label' (int): The gold standard emotion label (0-5) - task_name (str, optional): Name of the task for identification purposes. - Defaults to None. - - Returns: - Doc: A formatted document object containing: - - task_name: Task identifier - - query: The formatted prompt with text and emotion definitions - - choices: List of available emotion labels - - gold_index: The correct emotion label index - - instruction: Empty string (instructions are embedded in query) - - Examples: - >>> line = {'text': 'I am so excited for tomorrow!', 'label': 2} - >>> doc = prompt_emotion_classification(line, 'emotion_test') - >>> print(doc.query) - Classify the emotion expressed in the following text: "I am so excited for tomorrow!" - ... - >>> doc.gold_index - 2 - >>> doc.choices - ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'] - - Note: - - The prompt includes detailed definitions for each emotion to improve accuracy - - Emotion definitions are based on common psychological categorizations - - The format is optimized for both human readability and model understanding - """ - # Extract the text to be classified - text = line["text"] - - # Create a comprehensive classification prompt with detailed emotion definitions - # This approach helps models understand the subtle differences between emotions - prompt = f"""Classify the emotion expressed in the following text: "{text}" - -Available emotion labels and their meanings: -- sadness: Feeling of sorrow, grief, or unhappiness. Covers melancholy, disappointment, - loss, or general negative emotional states related to unfortunate circumstances. -- joy: Feeling of happiness, delight, or pleasure. Encompasses positive emotions like - excitement, satisfaction, contentment, and general well-being. -- love: Feeling of affection, care, or romantic attachment. Includes expressions of - deep fondness, romantic interest, or strong positive feelings toward people or things. -- anger: Feeling of displeasure, hostility, or annoyance. Often involves frustration, - irritation, or aggressive sentiments toward people, situations, or objects. -- fear: Feeling of anxiety, worry, or being afraid. Includes nervousness, concern - about future events, or apprehension about potential threats or negative outcomes. -- surprise: Feeling of astonishment or being caught off guard. Includes unexpected - reactions, amazement, or responses to sudden or unanticipated events. - -Choose the emotion that best matches the sentiment expressed in the text.""" - - return Doc( - task_name=task_name, - query=prompt, - choices=EMOTION_LABELS, # Available emotion label options - gold_index=line["label"], # Gold standard emotion index (0-5) - instruction="", # Instructions are embedded in the query - ) - - -def get_emotion_classification_grammar() -> TextGenerationInputGrammarType: - """Define the JSON schema grammar for constrained emotion classification responses. - - This function creates a strict JSON schema that constrains the model's output - to only valid emotion labels, preventing hallucination and ensuring consistent - response format. The grammar constraint is enforced during text generation. - - Returns: - TextGenerationInputGrammarType: A JSON schema grammar specification that: - - Enforces JSON object structure with required "classification" field - - Constrains classification values to only valid emotion labels - - Ensures consistent response parsing across different models - - Schema Structure: - { - "type": "object", - "properties": { - "classification": { - "type": "string", - "description": "Emotion classification", - "enum": ["anger", "fear", "joy", "love", "sadness", "surprise"] - } - }, - "required": ["classification"] - } - - Examples: - Valid responses that match this grammar: - - {"classification": "joy"} - - {"classification": "anger"} - - Invalid responses that would be rejected: - - {"emotion": "joy"} # Wrong field name - - {"classification": "happy"} # Invalid emotion label - - "joy" # Not a JSON object - - Note: - - This grammar constraint significantly improves response consistency - - It prevents the model from generating invalid emotion labels - - Compatible with grammar-enabled backends like vLLM, TGI, and others - - The enum constraint is crucial for maintaining label consistency - """ - return TextGenerationInputGrammarType( - type="json", # Specify JSON schema grammar type - value={ - "type": "object", # Require JSON object structure - "properties": { - "classification": { - "type": "string", # Classification must be a string - "description": "Emotion classification from the provided list", - "enum": EMOTION_LABELS, # Strictly constrain to valid emotion labels only - }, - }, - "required": ["classification"], # Classification field is mandatory - "additionalProperties": False, # Prevent extra fields in response - }, - ) - - -# Task configuration for emotion classification using the HuggingFace emotion dataset -# This configuration optimizes for accuracy while maintaining efficient resource usage -EMOTION_CLASSIFICATION_TASK = LightevalTaskConfig( - name="emotion_classification", # Unique task identifier - prompt_function=prompt_emotion_classification, # Custom prompt formatting function - suite=["custom"], # Classification as a community/custom task - hf_repo="emotion", # HuggingFace Hub dataset repository - hf_subset=None, # Use default subset (no subset specified) - metrics=[emotion_classification_group], # Evaluation metrics configuration - generation_size=64, # Conservative token limit for JSON responses (~30-40 tokens typical) - generation_grammar=get_emotion_classification_grammar(), # JSON schema constraint - stop_sequence=["\n\n"], # Early stopping on double newline - evaluation_splits=["test"], # Evaluate on test split only - hf_avail_splits=["train", "validation", "test"], # Available dataset splits -) - -# Export the task for LightEval discovery -# This list is automatically detected by LightEval when loading custom tasks -TASKS_TABLE = [EMOTION_CLASSIFICATION_TASK] - -# Development and testing utilities -if __name__ == "__main__": - # Print available tasks for verification - print("Available tasks:", [t.name for t in TASKS_TABLE]) - print("Total tasks:", len(TASKS_TABLE)) - - # Print task configuration summary for debugging - task = TASKS_TABLE[0] - print("\nTask Configuration Summary:") - print(f" Name: {task.name}") - print(f" Dataset: {task.hf_repo}") - print(f" Splits: {task.evaluation_splits}") - print(f" Metrics: {[m.metric_name for m in task.metric]}") - print(f" Generation size: {task.generation_size}") - print(f" Grammar constrained: {task.generation_grammar is not None}") - print(f" Stop sequences: {task.stop_sequence}") - - # Verify emotion labels configuration - print(f"\nEmotion Labels ({len(EMOTION_LABELS)}):") - for i, label in enumerate(EMOTION_LABELS): - print(f" {i}: {label}") - - print("\nUsage Examples:") - print( - f" TGI: uv run lighteval endpoint tgi config/tgi/tgi.yaml 'custom|{task.name}|0' --custom-tasks {__file__} --output-dir results --override-batch-size 1 --use-chat-template --save-details --no-public-run --max-samples 10" - ) - print( - f" Full: uv run lighteval endpoint tgi config/tgi/tgi.yaml 'custom|{task.name}|5' --custom-tasks {__file__} --output-dir results --override-batch-size 1 --use-chat-template --save-details --no-public-run" - ) diff --git a/community_tasks/filipino_evals.py b/community_tasks/filipino_evals.py deleted file mode 100644 index 45011535e..000000000 --- a/community_tasks/filipino_evals.py +++ /dev/null @@ -1,800 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# ruff: noqa: F405, F403, F401 - -""" -This file contains the tasks for the Filipino language, collectively known as FilBench. -It includes several tasks for the following categories: Cultural Knowledge, Classical NLP, Reading Comprehension, and Generation. -For more information, please read the paper: https://github.com/filbench/filbench-eval/blob/main/filbench.pdf - -Contact: -- Lester James V. Miranda -- Elyanah Aco -- Conner Manuel -- Jan Christian Blaise Cruz -- Joseph Imperial -""" - -from collections import OrderedDict -from functools import partial -from typing import Any - -from langcodes import Language as LangCodeLanguage -from langcodes import standardize_tag - -from lighteval.metrics.dynamic_metrics import LogLikelihoodAccMetric -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import ( - LogProbCharNorm, - LogProbPMINorm, - LogProbTokenNorm, -) -from lighteval.tasks.default_prompts import LETTER_INDICES -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.tasks import MMLU_SUBSETS -from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation -from lighteval.tasks.requests import Doc -from lighteval.tasks.templates.multichoice import get_mcq_prompt_function -from lighteval.tasks.templates.nli import get_nli_prompt_function -from lighteval.tasks.templates.translation import get_translation_prompt_function -from lighteval.tasks.templates.utils.formulation import ( - CFFormulation, - HybridFormulation, - MCFFormulation, -) -from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro - - -# Balita NLP -FILIPINO_BALITA_TASKS = [ - LightevalTaskConfig( - name=f"balita_tgl_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - language=Language.TAGALOG, - adapter=lambda line: { - "question": "Alin sa mga titlulong nakalista sa ibaba ang pinaka-angkop para sa teksto?", - "context": f"Teksto: {line['title_choice_first_paragraph']}", - "choices": line["title_choices"], - "gold_idx": line["title_choice_gold_idx"], - }, - formulation=formulation, - ), - suite=("community",), - hf_repo="LanceBunag/BalitaNLP", - hf_subset="no-image", - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=("validation", "test"), - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [MCFFormulation(), HybridFormulation()] -] - -# Belebele -FILIPINO_BELEBELE_TASKS = [ - LightevalTaskConfig( - name=f"belebele_{LangCodeLanguage.get(language).to_alpha3()}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - iso_639_3_ind_to_iso_639_3_macro[LangCodeLanguage.get(language).to_alpha3()], - lambda line: { - "question": line["question"], - "context": line["flores_passage"], - "choices": [line[f"mc_answer{i}"] for i in range(1, 5)], - "gold_idx": int(line["correct_answer_num"]) - 1, - }, - formulation=formulation, - ), - suite=("community",), - hf_repo="facebook/belebele", - hf_subset=language, - evaluation_splits=("test",), - hf_avail_splits=["test"], - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - ) - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] - for language in ["tgl_Latn", "ceb_Latn"] -] - -# CebuaNER -cebuaner_choices = ["PERSON", "ORGANIZATION", "LOCATION", "OTHER"] -cebuaner_answer_idx = ["A", "B", "C", "D"] -question = "Unsa ang ginganlan nga named-entity sa pulong '{entity}' niini nga sentence: {text}" -FILIPINO_CEBUANER_TASKS = [ - LightevalTaskConfig( - name=f"cebuaner_ceb_{formulation.name.lower()}", - hf_subset="default", - prompt_function=get_mcq_prompt_function( - Language.CEBUANO, - lambda line: { - "question": question.format(entity=line["entity"], text=line["text"]), - "choices": cebuaner_choices, - "gold_idx": cebuaner_answer_idx.index(line["answer"]), - }, - formulation=formulation, - ), - hf_repo="UD-Filipino/cebuaner-instruction", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split="test", - few_shots_select="random", - suite=["community"], - generation_size=-1, - trust_dataset=True, - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - version=0, - ) - for formulation in [MCFFormulation(), HybridFormulation()] -] - -# Cebuano Readability -cebuano_readability_choices = ["Grade 1", "Grade 2", "Grade 3"] -cebuano_readability_instruction = """ -Unsa ang angay nga lebel sa grado alang sa mosunod nga teksto? - -Grade 1 - ang teksto mahimong basahon sa usa ka tawo tali sa edad nga 6-7. -Grade 2 - ang teksto mahimong basahon sa usa ka tawo tali sa edad nga 7-8. -Grade 3 - ang teksto mahimong basahon sa usa ka tawo tali sa edad nga 8-9. -""" -FILIPINO_READABILITY_TASKS = [ - LightevalTaskConfig( - name=f"readability_ceb_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - Language.CEBUANO, - lambda line: { - "question": cebuano_readability_instruction + line["text"], - "choices": cebuano_readability_choices, - "gold_idx": cebuano_readability_choices.index(f"Grade {line['label']}"), - }, - formulation=formulation, - ), - suite=("community",), - hf_subset="default", - hf_repo="UD-Filipino/cebuano-readability", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split="test", - few_shots_select="random", - generation_size=-1, - trust_dataset=True, - version=0, - ) - for formulation in [MCFFormulation(), HybridFormulation()] -] - -# Dengue -dengue_filipino_subsets = { - "absent": "pagiging absent", - "dengue": "dengue", - "health": "kalusugan", - "mosquito": "lamok", - "sick": "sakit", -} - - -def filipino_dengue_pfn(line, task_name: str) -> Doc: - subset = task_name.split(":")[-1] - subset_keyword = dengue_filipino_subsets[subset] - - instruction = f"Tungkol ba sa {subset_keyword} ang sumusunod na pangungusap? Piliin ang tamang sagot:\n\n" - choices: dict[str, str] = OrderedDict({"A": "Hindi", "B": "Oo"}) - - answer_index = int(line.get(subset)) - query = f"{instruction}{line['text']}\n" - query += "".join([f"{key}. {choice}\n" for key, choice in choices.items()]) - query += "Sagot:" - return Doc( - task_name=task_name, - query=query, - choices=list(choices.keys()), - gold_index=answer_index, - instruction=instruction, - ) - - -FILIPINO_DENGUE_TASKS = [ - LightevalTaskConfig( - name=f"dengue_filipino_fil:{subset}", - hf_subset="default", - prompt_function=filipino_dengue_pfn, - hf_repo="jcblaise/dengue_filipino", - metrics=[Metrics.loglikelihood_acc_norm], - hf_avail_splits=["train", "test", "validation"], - evaluation_splits=["train"], - few_shots_split="train", - few_shots_select="random", - suite=("community",), - generation_size=-1, - trust_dataset=True, - version=0, - ) - for subset in dengue_filipino_subsets -] - -# FireCS -firecs_choices = ["Negatibo", "Neutral", "Positibo"] - -FILIPINO_FIRECS_TASK = [ - LightevalTaskConfig( - name=f"firecs_fil_{formulation.name.lower()}", - hf_subset="default", - prompt_function=get_mcq_prompt_function( - Language.TAGALOG, - lambda line: { - "question": f"Ano ang damdamin o sentimiyento ng sumusunod na pangungusap: {line['review']}", - "choices": firecs_choices, - "gold_idx": int(line["label"]), - }, - ), - hf_repo="ccosme/FiReCS", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - hf_avail_splits=["train", "test"], - evaluation_splits=["train"], - few_shots_split="train", - few_shots_select="random", - suite=["community"], - generation_size=-1, - trust_dataset=True, - version=0, - ) - for formulation in [MCFFormulation(), HybridFormulation()] -] - -# Global-MMLU (FIl) - -FILIPINO_GLOBAL_MMLU_TASKS = [ - LightevalTaskConfig( - name=f"global_mmlu_{sensitivity_label.lower()}_{language.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - language, - lambda line: { - "question": line["question"], - "choices": [ - line["option_a"], - line["option_b"], - line["option_c"], - line["option_d"], - ], - "gold_idx": LETTER_INDICES.index(line["answer"]), - }, - formulation=formulation, - ), - suite=("community",), - hf_repo="CohereForAI/Global-MMLU", - hf_subset=standardize_tag(language.value), - evaluation_splits=("test",), - few_shots_split="dev", - hf_filter=partial( - lambda subset, sensitivity_label, x: x["subject"].lower() == subset - and ( - sensitivity_label == "ALL" or sensitivity_label in x["cultural_sensitivity_label"].replace("-", "UNK") - ), - subset, - sensitivity_label, - ), - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - ) - for subset in MMLU_SUBSETS - for language in [Language.TAGALOG] - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] - for sensitivity_label in ["ALL", "CA", "CS", "UNK"] -] - -# INCLUDE - -FILIPINO_INCLUDE_TASKS = [ - LightevalTaskConfig( - name=f"include_{language.value}_{formulation.name.lower()}:{subset}", - prompt_function=get_mcq_prompt_function( - language, - lambda line: { - "question": line["question"], - "choices": [line[f"option_{i}"] for i in ("a", "b", "c", "d")], - "gold_idx": line["answer"], - }, - formulation=formulation, - ), - suite=("community",), - hf_subset="Tagalog", - hf_repo="CohereForAI/include-base-44", - hf_filter=partial(lambda subset, x: x["subject"].replace(" ", "_").lower() == subset, subset), - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split="test", - few_shots_select="random", - generation_size=-1, - trust_dataset=True, - version=0, - ) - for subset in ["culturology", "history", "language", "driving_license"] - for language in [Language.TAGALOG] - for formulation in [MCFFormulation(), HybridFormulation()] -] - -# KALAHI -FILIPINO_KALAHI_TASKS = [ - LightevalTaskConfig( - name=f"kalahi_tgl_{formulation.name.lower()}", - suite=["community"], - prompt_function=get_mcq_prompt_function( - language=Language.TAGALOG, - adapter=lambda line: { - "question": line["prompts"][0]["question"], - "choices": [entry[3:] for entry in line["prompts"][0]["mcq"].split("\n")], - "gold_idx": LETTER_INDICES.index(line["label"]), - }, - formulation=formulation, - ), - hf_repo="aisingapore/cultural_evaluation-kalahi", - hf_subset="default", - evaluation_splits=["tl"], - metrics=[ - LogLikelihoodAccMetric(normalization=None), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ) - for formulation in [HybridFormulation(), MCFFormulation()] -] - -# NewsPH NLI -FILIPINO_NEWSPH_NLI_TASKS = [ - LightevalTaskConfig( - name=f"newsphnli_fil_{formulation.name.lower()}", - suite=["community"], - prompt_function=get_nli_prompt_function( - language=Language.TAGALOG, - adapter=lambda line: { - "premise": line["premise"], - "hypothesis": line["hypothesis"], - # Since there is no neutral label - "gold_idx": line["label"], - }, - relations=["entailment", "contradiction"], - formulation=formulation, - ), - hf_repo="jcblaise/newsph_nli", - hf_subset="default", - evaluation_splits=["validation"], - few_shots_split="train", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=None), - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - ], - ), - trust_dataset=True, - ) - for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] -] - -# NTREX-128 -FILIPINO_NTREX_TASK = [ - LightevalTaskConfig( - name=f"ntrex128_{LangCodeLanguage.get(language).to_alpha3()}", - prompt_function=get_translation_prompt_function( - source_language=Language.ENGLISH, - target_language=iso_639_3_ind_to_iso_639_3_macro[LangCodeLanguage.get(language).to_alpha3()], - adapter=lambda line: { - "source_text": line["eng_Latn"], - "target_text": line[language], - }, - formulation=CFFormulation(), - ), - suite=("community",), - hf_repo="mteb/NTREX", - hf_subset="default", - metrics=[ - Metrics.rougeL, - Metrics.bleu, - Metrics.bleurt, - Metrics.chrf, - Metrics.ter, - ], - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - generation_size=64, - trust_dataset=True, - version=0, - ) - for language in ["fil_Latn"] -] - -# SIB-200 - -sib200_choices = [ - "geography", - "science/technology", - "entertainment", - "travel", - "sports", - "health", - "politics", -] - - -def get_instruction(language: Language) -> str: - if language == Language.CEBUANO: - return "Mahitungod sa unsa ang mosunod nga teksto?\n" - if language == Language.TAGALOG: - return "Tungkol saan ang sumusunod na pangungusap?\n" - - -def create_sib200_task(language: Language, formulation): - return LightevalTaskConfig( - name=f"sib200_{language.value}_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - language, - lambda line: { - "question": get_instruction(language) + line["text"], - "choices": sib200_choices, - "gold_idx": sib200_choices.index(line["category"]), - }, - formulation=formulation, - ), - suite=("community",), - hf_subset=f"{language.value}_Latn", - hf_repo="Davlan/sib200", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - hf_avail_splits=["test", "validation"], - evaluation_splits=["validation"], - few_shots_split="validation", - few_shots_select="random", - generation_size=-1, - trust_dataset=True, - version=0, - ) - - -FILIPINO_SIB_TASKS = [ - create_sib200_task(language, formulation) - for language in [Language.TAGALOG, Language.CEBUANO] - for formulation in [MCFFormulation(), HybridFormulation()] -] - - -def prepare_stingray_correctness(line: dict[str, str]) -> dict[str, Any]: - # lang2 is Tagalog - word = line["word"] - sentence = line["lang2_sentence"] - question = f"Is the usage of {word} in this sentence correct? \n{sentence}" - choices = ["Yes", "No"] - gold_idx = choices.index(line["usage_correctness_lang2_answer"]) - return {"question": question, "choices": choices, "gold_idx": gold_idx} - - -def prepare_stingray_semantic_appropriateness(line: dict[str, str]) -> dict[str, Any]: - lang1 = line["lang1_sentence"] - lang2 = line["lang2_sentence"] - question = "Which sentence is more semantically appropriate?" - choices = [lang1, lang2, "Both"] - choice_letters = ["A", "B", "C"] - gold_idx = choice_letters.index(line["semantic_appropriate_answer"]) - return {"question": question, "choices": choices, "gold_idx": gold_idx} - - -FILIPINO_STINGRAY_CORRECTNESS_TASKS = [ - LightevalTaskConfig( - name=f"stingraybench_correctness_tgl_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - Language.ENGLISH, # the orig instruction is in English, so we replicate it. - adapter=prepare_stingray_correctness, - formulation=formulation, - ), - suite=("community",), - hf_subset="id_tl", - hf_repo="StingrayBench/StingrayBench", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split="test", - few_shots_select="random", - generation_size=-1, - trust_dataset=True, - version=0, - ) - for formulation in [MCFFormulation(), HybridFormulation()] -] - -FILIPINO_STINGRAY_SEMANTIC_TASKS = [ - LightevalTaskConfig( - name=f"stingraybench_semantic_appropriateness_tgl_{formulation.name.lower()}", - prompt_function=get_mcq_prompt_function( - Language.ENGLISH, # the orig instruction is in English, so we replicate it. - adapter=prepare_stingray_semantic_appropriateness, - formulation=formulation, - ), - suite=("community",), - hf_subset="id_tl", - hf_repo="StingrayBench/StingrayBench", - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split="test", - few_shots_select="random", - generation_size=-1, - trust_dataset=True, - version=0, - ) - for formulation in [MCFFormulation(), HybridFormulation()] -] - -FILIPINO_STINGRAY_TASKS = FILIPINO_STINGRAY_SEMANTIC_TASKS + FILIPINO_STINGRAY_CORRECTNESS_TASKS - -# Tatoeba -# We follow the original translation direction from tatoeba -lang_dict = { - "ceb": { - "subset": "ceb-eng", - "source_language": Language.CEBUANO, - "target_language": Language.ENGLISH, - }, - "tgl": { - "subset": "eng-tgl", - "source_language": Language.ENGLISH, - "target_language": Language.TAGALOG, - }, -} - -FILIPINO_TATOEBA_TASKS = [ - LightevalTaskConfig( - name=f"tatoeba_{language}", - prompt_function=get_translation_prompt_function( - source_language=meta.get("source_language"), - target_language=meta.get("target_language"), - adapter=lambda line: { - "source_text": line["sourceString"], - "target_text": line["targetString"], - }, - formulation=CFFormulation(), - ), - suite=("community",), - hf_repo="Helsinki-NLP/tatoeba_mt", - hf_subset=meta.get("subset"), - metrics=[ - Metrics.rougeL, - Metrics.bleu, - Metrics.bleurt, - Metrics.chrf, - Metrics.ter, - ], - hf_avail_splits=["test"], - evaluation_splits=["test"], - trust_dataset=True, - generation_size=64, - ) - for language, meta in lang_dict.items() -] - -# TICO-19 -FILIPINO_TICO19_TASKS = [ - LightevalTaskConfig( - name="tico19_tgl", - prompt_function=get_translation_prompt_function( - source_language=Language.ENGLISH, - target_language=Language.TAGALOG, - adapter=lambda line: { - "source_text": line["sourceString"], - "target_text": line["targetString"], - }, - formulation=CFFormulation(), - ), - suite=("community",), - hf_repo="gmnlp/tico19", - hf_subset="en-tl", - metrics=[ - Metrics.rougeL, - Metrics.bleu, - Metrics.bleurt, - Metrics.chrf, - Metrics.ter, - ], - hf_avail_splits=["test", "validation"], - evaluation_splits=["validation"], - few_shots_split=["validation"], - few_shots_select="random", - trust_dataset=True, - generation_size=64, - ) -] - -# TLUnified-NER -tlunified_ner_choices = ["PERSON", "ORGANIZATION", "LOCATION"] -tlunified_ner_answer_idx = ["A", "B", "C"] - -FILIPINO_TLUNIFIED_NER_TASK = [ - LightevalTaskConfig( - name=f"tlunifiedner_tgl_{formulation.name.lower()}", - hf_subset="instruction", - prompt_function=get_mcq_prompt_function( - Language.TAGALOG, - lambda line: { - "question": f"Ano ang named-entity ng salitang '{line['entity']}' sa pangungusap na ito: {line['text']}", - "choices": tlunified_ner_choices, - "gold_idx": tlunified_ner_answer_idx.index(line["answer"]), - }, - formulation=formulation, - ), - hf_repo="ljvmiranda921/tlunified-ner", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split="test", - few_shots_select="random", - suite=["community"], - generation_size=-1, - trust_dataset=True, - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - version=0, - ) - for formulation in [MCFFormulation(), HybridFormulation()] -] - -# Universal NER -universalner_choices = ["PERSON", "ORGANIZATION", "LOCATION"] -universalner_answer_idx = ["A", "B", "C"] - - -def create_universalner_task(language: Language, formulation): - if language == Language.CEBUANO: - question = "Unsa ang ginganlan nga named-entity sa pulong '{entity}' niini nga sentence: {text}" - if language == Language.TAGALOG: - question = "Ano ang named-entity ng salitang '{entity}' sa pangungusap na ito: {text}" - - return LightevalTaskConfig( - name=f"universalner_{language.value}_{formulation.name.lower()}", - hf_subset=language.value, - prompt_function=get_mcq_prompt_function( - language, - lambda line: { - "question": question.format(entity=line["entity"], text=line["text"]), - "choices": universalner_choices, - "gold_idx": universalner_answer_idx.index(line["answer"]), - }, - formulation=formulation, - ), - hf_repo="UD-Filipino/universalner-instruction", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split="test", - few_shots_select="random", - suite=["community"], - generation_size=-1, - trust_dataset=True, - metrics=get_metrics_for_formulation( - formulation, - [ - LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), - LogLikelihoodAccMetric(normalization=LogProbCharNorm()), - LogLikelihoodAccMetric(normalization=LogProbPMINorm()), - ], - ), - version=0, - ) - - -FILIPINO_UNIVERSALNER_TASKS = [ - create_universalner_task(language, formulation) - for language in [Language.CEBUANO, Language.TAGALOG] - for formulation in [MCFFormulation(), HybridFormulation()] -] - -# Tasks Table - -TASKS_TABLE: list[LightevalTaskConfig] = ( - FILIPINO_BALITA_TASKS - + FILIPINO_BELEBELE_TASKS - + FILIPINO_CEBUANER_TASKS - + FILIPINO_READABILITY_TASKS - + FILIPINO_DENGUE_TASKS - + FILIPINO_FIRECS_TASK - + FILIPINO_GLOBAL_MMLU_TASKS - + FILIPINO_INCLUDE_TASKS - + FILIPINO_KALAHI_TASKS - + FILIPINO_NEWSPH_NLI_TASKS - + FILIPINO_NTREX_TASK - + FILIPINO_SIB_TASKS - + FILIPINO_STINGRAY_TASKS - + FILIPINO_TATOEBA_TASKS - + FILIPINO_TICO19_TASKS - + FILIPINO_TLUNIFIED_NER_TASK - + FILIPINO_UNIVERSALNER_TASKS -) diff --git a/community_tasks/french_evals.py b/community_tasks/french_evals.py deleted file mode 100644 index 8e0480aac..000000000 --- a/community_tasks/french_evals.py +++ /dev/null @@ -1,149 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# ruff: noqa: F405, F403, F401 -""" -Custom evaluation tasks for lighteval. - -This file generally creates just a TASKS_TABLE and TASKS_GROUPS which are then imported by LightEval. - -This module implements tasks for the french specific datasets -See : https://huggingface.co/fr-gouv-coordination-ia -""" - -import random - -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import math_normalizer -from lighteval.tasks.default_prompts import LETTER_INDICES -from lighteval.tasks.extended.ifeval.main import ifeval_metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc -from lighteval.utils.utils import as_list - - -# Ifeval-fr prompt function -def prompt_ifeval_fr(line, task_name: str = None): - return Doc( - task_name=task_name, - query=line["prompt"], - choices=[""], - gold_index=0, - instruction="", - specific={"instructions_id_list": line["instruction_id_list"], "kwargs": line["kwargs"]}, - ) - - -# qpqa-fr prompt function -def prompt_gpqa_fr(line, task_name: str = None): - gold_index = random.randint(0, 3) - choices = [line["Réponse incorrecte 1"], line["Réponse incorrecte 2"], line["Réponse incorrecte 3"]] - choices.insert(gold_index, line["Réponse correcte"]) - - instruction = "Choisissez la réponse correcte aux questions suivantes.\n\n" - - query = f"Question: {line['Question']}\n" - query += "".join([f"{key}. {choice}\n" for key, choice in zip(LETTER_INDICES, choices)]) - query += "Réponse: " - return Doc( - task_name=task_name, - query=f"{instruction}{query}", - choices=LETTER_INDICES[: len(choices)], - gold_index=gold_index, - instruction=instruction, - ) - - -# BAC-fr prompt function -def prompt_bac_fr(line, task_name: str = None): - prompt = f"Enoncé: {line['enonce']}\n{line['instruction']}\n" - if line["choix"] is not None: # Multichoice evaluation - # prompt += "\n".join([f"{LETTER_INDICES[ix]}.{choix}" for ix, choix in enumerate(line["choix"])]) - return Doc( - task_name=task_name, - query=prompt, - choices=as_list(line["choix"]), - gold_index=line["choix"].index(line["choix correct"]), - instruction="", - ) - else: - return Doc(task_name=task_name, query=prompt, choices=[line["reponse"]], gold_index=0, instruction="") - - -# IFEVal-fr task - - -ifeval_fr_task = LightevalTaskConfig( - name="ifeval-fr", - prompt_function=prompt_ifeval_fr, # must be defined in the file or imported from src/lighteval/tasks/tasks_prompt_formatting.py - suite=["community"], - hf_repo="fr-gouv-coordination-ia/IFEval-fr", - hf_subset="default", - metrics=[ifeval_metrics], - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split="train", - few_shots_select="random_sampling", - generation_size=1280, - stop_sequence=[], # no stop sequence, will use eot token - version="0.1", # select your metric in Metrics -) - -# GPQA-fr task -gpqa_fr_task = LightevalTaskConfig( - name="gpqa-fr", - suite=["community"], - prompt_function=prompt_gpqa_fr, - hf_repo="fr-gouv-coordination-ia/gpqa-fr", - hf_subset="default", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[Metrics.loglikelihood_acc], - stop_sequence=["\n"], - version=0, -) - -# BAC-fr task -bac_fr_task = LightevalTaskConfig( - name="bac-fr", - suite=["community"], - prompt_function=prompt_bac_fr, - hf_repo="fr-gouv-coordination-ia/bac-fr", - hf_subset="default", - hf_avail_splits=["train"], - evaluation_splits=["train"], - few_shots_split=None, - few_shots_select="random_sampling", - generation_size=1, - metrics=[ - Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), - Metrics.exact_match, - ], - stop_sequence=["\n"], - version=0, -) - -# STORE YOUR EVALS -TASKS_TABLE = [ifeval_fr_task, gpqa_fr_task, bac_fr_task] diff --git a/community_tasks/german_rag_evals.py b/community_tasks/german_rag_evals.py deleted file mode 100644 index 052826287..000000000 --- a/community_tasks/german_rag_evals.py +++ /dev/null @@ -1,223 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team -# Copyright (c) 2024 Philip May, Deutsche Telekom AG - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# ruff: noqa: F405, F403, F401 -""" -Custom evaluation tasks for lighteval. - -This file generally creates just a TASKS_TABLE and TASKS_GROUPS which are then imported by LightEval. -This module implements the 4 tasks of deutsche-telekom/Ger-RAG-eval. -See: https://huggingface.co/datasets/deutsche-telekom/Ger-RAG-eval -""" - -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc - - -def prompt_fn_choose_question_by_context(line, task_name: str = None): - instruction = "Welche der folgenden Fragen (A oder B oder C oder D) lässt sich anhand des Kontext beantworten?\n\n" - query_template = """\ -Kontext: -{context} - -Fragen: -A: {choice_a} -B: {choice_b} -C: {choice_c} -D: {choice_d} - -Antwort:""" - query = instruction + query_template.format( - context=line["context"], - choice_a=line["choice_a"], - choice_b=line["choice_b"], - choice_c=line["choice_c"], - choice_d=line["choice_d"], - ) - choices = ["A", "B", "C", "D"] - return Doc( - task_name=task_name, - instruction=instruction, - query=query, - choices=choices, - gold_index=choices.index(line["target"]), - ) - - -def prompt_fn_choose_context_by_question(line, task_name: str = None): - instruction = ( - "Auf Basis welcher der folgenden Kontexte (A oder B oder C oder D) lässt sich die Frage beantworten?\n\n" - ) - query_template = """\ -Frage: {question} - -Kontexte: - -A: -{choice_a} - -B: -{choice_b} - -C: -{choice_c} - -D: -{choice_d} - -Antwort:""" - query = instruction + query_template.format( - question=line["question"], - choice_a=line["choice_a"], - choice_b=line["choice_b"], - choice_c=line["choice_c"], - choice_d=line["choice_d"], - ) - choices = ["A", "B", "C", "D"] - return Doc( - task_name=task_name, - instruction=instruction, - query=query, - choices=choices, - gold_index=choices.index(line["target"]), - ) - - -def prompt_fn_question_answer_match(line, task_name: str = None): - instruction = "Beantwortet die Antwort wirklich die Frage? Antworte mit J für ja oder N für nein.\n\n" - query_template = """\ -Die Frage: {question} - -Die Antwort: {answer} - -Auswahl (J/N):""" - query = instruction + query_template.format( - question=line["question"], - answer=line["answer"], - ) - choices = ["J", "N"] - return Doc( - task_name=task_name, - instruction=instruction, - query=query, - choices=choices, - gold_index=choices.index(line["target"]), - ) - - -def prompt_fn_context_question_match(line, task_name: str = None): - instruction = "Lässt sich die Frage mithilfe der Informationen aus dem Kontext beantworten? Antworte mit J für ja oder N für nein.\n\n" - query_template = """\ -Kontext: -{context} - -Die Frage: {question} - -Auswahl (J/N):""" - query = instruction + query_template.format( - question=line["question"], - context=line["context"], - ) - choices = ["J", "N"] - return Doc( - task_name=task_name, - instruction=instruction, - query=query, - choices=choices, - gold_index=choices.index(line["target"]), - ) - - -# Task 1: Choose question by context. -# Given is a context and 4 questions. -# The task is to decide which question can be answered by the context. -task1 = LightevalTaskConfig( - name="german_rag_eval:choose_question_by_context", - prompt_function=prompt_fn_choose_question_by_context, - suite=["community"], - hf_repo="deutsche-telekom/Ger-RAG-eval", - hf_subset="task1", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split="test", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc], - version=1, -) - -# Task 2: Choose context by question. -# Given is a question and 4 contexts. -# The task is to decide which context can answer the question. -task2 = LightevalTaskConfig( - name="german_rag_eval:choose_context_by_question", - prompt_function=prompt_fn_choose_context_by_question, - suite=["community"], - hf_repo="deutsche-telekom/Ger-RAG-eval", - hf_subset="task2", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split="test", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc], - version=1, -) - - -# Task 3: Question-answer match. -# Given is a question and an answer. -# The task is to decide whether the answer actually answers the question. -task3 = LightevalTaskConfig( - name="german_rag_eval:question_answer_match", - prompt_function=prompt_fn_question_answer_match, - suite=["community"], - hf_repo="deutsche-telekom/Ger-RAG-eval", - hf_subset="task3", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split="test", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc], - version=1, -) - -# Task 4: Context-question match. -# Given is a context and a question. -# The task is to decide whether the question can be answered by the context or not. -task4 = LightevalTaskConfig( - name="german_rag_eval:context_question_match", - prompt_function=prompt_fn_context_question_match, - suite=["community"], - hf_repo="deutsche-telekom/Ger-RAG-eval", - hf_subset="task4", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split="test", - few_shots_select="sequential", - metrics=[Metrics.loglikelihood_acc], - version=1, -) - - -# STORE YOUR EVALS -TASKS_TABLE = [task1, task2, task3, task4] diff --git a/community_tasks/oz_evals.py b/community_tasks/oz_evals.py deleted file mode 100644 index 61c762bef..000000000 --- a/community_tasks/oz_evals.py +++ /dev/null @@ -1,87 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# ruff: noqa: F405, F403, F401 -""" -Custom evaluation tasks for lighteval. - -This file generally creates just a TASKS_TABLE and TASKS_GROUPS which are then imported by LightEval. - -OZ Eval (sr. Opšte Znanje Evaluacija) dataset was created for the purposes of evaluating General Knowledge of LLM models in Serbian language. -Data consists of 1k+ high-quality questions and answers which were used as part of entry exams at the Faculty of Philosophy and Faculty of Organizational Sciences, University of Belgrade. -The exams test the General Knowledge of students and were used in the enrollment periods from 2003 to 2024. -For more details and results see: https://huggingface.co/datasets/DjMel/oz-eval -""" - -from lighteval.metrics.metrics import Metrics -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc - - -def prompt_fn_oz_eval_task(line, task_name: str = None): - query_template = """Pitanje: {question}\n - Ponuđeni odgovori: - A. {choice_a} - B. {choice_b} - C. {choice_c} - D. {choice_d} - E. {choice_e} - - Krajnji odgovor:""" - - options = line["options"] - - query = query_template.format( - question=line["questions"], - choice_a=options[0], - choice_b=options[1], - choice_c=options[2], - choice_d=options[3], - choice_e=options[4], - ) - - choices = ["A", "B", "C", "D", "E"] - return Doc( - task_name=task_name, - query=query, - choices=choices, - gold_index=choices.index(line["answer"]), - ) - - -oz_eval_task = LightevalTaskConfig( - name="serbian_evals:oz_task", - prompt_function=prompt_fn_oz_eval_task, - suite=["community"], - hf_repo="DjMel/oz-eval", - hf_subset="default", - hf_avail_splits=["test"], - evaluation_splits=["test"], - few_shots_split=None, - few_shots_select=None, - metrics=[Metrics.loglikelihood_acc], - version=0, -) - - -# STORE YOUR EVALS -TASKS_TABLE = [oz_eval_task] diff --git a/community_tasks/serbian_eval.py b/community_tasks/serbian_eval.py deleted file mode 100644 index c235c7e47..000000000 --- a/community_tasks/serbian_eval.py +++ /dev/null @@ -1,779 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - - -""" -This module contains task configurations and prompt functions for evaluating -LLM models on Serbian datasets. -Each task is defined using the `LightevalTaskConfig` class with its respective -prompt function. -The tasks cover a variety of benchmarks, including: standard task like ARC[E][C], -BoolQ, Hellaswag, OpenBookQA,PIQA, Winogrande and a custom OZ Eval. -MMLU is separated by subject and also all in one. -""" - -from enum import Enum -from typing import List, Optional - -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc - - -class HFSubsets(Enum): - """Enum for all available Hugging Face dataset subsets in Serbian evaluation tasks.""" - - HF_BASE_REPO = "datatab/serbian-llm-benchmark" - HF_REVISION = "209c5b5f999cae5c02eef5735eb817ead18ac214" - - # ARC (AI2 Reasoning Challenge) - ARC_EASY = "arc_easy_serbian" - ARC_CHALLENGE = "arc_challenge_serbian" - # Question Answering and Knowledge - BOOLQ = "boolq_serbian" - OPENBOOK = "openbookq_serbian" - # Commonsense Reasoning - HELLASWAG = "hellaswag_serbian" - PIQA = "piqa_serbian" - WINOGRANDE = "winogrande_serbian" - # Custom/Other Task - OZ_EVAL = "oz_eval_serbian" - # MMLU (Miscellaneous) - MMLU_ANATOMY = "mmlu_anatomija_serbian" - MMLU_ASTRONOMY = "mmlu_astronomija_serbian" - MMLU_BUSINESS_ETHICS = "mmlu_poslovna_etika_serbian" - MMLU_CLINICAL_KNOWLEDGE = "mmlu_kliničko_znanje_serbian" - MMLU_MISCELLANEOUS = "mmlu_miscellaneous_serbian" - MMLU_ELECTRONIC_ENGINEERING = "mmlu_electrical_engineering_serbian" - # MMLU (Business Professional) - MMLU_MARKETING = "mmlu_marketing_serbian" - MMLU_MANAGEMENT = "mmlu_management_serbian" - # MMLU (College Level Tasks) - MMLU_COLLEGE_BIOLOGY = "mmlu_college_biology_serbian" - MMLU_COLLEGE_CHEMISTRY = "mmlu_college_chemistry_serbian" - MMLU_COLLEGE_COMPUTER_SCIENCE = "mmlu_college_computer_science_serbian" - MMLU_COLLEGE_MATHEMATICS = "mmlu_college_mathematics_serbian" - MMLU_COLLEGE_MEDICINE = "mmlu_college_medicine_serbian" - MMLU_COLLEGE_PHYSICS = "mmlu_college_physics_serbian" - MMLU_COLLEGE_COMPUTER_SECURITY = "mmlu_computer_security_serbian" - # MMLU (Ethics, Philosophy) - MMLU_MORAL_DISPUTES = "mmlu_moral_disputes_serbian" - MMLU_MORAL_SCENARIOS = "mmlu_moral_scenarios_serbian" - MMLU_PHILOSOPHY = "mmlu_philosophy_serbian" - MMLU_WORLD_RELIGIONS = "mmlu_world_religions_serbian" - # MMLU (High School Level Tasks) - MMLU_HIGH_SCHOOL_BIOLOGY = "mmlu_high_school_biology_serbian" - MMLU_HIGH_SCHOOL_CHEMISTRY = "mmlu_high_school_chemistry_serbian" - MMLU_HIGH_SCHOOL_COMPUTER_SCIENCE = "mmlu_high_school_computer_science_serbian" - MMLU_HIGH_SCHOOL_EURO_HISTORY = "mmlu_high_school_european_history_serbian" - MMLU_HIGH_SCHOOL_GEOGRAPHY = "mmlu_high_school_geography_serbian" - MMLU_HIGH_SCHOOL_MATHEMATICS = "mmlu_high_school_mathematics_serbian" - MMLU_HIGH_SCHOOL_MICROECONOMICS = "mmlu_high_school_microeconomics_serbian" - MMLU_HIGH_SCHOOL_PHYSICS = "mmlu_high_school_physics_serbian" - MMLU_HIGH_SCHOOL_PSYCHOLOGY = "mmlu_high_school_psychology_serbian" - MMLU_HIGH_SCHOOL_STATISTICS = "mmlu_high_school_statistics_serbian" - MMLU_HIGH_SCHOOL_WORLD_HISTORY = "mmlu_high_school_world_history" - # MMLU (Math, Logic) - MMLU_ABSTRACT_ALGEBRA = "mmlu_abstract_algebra_serbian" - MMLU_ELEMENTARY_MATHEMATICS = "mmlu_osnovna_matematika_serbian" - MMLU_FORMAL_LOGIC = "mmlu_formalna_logika_serbian" - MMLU_CONCEPTUAL_PHYSICS = "mmlu_conceptual_physics_serbian" - MMLU_ECONOMETRICS = "mmlu_econometrics_serbian" - MMLU_MACHINE_LEARNING = "mmlu_machine_learning_serbian" - # MMLU (Social Sciences) - MMLU_GLOBAL_FACT = "mmlu_global_facts_serbian" - MMLU_LOGICAL_FALLACIES = "mmlu_logicke_zablude_serbian" - MMLU_SOCIOLOGY = "mmlu_sociology_serbian" - MMLU_HUMAN_AGING = "mmlu_human_aging_serbian" - # MMLU (All-inclusive Task Entry) - MMLU_SERBIAN_ALL = "mmlu_all_serbian" - - -def prompt_fn_oz_eval_task(line, task_name: str = None): - """ - Prepares a question and answer set in Serbian from the OZ Eval (Opšte Znanje Evaluacija) dataset - for use in a LightEval task. This dataset, specifically designed for evaluating general knowledge - in Serbian, contains questions derived from entrance exams at the University of Belgrade's Faculty - of Philosophy and Faculty of Organizational Sciences, covering enrollment periods from 2003 to 2024. - - The function accepts a dictionary with a question, five answer choices, and a correct answer - designation, returning a structured `Doc` object formatted for LightEval's TASKS_TABLE or TASKS_GROUPS. - - Args: - line (dict): A dictionary with required keys: - - 'query' (str): The main question string. - - 'choices' (list of str): A list containing exactly five answer options. - - 'answer_str' (str): A single character from "A" to "E" representing the correct answer. - task_name (str, optional): An optional string specifying the evaluation task name. - - Returns: - Doc: A structured object for LightEval containing: - - task_name (str): The task name, if provided. - - query (str): Formatted question with embedded answer choices. - - choices (list of str): List of option identifiers ["A", "B", "C", "D", "E"]. - - gold_index (int): Index of the correct answer within the 'choices' list. - - Note: - The OZ Eval dataset is available at https://huggingface.co/datasets/DjMel/oz-eval. - - """ - query_template = """Pitanje: {question}\n - Ponuđeni odgovori: - A. {choice_a} - B. {choice_b} - C. {choice_c} - D. {choice_d} - E. {choice_e} - - Krajnji odgovor:""" - - options = line["choices"] - - query = query_template.format( - question=line["query"], - choice_a=options[0], - choice_b=options[1], - choice_c=options[2], - choice_d=options[3], - choice_e=options[4], - ) - - choices = ["A", "B", "C", "D", "E"] - return Doc( - task_name=task_name, - query=query, - choices=choices, - gold_index=choices.index(line["answer_str"]), - ) - - -def serbian_eval_prompt(line: dict, task_name: Optional[str] = None) -> Doc: - """ - Creates a prompt for a multiple-choice task in Serbian. This function formats the prompt - based on the provided query and choices, handling both standard tasks and MMLU-specific - tasks (if "mmlu" is part of the task name). - - The prompt includes an instruction in Serbian, followed by the query, available choices, - and finally the correct answer. The function determines how to compute the correct answer - based on whether the task name contains "mmlu". - - Args: - line (dict): A dictionary containing the following keys: - - "query" (str): The question or query to present to the user. - - "choices" (list of str): A list of possible answer choices. - - "answer" (int or str): The correct answer, either as an index (for regular tasks) - or as a string (for MMLU tasks). - task_name (Optional[str]): The name of the task. If "mmlu" is in the task name, the - function treats the task as an MMLU task and searches for the correct answer - by matching the string value of the answer. - - Returns: - Doc: A `Doc` object containing the formatted prompt, choices, and the correct answer index. - The `Doc` object includes the following fields: - - task_name (Optional[str]): The name of the task. - - query (str): The formatted query prompt in Serbian, including instructions and choices. - - choices (list of str): The list of available answer choices. - - gold_index (int): The index of the correct answer. - - instruction (str): The instruction shown to the user in Serbian. - """ - - question = line["query"] - choices = line["choices"] - instruction = "Na osnovu sledećeg pitanja, izaberite tačanu opciju iz ponuđenih odgovora.\n" - - # Build the query and determine the gold_index in a single pass - query = f"{instruction}Pitanje: {question}\n\nPonuđeni odgovori:\n" - - gold_index = None - - # ARC is base gold index, but MMLU we handle gold index as - if task_name and "mmlu" in task_name: - correct_answer = str(line["answer"]) - gold_index = next((i for i, choice in enumerate(choices) if correct_answer in choice), None) - else: - gold_index = int(line["answer"]) - - # Show all choises - for i, choice in enumerate(choices): - query += f"{i}. {choice}\n" - - query += "\n\nKrajnji odgovor:" - - return Doc( - task_name=task_name, - query=query, - choices=choices, - gold_index=gold_index, - instruction=instruction, - ) - - -def boolq_serbian(line, task_name: str = None): - # remove extra `?` - question = line["question"][:-1] if line["question"][-2:] == "??" else line["question"] - return Doc( - task_name=task_name, - query=f"Passage: {line['passage']}\nQuestion: {question}\nAnswer:", - choices=[" Da", " Ne"], - gold_index=["Da", "Ne"].index(line["answer"]), - ) - - -def create_task_config( - task_name: str, - prompt_function, - hf_repo: str, - hf_subset: str, - metrics: List, - evaluation_splits: List[str] = ["test"], - suite: List[str] = ["community"], - hf_avail_splits: List[str] = ["test", "validation"], - few_shots_split: str = "validation", - generation_size=5, -) -> LightevalTaskConfig: - """ - Creates a task configuration using dependency injection for flexible task creation. - - Args: - task_name: The name of the task. - prompt_function: The function to generate task prompts. - hf_repo: Hugging Face repository. - hf_subset: Subset of the dataset. - metrics: The metrics to use for the task. - evaluation_splits: The evaluation splits to use (default is "test"). - suite: The suite of tasks. - hf_avail_splits: Available splits (default is "test", "validation"). - few_shots_split: Split used for few-shot examples. - generation_size: Number of generations to produce (default is 5). - - Returns: - A `LightevalTaskConfig` object for the task configuration. - """ - return LightevalTaskConfig( - name=task_name, - prompt_function=prompt_function, - suite=suite, - hf_repo=hf_repo, - hf_subset=hf_subset, - hf_avail_splits=hf_avail_splits, - evaluation_splits=evaluation_splits, - few_shots_split=few_shots_split, - few_shots_select="sequential", - metrics=metrics, - generation_size=generation_size, - hf_revision=HFSubsets.HF_REVISION.value, - version=0, - ) - - -# ============================================ -# ===== ARC (AI2 Reasoning Challenge)========= -# ============================================ - -arc_easy = create_task_config( - task_name="serbian_evals:arc_easy", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.ARC_EASY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -arc_challenge = create_task_config( - task_name="serbian_evals:arc_challenge", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.ARC_CHALLENGE.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -# ============================================ -# ========= Commonsense Reasoning ============ -# ============================================ - -hellaswag = create_task_config( - task_name="serbian_evals:hellaswag", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.HELLASWAG.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) -piqa = create_task_config( - task_name="serbian_evals:piqa", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.PIQA.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -winogrande = create_task_config( - task_name="serbian_evals:winogrande", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.WINOGRANDE.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -# ============================================ -# =========== Custom/Other Task ============== -# ============================================ - -oz_eval = create_task_config( - task_name="serbian_evals:oz_eval", - prompt_function=prompt_fn_oz_eval_task, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.OZ_EVAL.value, - metrics=[Metrics.loglikelihood_acc], -) - -# ============================================ -# ========== MMLU (Miscellaneous) ============ -# ============================================ - -mmlu_anatomy = create_task_config( - task_name="serbian_evals:mmlu_anatomija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_ANATOMY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_astronomy = create_task_config( - task_name="serbian_evals:mmlu_astronomija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_ASTRONOMY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_business_ethics = create_task_config( - task_name="serbian_evals:mmlu_poslovna_etika", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_BUSINESS_ETHICS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_clinical_knowledge = create_task_config( - task_name="serbian_evals:mmlu_kliničko_znanje", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_CLINICAL_KNOWLEDGE.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_miscellaneous = create_task_config( - task_name="serbian_evals:mmlu_razno", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_MISCELLANEOUS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_electrical_engineering = create_task_config( - task_name="serbian_evals:mmlu_elektrotehnika", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_ELECTRONIC_ENGINEERING.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -# ============================================ -# ====== MMLU (All-inclusive Task Entry) ===== -# ============================================ - -mmlu_all = create_task_config( - task_name="serbian_evals:mmlu", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_SERBIAN_ALL.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -# ============================================ -# ======= MMLU (Business Professional) ======= -# ============================================ - -mmlu_marketing = create_task_config( - task_name="serbian_evals:mmlu_marketing", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_MARKETING.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_management = create_task_config( - task_name="serbian_evals:mmlu_manadzment", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_MANAGEMENT.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -# ============================================ -# ======== MMLU (College Level Tasks) ======== -# ============================================ - -mmlu_college_biology = create_task_config( - task_name="serbian_evals:mmlu_fakultet_biologija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_COLLEGE_BIOLOGY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_college_chemistry = create_task_config( - task_name="serbian_evals:mmlu_fakultet_hemija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_COLLEGE_CHEMISTRY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_college_computer_science = create_task_config( - task_name="serbian_evals:mmlu_fakultet_racunari", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_COLLEGE_COMPUTER_SCIENCE.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_college_mathematics = create_task_config( - task_name="serbian_evals:mmlu_fakultet_matematika", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_COLLEGE_MATHEMATICS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_college_medicine = create_task_config( - task_name="serbian_evals:mmlu_fakultet_medicina", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_COLLEGE_MEDICINE.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_college_physics = create_task_config( - task_name="serbian_evals:mmlu_fakultet_fizika", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_COLLEGE_PHYSICS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_computer_security = create_task_config( - task_name="serbian_evals:mmlu_sigurnost_racunara", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_COLLEGE_COMPUTER_SECURITY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -# ============================================ -# ======== MMLU (Ethics, Philosophy) ========= -# ============================================ - -mmlu_moral_disputes = create_task_config( - task_name="serbian_evals:mmlu_moralni_sporovi", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_MORAL_DISPUTES.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_moral_scenarios = create_task_config( - task_name="serbian_evals:mmlu_moralne_dileme", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_MORAL_SCENARIOS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_philosophy = create_task_config( - task_name="serbian_evals:mmlu_filozofija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_PHILOSOPHY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_world_religions = create_task_config( - task_name="serbian_evals:mmlu_svetska_religija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_WORLD_RELIGIONS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -# ============================================ -# ====== MMLU (High School Level Tasks) ====== -# ============================================ - -mmlu_high_school_biology = create_task_config( - task_name="serbian_evals:mmlu_srednja_skola_biologija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_BIOLOGY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_high_school_chemistry = create_task_config( - task_name="serbian_evals:mmlu_srednja_skola_hemija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_CHEMISTRY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_high_school_computer_science = create_task_config( - task_name="serbian_evals:mmlu_srednja_skola_racunari", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_COMPUTER_SCIENCE.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_high_school_european_history = create_task_config( - task_name="serbian_evals:mmlu_srednja_skola_istorija_evrope", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_EURO_HISTORY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_high_school_geography = create_task_config( - task_name="serbian_evals:mmlu_srednja_skola_geografija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_GEOGRAPHY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_high_school_mathematics = create_task_config( - task_name="serbian_evals:mmlu_srednja_skola_matematika", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_MATHEMATICS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_high_school_microeconomics = create_task_config( - task_name="serbian_evals:mmlu_srednja_skola_mikroekonomija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_MICROECONOMICS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_high_school_physics = create_task_config( - task_name="serbian_evals:mmlu_srednja_skola_fizika", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_PHYSICS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_high_school_psychology = create_task_config( - task_name="serbian_evals:mmlu_srednja_skola_psihologija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_PSYCHOLOGY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_high_school_statistics = create_task_config( - task_name="serbian_evals:mmlu_srednja_skola_statistika", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_STATISTICS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_high_school_world_history = create_task_config( - task_name="serbian_evals:mmlu_srednja_skola_svetska_istorija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_WORLD_HISTORY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -# ============================================ -# ============ MMLU (Math, Logic) ============ -# ============================================ - -mmlu_abstract_algebra = create_task_config( - task_name="serbian_evals:mmlu_abstract_algebra", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_ABSTRACT_ALGEBRA.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_elementary_mathematics = create_task_config( - task_name="serbian_evals:mmlu_osnovna_matematika", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_ELEMENTARY_MATHEMATICS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_formal_logic = create_task_config( - task_name="serbian_evals:mmlu_formalna_logika", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_FORMAL_LOGIC.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_conceptual_physics = create_task_config( - task_name="serbian_evals:mmlu_konceptualna_fizika", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_CONCEPTUAL_PHYSICS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_econometrics = create_task_config( - task_name="serbian_evals:mmlu_metrika_ekonomije", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_ECONOMETRICS.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_machine_learning = create_task_config( - task_name="serbian_evals:mmlu_masinsko_ucenje", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_MACHINE_LEARNING.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -# ============================================ -# ========== MMLU (Social Sciences) ========== -# ============================================ - -mmlu_global_facts = create_task_config( - task_name="serbian_evals:mmlu_globalne_cinjenice", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_GLOBAL_FACT.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_logical_fallacies = create_task_config( - task_name="serbian_evals:mmlu_logicke_zablude", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_LOGICAL_FALLACIES.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_sociology = create_task_config( - task_name="serbian_evals:mmlu_sociologija", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_SOCIOLOGY.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -mmlu_human_aging = create_task_config( - task_name="serbian_evals:mmlu_human_aging", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.MMLU_HUMAN_AGING.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -# ============================================ -# ===== Question Answering and Knowledge ===== -# ============================================ - -boolq = create_task_config( - task_name="serbian_evals:boolq", - prompt_function=boolq_serbian, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.BOOLQ.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - -openbook_qa = create_task_config( - task_name="serbian_evals:openbook", - prompt_function=serbian_eval_prompt, - hf_repo=HFSubsets.HF_BASE_REPO.value, - hf_subset=HFSubsets.OPENBOOK.value, - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], -) - - -TASKS_TABLE = [ - arc_easy, - arc_challenge, - boolq, - hellaswag, - openbook_qa, - piqa, - oz_eval, - winogrande, - mmlu_abstract_algebra, - mmlu_anatomy, - mmlu_astronomy, - mmlu_business_ethics, - mmlu_clinical_knowledge, - mmlu_college_biology, - mmlu_college_chemistry, - mmlu_college_computer_science, - mmlu_college_mathematics, - mmlu_college_medicine, - mmlu_college_physics, - mmlu_computer_security, - mmlu_conceptual_physics, - mmlu_econometrics, - mmlu_electrical_engineering, - mmlu_elementary_mathematics, - mmlu_formal_logic, - mmlu_global_facts, - mmlu_high_school_biology, - mmlu_high_school_chemistry, - mmlu_high_school_computer_science, - mmlu_high_school_european_history, - mmlu_high_school_geography, - mmlu_high_school_mathematics, - mmlu_high_school_microeconomics, - mmlu_high_school_physics, - mmlu_high_school_psychology, - mmlu_high_school_statistics, - mmlu_high_school_world_history, - mmlu_human_aging, - mmlu_logical_fallacies, - mmlu_marketing, - mmlu_machine_learning, - mmlu_management, - mmlu_moral_disputes, - mmlu_miscellaneous, - mmlu_moral_scenarios, - mmlu_sociology, - mmlu_philosophy, - mmlu_world_religions, - mmlu_all, -] diff --git a/community_tasks/slr_bench_evals.py b/community_tasks/slr_bench_evals.py deleted file mode 100644 index b6d60ff43..000000000 --- a/community_tasks/slr_bench_evals.py +++ /dev/null @@ -1,125 +0,0 @@ -# MIT License - -# Copyright (c) 2025 Lukas Helff - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -""" -SLR-Bench is a large-scale benchmark for scalable logical reasoning with language models, comprising 19,000 prompts organized into 20 curriculum levels. -The tasks progressively increase in relational, arithmetic, and recursive complexity, requiring models to synthesize Prolog rules that classify train compositions. -For more details see: https://huggingface.co/datasets/AIML-TUDA/SLR-Bench -The paper can be found here: https://arxiv.org/abs/2506.15787 -Before using this task, please ensure that SWI-Prolog and evaluate are installed on your system, as they are required for symbolic verification of the generated Prolog programs. -""" - -import logging -import shutil - -import numpy as np -from evaluate import load - -from lighteval.metrics.utils.metric_utils import SampleLevelComputation, SampleLevelMetric -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc, SamplingMethod - - -logger = logging.getLogger(__name__) - - -# Check for SWI-Prolog installation -if shutil.which("swipl") is None: - raise ImportError( - "SWI-Prolog (swipl) is not installed or not in PATH. " - "Please install SWI-Prolog to use this task. " - "You can install required dependencies with: pip install -r community_tasks/slr_bench_requirements.txt" - ) - -# Load the symbolic judge for evaluating Prolog programs -symbolic_judge = load("AIML-TUDA/VerifiableRewardsForScalableLogicalReasoning") - - -def prompt_fn(line: dict, task_name: str): - """Defines how to go from a dataset line to a doc object.""" - return Doc( - task_name=task_name, query=line["prompt"], choices=[str(line.get("validation program", ""))], gold_index=0 - ) - - -class VerifiableRewardMetric(SampleLevelComputation): - def compute(self, doc, model_response, **kwargs): - try: - prediction = model_response.final_text[0] - validation_program = doc.choices[0] if doc.choices else "" - ref_format = [ - { - "validation_program": validation_program, - "evaluation_config": {"positive_predicate": "eastbound", "negative_predicate": "westbound"}, - } - ] - - results = symbolic_judge.compute(predictions=[prediction], references=ref_format) - return results["accuracy"] - - except Exception as e: - logger.error("Error during the computation of the metric") - raise RuntimeError(f"Failed to compute verifiable reward metric: {e}") - - -custom_metric = SampleLevelMetric( - metric_name="verifiable_reward", - higher_is_better=True, - category=SamplingMethod.GENERATIVE, - sample_level_fn=VerifiableRewardMetric(), - corpus_level_fn=np.mean, -) - -# Define the subsets available in the SLR-Bench dataset -CONFIGURATIONS = ["All", "Basic", "Easy", "Medium", "Hard"] - - -class SLRBenchTask(LightevalTaskConfig): - """Task configuration for SLR-Bench evaluation.""" - - def __init__( - self, - config: str, - ): - name = f"slr_bench_{config.lower()}" - super().__init__( - name=name, - hf_subset=f"v1-{config}", - prompt_function=prompt_fn, - hf_repo="AIML-TUDA/SLR-Bench", - metrics=[custom_metric], - hf_avail_splits=["train", "validation", "test"], - evaluation_splits=["test"], - few_shots_split="train", - few_shots_select="random_sampling_from_train", - suite=["community"], - generation_size=4096, - stop_sequence=None, - version=1, - ) - - -# Create a single task instance for each configuration -TASKS = [SLRBenchTask(config) for config in CONFIGURATIONS] - -# Export tasks table -TASKS_TABLE = TASKS diff --git a/community_tasks/slr_bench_requirements.txt b/community_tasks/slr_bench_requirements.txt deleted file mode 100644 index 57953d68e..000000000 --- a/community_tasks/slr_bench_requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -evaluate -swipl diff --git a/community_tasks/turkic_evals.py b/community_tasks/turkic_evals.py deleted file mode 100644 index 242b25f81..000000000 --- a/community_tasks/turkic_evals.py +++ /dev/null @@ -1,140 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# ruff: noqa: F405, F403, F401 -""" -Task to evaluate LLMs on TUMLU-mini benchmark: https://huggingface.co/datasets/jafarisbarov/TUMLU-mini - -For more details, see the associated paper: - -@misc{isbarov2025tumluunifiednativelanguage, - title={{TUMLU: A Unified and Native Language Understanding Benchmark for Turkic Languages}}, - author={Jafar Isbarov and Arofat Akhundjanova and Mammad Hajili and Kavsar Huseynova and Dmitry Gaynullin and Anar Rzayev and Osman Tursun and Ilshat Saetov and Rinat Kharisov and Saule Belginova and Ariana Kenbayeva and Amina Alisheva and Aizirek Turdubaeva and Abdullatif Köksal and Samir Rustamov and Duygu Ataman}, - year={2025}, - eprint={2502.11020}, - archivePrefix={arXiv}, - primaryClass={cs.CL}, - url={https://arxiv.org/abs/2502.11020}, -} -""" - -from functools import partial - -from lighteval.metrics.metrics import Metrics -from lighteval.metrics.normalizations import LogProbCharNorm -from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.requests import Doc - - -# TUMLU -# fmt: off -TUMLU_SUBSETS = [ - "azerbaijani", - "crimean-tatar", - "karakalpak", - "kazakh", - "tatar", - "turkish", - "uyghur", - "uzbek", - "kyrgyz" -] -# fmt: on - -INSTRUCTION_BY_LANGUAGE = { - "azerbaijani": "Aşağıdakı sual çoxvariantlı sualdır. Düzgün cavabı seçin:\n\n", - "crimean-tatar": "Aşağıdaki sual çoqtan-çoq cevaplı sualdir. Doğru cevapnı seçip alıñız:\n\n", - "karakalpak": "Tómendegi soraw kóp tańlawlı soraw Tuwrı juwaptı saylań:\n\n", - "kazakh": "Төмендегі сұрақ көп таңдау мүмкіндігі бар сұрақ. Дұрыс жауапты таңдаңыз:\n\n", - "tatar": "Түбәндәге сорау - күп сорау. Дөрес җавапны сайлагыз:\n\n", - "turkish": "Aşağıdaki soru çoktan seçmeli bir sorudur. Doğru cevabı seçin:\n\n", - "uyghur": "تۆۋەندىكى سوئال كۆپ تاللاش سوئالى. توغرا جاۋابنى تاللاڭ:\n\n", - "uzbek": "Quyidagi savol tanlovli savoldir. To‘g‘ri javobni tanlang:\n\n", - "kyrgyz": "Төмөнкү суроо бир нече варианттуу суроо. Туура жоопту тандаңыз:\n\n", -} - -ANSWER_BY_LANGUAGE = { - "uzbek": "Javob:", - "uzbek-cyrillic": "Жавоб", - "crimean-tatar": "Cevap:", - "crimean-tatar-cyrillic": "Джевап", - "tatar": "Җавап:", - "kazakh": "Жауап:", - "kazakh-latin": "Jawap", - "karakalpak": "Juwap:", - "kyrgyz": "Жооп:", - "turkish": "Cevap:", - "uyghur": "جاۋاب:", - "uyghur-latin": "Jawab:", - "azerbaijani": "Cavab:", -} - - -def tumlu_pfn(line, task_name: str = None, language: str = None): - instruction = INSTRUCTION_BY_LANGUAGE[language] - - # Create a list of valid choices with corresponding keys - choices = line.get("choices") - valid_keys = ["A", "B", "C", "D", "E"][: len(choices)] - - answer_index = valid_keys.index(line.get("answer")) - - # Construct the query - query = f"{instruction}{line['question']}\n" - query += "".join([f"{key}. {choice}\n" for key, choice in zip(valid_keys, choices)]) - query += ANSWER_BY_LANGUAGE[language] - - return Doc( - task_name=task_name, - query=query, - choices=valid_keys, # Return only valid choices - gold_index=answer_index, # Correct index - instruction=instruction, - ) - - -class CustomTUMLUTask(LightevalTaskConfig): - def __init__( - self, - name, - hf_subset, - ): - super().__init__( - name=name, - hf_subset=hf_subset, - prompt_function=partial(tumlu_pfn, language=hf_subset), - hf_repo="jafarisbarov/TUMLU-mini", - metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], - hf_avail_splits=["test", "dev"], - evaluation_splits=["test"], - few_shots_split=["dev"], - few_shots_select="sequential", - suite=["community"], - generation_size=-1, - stop_sequence=None, - version=0, - ) - - -TUMLU_TASKS = [CustomTUMLUTask(name=f"tumlu:{subset}", hf_subset=subset) for subset in TUMLU_SUBSETS] - -TASKS_TABLE = TUMLU_TASKS diff --git a/docs/source/adding-a-custom-task.mdx b/docs/source/adding-a-custom-task.mdx index a97a0fd42..52e6d4aa2 100644 --- a/docs/source/adding-a-custom-task.mdx +++ b/docs/source/adding-a-custom-task.mdx @@ -2,37 +2,17 @@ Lighteval provides a flexible framework for creating custom evaluation tasks. This guide explains how to create and integrate new tasks into the evaluation system. -## Task Categories - -Before creating a custom task, consider which category it belongs to: - -### Core Evaluations -Core evaluations are evaluations that only require standard logic in their -metrics and processing, and that we will add to our test suite to ensure non-regression through time. They already see high usage in the community. - -### Extended Evaluations -Extended evaluations are evaluations that require custom logic in their -metrics (complex normalization, an LLM as a judge, etc.), that we added to -facilitate the life of users. They already see high usage in the community. - -### Community Evaluations -Community evaluations are submissions by the community of new tasks. - -A popular community evaluation can move to become an extended or core evaluation over time. - -> [!TIP] -> You can find examples of custom tasks in the [community_tasks](https://github.com/huggingface/lighteval/tree/main/community_tasks) directory. - -## Step-by-Step Creation of a Custom Task +## Step-by-Step Creation of a Task > [!WARNING] -> To contribute your custom task to the Lighteval repository, you would first need +> To contribute your task to the Lighteval repository, you would first need > to install the required dev dependencies by running `pip install -e .[dev]` > and then run `pre-commit install` to install the pre-commit hooks. ### Step 1: Create the Task File -First, create a Python file under the `community_tasks` directory. +First, create a Python file or directory under the `src/lighteval/tasks/tasks` directory. +A directory is helpfull if you need to split your file into multiple ones, just make sure to have one of the file named `main.py`. ### Step 2: Define the Prompt Function @@ -135,12 +115,12 @@ class CustomSubsetTask(LightevalTaskConfig): evaluation_splits=["test"], few_shots_split="train", few_shots_select="random_sampling_from_train", - suite=["community"], + suite=["lighteval"], generation_size=256, stop_sequence=["\n", "Question:"], ) -SUBSET_TASKS = [CustomSubsetTask(name=f"mytask:{subset}", hf_subset=subset) for subset in SAMPLE_SUBSETS] +SUBSET_TASKS = [CustomSubsetTask(name=f"task:{subset}", hf_subset=subset) for subset in SAMPLE_SUBSETS] ``` ### Step 5: Add Tasks to the Table @@ -169,7 +149,7 @@ Once your file is created, you can run the evaluation with the following command ```bash lighteval accelerate \ "model_name=HuggingFaceH4/zephyr-7b-beta" \ - "community|{custom_task}|{fewshots}" \ + "lighteval|{task}|{fewshots}" \ --custom-tasks {path_to_your_custom_task_file} ``` @@ -179,12 +159,12 @@ lighteval accelerate \ # Run a custom task with zero-shot evaluation lighteval accelerate \ "model_name=openai-community/gpt2" \ - "community|myothertask|0" \ + "lighteval|myothertask|0" \ --custom-tasks community_tasks/my_custom_task.py # Run a custom task with few-shot evaluation lighteval accelerate \ "model_name=openai-community/gpt2" \ - "community|myothertask|3" \ + "lighteval|myothertask|3" \ --custom-tasks community_tasks/my_custom_task.py ``` diff --git a/docs/source/available-tasks.mdx b/docs/source/available-tasks.mdx index 2acb4ef95..65c7454cc 100644 --- a/docs/source/available-tasks.mdx +++ b/docs/source/available-tasks.mdx @@ -1,6 +1,14 @@ # Available Tasks -## Discovering Available Tasks + + + + ### List All Tasks @@ -10,8 +18,6 @@ You can get a list of all available tasks by running: lighteval tasks list ``` -This command will display all tasks organized by their suites (e.g., leaderboard, lighteval, community). - ### Inspect Specific Tasks You can inspect a specific task to see its configuration, metrics, and requirements by running: diff --git a/pyproject.toml b/pyproject.toml index c75af3b95..8ce602db9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -108,7 +108,8 @@ extended_tasks = [ "langdetect", # ifeval "openai>1.87", # llm as a judge using openai models "tiktoken", - "emoji", "spacy", "syllapy" # ifbench + "emoji", "spacy", "syllapy", # ifbench + "evaluate", "pyswip", # slr_bench ] s3 = ["s3fs"] multilingual = [ diff --git a/src/lighteval/main_tasks.py b/src/lighteval/main_tasks.py index 62f1129f4..226f6a463 100644 --- a/src/lighteval/main_tasks.py +++ b/src/lighteval/main_tasks.py @@ -46,7 +46,7 @@ def inspect( from lighteval.tasks.registry import Registry - registry = Registry(custom_tasks=custom_tasks, load_community=True, load_extended=True, load_multilingual=True) + registry = Registry(custom_tasks=custom_tasks, load_multilingual=True) # Loading task task_dict = registry.load_tasks() @@ -75,7 +75,7 @@ def list( """List all tasks""" from lighteval.tasks.registry import Registry - registry = Registry(custom_tasks=custom_tasks, load_community=True, load_extended=True, load_multilingual=True) + registry = Registry(custom_tasks=custom_tasks, load_multilingual=True) registry.print_all_tasks(suites=suites) diff --git a/src/lighteval/tasks/tasks/winogrande.py b/src/lighteval/tasks/tasks/winogrande.py index c12bed1bf..bcc49899b 100644 --- a/src/lighteval/tasks/tasks/winogrande.py +++ b/src/lighteval/tasks/tasks/winogrande.py @@ -29,7 +29,7 @@ winogrande = LightevalTaskConfig( name="winogrande", - suite=["leaderboard"], + suite=["lighteval"], prompt_function=prompt.winogrande, hf_repo="allenai/winogrande", hf_subset="winogrande_xl", From 5445f5c098cf863ee0236363f5e2c02cfe86e1eb Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 14:15:35 +0200 Subject: [PATCH 25/43] move community tasks to default tasks and update doc --- .../tasks/multilingual/tasks/arabic.py | 1041 +++++++++++++++++ .../tasks/multilingual/tasks/filipino.py | 790 +++++++++++++ .../tasks/multilingual/tasks/french.py | 137 +++ .../tasks/multilingual/tasks/german_rag.py | 211 ++++ src/lighteval/tasks/multilingual/tasks/oz.py | 77 ++ .../tasks/multilingual/tasks/serbian_eval.py | 767 ++++++++++++ .../tasks/multilingual/tasks/turkic.py | 122 ++ 7 files changed, 3145 insertions(+) create mode 100644 src/lighteval/tasks/multilingual/tasks/arabic.py create mode 100644 src/lighteval/tasks/multilingual/tasks/filipino.py create mode 100644 src/lighteval/tasks/multilingual/tasks/french.py create mode 100644 src/lighteval/tasks/multilingual/tasks/german_rag.py create mode 100644 src/lighteval/tasks/multilingual/tasks/oz.py create mode 100644 src/lighteval/tasks/multilingual/tasks/serbian_eval.py create mode 100644 src/lighteval/tasks/multilingual/tasks/turkic.py diff --git a/src/lighteval/tasks/multilingual/tasks/arabic.py b/src/lighteval/tasks/multilingual/tasks/arabic.py new file mode 100644 index 000000000..c85d2ecbd --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/arabic.py @@ -0,0 +1,1041 @@ +""" +name: +Arabic Evals + +dataset: +MBZUAI/ArabicMMLU, MBZUAI/human_translated_arabic_mmlu, OALL/Arabic_MMLU, OALL/ACVA, asas-ai/AraTrust-categorized + +abstract: +Collection of benchmarks for Arabic language. + +languages: +arabic + +tags: +knowledge, multilingual, multiple-choice + +paper: +""" + +import random +import re +from typing import Any, Dict, List, Optional, Union + +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm +from lighteval.metrics.utils.llm_as_judge import JudgeLM +from lighteval.metrics.utils.metric_utils import Metric +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc, SamplingMethod + + +# fmt: off +LETTER_INDICES_AR = ["أ", "ب", "ج", "د", "هـ", "و", "ز", "ح", "ط", "ي", "ك", "ل", "م", "ن", "س", "ع", "ف", "ص", "ق", "ر", "ش", "ت", "ث", "خ", "ذ", "ض", "ظ", "غ"] +# fmt: on + +# ArabicMMLU +# fmt: off +ARABIC_MMLU_SUBSETS = [ + "All", "Islamic Studies", "Islamic Studies (Middle School)", "Islamic Studies (Primary School)", "Islamic Studies (High School)", "Driving Test", + "Natural Science (Middle School)", "Natural Science (Primary School)", "History (Middle School)", "History (Primary School)", "History (High School)", "General Knowledge", + "General Knowledge (Middle School)", "General Knowledge (Primary School)", "Law (Professional)", "Physics (High School)", "Social Science (Middle School)", + "Social Science (Primary School)", "Management (University)", "Arabic Language (Middle School)", "Arabic Language (Primary School)", "Arabic Language (High School)", "Political Science (University)", + "Philosophy (High School)", "Accounting (University)", "Computer Science (Middle School)", "Computer Science (Primary School)", "Computer Science (High School)", "Computer Science (University)", + "Geography (Middle School)", "Geography (Primary School)", "Geography (High School)", "Math (Primary School)", "Biology (High School)", "Economics (Middle School)", + "Economics (High School)", "Economics (University)", "Arabic Language (General)", "Arabic Language (Grammar)", "Civics (Middle School)", "Civics (High School)" +] +# fmt: on + + +def arabic_mmlu_pfn(line, task_name: str = None): + instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة:\n\n" + + # Define the mapping from Latin to Arabic letters + latin_to_arabic = {"A": "أ", "B": "ب", "C": "ج", "D": "د", "E": "هـ"} + + # Create a list of valid choices with corresponding Arabic keys + choices = [] + valid_keys_latin = [] + valid_keys_arabic = [] + + # Enumerate through the options and append the valid ones + for idx, key in enumerate(["A", "B", "C", "D", "E"]): + option = line.get(f"Option {idx + 1}") + if option: # Check if option is not null + choices.append(option) + valid_keys_latin.append(key) # Append the Latin key (A, B, C, D, E) + valid_keys_arabic.append(latin_to_arabic[key]) # Append the corresponding Arabic letter + + # Find the correct index for the answer key in the Arabic version + answer_index = valid_keys_latin.index(line["Answer Key"]) + + # Construct the query with Arabic letters + query = f"{instruction}{line['Question']}\n" + query += "".join([f"{key}. {choice}\n" for key, choice in zip(valid_keys_arabic, choices)]) + query += "الإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=valid_keys_arabic, # Return only valid choices (Arabic keys) + gold_index=answer_index, # Correct index in the valid Arabic keys + instruction=instruction, + ) + + +class CustomArabicMMLUTask(LightevalTaskConfig): + def __init__( + self, + name, + hf_subset, + ): + super().__init__( + name=name, + hf_subset=hf_subset, + prompt_function=arabic_mmlu_pfn, + hf_repo="MBZUAI/ArabicMMLU", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=["dev"], + few_shots_select="sequential", + suite=["community"], + generation_size=-1, + stop_sequence=None, + version=0, + ) + + +ARABIC_MMLU_TASKS = [ + CustomArabicMMLUTask(name=f"arabic_mmlu:{subset}", hf_subset=subset) for subset in ARABIC_MMLU_SUBSETS +] + + +# ARABIC MMLU HT ## +# fmt: off +ARABIC_MMLU_HT_SUBSETS = [ + "abstract_algebra", "anatomy", "astronomy", "business_ethics", "clinical_knowledge", "college_biology", "college_chemistry", "college_computer_science", + "college_mathematics", "college_medicine", "college_physics", "computer_security", "conceptual_physics", "econometrics", "electrical_engineering", + "elementary_mathematics", "formal_logic", "global_facts", "high_school_biology", "high_school_chemistry", "high_school_computer_science", + "high_school_european_history", "high_school_geography", "high_school_government_and_politics", "high_school_macroeconomics", "high_school_mathematics", + "high_school_microeconomics", "high_school_physics", "high_school_psychology", "high_school_statistics", "high_school_us_history", "high_school_world_history", + "human_aging", "human_sexuality", "international_law", "jurisprudence", "logical_fallacies", "machine_learning", "management", "marketing", "medical_genetics", + "miscellaneous", "moral_disputes", "moral_scenarios", "nutrition", "philosophy", "prehistory", "professional_accounting", "professional_law", + "professional_medicine", "professional_psychology", "public_relations", "security_studies", "sociology", "us_foreign_policy", "virology", "world_religions" +] +# fmt: on + + +def arabic_mmlu_ht_pfn(line, task_name: str = None): + instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة:\n\n" + choices = line["choices"] + answer_index = line["answer"] # It is an int reflecting the index of correct answer in line["choices"] + + query = f"{instruction}{line['question']}\n" + query += "".join([f"{idx}. {choice}\n" for idx, choice in enumerate(choices, start=1)]) + query += "الإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=[str(i) for i in range(1, len(choices) + 1)], # List of strings instead of ints + gold_index=answer_index, + instruction=instruction, + ) + + +class CustomArabicMMLUHTTask(LightevalTaskConfig): + def __init__( + self, + name, + hf_subset, + ): + super().__init__( + name=name, + hf_subset=hf_subset, + prompt_function=arabic_mmlu_ht_pfn, + hf_repo="MBZUAI/human_translated_arabic_mmlu", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + suite=["community"], + generation_size=-1, + stop_sequence=None, + version=0, + ) + + +ARABIC_MMLU_HT_TASKS = [ + CustomArabicMMLUHTTask(name=f"arabic_mmlu_ht:{subset}", hf_subset=subset) for subset in ARABIC_MMLU_HT_SUBSETS +] + +# ARABIC MMLU MT ## +# fmt: off +ARABIC_MMLU_MT_SUBSETS = [ + "abstract_algebra", "anatomy", "astronomy", "business_ethics", "clinical_knowledge", "college_biology", "college_chemistry", "college_computer_science", + "college_mathematics", "college_medicine", "college_physics", "computer_security", "conceptual_physics", "econometrics", "electrical_engineering", + "elementary_mathematics", "formal_logic", "global_facts", "high_school_biology", "high_school_chemistry", "high_school_computer_science", + "high_school_european_history", "high_school_geography", "high_school_government_and_politics", "high_school_macroeconomics", "high_school_mathematics", + "high_school_microeconomics", "high_school_physics", "high_school_psychology", "high_school_statistics", "high_school_us_history", "high_school_world_history", + "human_aging", "human_sexuality", "international_law", "jurisprudence", "logical_fallacies", "machine_learning", "management", "marketing", "medical_genetics", + "miscellaneous", "moral_disputes", "moral_scenarios", "nutrition", "philosophy", "prehistory", "professional_accounting", "professional_law", + "professional_medicine", "professional_psychology", "public_relations", "security_studies", "sociology", "us_foreign_policy", "virology", "world_religions" +] +# fmt: on + + +def arabic_mmlu_mt_pfn(line, task_name: str = None): + instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة: أ، ب، ج، أو د... إلخ. \n\n" + choices = [line["A"], line["B"], line["C"], line["D"]] + # Answers are provided with roman letters - we look for the correct index in LETTER_INDICES, + # it will then be applied to arabic letters + answer_index = LETTER_INDICES.index( + line["answer"] + ) # line["answer"] is the correct answer. That's why we need to index it ! + + query = f"{instruction}{line['question']}\n" + query += "".join([f"{key}. {choice}\n" for key, choice in zip(LETTER_INDICES_AR[:4], choices)]) + query += "الإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=LETTER_INDICES_AR[:4], + gold_index=answer_index, + instruction=instruction, + ) + + +class CustomArabicMMLUMTTask(LightevalTaskConfig): + def __init__( + self, + name, + hf_subset, + ): + super().__init__( + name=name, + hf_subset=hf_subset, + prompt_function=arabic_mmlu_mt_pfn, + hf_repo="OALL/Arabic_MMLU", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + hf_avail_splits=["test", "dev"], + evaluation_splits=["test"], + few_shots_split="dev", + few_shots_select="sequential", + suite=["community"], + generation_size=-1, + stop_sequence=None, + version=0, + ) + + +ARABIC_MMLU_MT_TASKS = [ + CustomArabicMMLUMTTask(name=f"arabic_mmlu_mt:{subset}", hf_subset=subset) for subset in ARABIC_MMLU_MT_SUBSETS +] + + +# ACVA ## +# fmt: off +ACVA_SUBSETS = [ + "Algeria", "Ancient_Egypt", "Arab_Empire", "Arabic_Architecture", "Arabic_Art", "Arabic_Astronomy", "Arabic_Calligraphy", "Arabic_Ceremony", + "Arabic_Clothing", "Arabic_Culture", "Arabic_Food", "Arabic_Funeral", "Arabic_Geography", "Arabic_History", "Arabic_Language_Origin", + "Arabic_Literature", "Arabic_Math", "Arabic_Medicine", "Arabic_Music", "Arabic_Ornament", "Arabic_Philosophy", "Arabic_Physics_and_Chemistry", + "Arabic_Wedding", "Bahrain", "Comoros", "Egypt_modern", "InfluenceFromAncientEgypt", "InfluenceFromByzantium", "InfluenceFromChina", + "InfluenceFromGreece", "InfluenceFromIslam", "InfluenceFromPersia", "InfluenceFromRome", "Iraq", "Islam_Education", "Islam_branches_and_schools", + "Islamic_law_system", "Jordan", "Kuwait", "Lebanon", "Libya", "Mauritania", "Mesopotamia_civilization", "Morocco", "Oman", "Palestine", "Qatar", + "Saudi_Arabia", "Somalia", "Sudan", "Syria", "Tunisia", "United_Arab_Emirates", "Yemen", + "communication", "computer_and_phone", "daily_life", "entertainment" +] +# fmt: on + + +def acva_pfn(line, task_name: str = None): + question = line["question"] + answer = line["answer"] + + return Doc( + task_name=task_name, + query=f"السؤال: {question}\nالإجابة:", + choices=["صح", "خطأ"], + gold_index=["صح", "خطأ"].index(answer), + ) + + +class CustomACVATask(LightevalTaskConfig): + def __init__( + self, + name, + hf_subset, + ): + super().__init__( + name=name, + hf_subset=hf_subset, + prompt_function=acva_pfn, + hf_repo="OALL/ACVA", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + suite=["community"], + generation_size=-1, + stop_sequence=None, + version=0, + ) + + +ACVA_TASKS = [CustomACVATask(name=f"acva:{subset}", hf_subset=subset) for subset in ACVA_SUBSETS] + + +# AraTrust ## +# fmt: off +ARATRUST_SUBSETS = [ + "Trustfulness", "MentalHealth", "PhysicalHealth", "Offensive", "Ethics", "Privacy", "Unfairness", "Illegal", +] +# fmt: on + + +def aratrust_pfn(line, task_name: str = None): + instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة: أ، ب أو ج. \n\n" + choices = [line["A"], line["B"], line["C"]] + # Answers are provided with roman letters - we look for the correct index in LETTER_INDICES, + # it will then be applied to arabic letters + answer_index = LETTER_INDICES_AR.index( + line["Answer"] + ) # line["answer"] is the correct answer. That's why we need to index it ! + + query = f"{instruction}{line['Question']}\n" + query += "".join([f"{choice}\n" for choice in choices]) + query += "الإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=LETTER_INDICES_AR[:3], + gold_index=answer_index, + instruction=instruction, + ) + + +class CustomAraTrustTask(LightevalTaskConfig): + def __init__( + self, + name, + hf_subset, + ): + super().__init__( + name=name, + hf_subset=hf_subset, + prompt_function=aratrust_pfn, + hf_repo="asas-ai/AraTrust-categorized", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + suite=["community"], + generation_size=-1, + stop_sequence=None, + version=0, + ) + + +ARATRUST_TASKS = [CustomAraTrustTask(name=f"aratrust:{subset}", hf_subset=subset) for subset in ARATRUST_SUBSETS] + + +def arabic_exams_pfn(line, task_name: str = None): + topic = line["subject"] + question = line["question"] + choices = [line["A"], line["B"], line["C"], line["D"]] + choices_formatted = [f" {LETTER_INDICES_AR[i]}) {choice}\n" for i, choice in enumerate(choices)] + answer = line["answer"] + answer_index = LETTER_INDICES.index(answer) + + instruction = f"الأسئلة التالية هي أسئلة متعددة الإختيارات مع الجواب الصحيح حول {topic.replace('_', ' ')}. \n\n" + query = f"{instruction}السؤال: {question}\n" + query += "\n".join(choices_formatted) + query += "\nالإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=LETTER_INDICES_AR[:4], + gold_index=answer_index, + instruction=instruction, + ) + + +# ARABIC EXAMS ## +arabic_exams_task = LightevalTaskConfig( + name="arabic_exams", + prompt_function=arabic_exams_pfn, + suite=["community"], + hf_repo="OALL/Arabic_EXAMS", + hf_subset="default", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + version=0, +) + + +# ALGHAFA NATIVE ## +# fmt: off +ALGHAFA_SUBSETS = [ + "mcq_exams_test_ar", "meta_ar_dialects", "meta_ar_msa", "multiple_choice_facts_truefalse_balanced_task", "multiple_choice_grounded_statement_soqal_task", + "multiple_choice_grounded_statement_xglue_mlqa_task", "multiple_choice_rating_sentiment_no_neutral_task", "multiple_choice_rating_sentiment_task", + "multiple_choice_sentiment_task" +] +# fmt: on + + +def alghafa_pfn(line, task_name: str = None): + question = line["query"] + answer_index = int(line["label"]) + allowed_keys = [f"sol{i}" for i in range(1, 6)] + extracted_choices = [line[key] for key in allowed_keys if key in line] + choices = [str(i) for i in range(len(extracted_choices))] + + instruction = "الأسئلة التالية هي أسئلة متعددة الإختيارات مع الجواب الصحيح\n\n" + query = f"{instruction}السؤال: {question}\n" + + for index, choice in enumerate(extracted_choices): + query += f"{index}) {choice}\n" + + query += "الإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=choices, + gold_index=answer_index, + instruction=instruction, + ) + + +class CustomAlGhafaNativeTask(LightevalTaskConfig): + def __init__( + self, + name, + hf_subset, + ): + super().__init__( + name=name, + hf_subset=hf_subset, + prompt_function=alghafa_pfn, + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Native", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + suite=["community"], + generation_size=-1, + stop_sequence=None, + version=0, + ) + + +ALGHAFA_TASKS = [CustomAlGhafaNativeTask(name=f"alghafa:{subset}", hf_subset=subset) for subset in ALGHAFA_SUBSETS] + +# ALGHAFA TRANSLATED ## +# race_ar +race_ar_task = LightevalTaskConfig( + name="race_ar", + prompt_function=alghafa_pfn, + suite=["community"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_subset="race_ar", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + version=0, +) + + +# piqa_ar +piqa_ar_task = LightevalTaskConfig( + name="piqa_ar", + prompt_function=alghafa_pfn, + suite=["community"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_subset="piqa_ar", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + version=0, +) + + +# arc_easy_ar +arc_easy_ar_task = LightevalTaskConfig( + name="arc_easy_ar", + prompt_function=alghafa_pfn, + suite=["community"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_subset="arc_easy_ar", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + version=0, +) + + +# arc_challenge_okapi_ar +arc_challenge_okapi_ar_task = LightevalTaskConfig( + name="arc_challenge_okapi_ar", + prompt_function=alghafa_pfn, + suite=["community"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_subset="arc_challenge_okapi_ar", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + version=0, +) + + +# mmlu_okapi_ar +mmlu_okapi_ar_task = LightevalTaskConfig( + name="mmlu_okapi_ar", + prompt_function=alghafa_pfn, + suite=["community"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_subset="mmlu_okapi_ar", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + version=0, +) + + +# openbook_qa_ext_ar +openbook_qa_ext_ar_task = LightevalTaskConfig( + name="openbook_qa_ext_ar", + prompt_function=alghafa_pfn, + suite=["community"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_subset="openbook_qa_ext_ar", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + version=0, +) + + +# boolq_ar +def boolq_arabic_pfn(line, task_name: str = None): + question = line["question"] + passage = line["passage"] + instruction = "بناء على المقطع التالي، أجب عن السؤال ب نعم أو لا" + query = f"""{instruction} + المقطع : + {passage} + السؤال: + {question} + الإجابة: + """ + + return Doc( + task_name=task_name, + query=query, + choices=["نعم", "لا"], + gold_index=0 if line["answer"] else 1, + instruction=instruction, + ) + + +boolq_ar_task = LightevalTaskConfig( + name="boolq_ar", + prompt_function=boolq_arabic_pfn, + suite=["community"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_subset="boolq_ar", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + version=0, +) + + +# copa_ext_ar +def copa_arabic_pfn(line, task_name: str = None): + premise = line["premise"] + choices = [line["choice1"], line["choice2"]] + question_map = {"cause": "لأن", "effect": "لذلك"} + question = question_map[line["question"]] + answer = line["label"] + + query = "{}، {} :\n0) {}\n1) {}\nالإجابة:".format(premise, question, choices[0], choices[1]) + + return Doc( + task_name=task_name, + query=query, + choices=choices, + gold_index=answer, + instruction="", + ) + + +copa_ext_ar_task = LightevalTaskConfig( + name="copa_ext_ar", + prompt_function=copa_arabic_pfn, + suite=["community"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_subset="copa_ext_ar", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + version=0, +) + + +# hellaswag_okapi_ar +def hellaswag_arabic_pfn(line, task_name: str = None): + ctx = re.sub(r"\[.*?\]", "", line["ctx"]) # Remove latin words within brackets + endings = [ + re.sub(r"\[.*?\]", "", e) for e in eval(line["endings"]) + ] # endings is a string representation of a list + answer_index = line["label"] + instruction = "بناء على السياق التالي، اختر النهاية الصحيحة من الاقتراحات التالية" + + query = f"""{instruction} + السياق: + {ctx} + الاقتراحات: + + """ + for i, ending in enumerate(endings): + query += f"{i}) {ending}\n" + query += "الإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=endings, + gold_index=answer_index, + instruction=instruction, + ) + + +hellaswag_okapi_ar_task = LightevalTaskConfig( + name="hellaswag_okapi_ar", + prompt_function=hellaswag_arabic_pfn, + suite=["community"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_subset="hellaswag_okapi_ar", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + version=0, +) + + +# toxigen_ar +def toxigen_arabic_pfn(line, task_name: str = None): + text = line["text"] + label = 1 if ((line["toxicity_ai"] + line["toxicity_human"]) > 5.5) else 0 + instruction = 'هل العبارة التالية تحض على الكراهية؟ قم بالرد بـ"نعم" أو "لا".' + + query = f"""{instruction} + العبارة: + '{text}' + الإجابة: + """ + + return Doc( + task_name=task_name, + query=query, + choices=["لا", "نعم"], + gold_index=label, + instruction=instruction, + ) + + +toxigen_ar_task = LightevalTaskConfig( + name="toxigen_ar", + prompt_function=toxigen_arabic_pfn, + suite=["community"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_subset="toxigen_ar", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + version=0, +) + + +# sciq_ar +def sciq_arabic_pfn(line, task_name: str = None): + support = line["support"] + question = line["question"] + correct_answer = line["correct_answer"] + choices = [line["distractor1"], line["distractor2"], line["distractor3"], correct_answer] + + # Shuffle the choices + random.shuffle(choices) + + answer_index = choices.index(correct_answer) + + instruction = "بناءً على السياق أدناه، اختر الإجابة الصحيحة للسؤال التالي من قائمة الاقتراحات" + + query = f"""{instruction} + السياق: + {support} + السؤال: + {question} + الإجابات المحتملة: + + """ + for i, choice in enumerate(choices): + query += f"{i}) {choice}\n" + query += "الإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=choices, + gold_index=answer_index, + instruction=instruction, + ) + + +sciq_ar_task = LightevalTaskConfig( + name="sciq_ar", + prompt_function=sciq_arabic_pfn, + suite=["community"], + hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", + hf_subset="sciq_ar", + hf_avail_splits=["test", "validation"], + evaluation_splits=["test"], + few_shots_split="validation", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + version=0, +) + + +# madinah_qa +# fmt: off +MADINAH_QA_SUBSETS = ["Arabic Language (General)", "Arabic Language (Grammar)"] +# fmt: on + + +def madinah_qa_pfn(line, task_name: str = None): + instruction = "بناءً على السياق أدناه، اختر الإجابة الصحيحة للسؤال التالي من قائمة الأجوبة:\n\n" + + # Define the mapping from Latin to Arabic letters + latin_to_arabic = {"A": "أ", "B": "ب", "C": "ج", "D": "د", "E": "هـ"} + + # Create a list of valid choices with corresponding Arabic keys + choices = [] + valid_keys_latin = [] + valid_keys_arabic = [] + + # Enumerate through the options and append the valid ones + for idx, key in enumerate(["A", "B", "C", "D", "E"]): + option = line.get(f"Option {idx + 1}") + if option: # Check if option is not null + choices.append(option) + valid_keys_latin.append(key) # Append the Latin key (A, B, C, D, E) + valid_keys_arabic.append(latin_to_arabic[key]) # Append the corresponding Arabic letter + + # Find the correct index for the answer key in the Arabic version + answer_index = valid_keys_latin.index(line["Answer Key"]) + + query = f"{instruction}\nالسياق:\n{line['Context']}\nالسؤال:\n{line['Question']}\n" + query += "".join([f"{key}. {choice}\n" for key, choice in zip(valid_keys_arabic, choices)]) + query += "الإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=valid_keys_arabic, + gold_index=answer_index, # Correct index in the valid keys + instruction=instruction, + ) + + +class CustomMadinahQATask(LightevalTaskConfig): + def __init__( + self, + name, + hf_subset, + ): + super().__init__( + name=name, + hf_subset=hf_subset, + prompt_function=madinah_qa_pfn, + hf_repo="MBZUAI/MadinahQA", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=["dev"], + few_shots_select="sequential", + suite=["community"], + generation_size=-1, + stop_sequence=None, + version=0, + ) + + +MADINAH_QA_TASKS = [ + CustomMadinahQATask(name=f"madinah_qa:{subset}", hf_subset=subset) for subset in MADINAH_QA_SUBSETS +] + + +class JudgeMetricWrapper(Metric): + """Wrapper class for LLM-based judge metric implementation.""" + + def __init__(self, judge: JudgeLM): + """ + Initializes the judge metric wrapper. + + Args: + judge (JudgeLM): The LLM judge instance to use for evaluation. + """ + self.judge = judge + self.metric_name = "llm_as_judge" + self.category = SamplingMethod.GENERATIVE + self.corpus_level_fn = self.aggregate_scores + self.sample_level_fn = self._sample_level_fn + self.higher_is_better = True # Fixed tuple syntax + + def compute(self, responses: list[str], formatted_docs: list[Doc], **kwargs) -> dict[str, float]: + """ + Computes evaluation scores using the judge's evaluate_answer method. + + Args: + responses (list[str]): The predicted answers + formatted_docs (list[Doc]): Documents containing questions and gold answers + kwargs: Additional keyword arguments (not used) + + Returns: + dict[str, float]: Dictionary containing evaluation scores + """ + results = [] + for i, doc in enumerate(formatted_docs): + question = doc.query + gold = doc.choices[doc.gold_index] if doc.gold_index is not None else None + answer = responses[i][0].result[0] + + score, _, _ = self.judge.evaluate_answer(question=question, answer=answer, options=None, gold=gold) + results.append({self.metric_name: score}) + + return results + + def aggregate_scores(self, scores: list[dict]) -> float: + return sum(scores) / len(scores) if scores else 0.0 + + def _sample_level_fn(self): + return None + + +def parse_candidates(candidates: Union[List[str], str]) -> List[str]: + """ + Parses and validates candidate answers from either list or string format. + + Args: + candidates: Either a list of candidate answers or a newline-separated string + + Returns: + List[str]: List of validated candidate answers + + Raises: + ValueError: If candidates cannot be parsed or are empty + """ + try: + if isinstance(candidates, list): + parsed_candidates = [str(c).strip() for c in candidates if c] + else: + parsed_candidates = [c.strip() for c in str(candidates).split("\n") if c.strip()] + + if not parsed_candidates: + raise ValueError("No valid candidates found after parsing") + + return parsed_candidates + except Exception as e: + raise ValueError(f"Failed to parse candidates: {str(e)}") + + +def qa_prompt_arabic(line: Dict[str, Any], task_name: str = None) -> Doc: + """ + Formats the prompt for Arabic question answering with candidates. + + Args: + line: Dictionary containing question and candidate information + task_name: Optional name for the task + + Returns: + Doc: Formatted document for evaluation + + Raises: + ValueError: If required fields are missing or invalid + """ + try: + # Validates and extracts the question + if not isinstance(line.get("question"), str): + raise ValueError("Question must be a string") + question = line["question"] + + # Processes candidate answers + candidates = parse_candidates(line["candidates"]) + + # Validates gold answer + if "gold_answer" not in line: + raise ValueError("Gold answer is required") + gold_answer = str(line["gold_answer"]) + + # Constructs the prompt + instruction = "بناءً على السياقات المقترحة التالية، اجب عن السؤال التالي" + query = f"{instruction}\n\nالسؤال:\n{question}\n\nالسياقات المقترحة:\n{', '.join(candidates)}\n" + + return Doc( + task_name=task_name or "alrage", + query=query, + instruction=instruction, + choices=[gold_answer], # Gold answer is used as the only valid choice + gold_index=0, # Index of the correct answer in choices + ) + except Exception as e: + raise ValueError(f"Failed to create QA prompt: {str(e)}") + + +def judge_template(question: str, answer: str, gold: str, options: Optional[List[str]] = None) -> List[Dict[str, str]]: + """ + Template for the Arabic judge prompt. + + System prompt translation: + You are a neutral expert evaluator. Your tasks are: + 1. Evaluate the answer's accuracy compared to the correct answer + 2. Verify that the answer is supported by the provided context + 3. Evaluate the quality and comprehensiveness of the answer + Rate the answer on a scale from 0 to 10. + + Args: + question: The question being evaluated + answer: The provided answer + gold: The correct answer + options: Optional list of answer choices + + Returns: + List[Dict[str, str]]: Formatted messages for the judge + """ + messages = [ + { + "role": "system", + "content": """أنت مقيّم محايد خبير باللغة العربية. يجب عليك: +1. تقييم دقة الإجابة مقارنة بالإجابة الصحيحة +2. التحقق من أن الإجابة مدعومة بالسياق المقدم +3. تقييم جودة وشمولية الإجابة + +مهم جداً: يجب أن يكون ردك رقماً فقط من 0 إلى 10. لا تضف أي نص أو تفسير.""", + }, + { + "role": "user", + "content": f"""السؤال: {question} + +الإجابة المقدمة: {answer} + +الإجابة الصحيحة: {gold} + +أعط تقييماً من 0 إلى 10: +0-2: إجابة خاطئة تماماً +3-4: إجابة جزئية مع أخطاء +5-6: إجابة متوسطة +7-8: إجابة جيدة +9-10: إجابة ممتازة + +اكتب رقماً فقط من 0 إلى 10 بدون أي نص إضافي:""", + }, + ] + return messages + + +def process_judge_response(response) -> float: + """Process the judge's response to extract the score""" + # If response is a list, extract the content from the user role + if isinstance(response, list): + response_content = " ".join(item["content"] for item in response if item["role"] == "user") + else: + response_content = response # If it's not a list, use it directly + + try: + # Extract the score from the response content + score = float(next(num for num in response_content.split() if num.replace(".", "", 1).isdigit())) + return min(max(score / 10.0, 0.0), 1.0) + except (StopIteration, ValueError): + return 0.0 + + +judge = JudgeLM( + model="Qwen/Qwen2.5-72B-Instruct", + templates=judge_template, + process_judge_response=process_judge_response, + judge_backend="vllm", +) + +wrapped_judge = JudgeMetricWrapper(judge) + +# Task configuration +alrage_qa_task = LightevalTaskConfig( + name="alrage_qa", + prompt_function=qa_prompt_arabic, + suite=["community"], + hf_repo="OALL/ALRAGE", + hf_subset=None, + hf_avail_splits=["train"], + evaluation_splits=["train"], + metrics=[wrapped_judge], + generation_size=200, + stop_sequence=[], + version=0, +) + +TASKS_TABLE = ( + ARABIC_MMLU_TASKS + + ARABIC_MMLU_HT_TASKS + + ARABIC_MMLU_MT_TASKS + + ACVA_TASKS + + ALGHAFA_TASKS + + ARATRUST_TASKS + + MADINAH_QA_TASKS + + [arabic_exams_task] + + [race_ar_task] + + [piqa_ar_task] + + [arc_easy_ar_task] + + [arc_challenge_okapi_ar_task] + + [mmlu_okapi_ar_task] + + [openbook_qa_ext_ar_task] + + [boolq_ar_task] + + [copa_ext_ar_task] + + [hellaswag_okapi_ar_task] + + [toxigen_ar_task] + + [sciq_ar_task] + + [alrage_qa_task] +) diff --git a/src/lighteval/tasks/multilingual/tasks/filipino.py b/src/lighteval/tasks/multilingual/tasks/filipino.py new file mode 100644 index 000000000..66d5ddcd0 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/filipino.py @@ -0,0 +1,790 @@ +""" +name: +Filipino Evals + +dataset: +filbench/filbench-eval + +abstract: +Collection of benchmarks for Filipino language. + +languages: +filipino + +tags: +knowledge, multilingual, multiple-choice + +paper: +https://github.com/filbench/filbench-eval/blob/main/filbench.pdf + +Contact: +- Lester James V. Miranda +- Elyanah Aco +- Conner Manuel +- Jan Christian Blaise Cruz +- Joseph Imperial +""" + +from collections import OrderedDict +from functools import partial +from typing import Any + +from langcodes import Language as LangCodeLanguage +from langcodes import standardize_tag + +from lighteval.metrics.dynamic_metrics import LogLikelihoodAccMetric +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import ( + LogProbCharNorm, + LogProbPMINorm, + LogProbTokenNorm, +) +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.multilingual.tasks import MMLU_SUBSETS +from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation +from lighteval.tasks.requests import Doc +from lighteval.tasks.templates.multichoice import get_mcq_prompt_function +from lighteval.tasks.templates.nli import get_nli_prompt_function +from lighteval.tasks.templates.translation import get_translation_prompt_function +from lighteval.tasks.templates.utils.formulation import ( + CFFormulation, + HybridFormulation, + MCFFormulation, +) +from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro + + +# Balita NLP +FILIPINO_BALITA_TASKS = [ + LightevalTaskConfig( + name=f"balita_tgl_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + language=Language.TAGALOG, + adapter=lambda line: { + "question": "Alin sa mga titlulong nakalista sa ibaba ang pinaka-angkop para sa teksto?", + "context": f"Teksto: {line['title_choice_first_paragraph']}", + "choices": line["title_choices"], + "gold_idx": line["title_choice_gold_idx"], + }, + formulation=formulation, + ), + suite=("community",), + hf_repo="LanceBunag/BalitaNLP", + hf_subset="no-image", + hf_avail_splits=["train", "validation", "test"], + evaluation_splits=("validation", "test"), + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [MCFFormulation(), HybridFormulation()] +] + +# Belebele +FILIPINO_BELEBELE_TASKS = [ + LightevalTaskConfig( + name=f"belebele_{LangCodeLanguage.get(language).to_alpha3()}_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + iso_639_3_ind_to_iso_639_3_macro[LangCodeLanguage.get(language).to_alpha3()], + lambda line: { + "question": line["question"], + "context": line["flores_passage"], + "choices": [line[f"mc_answer{i}"] for i in range(1, 5)], + "gold_idx": int(line["correct_answer_num"]) - 1, + }, + formulation=formulation, + ), + suite=("community",), + hf_repo="facebook/belebele", + hf_subset=language, + evaluation_splits=("test",), + hf_avail_splits=["test"], + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + ) + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] + for language in ["tgl_Latn", "ceb_Latn"] +] + +# CebuaNER +cebuaner_choices = ["PERSON", "ORGANIZATION", "LOCATION", "OTHER"] +cebuaner_answer_idx = ["A", "B", "C", "D"] +question = "Unsa ang ginganlan nga named-entity sa pulong '{entity}' niini nga sentence: {text}" +FILIPINO_CEBUANER_TASKS = [ + LightevalTaskConfig( + name=f"cebuaner_ceb_{formulation.name.lower()}", + hf_subset="default", + prompt_function=get_mcq_prompt_function( + Language.CEBUANO, + lambda line: { + "question": question.format(entity=line["entity"], text=line["text"]), + "choices": cebuaner_choices, + "gold_idx": cebuaner_answer_idx.index(line["answer"]), + }, + formulation=formulation, + ), + hf_repo="UD-Filipino/cebuaner-instruction", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split="test", + few_shots_select="random", + suite=["community"], + generation_size=-1, + trust_dataset=True, + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + version=0, + ) + for formulation in [MCFFormulation(), HybridFormulation()] +] + +# Cebuano Readability +cebuano_readability_choices = ["Grade 1", "Grade 2", "Grade 3"] +cebuano_readability_instruction = """ +Unsa ang angay nga lebel sa grado alang sa mosunod nga teksto? + +Grade 1 - ang teksto mahimong basahon sa usa ka tawo tali sa edad nga 6-7. +Grade 2 - ang teksto mahimong basahon sa usa ka tawo tali sa edad nga 7-8. +Grade 3 - ang teksto mahimong basahon sa usa ka tawo tali sa edad nga 8-9. +""" +FILIPINO_READABILITY_TASKS = [ + LightevalTaskConfig( + name=f"readability_ceb_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + Language.CEBUANO, + lambda line: { + "question": cebuano_readability_instruction + line["text"], + "choices": cebuano_readability_choices, + "gold_idx": cebuano_readability_choices.index(f"Grade {line['label']}"), + }, + formulation=formulation, + ), + suite=("community",), + hf_subset="default", + hf_repo="UD-Filipino/cebuano-readability", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split="test", + few_shots_select="random", + generation_size=-1, + trust_dataset=True, + version=0, + ) + for formulation in [MCFFormulation(), HybridFormulation()] +] + +# Dengue +dengue_filipino_subsets = { + "absent": "pagiging absent", + "dengue": "dengue", + "health": "kalusugan", + "mosquito": "lamok", + "sick": "sakit", +} + + +def filipino_dengue_pfn(line, task_name: str) -> Doc: + subset = task_name.split(":")[-1] + subset_keyword = dengue_filipino_subsets[subset] + + instruction = f"Tungkol ba sa {subset_keyword} ang sumusunod na pangungusap? Piliin ang tamang sagot:\n\n" + choices: dict[str, str] = OrderedDict({"A": "Hindi", "B": "Oo"}) + + answer_index = int(line.get(subset)) + query = f"{instruction}{line['text']}\n" + query += "".join([f"{key}. {choice}\n" for key, choice in choices.items()]) + query += "Sagot:" + return Doc( + task_name=task_name, + query=query, + choices=list(choices.keys()), + gold_index=answer_index, + instruction=instruction, + ) + + +FILIPINO_DENGUE_TASKS = [ + LightevalTaskConfig( + name=f"dengue_filipino_fil:{subset}", + hf_subset="default", + prompt_function=filipino_dengue_pfn, + hf_repo="jcblaise/dengue_filipino", + metrics=[Metrics.loglikelihood_acc_norm], + hf_avail_splits=["train", "test", "validation"], + evaluation_splits=["train"], + few_shots_split="train", + few_shots_select="random", + suite=("community",), + generation_size=-1, + trust_dataset=True, + version=0, + ) + for subset in dengue_filipino_subsets +] + +# FireCS +firecs_choices = ["Negatibo", "Neutral", "Positibo"] + +FILIPINO_FIRECS_TASK = [ + LightevalTaskConfig( + name=f"firecs_fil_{formulation.name.lower()}", + hf_subset="default", + prompt_function=get_mcq_prompt_function( + Language.TAGALOG, + lambda line: { + "question": f"Ano ang damdamin o sentimiyento ng sumusunod na pangungusap: {line['review']}", + "choices": firecs_choices, + "gold_idx": int(line["label"]), + }, + ), + hf_repo="ccosme/FiReCS", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + hf_avail_splits=["train", "test"], + evaluation_splits=["train"], + few_shots_split="train", + few_shots_select="random", + suite=["community"], + generation_size=-1, + trust_dataset=True, + version=0, + ) + for formulation in [MCFFormulation(), HybridFormulation()] +] + +# Global-MMLU (FIl) + +FILIPINO_GLOBAL_MMLU_TASKS = [ + LightevalTaskConfig( + name=f"global_mmlu_{sensitivity_label.lower()}_{language.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + language, + lambda line: { + "question": line["question"], + "choices": [ + line["option_a"], + line["option_b"], + line["option_c"], + line["option_d"], + ], + "gold_idx": LETTER_INDICES.index(line["answer"]), + }, + formulation=formulation, + ), + suite=("community",), + hf_repo="CohereForAI/Global-MMLU", + hf_subset=standardize_tag(language.value), + evaluation_splits=("test",), + few_shots_split="dev", + hf_filter=partial( + lambda subset, sensitivity_label, x: x["subject"].lower() == subset + and ( + sensitivity_label == "ALL" or sensitivity_label in x["cultural_sensitivity_label"].replace("-", "UNK") + ), + subset, + sensitivity_label, + ), + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + ) + for subset in MMLU_SUBSETS + for language in [Language.TAGALOG] + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] + for sensitivity_label in ["ALL", "CA", "CS", "UNK"] +] + +# INCLUDE + +FILIPINO_INCLUDE_TASKS = [ + LightevalTaskConfig( + name=f"include_{language.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + language, + lambda line: { + "question": line["question"], + "choices": [line[f"option_{i}"] for i in ("a", "b", "c", "d")], + "gold_idx": line["answer"], + }, + formulation=formulation, + ), + suite=("community",), + hf_subset="Tagalog", + hf_repo="CohereForAI/include-base-44", + hf_filter=partial(lambda subset, x: x["subject"].replace(" ", "_").lower() == subset, subset), + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split="test", + few_shots_select="random", + generation_size=-1, + trust_dataset=True, + version=0, + ) + for subset in ["culturology", "history", "language", "driving_license"] + for language in [Language.TAGALOG] + for formulation in [MCFFormulation(), HybridFormulation()] +] + +# KALAHI +FILIPINO_KALAHI_TASKS = [ + LightevalTaskConfig( + name=f"kalahi_tgl_{formulation.name.lower()}", + suite=["community"], + prompt_function=get_mcq_prompt_function( + language=Language.TAGALOG, + adapter=lambda line: { + "question": line["prompts"][0]["question"], + "choices": [entry[3:] for entry in line["prompts"][0]["mcq"].split("\n")], + "gold_idx": LETTER_INDICES.index(line["label"]), + }, + formulation=formulation, + ), + hf_repo="aisingapore/cultural_evaluation-kalahi", + hf_subset="default", + evaluation_splits=["tl"], + metrics=[ + LogLikelihoodAccMetric(normalization=None), + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ) + for formulation in [HybridFormulation(), MCFFormulation()] +] + +# NewsPH NLI +FILIPINO_NEWSPH_NLI_TASKS = [ + LightevalTaskConfig( + name=f"newsphnli_fil_{formulation.name.lower()}", + suite=["community"], + prompt_function=get_nli_prompt_function( + language=Language.TAGALOG, + adapter=lambda line: { + "premise": line["premise"], + "hypothesis": line["hypothesis"], + # Since there is no neutral label + "gold_idx": line["label"], + }, + relations=["entailment", "contradiction"], + formulation=formulation, + ), + hf_repo="jcblaise/newsph_nli", + hf_subset="default", + evaluation_splits=["validation"], + few_shots_split="train", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=None), + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + ], + ), + trust_dataset=True, + ) + for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] +] + +# NTREX-128 +FILIPINO_NTREX_TASK = [ + LightevalTaskConfig( + name=f"ntrex128_{LangCodeLanguage.get(language).to_alpha3()}", + prompt_function=get_translation_prompt_function( + source_language=Language.ENGLISH, + target_language=iso_639_3_ind_to_iso_639_3_macro[LangCodeLanguage.get(language).to_alpha3()], + adapter=lambda line: { + "source_text": line["eng_Latn"], + "target_text": line[language], + }, + formulation=CFFormulation(), + ), + suite=("community",), + hf_repo="mteb/NTREX", + hf_subset="default", + metrics=[ + Metrics.rougeL, + Metrics.bleu, + Metrics.bleurt, + Metrics.chrf, + Metrics.ter, + ], + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + generation_size=64, + trust_dataset=True, + version=0, + ) + for language in ["fil_Latn"] +] + +# SIB-200 + +sib200_choices = [ + "geography", + "science/technology", + "entertainment", + "travel", + "sports", + "health", + "politics", +] + + +def get_instruction(language: Language) -> str: + if language == Language.CEBUANO: + return "Mahitungod sa unsa ang mosunod nga teksto?\n" + if language == Language.TAGALOG: + return "Tungkol saan ang sumusunod na pangungusap?\n" + + +def create_sib200_task(language: Language, formulation): + return LightevalTaskConfig( + name=f"sib200_{language.value}_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + language, + lambda line: { + "question": get_instruction(language) + line["text"], + "choices": sib200_choices, + "gold_idx": sib200_choices.index(line["category"]), + }, + formulation=formulation, + ), + suite=("community",), + hf_subset=f"{language.value}_Latn", + hf_repo="Davlan/sib200", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + hf_avail_splits=["test", "validation"], + evaluation_splits=["validation"], + few_shots_split="validation", + few_shots_select="random", + generation_size=-1, + trust_dataset=True, + version=0, + ) + + +FILIPINO_SIB_TASKS = [ + create_sib200_task(language, formulation) + for language in [Language.TAGALOG, Language.CEBUANO] + for formulation in [MCFFormulation(), HybridFormulation()] +] + + +def prepare_stingray_correctness(line: dict[str, str]) -> dict[str, Any]: + # lang2 is Tagalog + word = line["word"] + sentence = line["lang2_sentence"] + question = f"Is the usage of {word} in this sentence correct? \n{sentence}" + choices = ["Yes", "No"] + gold_idx = choices.index(line["usage_correctness_lang2_answer"]) + return {"question": question, "choices": choices, "gold_idx": gold_idx} + + +def prepare_stingray_semantic_appropriateness(line: dict[str, str]) -> dict[str, Any]: + lang1 = line["lang1_sentence"] + lang2 = line["lang2_sentence"] + question = "Which sentence is more semantically appropriate?" + choices = [lang1, lang2, "Both"] + choice_letters = ["A", "B", "C"] + gold_idx = choice_letters.index(line["semantic_appropriate_answer"]) + return {"question": question, "choices": choices, "gold_idx": gold_idx} + + +FILIPINO_STINGRAY_CORRECTNESS_TASKS = [ + LightevalTaskConfig( + name=f"stingraybench_correctness_tgl_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + Language.ENGLISH, # the orig instruction is in English, so we replicate it. + adapter=prepare_stingray_correctness, + formulation=formulation, + ), + suite=("community",), + hf_subset="id_tl", + hf_repo="StingrayBench/StingrayBench", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split="test", + few_shots_select="random", + generation_size=-1, + trust_dataset=True, + version=0, + ) + for formulation in [MCFFormulation(), HybridFormulation()] +] + +FILIPINO_STINGRAY_SEMANTIC_TASKS = [ + LightevalTaskConfig( + name=f"stingraybench_semantic_appropriateness_tgl_{formulation.name.lower()}", + prompt_function=get_mcq_prompt_function( + Language.ENGLISH, # the orig instruction is in English, so we replicate it. + adapter=prepare_stingray_semantic_appropriateness, + formulation=formulation, + ), + suite=("community",), + hf_subset="id_tl", + hf_repo="StingrayBench/StingrayBench", + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split="test", + few_shots_select="random", + generation_size=-1, + trust_dataset=True, + version=0, + ) + for formulation in [MCFFormulation(), HybridFormulation()] +] + +FILIPINO_STINGRAY_TASKS = FILIPINO_STINGRAY_SEMANTIC_TASKS + FILIPINO_STINGRAY_CORRECTNESS_TASKS + +# Tatoeba +# We follow the original translation direction from tatoeba +lang_dict = { + "ceb": { + "subset": "ceb-eng", + "source_language": Language.CEBUANO, + "target_language": Language.ENGLISH, + }, + "tgl": { + "subset": "eng-tgl", + "source_language": Language.ENGLISH, + "target_language": Language.TAGALOG, + }, +} + +FILIPINO_TATOEBA_TASKS = [ + LightevalTaskConfig( + name=f"tatoeba_{language}", + prompt_function=get_translation_prompt_function( + source_language=meta.get("source_language"), + target_language=meta.get("target_language"), + adapter=lambda line: { + "source_text": line["sourceString"], + "target_text": line["targetString"], + }, + formulation=CFFormulation(), + ), + suite=("community",), + hf_repo="Helsinki-NLP/tatoeba_mt", + hf_subset=meta.get("subset"), + metrics=[ + Metrics.rougeL, + Metrics.bleu, + Metrics.bleurt, + Metrics.chrf, + Metrics.ter, + ], + hf_avail_splits=["test"], + evaluation_splits=["test"], + trust_dataset=True, + generation_size=64, + ) + for language, meta in lang_dict.items() +] + +# TICO-19 +FILIPINO_TICO19_TASKS = [ + LightevalTaskConfig( + name="tico19_tgl", + prompt_function=get_translation_prompt_function( + source_language=Language.ENGLISH, + target_language=Language.TAGALOG, + adapter=lambda line: { + "source_text": line["sourceString"], + "target_text": line["targetString"], + }, + formulation=CFFormulation(), + ), + suite=("community",), + hf_repo="gmnlp/tico19", + hf_subset="en-tl", + metrics=[ + Metrics.rougeL, + Metrics.bleu, + Metrics.bleurt, + Metrics.chrf, + Metrics.ter, + ], + hf_avail_splits=["test", "validation"], + evaluation_splits=["validation"], + few_shots_split=["validation"], + few_shots_select="random", + trust_dataset=True, + generation_size=64, + ) +] + +# TLUnified-NER +tlunified_ner_choices = ["PERSON", "ORGANIZATION", "LOCATION"] +tlunified_ner_answer_idx = ["A", "B", "C"] + +FILIPINO_TLUNIFIED_NER_TASK = [ + LightevalTaskConfig( + name=f"tlunifiedner_tgl_{formulation.name.lower()}", + hf_subset="instruction", + prompt_function=get_mcq_prompt_function( + Language.TAGALOG, + lambda line: { + "question": f"Ano ang named-entity ng salitang '{line['entity']}' sa pangungusap na ito: {line['text']}", + "choices": tlunified_ner_choices, + "gold_idx": tlunified_ner_answer_idx.index(line["answer"]), + }, + formulation=formulation, + ), + hf_repo="ljvmiranda921/tlunified-ner", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split="test", + few_shots_select="random", + suite=["community"], + generation_size=-1, + trust_dataset=True, + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + version=0, + ) + for formulation in [MCFFormulation(), HybridFormulation()] +] + +# Universal NER +universalner_choices = ["PERSON", "ORGANIZATION", "LOCATION"] +universalner_answer_idx = ["A", "B", "C"] + + +def create_universalner_task(language: Language, formulation): + if language == Language.CEBUANO: + question = "Unsa ang ginganlan nga named-entity sa pulong '{entity}' niini nga sentence: {text}" + if language == Language.TAGALOG: + question = "Ano ang named-entity ng salitang '{entity}' sa pangungusap na ito: {text}" + + return LightevalTaskConfig( + name=f"universalner_{language.value}_{formulation.name.lower()}", + hf_subset=language.value, + prompt_function=get_mcq_prompt_function( + language, + lambda line: { + "question": question.format(entity=line["entity"], text=line["text"]), + "choices": universalner_choices, + "gold_idx": universalner_answer_idx.index(line["answer"]), + }, + formulation=formulation, + ), + hf_repo="UD-Filipino/universalner-instruction", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split="test", + few_shots_select="random", + suite=["community"], + generation_size=-1, + trust_dataset=True, + metrics=get_metrics_for_formulation( + formulation, + [ + LogLikelihoodAccMetric(normalization=LogProbTokenNorm()), + LogLikelihoodAccMetric(normalization=LogProbCharNorm()), + LogLikelihoodAccMetric(normalization=LogProbPMINorm()), + ], + ), + version=0, + ) + + +FILIPINO_UNIVERSALNER_TASKS = [ + create_universalner_task(language, formulation) + for language in [Language.CEBUANO, Language.TAGALOG] + for formulation in [MCFFormulation(), HybridFormulation()] +] + +# Tasks Table + +TASKS_TABLE: list[LightevalTaskConfig] = ( + FILIPINO_BALITA_TASKS + + FILIPINO_BELEBELE_TASKS + + FILIPINO_CEBUANER_TASKS + + FILIPINO_READABILITY_TASKS + + FILIPINO_DENGUE_TASKS + + FILIPINO_FIRECS_TASK + + FILIPINO_GLOBAL_MMLU_TASKS + + FILIPINO_INCLUDE_TASKS + + FILIPINO_KALAHI_TASKS + + FILIPINO_NEWSPH_NLI_TASKS + + FILIPINO_NTREX_TASK + + FILIPINO_SIB_TASKS + + FILIPINO_STINGRAY_TASKS + + FILIPINO_TATOEBA_TASKS + + FILIPINO_TICO19_TASKS + + FILIPINO_TLUNIFIED_NER_TASK + + FILIPINO_UNIVERSALNER_TASKS +) diff --git a/src/lighteval/tasks/multilingual/tasks/french.py b/src/lighteval/tasks/multilingual/tasks/french.py new file mode 100644 index 000000000..e7b3a5a0d --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/french.py @@ -0,0 +1,137 @@ +""" +name: +French Evals + +dataset: +fr-gouv-coordination-ia/IFEval-fr, fr-gouv-coordination-ia/gpqa-fr, fr-gouv-coordination-ia/bac-fr + +abstract: +Collection of benchmarks for the french language. + +languages: +french + +tags: +knowledge, multiple-choice, qa + +paper: +https://huggingface.co/fr-gouv-coordination-ia +""" + +import random + +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import math_normalizer +from lighteval.tasks.default_prompts import LETTER_INDICES +from lighteval.tasks.extended.ifeval.main import ifeval_metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc +from lighteval.utils.utils import as_list + + +# Ifeval-fr prompt function +def prompt_ifeval_fr(line, task_name: str = None): + return Doc( + task_name=task_name, + query=line["prompt"], + choices=[""], + gold_index=0, + instruction="", + specific={"instructions_id_list": line["instruction_id_list"], "kwargs": line["kwargs"]}, + ) + + +# qpqa-fr prompt function +def prompt_gpqa_fr(line, task_name: str = None): + gold_index = random.randint(0, 3) + choices = [line["Réponse incorrecte 1"], line["Réponse incorrecte 2"], line["Réponse incorrecte 3"]] + choices.insert(gold_index, line["Réponse correcte"]) + + instruction = "Choisissez la réponse correcte aux questions suivantes.\n\n" + + query = f"Question: {line['Question']}\n" + query += "".join([f"{key}. {choice}\n" for key, choice in zip(LETTER_INDICES, choices)]) + query += "Réponse: " + return Doc( + task_name=task_name, + query=f"{instruction}{query}", + choices=LETTER_INDICES[: len(choices)], + gold_index=gold_index, + instruction=instruction, + ) + + +# BAC-fr prompt function +def prompt_bac_fr(line, task_name: str = None): + prompt = f"Enoncé: {line['enonce']}\n{line['instruction']}\n" + if line["choix"] is not None: # Multichoice evaluation + # prompt += "\n".join([f"{LETTER_INDICES[ix]}.{choix}" for ix, choix in enumerate(line["choix"])]) + return Doc( + task_name=task_name, + query=prompt, + choices=as_list(line["choix"]), + gold_index=line["choix"].index(line["choix correct"]), + instruction="", + ) + else: + return Doc(task_name=task_name, query=prompt, choices=[line["reponse"]], gold_index=0, instruction="") + + +# IFEVal-fr task + + +ifeval_fr_task = LightevalTaskConfig( + name="ifeval-fr", + prompt_function=prompt_ifeval_fr, # must be defined in the file or imported from src/lighteval/tasks/tasks_prompt_formatting.py + suite=["community"], + hf_repo="fr-gouv-coordination-ia/IFEval-fr", + hf_subset="default", + metrics=[ifeval_metrics], + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split="train", + few_shots_select="random_sampling", + generation_size=1280, + stop_sequence=[], # no stop sequence, will use eot token + version="0.1", # select your metric in Metrics +) + +# GPQA-fr task +gpqa_fr_task = LightevalTaskConfig( + name="gpqa-fr", + suite=["community"], + prompt_function=prompt_gpqa_fr, + hf_repo="fr-gouv-coordination-ia/gpqa-fr", + hf_subset="default", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[Metrics.loglikelihood_acc], + stop_sequence=["\n"], + version=0, +) + +# BAC-fr task +bac_fr_task = LightevalTaskConfig( + name="bac-fr", + suite=["community"], + prompt_function=prompt_bac_fr, + hf_repo="fr-gouv-coordination-ia/bac-fr", + hf_subset="default", + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select="random_sampling", + generation_size=1, + metrics=[ + Metrics.exact_match(sample_params={"normalize_gold": math_normalizer, "normalize_pred": math_normalizer}), + Metrics.exact_match, + ], + stop_sequence=["\n"], + version=0, +) + +# STORE YOUR EVALS +TASKS_TABLE = [ifeval_fr_task, gpqa_fr_task, bac_fr_task] diff --git a/src/lighteval/tasks/multilingual/tasks/german_rag.py b/src/lighteval/tasks/multilingual/tasks/german_rag.py new file mode 100644 index 000000000..06eb398d7 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/german_rag.py @@ -0,0 +1,211 @@ +""" +name: +German RAG Evals + +dataset: +deutsche-telekom/Ger-RAG-eval + +abstract: +Collection of benchmarks for the German language. + +languages: +german + +tags: +knowledge, reasoning, multiple-choice + +paper: +https://huggingface.co/datasets/deutsche-telekom/Ger-RAG-eval +""" + +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc + + +def prompt_fn_choose_question_by_context(line, task_name: str = None): + instruction = "Welche der folgenden Fragen (A oder B oder C oder D) lässt sich anhand des Kontext beantworten?\n\n" + query_template = """\ +Kontext: +{context} + +Fragen: +A: {choice_a} +B: {choice_b} +C: {choice_c} +D: {choice_d} + +Antwort:""" + query = instruction + query_template.format( + context=line["context"], + choice_a=line["choice_a"], + choice_b=line["choice_b"], + choice_c=line["choice_c"], + choice_d=line["choice_d"], + ) + choices = ["A", "B", "C", "D"] + return Doc( + task_name=task_name, + instruction=instruction, + query=query, + choices=choices, + gold_index=choices.index(line["target"]), + ) + + +def prompt_fn_choose_context_by_question(line, task_name: str = None): + instruction = ( + "Auf Basis welcher der folgenden Kontexte (A oder B oder C oder D) lässt sich die Frage beantworten?\n\n" + ) + query_template = """\ +Frage: {question} + +Kontexte: + +A: +{choice_a} + +B: +{choice_b} + +C: +{choice_c} + +D: +{choice_d} + +Antwort:""" + query = instruction + query_template.format( + question=line["question"], + choice_a=line["choice_a"], + choice_b=line["choice_b"], + choice_c=line["choice_c"], + choice_d=line["choice_d"], + ) + choices = ["A", "B", "C", "D"] + return Doc( + task_name=task_name, + instruction=instruction, + query=query, + choices=choices, + gold_index=choices.index(line["target"]), + ) + + +def prompt_fn_question_answer_match(line, task_name: str = None): + instruction = "Beantwortet die Antwort wirklich die Frage? Antworte mit J für ja oder N für nein.\n\n" + query_template = """\ +Die Frage: {question} + +Die Antwort: {answer} + +Auswahl (J/N):""" + query = instruction + query_template.format( + question=line["question"], + answer=line["answer"], + ) + choices = ["J", "N"] + return Doc( + task_name=task_name, + instruction=instruction, + query=query, + choices=choices, + gold_index=choices.index(line["target"]), + ) + + +def prompt_fn_context_question_match(line, task_name: str = None): + instruction = "Lässt sich die Frage mithilfe der Informationen aus dem Kontext beantworten? Antworte mit J für ja oder N für nein.\n\n" + query_template = """\ +Kontext: +{context} + +Die Frage: {question} + +Auswahl (J/N):""" + query = instruction + query_template.format( + question=line["question"], + context=line["context"], + ) + choices = ["J", "N"] + return Doc( + task_name=task_name, + instruction=instruction, + query=query, + choices=choices, + gold_index=choices.index(line["target"]), + ) + + +# Task 1: Choose question by context. +# Given is a context and 4 questions. +# The task is to decide which question can be answered by the context. +task1 = LightevalTaskConfig( + name="german_rag_eval:choose_question_by_context", + prompt_function=prompt_fn_choose_question_by_context, + suite=["community"], + hf_repo="deutsche-telekom/Ger-RAG-eval", + hf_subset="task1", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split="test", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc], + version=1, +) + +# Task 2: Choose context by question. +# Given is a question and 4 contexts. +# The task is to decide which context can answer the question. +task2 = LightevalTaskConfig( + name="german_rag_eval:choose_context_by_question", + prompt_function=prompt_fn_choose_context_by_question, + suite=["community"], + hf_repo="deutsche-telekom/Ger-RAG-eval", + hf_subset="task2", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split="test", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc], + version=1, +) + + +# Task 3: Question-answer match. +# Given is a question and an answer. +# The task is to decide whether the answer actually answers the question. +task3 = LightevalTaskConfig( + name="german_rag_eval:question_answer_match", + prompt_function=prompt_fn_question_answer_match, + suite=["community"], + hf_repo="deutsche-telekom/Ger-RAG-eval", + hf_subset="task3", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split="test", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc], + version=1, +) + +# Task 4: Context-question match. +# Given is a context and a question. +# The task is to decide whether the question can be answered by the context or not. +task4 = LightevalTaskConfig( + name="german_rag_eval:context_question_match", + prompt_function=prompt_fn_context_question_match, + suite=["community"], + hf_repo="deutsche-telekom/Ger-RAG-eval", + hf_subset="task4", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split="test", + few_shots_select="sequential", + metrics=[Metrics.loglikelihood_acc], + version=1, +) + + +# STORE YOUR EVALS +TASKS_TABLE = [task1, task2, task3, task4] diff --git a/src/lighteval/tasks/multilingual/tasks/oz.py b/src/lighteval/tasks/multilingual/tasks/oz.py new file mode 100644 index 000000000..dde7552a1 --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/oz.py @@ -0,0 +1,77 @@ +""" +name: +OZ Serbian Evals + +dataset: +DjMel/oz-eval + +abstract: +OZ Eval (sr. Opšte Znanje Evaluacija) dataset was created for the purposes of +evaluating General Knowledge of LLM models in Serbian language. Data consists +of 1k+ high-quality questions and answers which were used as part of entry exams +at the Faculty of Philosophy and Faculty of Organizational Sciences, University +of Belgrade. The exams test the General Knowledge of students and were used in +the enrollment periods from 2003 to 2024. + +languages: +serbian + +tags: +knowledge, multiple-choice + +paper: +""" + +from lighteval.metrics.metrics import Metrics +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc + + +def prompt_fn_oz_eval_task(line, task_name: str = None): + query_template = """Pitanje: {question}\n + Ponuđeni odgovori: + A. {choice_a} + B. {choice_b} + C. {choice_c} + D. {choice_d} + E. {choice_e} + + Krajnji odgovor:""" + + options = line["options"] + + query = query_template.format( + question=line["questions"], + choice_a=options[0], + choice_b=options[1], + choice_c=options[2], + choice_d=options[3], + choice_e=options[4], + ) + + choices = ["A", "B", "C", "D", "E"] + return Doc( + task_name=task_name, + query=query, + choices=choices, + gold_index=choices.index(line["answer"]), + ) + + +oz_eval_task = LightevalTaskConfig( + name="serbian_evals:oz_task", + prompt_function=prompt_fn_oz_eval_task, + suite=["community"], + hf_repo="DjMel/oz-eval", + hf_subset="default", + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + metrics=[Metrics.loglikelihood_acc], + version=0, +) + + +# STORE YOUR EVALS +TASKS_TABLE = [oz_eval_task] diff --git a/src/lighteval/tasks/multilingual/tasks/serbian_eval.py b/src/lighteval/tasks/multilingual/tasks/serbian_eval.py new file mode 100644 index 000000000..e2df1f57a --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/serbian_eval.py @@ -0,0 +1,767 @@ +""" +name: +Serbian Evals + +dataset: +datatab/serbian-llm-benchmark + +abstract: +The tasks cover a variety of benchmarks, including: standard task like ARC[E][C], +BoolQ, Hellaswag, OpenBookQA,PIQA, Winogrande and a custom OZ Eval. +MMLU is separated by subject and also all in one. + +languages: +serbian + +tags: +knowledge, multiple-choice + +paper: +""" + +from enum import Enum +from typing import List, Optional + +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc + + +class HFSubsets(Enum): + """Enum for all available Hugging Face dataset subsets in Serbian evaluation tasks.""" + + HF_BASE_REPO = "datatab/serbian-llm-benchmark" + HF_REVISION = "209c5b5f999cae5c02eef5735eb817ead18ac214" + + # ARC (AI2 Reasoning Challenge) + ARC_EASY = "arc_easy_serbian" + ARC_CHALLENGE = "arc_challenge_serbian" + # Question Answering and Knowledge + BOOLQ = "boolq_serbian" + OPENBOOK = "openbookq_serbian" + # Commonsense Reasoning + HELLASWAG = "hellaswag_serbian" + PIQA = "piqa_serbian" + WINOGRANDE = "winogrande_serbian" + # Custom/Other Task + OZ_EVAL = "oz_eval_serbian" + # MMLU (Miscellaneous) + MMLU_ANATOMY = "mmlu_anatomija_serbian" + MMLU_ASTRONOMY = "mmlu_astronomija_serbian" + MMLU_BUSINESS_ETHICS = "mmlu_poslovna_etika_serbian" + MMLU_CLINICAL_KNOWLEDGE = "mmlu_kliničko_znanje_serbian" + MMLU_MISCELLANEOUS = "mmlu_miscellaneous_serbian" + MMLU_ELECTRONIC_ENGINEERING = "mmlu_electrical_engineering_serbian" + # MMLU (Business Professional) + MMLU_MARKETING = "mmlu_marketing_serbian" + MMLU_MANAGEMENT = "mmlu_management_serbian" + # MMLU (College Level Tasks) + MMLU_COLLEGE_BIOLOGY = "mmlu_college_biology_serbian" + MMLU_COLLEGE_CHEMISTRY = "mmlu_college_chemistry_serbian" + MMLU_COLLEGE_COMPUTER_SCIENCE = "mmlu_college_computer_science_serbian" + MMLU_COLLEGE_MATHEMATICS = "mmlu_college_mathematics_serbian" + MMLU_COLLEGE_MEDICINE = "mmlu_college_medicine_serbian" + MMLU_COLLEGE_PHYSICS = "mmlu_college_physics_serbian" + MMLU_COLLEGE_COMPUTER_SECURITY = "mmlu_computer_security_serbian" + # MMLU (Ethics, Philosophy) + MMLU_MORAL_DISPUTES = "mmlu_moral_disputes_serbian" + MMLU_MORAL_SCENARIOS = "mmlu_moral_scenarios_serbian" + MMLU_PHILOSOPHY = "mmlu_philosophy_serbian" + MMLU_WORLD_RELIGIONS = "mmlu_world_religions_serbian" + # MMLU (High School Level Tasks) + MMLU_HIGH_SCHOOL_BIOLOGY = "mmlu_high_school_biology_serbian" + MMLU_HIGH_SCHOOL_CHEMISTRY = "mmlu_high_school_chemistry_serbian" + MMLU_HIGH_SCHOOL_COMPUTER_SCIENCE = "mmlu_high_school_computer_science_serbian" + MMLU_HIGH_SCHOOL_EURO_HISTORY = "mmlu_high_school_european_history_serbian" + MMLU_HIGH_SCHOOL_GEOGRAPHY = "mmlu_high_school_geography_serbian" + MMLU_HIGH_SCHOOL_MATHEMATICS = "mmlu_high_school_mathematics_serbian" + MMLU_HIGH_SCHOOL_MICROECONOMICS = "mmlu_high_school_microeconomics_serbian" + MMLU_HIGH_SCHOOL_PHYSICS = "mmlu_high_school_physics_serbian" + MMLU_HIGH_SCHOOL_PSYCHOLOGY = "mmlu_high_school_psychology_serbian" + MMLU_HIGH_SCHOOL_STATISTICS = "mmlu_high_school_statistics_serbian" + MMLU_HIGH_SCHOOL_WORLD_HISTORY = "mmlu_high_school_world_history" + # MMLU (Math, Logic) + MMLU_ABSTRACT_ALGEBRA = "mmlu_abstract_algebra_serbian" + MMLU_ELEMENTARY_MATHEMATICS = "mmlu_osnovna_matematika_serbian" + MMLU_FORMAL_LOGIC = "mmlu_formalna_logika_serbian" + MMLU_CONCEPTUAL_PHYSICS = "mmlu_conceptual_physics_serbian" + MMLU_ECONOMETRICS = "mmlu_econometrics_serbian" + MMLU_MACHINE_LEARNING = "mmlu_machine_learning_serbian" + # MMLU (Social Sciences) + MMLU_GLOBAL_FACT = "mmlu_global_facts_serbian" + MMLU_LOGICAL_FALLACIES = "mmlu_logicke_zablude_serbian" + MMLU_SOCIOLOGY = "mmlu_sociology_serbian" + MMLU_HUMAN_AGING = "mmlu_human_aging_serbian" + # MMLU (All-inclusive Task Entry) + MMLU_SERBIAN_ALL = "mmlu_all_serbian" + + +def prompt_fn_oz_eval_task(line, task_name: str = None): + """ + Prepares a question and answer set in Serbian from the OZ Eval (Opšte Znanje Evaluacija) dataset + for use in a LightEval task. This dataset, specifically designed for evaluating general knowledge + in Serbian, contains questions derived from entrance exams at the University of Belgrade's Faculty + of Philosophy and Faculty of Organizational Sciences, covering enrollment periods from 2003 to 2024. + + The function accepts a dictionary with a question, five answer choices, and a correct answer + designation, returning a structured `Doc` object formatted for LightEval's TASKS_TABLE or TASKS_GROUPS. + + Args: + line (dict): A dictionary with required keys: + - 'query' (str): The main question string. + - 'choices' (list of str): A list containing exactly five answer options. + - 'answer_str' (str): A single character from "A" to "E" representing the correct answer. + task_name (str, optional): An optional string specifying the evaluation task name. + + Returns: + Doc: A structured object for LightEval containing: + - task_name (str): The task name, if provided. + - query (str): Formatted question with embedded answer choices. + - choices (list of str): List of option identifiers ["A", "B", "C", "D", "E"]. + - gold_index (int): Index of the correct answer within the 'choices' list. + + Note: + The OZ Eval dataset is available at https://huggingface.co/datasets/DjMel/oz-eval. + + """ + query_template = """Pitanje: {question}\n + Ponuđeni odgovori: + A. {choice_a} + B. {choice_b} + C. {choice_c} + D. {choice_d} + E. {choice_e} + + Krajnji odgovor:""" + + options = line["choices"] + + query = query_template.format( + question=line["query"], + choice_a=options[0], + choice_b=options[1], + choice_c=options[2], + choice_d=options[3], + choice_e=options[4], + ) + + choices = ["A", "B", "C", "D", "E"] + return Doc( + task_name=task_name, + query=query, + choices=choices, + gold_index=choices.index(line["answer_str"]), + ) + + +def serbian_eval_prompt(line: dict, task_name: Optional[str] = None) -> Doc: + """ + Creates a prompt for a multiple-choice task in Serbian. This function formats the prompt + based on the provided query and choices, handling both standard tasks and MMLU-specific + tasks (if "mmlu" is part of the task name). + + The prompt includes an instruction in Serbian, followed by the query, available choices, + and finally the correct answer. The function determines how to compute the correct answer + based on whether the task name contains "mmlu". + + Args: + line (dict): A dictionary containing the following keys: + - "query" (str): The question or query to present to the user. + - "choices" (list of str): A list of possible answer choices. + - "answer" (int or str): The correct answer, either as an index (for regular tasks) + or as a string (for MMLU tasks). + task_name (Optional[str]): The name of the task. If "mmlu" is in the task name, the + function treats the task as an MMLU task and searches for the correct answer + by matching the string value of the answer. + + Returns: + Doc: A `Doc` object containing the formatted prompt, choices, and the correct answer index. + The `Doc` object includes the following fields: + - task_name (Optional[str]): The name of the task. + - query (str): The formatted query prompt in Serbian, including instructions and choices. + - choices (list of str): The list of available answer choices. + - gold_index (int): The index of the correct answer. + - instruction (str): The instruction shown to the user in Serbian. + """ + + question = line["query"] + choices = line["choices"] + instruction = "Na osnovu sledećeg pitanja, izaberite tačanu opciju iz ponuđenih odgovora.\n" + + # Build the query and determine the gold_index in a single pass + query = f"{instruction}Pitanje: {question}\n\nPonuđeni odgovori:\n" + + gold_index = None + + # ARC is base gold index, but MMLU we handle gold index as + if task_name and "mmlu" in task_name: + correct_answer = str(line["answer"]) + gold_index = next((i for i, choice in enumerate(choices) if correct_answer in choice), None) + else: + gold_index = int(line["answer"]) + + # Show all choises + for i, choice in enumerate(choices): + query += f"{i}. {choice}\n" + + query += "\n\nKrajnji odgovor:" + + return Doc( + task_name=task_name, + query=query, + choices=choices, + gold_index=gold_index, + instruction=instruction, + ) + + +def boolq_serbian(line, task_name: str = None): + # remove extra `?` + question = line["question"][:-1] if line["question"][-2:] == "??" else line["question"] + return Doc( + task_name=task_name, + query=f"Passage: {line['passage']}\nQuestion: {question}\nAnswer:", + choices=[" Da", " Ne"], + gold_index=["Da", "Ne"].index(line["answer"]), + ) + + +def create_task_config( + task_name: str, + prompt_function, + hf_repo: str, + hf_subset: str, + metrics: List, + evaluation_splits: List[str] = ["test"], + suite: List[str] = ["community"], + hf_avail_splits: List[str] = ["test", "validation"], + few_shots_split: str = "validation", + generation_size=5, +) -> LightevalTaskConfig: + """ + Creates a task configuration using dependency injection for flexible task creation. + + Args: + task_name: The name of the task. + prompt_function: The function to generate task prompts. + hf_repo: Hugging Face repository. + hf_subset: Subset of the dataset. + metrics: The metrics to use for the task. + evaluation_splits: The evaluation splits to use (default is "test"). + suite: The suite of tasks. + hf_avail_splits: Available splits (default is "test", "validation"). + few_shots_split: Split used for few-shot examples. + generation_size: Number of generations to produce (default is 5). + + Returns: + A `LightevalTaskConfig` object for the task configuration. + """ + return LightevalTaskConfig( + name=task_name, + prompt_function=prompt_function, + suite=suite, + hf_repo=hf_repo, + hf_subset=hf_subset, + hf_avail_splits=hf_avail_splits, + evaluation_splits=evaluation_splits, + few_shots_split=few_shots_split, + few_shots_select="sequential", + metrics=metrics, + generation_size=generation_size, + hf_revision=HFSubsets.HF_REVISION.value, + version=0, + ) + + +# ============================================ +# ===== ARC (AI2 Reasoning Challenge)========= +# ============================================ + +arc_easy = create_task_config( + task_name="serbian_evals:arc_easy", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.ARC_EASY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +arc_challenge = create_task_config( + task_name="serbian_evals:arc_challenge", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.ARC_CHALLENGE.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +# ============================================ +# ========= Commonsense Reasoning ============ +# ============================================ + +hellaswag = create_task_config( + task_name="serbian_evals:hellaswag", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.HELLASWAG.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) +piqa = create_task_config( + task_name="serbian_evals:piqa", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.PIQA.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +winogrande = create_task_config( + task_name="serbian_evals:winogrande", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.WINOGRANDE.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +# ============================================ +# =========== Custom/Other Task ============== +# ============================================ + +oz_eval = create_task_config( + task_name="serbian_evals:oz_eval", + prompt_function=prompt_fn_oz_eval_task, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.OZ_EVAL.value, + metrics=[Metrics.loglikelihood_acc], +) + +# ============================================ +# ========== MMLU (Miscellaneous) ============ +# ============================================ + +mmlu_anatomy = create_task_config( + task_name="serbian_evals:mmlu_anatomija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_ANATOMY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_astronomy = create_task_config( + task_name="serbian_evals:mmlu_astronomija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_ASTRONOMY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_business_ethics = create_task_config( + task_name="serbian_evals:mmlu_poslovna_etika", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_BUSINESS_ETHICS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_clinical_knowledge = create_task_config( + task_name="serbian_evals:mmlu_kliničko_znanje", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_CLINICAL_KNOWLEDGE.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_miscellaneous = create_task_config( + task_name="serbian_evals:mmlu_razno", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_MISCELLANEOUS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_electrical_engineering = create_task_config( + task_name="serbian_evals:mmlu_elektrotehnika", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_ELECTRONIC_ENGINEERING.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +# ============================================ +# ====== MMLU (All-inclusive Task Entry) ===== +# ============================================ + +mmlu_all = create_task_config( + task_name="serbian_evals:mmlu", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_SERBIAN_ALL.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +# ============================================ +# ======= MMLU (Business Professional) ======= +# ============================================ + +mmlu_marketing = create_task_config( + task_name="serbian_evals:mmlu_marketing", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_MARKETING.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_management = create_task_config( + task_name="serbian_evals:mmlu_manadzment", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_MANAGEMENT.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +# ============================================ +# ======== MMLU (College Level Tasks) ======== +# ============================================ + +mmlu_college_biology = create_task_config( + task_name="serbian_evals:mmlu_fakultet_biologija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_COLLEGE_BIOLOGY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_college_chemistry = create_task_config( + task_name="serbian_evals:mmlu_fakultet_hemija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_COLLEGE_CHEMISTRY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_college_computer_science = create_task_config( + task_name="serbian_evals:mmlu_fakultet_racunari", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_COLLEGE_COMPUTER_SCIENCE.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_college_mathematics = create_task_config( + task_name="serbian_evals:mmlu_fakultet_matematika", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_COLLEGE_MATHEMATICS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_college_medicine = create_task_config( + task_name="serbian_evals:mmlu_fakultet_medicina", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_COLLEGE_MEDICINE.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_college_physics = create_task_config( + task_name="serbian_evals:mmlu_fakultet_fizika", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_COLLEGE_PHYSICS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_computer_security = create_task_config( + task_name="serbian_evals:mmlu_sigurnost_racunara", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_COLLEGE_COMPUTER_SECURITY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +# ============================================ +# ======== MMLU (Ethics, Philosophy) ========= +# ============================================ + +mmlu_moral_disputes = create_task_config( + task_name="serbian_evals:mmlu_moralni_sporovi", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_MORAL_DISPUTES.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_moral_scenarios = create_task_config( + task_name="serbian_evals:mmlu_moralne_dileme", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_MORAL_SCENARIOS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_philosophy = create_task_config( + task_name="serbian_evals:mmlu_filozofija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_PHILOSOPHY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_world_religions = create_task_config( + task_name="serbian_evals:mmlu_svetska_religija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_WORLD_RELIGIONS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +# ============================================ +# ====== MMLU (High School Level Tasks) ====== +# ============================================ + +mmlu_high_school_biology = create_task_config( + task_name="serbian_evals:mmlu_srednja_skola_biologija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_BIOLOGY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_high_school_chemistry = create_task_config( + task_name="serbian_evals:mmlu_srednja_skola_hemija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_CHEMISTRY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_high_school_computer_science = create_task_config( + task_name="serbian_evals:mmlu_srednja_skola_racunari", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_COMPUTER_SCIENCE.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_high_school_european_history = create_task_config( + task_name="serbian_evals:mmlu_srednja_skola_istorija_evrope", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_EURO_HISTORY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_high_school_geography = create_task_config( + task_name="serbian_evals:mmlu_srednja_skola_geografija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_GEOGRAPHY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_high_school_mathematics = create_task_config( + task_name="serbian_evals:mmlu_srednja_skola_matematika", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_MATHEMATICS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_high_school_microeconomics = create_task_config( + task_name="serbian_evals:mmlu_srednja_skola_mikroekonomija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_MICROECONOMICS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_high_school_physics = create_task_config( + task_name="serbian_evals:mmlu_srednja_skola_fizika", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_PHYSICS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_high_school_psychology = create_task_config( + task_name="serbian_evals:mmlu_srednja_skola_psihologija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_PSYCHOLOGY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_high_school_statistics = create_task_config( + task_name="serbian_evals:mmlu_srednja_skola_statistika", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_STATISTICS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_high_school_world_history = create_task_config( + task_name="serbian_evals:mmlu_srednja_skola_svetska_istorija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_HIGH_SCHOOL_WORLD_HISTORY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +# ============================================ +# ============ MMLU (Math, Logic) ============ +# ============================================ + +mmlu_abstract_algebra = create_task_config( + task_name="serbian_evals:mmlu_abstract_algebra", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_ABSTRACT_ALGEBRA.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_elementary_mathematics = create_task_config( + task_name="serbian_evals:mmlu_osnovna_matematika", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_ELEMENTARY_MATHEMATICS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_formal_logic = create_task_config( + task_name="serbian_evals:mmlu_formalna_logika", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_FORMAL_LOGIC.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_conceptual_physics = create_task_config( + task_name="serbian_evals:mmlu_konceptualna_fizika", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_CONCEPTUAL_PHYSICS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_econometrics = create_task_config( + task_name="serbian_evals:mmlu_metrika_ekonomije", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_ECONOMETRICS.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_machine_learning = create_task_config( + task_name="serbian_evals:mmlu_masinsko_ucenje", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_MACHINE_LEARNING.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +# ============================================ +# ========== MMLU (Social Sciences) ========== +# ============================================ + +mmlu_global_facts = create_task_config( + task_name="serbian_evals:mmlu_globalne_cinjenice", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_GLOBAL_FACT.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_logical_fallacies = create_task_config( + task_name="serbian_evals:mmlu_logicke_zablude", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_LOGICAL_FALLACIES.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_sociology = create_task_config( + task_name="serbian_evals:mmlu_sociologija", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_SOCIOLOGY.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +mmlu_human_aging = create_task_config( + task_name="serbian_evals:mmlu_human_aging", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.MMLU_HUMAN_AGING.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +# ============================================ +# ===== Question Answering and Knowledge ===== +# ============================================ + +boolq = create_task_config( + task_name="serbian_evals:boolq", + prompt_function=boolq_serbian, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.BOOLQ.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + +openbook_qa = create_task_config( + task_name="serbian_evals:openbook", + prompt_function=serbian_eval_prompt, + hf_repo=HFSubsets.HF_BASE_REPO.value, + hf_subset=HFSubsets.OPENBOOK.value, + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], +) + + +TASKS_TABLE = [ + arc_easy, + arc_challenge, + boolq, + hellaswag, + openbook_qa, + piqa, + oz_eval, + winogrande, + mmlu_abstract_algebra, + mmlu_anatomy, + mmlu_astronomy, + mmlu_business_ethics, + mmlu_clinical_knowledge, + mmlu_college_biology, + mmlu_college_chemistry, + mmlu_college_computer_science, + mmlu_college_mathematics, + mmlu_college_medicine, + mmlu_college_physics, + mmlu_computer_security, + mmlu_conceptual_physics, + mmlu_econometrics, + mmlu_electrical_engineering, + mmlu_elementary_mathematics, + mmlu_formal_logic, + mmlu_global_facts, + mmlu_high_school_biology, + mmlu_high_school_chemistry, + mmlu_high_school_computer_science, + mmlu_high_school_european_history, + mmlu_high_school_geography, + mmlu_high_school_mathematics, + mmlu_high_school_microeconomics, + mmlu_high_school_physics, + mmlu_high_school_psychology, + mmlu_high_school_statistics, + mmlu_high_school_world_history, + mmlu_human_aging, + mmlu_logical_fallacies, + mmlu_marketing, + mmlu_machine_learning, + mmlu_management, + mmlu_moral_disputes, + mmlu_miscellaneous, + mmlu_moral_scenarios, + mmlu_sociology, + mmlu_philosophy, + mmlu_world_religions, + mmlu_all, +] diff --git a/src/lighteval/tasks/multilingual/tasks/turkic.py b/src/lighteval/tasks/multilingual/tasks/turkic.py new file mode 100644 index 000000000..074fc9b4a --- /dev/null +++ b/src/lighteval/tasks/multilingual/tasks/turkic.py @@ -0,0 +1,122 @@ +""" +name: +Turkic Evals + +dataset: +jafarisbarov/TUMLU-mini + +abstract: +TUMLU-mini is a benchmark for Turkic language understanding, comprising 1,000 +prompts organized into 10 subsets. + +languages: +turkic + +tags: +knowledge, multiple-choice + +paper: +https://arxiv.org/abs/2502.11020 +""" + +from functools import partial + +from lighteval.metrics.metrics import Metrics +from lighteval.metrics.normalizations import LogProbCharNorm +from lighteval.tasks.lighteval_task import LightevalTaskConfig +from lighteval.tasks.requests import Doc + + +# TUMLU +# fmt: off +TUMLU_SUBSETS = [ + "azerbaijani", + "crimean-tatar", + "karakalpak", + "kazakh", + "tatar", + "turkish", + "uyghur", + "uzbek", + "kyrgyz" +] +# fmt: on + +INSTRUCTION_BY_LANGUAGE = { + "azerbaijani": "Aşağıdakı sual çoxvariantlı sualdır. Düzgün cavabı seçin:\n\n", + "crimean-tatar": "Aşağıdaki sual çoqtan-çoq cevaplı sualdir. Doğru cevapnı seçip alıñız:\n\n", + "karakalpak": "Tómendegi soraw kóp tańlawlı soraw Tuwrı juwaptı saylań:\n\n", + "kazakh": "Төмендегі сұрақ көп таңдау мүмкіндігі бар сұрақ. Дұрыс жауапты таңдаңыз:\n\n", + "tatar": "Түбәндәге сорау - күп сорау. Дөрес җавапны сайлагыз:\n\n", + "turkish": "Aşağıdaki soru çoktan seçmeli bir sorudur. Doğru cevabı seçin:\n\n", + "uyghur": "تۆۋەندىكى سوئال كۆپ تاللاش سوئالى. توغرا جاۋابنى تاللاڭ:\n\n", + "uzbek": "Quyidagi savol tanlovli savoldir. To‘g‘ri javobni tanlang:\n\n", + "kyrgyz": "Төмөнкү суроо бир нече варианттуу суроо. Туура жоопту тандаңыз:\n\n", +} + +ANSWER_BY_LANGUAGE = { + "uzbek": "Javob:", + "uzbek-cyrillic": "Жавоб", + "crimean-tatar": "Cevap:", + "crimean-tatar-cyrillic": "Джевап", + "tatar": "Җавап:", + "kazakh": "Жауап:", + "kazakh-latin": "Jawap", + "karakalpak": "Juwap:", + "kyrgyz": "Жооп:", + "turkish": "Cevap:", + "uyghur": "جاۋاب:", + "uyghur-latin": "Jawab:", + "azerbaijani": "Cavab:", +} + + +def tumlu_pfn(line, task_name: str = None, language: str = None): + instruction = INSTRUCTION_BY_LANGUAGE[language] + + # Create a list of valid choices with corresponding keys + choices = line.get("choices") + valid_keys = ["A", "B", "C", "D", "E"][: len(choices)] + + answer_index = valid_keys.index(line.get("answer")) + + # Construct the query + query = f"{instruction}{line['question']}\n" + query += "".join([f"{key}. {choice}\n" for key, choice in zip(valid_keys, choices)]) + query += ANSWER_BY_LANGUAGE[language] + + return Doc( + task_name=task_name, + query=query, + choices=valid_keys, # Return only valid choices + gold_index=answer_index, # Correct index + instruction=instruction, + ) + + +class CustomTUMLUTask(LightevalTaskConfig): + def __init__( + self, + name, + hf_subset, + ): + super().__init__( + name=name, + hf_subset=hf_subset, + prompt_function=partial(tumlu_pfn, language=hf_subset), + hf_repo="jafarisbarov/TUMLU-mini", + metrics=[Metrics.loglikelihood_acc(sample_params={"logprob_normalization": LogProbCharNorm()})], + hf_avail_splits=["test", "dev"], + evaluation_splits=["test"], + few_shots_split=["dev"], + few_shots_select="sequential", + suite=["community"], + generation_size=-1, + stop_sequence=None, + version=0, + ) + + +TUMLU_TASKS = [CustomTUMLUTask(name=f"tumlu:{subset}", hf_subset=subset) for subset in TUMLU_SUBSETS] + +TASKS_TABLE = TUMLU_TASKS From 6a0c615d33d5b2fc5434f0d79b938e9238ef33a2 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 14:43:06 +0200 Subject: [PATCH 26/43] revert uneeded changes --- .../metrics/utils/extractive_match_utils.py | 6 +- src/lighteval/tasks/default_prompts.py | 64 +++++++++++-------- src/lighteval/tasks/lighteval_task.py | 23 ------- 3 files changed, 38 insertions(+), 55 deletions(-) diff --git a/src/lighteval/metrics/utils/extractive_match_utils.py b/src/lighteval/metrics/utils/extractive_match_utils.py index 3eb4508bb..cce2b1793 100644 --- a/src/lighteval/metrics/utils/extractive_match_utils.py +++ b/src/lighteval/metrics/utils/extractive_match_utils.py @@ -345,17 +345,13 @@ def lazy_indices_regex( def get_extraction_regexes( - # target_types: Sequence[ExtractionTarget], language: Language, len_choices: int = 1 - formatted_doc: Doc, - target_types: Sequence[ExtractionTarget], - language: Language, + formatted_doc: Doc, target_types: Sequence[ExtractionTarget], language: Language ) -> list[tuple[list[tuple[re.Pattern[str], int]], ExtractionTarget]]: extraction_regexes: list[tuple[list[tuple[re.Pattern[str], int]], ExtractionTarget]] = [ (lazy_latex_regex(target_type, language), target_type) if isinstance(target_type, LatexExtractionConfig) else (lazy_expr_regex(target_type, language), target_type) if isinstance(target_type, ExprExtractionConfig) - # else (lazy_indices_regex(target_type, len_choices, language), target_type) else (lazy_indices_regex(target_type, len(formatted_doc.choices), language), target_type) for target_type in target_types ] diff --git a/src/lighteval/tasks/default_prompts.py b/src/lighteval/tasks/default_prompts.py index 43f3658da..a78860168 100644 --- a/src/lighteval/tasks/default_prompts.py +++ b/src/lighteval/tasks/default_prompts.py @@ -30,7 +30,6 @@ import numpy as np import pycountry -from inspect_ai.dataset import Sample from lighteval.tasks.requests import Doc from lighteval.utils.utils import as_list @@ -131,14 +130,21 @@ def simpleqa(line, task_name: str = None): ) -def aime_prompt_fn(record): +def aime_prompt_fn(line, task_name: str = None): # Prompt template adapted from # - simple-evals: https://github.com/openai/simple-evals/blob/6e84f4e2aed6b60f6a0c7b8f06bbbf4bfde72e58/math_eval.py#L17 # - Llama 3: https://huggingface.co/datasets/meta-llama/Llama-3.2-1B-Instruct-evals/viewer/Llama-3.2-1B-Instruct-evals__math__details?views%5B%5D=llama_32_1b_instruct_evals__math__details # Note that it is important to have the final answer in a box for math-verify to work correctly - return Sample( - input=record["problem"], - target=record["answer"], + MATH_QUERY_TEMPLATE = """ +Solve the following math problem efficiently and clearly. The last line of your response should be of the following format: 'Therefore, the final answer is: $\\boxed{{ANSWER}}$. I hope it is correct' (without quotes) where ANSWER is just the final number or expression that solves the problem. Think step by step before answering. + +{Question} +""".strip() + return Doc( + task_name=task_name, + query=MATH_QUERY_TEMPLATE.format(Question=line["problem"]), + choices=[line["answer"]], + gold_index=0, ) @@ -330,7 +336,7 @@ def bbh_harness(line, task_name: str = None): ) -def bbh(line, task_name: str = None): +def bbh_lighteval(line, task_name: str = None): line = {k: v for k, v in line.items() if v is not None} query = line.get("task_prefix", "") @@ -349,6 +355,16 @@ def bbh(line, task_name: str = None): ) +def bbh(line, instruction, choices, task_name: str = None): + return Doc( + task_name=task_name, + query=f"{instruction}Q: {line['input']}\nA:", + choices=choices, + gold_index=choices.index(line["target"]), + instruction=instruction, + ) + + def bbh_boolean_expressions(line, task_name: str = None): instruction = "Evaluate the result of a random Boolean expression.\n\n" choices = ["False", "True"] @@ -878,19 +894,6 @@ def gpqa(line, task_name: str = None): ) -# def gpqa_instruct(record): -# """Prompt template adapted from simple-evals: https://github.com/openai/simple-evals/blob/83ed7640a7d9cd26849bcb3340125002ef14abbe/common.py#L14""" -# gold_index = random.randint(0, 3) -# choices = [record["Incorrect Answer 1"], record["Incorrect Answer 2"], record["Incorrect Answer 3"]] -# choices.insert(gold_index, record["Correct Answer"]) - -# return Sample( -# input=record["Question"].strip(), -# choices=choices, -# target=LETTER_INDICES[gold_index], -# ) - - def gpqa_instruct(line, task_name: str = None): """Prompt template adapted from simple-evals: https://github.com/openai/simple-evals/blob/83ed7640a7d9cd26849bcb3340125002ef14abbe/common.py#L14""" gold_index = random.randint(0, 3) @@ -917,22 +920,29 @@ def gpqa_instruct(line, task_name: str = None): ) -def gsm_plus(record): +def gsm_plus(line, task_name: str = None): # GSM8K with 8 prompt variations per sample # Some prompts require critical thinking (around 1k/10k), we skip them as # they are a bit trickier to eval with regular text extraction. + if line["perturbation_type"] == "critical thinking": + return None - return Sample( - input=record["question"], - target=record["answer"], + return Doc( + task_name=task_name, + query=f"Question: {line['question']}\n\nAnswer:", + choices=[line["answer"]], + gold_index=0, ) -def gsm8k(record): - return Sample( - input=record["question"], - target=record["answer"], +def gsm8k(line, task_name: str = None): + # Has special analysis in metric for number decomposition + return Doc( + task_name=task_name, + query=f"Question: {line['question']}\nAnswer:", + choices=[f" {line['answer']}"], + gold_index=0, ) diff --git a/src/lighteval/tasks/lighteval_task.py b/src/lighteval/tasks/lighteval_task.py index 9ca2c61c9..b84d421a6 100644 --- a/src/lighteval/tasks/lighteval_task.py +++ b/src/lighteval/tasks/lighteval_task.py @@ -27,7 +27,6 @@ from datasets import DatasetDict, load_dataset from huggingface_hub import TextGenerationInputGrammarType -from inspect_ai.dataset import Sample from multiprocess import Pool from pytablewriter import MarkdownTableWriter @@ -44,28 +43,6 @@ logger = logging.getLogger(__name__) -@dataclass -class LightevalTaskConfig_inspect: - """Configuration dataclass for a LightevalTask. - - This class stores all the configuration parameters needed to define and run - an evaluation task, including dataset information, prompt formatting, - evaluation metrics, and generation parameters. - """ - - name: str - prompt_function: Callable[[dict], Sample] - dataset_repo: str - dataset_subset: str - dataset_split: str - scorers: list - solvers: list | None = None - system_prompt: str | None = None - dataset_revision: str | None = None - epochs: int = 1 - epochs_reducer: str | None = None - - @dataclass class LightevalTaskConfig: """Configuration dataclass for a LightevalTask. From 1435e3825b28894e20f95da6cbbeb9faeb3bb532 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 14:55:41 +0200 Subject: [PATCH 27/43] fix doc build --- pyproject.toml | 2 +- src/lighteval/tasks/tasks/slr_bench.py | 13 ++++++++++--- 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 8ce602db9..a89024487 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -109,7 +109,7 @@ extended_tasks = [ "openai>1.87", # llm as a judge using openai models "tiktoken", "emoji", "spacy", "syllapy", # ifbench - "evaluate", "pyswip", # slr_bench + "evaluate", # slr_bench ] s3 = ["s3fs"] multilingual = [ diff --git a/src/lighteval/tasks/tasks/slr_bench.py b/src/lighteval/tasks/tasks/slr_bench.py index d9e6b1211..bad487b57 100644 --- a/src/lighteval/tasks/tasks/slr_bench.py +++ b/src/lighteval/tasks/tasks/slr_bench.py @@ -37,9 +37,16 @@ logger = logging.getLogger(__name__) -@requires("pyswip", "evaluate") +@requires("evaluate") def prompt_fn(line: dict, task_name: str): """Defines how to go from a dataset line to a doc object.""" + # Check for SWI-Prolog installation + import shutil + + if shutil.which("swipl") is None: + raise ImportError( + "SWI-Prolog (swipl) is not installed or not in PATH. Please install SWI-Prolog to use this task. " + ) return Doc( task_name=task_name, query=line["prompt"], choices=[str(line.get("validation program", ""))], gold_index=0 @@ -48,9 +55,9 @@ def prompt_fn(line: dict, task_name: str): class VerifiableRewardMetric(SampleLevelComputation): # Load the symbolic judge for evaluating Prolog programs - symbolic_judge = load("AIML-TUDA/VerifiableRewardsForScalableLogicalReasoning") def compute(self, doc, model_response, **kwargs): + symbolic_judge = load("AIML-TUDA/VerifiableRewardsForScalableLogicalReasoning") try: prediction = model_response.final_text[0] validation_program = doc.choices[0] if doc.choices else "" @@ -61,7 +68,7 @@ def compute(self, doc, model_response, **kwargs): } ] - results = self.symbolic_judge.compute(predictions=[prediction], references=ref_format) + results = symbolic_judge.compute(predictions=[prediction], references=ref_format) return results["accuracy"] except Exception as e: From 15f41f26933d1db1734fea03a2d3b551150738d2 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 15:00:40 +0200 Subject: [PATCH 28/43] fix doc build --- src/lighteval/tasks/tasks/ifbench/instructions.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/lighteval/tasks/tasks/ifbench/instructions.py b/src/lighteval/tasks/tasks/ifbench/instructions.py index c15a0dd02..f691a26f8 100644 --- a/src/lighteval/tasks/tasks/ifbench/instructions.py +++ b/src/lighteval/tasks/tasks/ifbench/instructions.py @@ -23,7 +23,6 @@ import unicodedata from collections import Counter -import emoji import nltk from lighteval.utils.imports import is_package_available, requires @@ -35,6 +34,9 @@ if is_package_available("spacy"): import spacy +if is_package_available("emoji"): + import emoji + import lighteval.tasks.tasks.ifeval.instructions_utils as instructions_util From 74e5c0f48a7da0d59d106c2488ba53f08049e14d Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 16:07:59 +0200 Subject: [PATCH 29/43] remove custom tasks and let user decide if loading multilingual tasks --- src/lighteval/cli_args.py | 21 ++- src/lighteval/main_accelerate.py | 6 +- src/lighteval/main_baseline.py | 6 +- src/lighteval/main_custom.py | 6 +- src/lighteval/main_endpoint.py | 18 +-- src/lighteval/main_nanotron.py | 4 +- src/lighteval/main_sglang.py | 6 +- src/lighteval/main_tasks.py | 17 +-- src/lighteval/main_vllm.py | 3 - src/lighteval/pipeline.py | 3 +- src/lighteval/tasks/__init__.py | 66 --------- .../tasks/multilingual/tasks/filipino.py | 77 +++++++--- .../tasks/multilingual/tasks/french.py | 2 +- src/lighteval/tasks/registry.py | 131 +++++++----------- 14 files changed, 152 insertions(+), 214 deletions(-) diff --git a/src/lighteval/cli_args.py b/src/lighteval/cli_args.py index 30e85a1a9..7a6869c54 100644 --- a/src/lighteval/cli_args.py +++ b/src/lighteval/cli_args.py @@ -58,17 +58,6 @@ class Arg: default=1, ) -custom_tasks = Arg( - type=Annotated[ - Optional[str], - Option( - help="Path to a Python file containing custom task definitions. The file should define a TASKS_TABLE with LightevalTaskConfig objects.", - rich_help_panel=HELP_PANEL_NAME_1, - ), - ], - default=None, -) - num_fewshot_seeds = Arg( type=Annotated[ int, @@ -113,6 +102,16 @@ class Arg: default="[('', '')]", ) +load_tasks_multilingual = Arg( + type=Annotated[ + bool, + Option( + help="Whether to load multilingual tasks.", + rich_help_panel=HELP_PANEL_NAME_1, + ), + ], + default=False, +) # Logging Parameters (HELP_PANEL_NAME_2) output_dir = Arg( diff --git a/src/lighteval/main_accelerate.py b/src/lighteval/main_accelerate.py index 3eca3b1c5..6e1454353 100644 --- a/src/lighteval/main_accelerate.py +++ b/src/lighteval/main_accelerate.py @@ -27,10 +27,10 @@ from lighteval.cli_args import ( HELP_PANEL_NAME_4, - custom_tasks, dataset_loading_processes, job_id, load_responses_from_details_date_id, + load_tasks_multilingual, max_samples, model_args, num_fewshot_seeds, @@ -59,8 +59,8 @@ def accelerate( # noqa C901 vision_model: Annotated[ bool, Option(help="Use vision model for evaluation.", rich_help_panel=HELP_PANEL_NAME_4) ] = False, + load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, - custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, load_responses_from_details_date_id: load_responses_from_details_date_id.type = load_responses_from_details_date_id.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, @@ -105,9 +105,9 @@ def accelerate( # noqa C901 ) pipeline_params = PipelineParameters( launcher_type=ParallelismManager.ACCELERATE, + load_tasks_multilingual=load_tasks_multilingual, job_id=job_id, dataset_loading_processes=dataset_loading_processes, - custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, remove_reasoning_tags=remove_reasoning_tags, diff --git a/src/lighteval/main_baseline.py b/src/lighteval/main_baseline.py index f082af726..57da79c55 100644 --- a/src/lighteval/main_baseline.py +++ b/src/lighteval/main_baseline.py @@ -22,8 +22,8 @@ from lighteval.cli_args import ( - custom_tasks, dataset_loading_processes, + load_tasks_multilingual, max_samples, output_dir, tasks, @@ -32,7 +32,7 @@ def baseline( tasks: tasks.type, - custom_tasks: custom_tasks.type = custom_tasks.default, + load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, output_dir: output_dir.type = output_dir.default, max_samples: max_samples.type = max_samples.default, @@ -55,7 +55,7 @@ def baseline( from lighteval.tasks.requests import SamplingMethod from lighteval.utils.utils import as_list - registry = Registry(tasks=tasks, custom_tasks=custom_tasks) + registry = Registry(tasks=tasks, load_multilingual=load_tasks_multilingual) tasks_dict: dict[str, LightevalTask] = registry.load_tasks() evaluation_tracker = EvaluationTracker( diff --git a/src/lighteval/main_custom.py b/src/lighteval/main_custom.py index 1cef8f3dc..18f9cdee3 100644 --- a/src/lighteval/main_custom.py +++ b/src/lighteval/main_custom.py @@ -26,9 +26,9 @@ from typing_extensions import Annotated from lighteval.cli_args import ( - custom_tasks, dataset_loading_processes, job_id, + load_tasks_multilingual, max_samples, num_fewshot_seeds, output_dir, @@ -55,8 +55,8 @@ def custom( model_definition_file_path: Annotated[str, Argument(help="The model definition file path to evaluate")], tasks: tasks.type, # === Common parameters === + load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, - custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, reasoning_tags: reasoning_tags.type = reasoning_tags.default, @@ -97,11 +97,11 @@ def custom( launcher_type=parallelism_manager, job_id=job_id, dataset_loading_processes=dataset_loading_processes, - custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, remove_reasoning_tags=remove_reasoning_tags, reasoning_tags=reasoning_tags, + load_tasks_multilingual=load_tasks_multilingual, ) pipeline = Pipeline( tasks=tasks, diff --git a/src/lighteval/main_endpoint.py b/src/lighteval/main_endpoint.py index 060b93822..f21ef8c84 100644 --- a/src/lighteval/main_endpoint.py +++ b/src/lighteval/main_endpoint.py @@ -27,10 +27,10 @@ from lighteval.cli_args import ( HELP_PANEL_NAME_4, - custom_tasks, dataset_loading_processes, job_id, load_responses_from_details_date_id, + load_tasks_multilingual, max_samples, num_fewshot_seeds, output_dir, @@ -65,8 +65,8 @@ def inference_endpoint( ), ] = False, # === Common parameters === + load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, - custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, load_responses_from_details_date_id: load_responses_from_details_date_id.type = load_responses_from_details_date_id.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, @@ -115,12 +115,12 @@ def inference_endpoint( launcher_type=parallelism_manager, job_id=job_id, dataset_loading_processes=dataset_loading_processes, - custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, load_responses_from_details_date_id=load_responses_from_details_date_id, remove_reasoning_tags=remove_reasoning_tags, reasoning_tags=reasoning_tags, + load_tasks_multilingual=load_tasks_multilingual, ) pipeline = Pipeline( tasks=tasks, @@ -148,8 +148,8 @@ def tgi( ], tasks: tasks.type, # === Common parameters === + load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, - custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, load_responses_from_details_date_id: load_responses_from_details_date_id.type = load_responses_from_details_date_id.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, @@ -193,9 +193,9 @@ def tgi( pipeline_params = PipelineParameters( launcher_type=parallelism_manager, + load_tasks_multilingual=load_tasks_multilingual, job_id=job_id, dataset_loading_processes=dataset_loading_processes, - custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, load_responses_from_details_date_id=load_responses_from_details_date_id, @@ -231,8 +231,8 @@ def litellm( ], tasks: tasks.type, # === Common parameters === + load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, - custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, load_responses_from_details_date_id: load_responses_from_details_date_id.type = load_responses_from_details_date_id.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, @@ -285,9 +285,9 @@ def litellm( pipeline_params = PipelineParameters( launcher_type=parallelism_manager, + load_tasks_multilingual=load_tasks_multilingual, job_id=job_id, dataset_loading_processes=dataset_loading_processes, - custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, load_responses_from_details_date_id=load_responses_from_details_date_id, @@ -324,8 +324,8 @@ def inference_providers( ], tasks: tasks.type, # === Common parameters === + load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, - custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, # === saving === output_dir: output_dir.type = output_dir.default, @@ -373,9 +373,9 @@ def inference_providers( pipeline_params = PipelineParameters( launcher_type=parallelism_manager, + load_tasks_multilingual=load_tasks_multilingual, job_id=job_id, dataset_loading_processes=dataset_loading_processes, - custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, load_responses_from_details_date_id=None, diff --git a/src/lighteval/main_nanotron.py b/src/lighteval/main_nanotron.py index b844a74a4..934b9a737 100644 --- a/src/lighteval/main_nanotron.py +++ b/src/lighteval/main_nanotron.py @@ -29,6 +29,7 @@ from yaml import SafeLoader from lighteval.cli_args import ( + load_tasks_multilingual, reasoning_tags, remove_reasoning_tags, ) @@ -44,6 +45,7 @@ def nanotron( str, Option(help="Path to the nanotron checkpoint YAML or python config file, potentially on s3.") ], lighteval_config_path: Annotated[str, Option(help="Path to a YAML config to be used for the evaluation.")], + load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, reasoning_tags: reasoning_tags.type = reasoning_tags.default, ): @@ -97,11 +99,11 @@ def nanotron( job_id=os.environ.get("SLURM_JOB_ID", 0), nanotron_checkpoint_path=checkpoint_config_path, dataset_loading_processes=lighteval_config.tasks.dataset_loading_processes, - custom_tasks_directory=lighteval_config.tasks.custom_tasks, num_fewshot_seeds=1, max_samples=lighteval_config.tasks.max_samples, remove_reasoning_tags=remove_reasoning_tags, reasoning_tags=reasoning_tags, + load_tasks_multilingual=load_tasks_multilingual, ) pipeline = Pipeline( diff --git a/src/lighteval/main_sglang.py b/src/lighteval/main_sglang.py index 0b506988e..65539cb86 100644 --- a/src/lighteval/main_sglang.py +++ b/src/lighteval/main_sglang.py @@ -21,10 +21,10 @@ # SOFTWARE. from lighteval.cli_args import ( - custom_tasks, dataset_loading_processes, job_id, load_responses_from_details_date_id, + load_tasks_multilingual, max_samples, model_args, num_fewshot_seeds, @@ -47,8 +47,8 @@ def sglang( model_args: model_args.type, tasks: tasks.type, # === Common parameters === + load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, - custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, load_responses_from_details_date_id: load_responses_from_details_date_id.type = load_responses_from_details_date_id.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, @@ -91,8 +91,8 @@ def sglang( pipeline_params = PipelineParameters( launcher_type=ParallelismManager.SGLANG, job_id=job_id, + load_tasks_multilingual=load_tasks_multilingual, dataset_loading_processes=dataset_loading_processes, - custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, load_responses_from_details_date_id=load_responses_from_details_date_id, diff --git a/src/lighteval/main_tasks.py b/src/lighteval/main_tasks.py index 226f6a463..04679c1c8 100644 --- a/src/lighteval/main_tasks.py +++ b/src/lighteval/main_tasks.py @@ -25,7 +25,7 @@ from typer import Argument, Option from typing_extensions import Annotated -from lighteval.cli_args import custom_tasks +from lighteval.cli_args import load_tasks_multilingual app = typer.Typer() @@ -34,7 +34,6 @@ @app.command() def inspect( tasks: Annotated[str, Argument(help="Id of tasks or path to a text file with a list of tasks")], - custom_tasks: custom_tasks.type = custom_tasks.default, num_samples: Annotated[int, Option(help="Number of samples to display")] = 10, show_config: Annotated[bool, Option(help="Will display the full task config")] = False, ): @@ -46,7 +45,7 @@ def inspect( from lighteval.tasks.registry import Registry - registry = Registry(custom_tasks=custom_tasks, load_multilingual=True) + registry = Registry(load_multilingual=True) # Loading task task_dict = registry.load_tasks() @@ -64,19 +63,13 @@ def inspect( @app.command() def list( - custom_tasks: custom_tasks.type = custom_tasks.default, - suites: Annotated[ - str | None, - Option( - help="Comma-separated list of suites to display (e.g., 'helm,harness'). Use 'all' for all suites. If not specified, shows core suites only." - ), - ] = None, + load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, ): """List all tasks""" from lighteval.tasks.registry import Registry - registry = Registry(custom_tasks=custom_tasks, load_multilingual=True) - registry.print_all_tasks(suites=suites) + registry = Registry(load_multilingual=load_tasks_multilingual) + registry.print_all_tasks() @app.command() diff --git a/src/lighteval/main_vllm.py b/src/lighteval/main_vllm.py index fcfb9652a..dc6d1d7d1 100644 --- a/src/lighteval/main_vllm.py +++ b/src/lighteval/main_vllm.py @@ -27,7 +27,6 @@ from lighteval.cli_args import ( HELP_PANEL_NAME_4, - custom_tasks, dataset_loading_processes, job_id, load_responses_from_details_date_id, @@ -57,7 +56,6 @@ def vllm( Optional[str], Option(help="Use chain of thought prompt for evaluation.", rich_help_panel=HELP_PANEL_NAME_4) ] = None, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, - custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, load_responses_from_details_date_id: load_responses_from_details_date_id.type = load_responses_from_details_date_id.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, @@ -101,7 +99,6 @@ def vllm( launcher_type=ParallelismManager.VLLM, job_id=job_id, dataset_loading_processes=dataset_loading_processes, - custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, cot_prompt=cot_prompt, diff --git a/src/lighteval/pipeline.py b/src/lighteval/pipeline.py index 0f02c4b38..6a39c5421 100644 --- a/src/lighteval/pipeline.py +++ b/src/lighteval/pipeline.py @@ -86,7 +86,6 @@ class PipelineParameters: dataset_loading_processes: int = 1 nanotron_checkpoint_path: str | None = None # only for nanotron models # Dataset - custom_tasks_directory: str | None = None num_fewshot_seeds: int = 1 max_samples: int | None = None cot_prompt: str | None = None @@ -210,7 +209,7 @@ def _init_tasks_and_requests(self, tasks: str): logger.info("--- LOADING TASKS ---") # The registry contains all the potential tasks - self.registry = Registry(tasks=tasks, custom_tasks=self.pipeline_parameters.custom_tasks_directory) + self.registry = Registry(tasks=tasks, load_multilingual=False) # load the tasks from the configs and their datasets self.tasks_dict: dict[str, LightevalTask] = self.registry.load_tasks() diff --git a/src/lighteval/tasks/__init__.py b/src/lighteval/tasks/__init__.py index 6cebcec89..e3e34484b 100644 --- a/src/lighteval/tasks/__init__.py +++ b/src/lighteval/tasks/__init__.py @@ -24,69 +24,3 @@ Automatically imports all task configs from the tasks/ directory. This module dynamically loads all Python files in tasks/ and exposes their LightevalTaskConfig objects. """ - -import importlib -import time -from pathlib import Path - - -# Get the tasks directory -TASKS_DIR = Path(__file__).parent / "tasks" -TASKS_DIR_MULTILINGUAL = Path(__file__).parent / "multilingual" / "tasks" - - -def _extract_configs(module): - configs = {} - if hasattr(module, "TASKS_TABLE"): - for config in getattr(module, "TASKS_TABLE"): - configs[config.name] = config - return configs - - -def _load_from_files(files, module_prefix: str): - configs = {} - for task_file in files: - module_name = task_file.stem - module = importlib.import_module(f"{module_prefix}.{module_name}") - configs.update(_extract_configs(module)) - return configs - - -def _load_from_subdirs(subdirs): - configs = {} - for task_dir in subdirs: - module_name = task_dir.name - module = importlib.import_module(f"lighteval.tasks.tasks.{module_name}.main") - configs.update(_extract_configs(module)) - return configs - - -def _load_all_task_configs(): - """Load all LightevalTaskConfig objects from all Python files in the tasks/ directory.""" - start_time = time.perf_counter() - loaded_configs = {} - - # Get all Python files in the tasks directory (excluding __init__.py) - task_files = [f for f in TASKS_DIR.glob("*.py") if f.name != "__init__.py"] - # task_files_multilingual = [f for f in TASKS_DIR_MULTILINGUAL.glob("*.py") if f.name != "__init__.py"] - - # Also get all subdirectories with main.py files - task_subdirs = [d for d in TASKS_DIR.iterdir() if d.is_dir() and (d / "main.py").exists()] - - loaded_configs.update(_load_from_files(task_files, "lighteval.tasks.tasks")) - # loaded_configs.update( - # _load_from_files(task_files_multilingual, "lighteval.tasks.multilingual.tasks") - # ) - loaded_configs.update(_load_from_subdirs(task_subdirs)) - - duration_s = time.perf_counter() - start_time - print(f"[lighteval.tasks] Loaded {len(loaded_configs)} task configs in {duration_s * 1000:.1f} ms") - return loaded_configs - - -# Load all configs and add them to module namespace -_configs = _load_all_task_configs() -globals().update(_configs) - -# Clean up -del _configs diff --git a/src/lighteval/tasks/multilingual/tasks/filipino.py b/src/lighteval/tasks/multilingual/tasks/filipino.py index 66d5ddcd0..daf29daa6 100644 --- a/src/lighteval/tasks/multilingual/tasks/filipino.py +++ b/src/lighteval/tasks/multilingual/tasks/filipino.py @@ -41,7 +41,6 @@ ) from lighteval.tasks.default_prompts import LETTER_INDICES from lighteval.tasks.lighteval_task import LightevalTaskConfig -from lighteval.tasks.multilingual.tasks import MMLU_SUBSETS from lighteval.tasks.multilingual.utils.task_utils import get_metrics_for_formulation from lighteval.tasks.requests import Doc from lighteval.tasks.templates.multichoice import get_mcq_prompt_function @@ -55,6 +54,66 @@ from lighteval.utils.language import Language, iso_639_3_ind_to_iso_639_3_macro +MMLU_SUBSETS = [ + "abstract_algebra", + "anatomy", + "astronomy", + "business_ethics", + "clinical_knowledge", + "college_biology", + "college_chemistry", + "college_computer_science", + "college_mathematics", + "college_medicine", + "college_physics", + "computer_security", + "conceptual_physics", + "econometrics", + "electrical_engineering", + "elementary_mathematics", + "formal_logic", + "global_facts", + "high_school_biology", + "high_school_chemistry", + "high_school_computer_science", + "high_school_european_history", + "high_school_geography", + "high_school_government_and_politics", + "high_school_macroeconomics", + "high_school_mathematics", + "high_school_microeconomics", + "high_school_physics", + "high_school_psychology", + "high_school_statistics", + "high_school_us_history", + "high_school_world_history", + "human_aging", + "human_sexuality", + "international_law", + "jurisprudence", + "logical_fallacies", + "machine_learning", + "management", + "marketing", + "medical_genetics", + "miscellaneous", + "moral_disputes", + "moral_scenarios", + "nutrition", + "philosophy", + "prehistory", + "professional_accounting", + "professional_law", + "professional_medicine", + "professional_psychology", + "public_relations", + "security_studies", + "sociology", + "us_foreign_policy", + "virology", + "world_religions", +] + # Balita NLP FILIPINO_BALITA_TASKS = [ LightevalTaskConfig( @@ -140,7 +199,6 @@ few_shots_select="random", suite=["community"], generation_size=-1, - trust_dataset=True, metrics=get_metrics_for_formulation( formulation, [ @@ -191,7 +249,6 @@ few_shots_split="test", few_shots_select="random", generation_size=-1, - trust_dataset=True, version=0, ) for formulation in [MCFFormulation(), HybridFormulation()] @@ -233,14 +290,13 @@ def filipino_dengue_pfn(line, task_name: str) -> Doc: hf_subset="default", prompt_function=filipino_dengue_pfn, hf_repo="jcblaise/dengue_filipino", - metrics=[Metrics.loglikelihood_acc_norm], + metrics=[LogLikelihoodAccMetric(normalization=LogProbTokenNorm())], hf_avail_splits=["train", "test", "validation"], evaluation_splits=["train"], few_shots_split="train", few_shots_select="random", suite=("community",), generation_size=-1, - trust_dataset=True, version=0, ) for subset in dengue_filipino_subsets @@ -276,7 +332,6 @@ def filipino_dengue_pfn(line, task_name: str) -> Doc: few_shots_select="random", suite=["community"], generation_size=-1, - trust_dataset=True, version=0, ) for formulation in [MCFFormulation(), HybridFormulation()] @@ -360,7 +415,6 @@ def filipino_dengue_pfn(line, task_name: str) -> Doc: few_shots_split="test", few_shots_select="random", generation_size=-1, - trust_dataset=True, version=0, ) for subset in ["culturology", "history", "language", "driving_license"] @@ -422,7 +476,6 @@ def filipino_dengue_pfn(line, task_name: str) -> Doc: LogLikelihoodAccMetric(normalization=LogProbCharNorm()), ], ), - trust_dataset=True, ) for formulation in [MCFFormulation(), CFFormulation(), HybridFormulation()] ] @@ -455,7 +508,6 @@ def filipino_dengue_pfn(line, task_name: str) -> Doc: few_shots_split=None, few_shots_select=None, generation_size=64, - trust_dataset=True, version=0, ) for language in ["fil_Latn"] @@ -509,7 +561,6 @@ def create_sib200_task(language: Language, formulation): few_shots_split="validation", few_shots_select="random", generation_size=-1, - trust_dataset=True, version=0, ) @@ -565,7 +616,6 @@ def prepare_stingray_semantic_appropriateness(line: dict[str, str]) -> dict[str, few_shots_split="test", few_shots_select="random", generation_size=-1, - trust_dataset=True, version=0, ) for formulation in [MCFFormulation(), HybridFormulation()] @@ -595,7 +645,6 @@ def prepare_stingray_semantic_appropriateness(line: dict[str, str]) -> dict[str, few_shots_split="test", few_shots_select="random", generation_size=-1, - trust_dataset=True, version=0, ) for formulation in [MCFFormulation(), HybridFormulation()] @@ -642,7 +691,6 @@ def prepare_stingray_semantic_appropriateness(line: dict[str, str]) -> dict[str, ], hf_avail_splits=["test"], evaluation_splits=["test"], - trust_dataset=True, generation_size=64, ) for language, meta in lang_dict.items() @@ -675,7 +723,6 @@ def prepare_stingray_semantic_appropriateness(line: dict[str, str]) -> dict[str, evaluation_splits=["validation"], few_shots_split=["validation"], few_shots_select="random", - trust_dataset=True, generation_size=64, ) ] @@ -704,7 +751,6 @@ def prepare_stingray_semantic_appropriateness(line: dict[str, str]) -> dict[str, few_shots_select="random", suite=["community"], generation_size=-1, - trust_dataset=True, metrics=get_metrics_for_formulation( formulation, [ @@ -748,7 +794,6 @@ def create_universalner_task(language: Language, formulation): few_shots_select="random", suite=["community"], generation_size=-1, - trust_dataset=True, metrics=get_metrics_for_formulation( formulation, [ diff --git a/src/lighteval/tasks/multilingual/tasks/french.py b/src/lighteval/tasks/multilingual/tasks/french.py index e7b3a5a0d..12cf3d928 100644 --- a/src/lighteval/tasks/multilingual/tasks/french.py +++ b/src/lighteval/tasks/multilingual/tasks/french.py @@ -23,9 +23,9 @@ from lighteval.metrics.metrics import Metrics from lighteval.metrics.normalizations import math_normalizer from lighteval.tasks.default_prompts import LETTER_INDICES -from lighteval.tasks.extended.ifeval.main import ifeval_metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.requests import Doc +from lighteval.tasks.tasks.ifeval.main import ifeval_metrics from lighteval.utils.utils import as_list diff --git a/src/lighteval/tasks/registry.py b/src/lighteval/tasks/registry.py index b9ce1c983..f99d2abab 100644 --- a/src/lighteval/tasks/registry.py +++ b/src/lighteval/tasks/registry.py @@ -28,12 +28,12 @@ import logging import os import sys +import time from functools import lru_cache from itertools import groupby from pathlib import Path from types import ModuleType -import lighteval.tasks as default_tasks from lighteval.tasks.lighteval_task import LightevalTask, LightevalTaskConfig @@ -113,8 +113,6 @@ class Registry: def __init__( self, tasks: str | Path | None = None, - custom_tasks: str | Path | ModuleType | None = None, - load_community: bool = False, load_multilingual: bool = False, ): """ @@ -143,8 +141,6 @@ def __init__( ) ] """ - self._custom_tasks = custom_tasks - if tasks is None: logger.warning( "You passed no task name. This should only occur if you are using the CLI to inspect tasks." @@ -152,15 +148,8 @@ def __init__( self.tasks_list = [] else: self.tasks_list = self._get_full_task_list_from_input_string(tasks) - # These parameters are dynamically set by the task names provided, thanks to `activate_suites_to_load`, - # except in the `tasks` CLI command to display the full list - self._load_community = load_community - self._load_multilingual = load_multilingual - self._activate_loading_of_optional_suite() # we dynamically set the loading parameters - - # We load all task to - self._task_registry = self._load_full_registry() + self._task_registry = Registry.load_all_task_configs(load_multilingual=load_multilingual) self.task_to_configs = self._update_task_configs() def _get_full_task_list_from_input_string(self, tasks: str | Path) -> list[str]: @@ -171,21 +160,7 @@ def _get_full_task_list_from_input_string(self, tasks: str | Path) -> list[str]: else: tasks_list = tasks.split(",") - # We might have tasks provided as task groups in the custom tasks - # We load the whole task_groups mapping - if self._custom_tasks is None: - task_groups = {} - else: - custom_tasks_module = Registry.create_custom_tasks_module(custom_tasks=self._custom_tasks) - tasks_group_dict = {} - if hasattr(custom_tasks_module, "TASKS_GROUPS"): - tasks_group_dict = custom_tasks_module.TASKS_GROUPS - - # We should allow defining task groups as comma-separated strings or lists of tasks - task_groups = {k: v if isinstance(v, list) else v.split(",") for k, v in tasks_group_dict.items()} - - # Then link actual task_group to task list if needed - # (At this point the strings are either task name/superset name or group names) + task_groups = {} expanded_tasks_list: list[str] = [] for maybe_task_group in tasks_list: # We either expand the group (in case it's a group name), or we keep it as is (in case it's a task name or superset name) @@ -199,43 +174,6 @@ def _get_full_task_list_from_input_string(self, tasks: str | Path) -> list[str]: return expanded_tasks_list - def _activate_loading_of_optional_suite(self) -> None: - """Dynamically selects which of the optional suite we want to load.""" - suites = {task.split("|")[0] for task in self.tasks_list} - - for suite_name in suites: - if suite_name not in DEFAULT_SUITES: - logger.warning( - f"Suite {suite_name} unknown. This is not normal, unless you are testing adding new evaluations." - ) - - if "extended" in suites: - self._load_extended = True - if "multilingual" in suites: - self._load_multilingual = True - if "community" in suites: - self._load_community = True - - def _load_full_registry(self) -> dict[str, LightevalTaskConfig]: - """ - Returns: - dict[str, LightevalTaskConfig]: A dictionary mapping task names (suite|task) to their corresponding LightevalTask classes. - - Example: - { - "arc_easy": LightevalTaskConfig(name="arc_easy", suite="lighteval", ...), - } - """ - - return Registry.create_task_config_dict() - - # Need to load multilingual tasks - if self._load_multilingual: - pass - tasks_registry = {} - - return tasks_registry - def _update_task_configs(self) -> dict[str, LightevalTaskConfig]: # noqa: C901 """ Updates each config depending on the input tasks (we replace all provided params, like few shot number, sampling params, etc) @@ -364,26 +302,57 @@ def create_custom_tasks_module(custom_tasks: str | Path | ModuleType) -> ModuleT return importlib.import_module(str(custom_tasks)) @staticmethod - def create_task_config_dict(meta_table: list[LightevalTaskConfig] | None = None) -> dict[str, LightevalTaskConfig]: - """Create configuration tasks based on the provided meta_table. + def _extract_configs(module: ModuleType) -> dict[str, LightevalTaskConfig]: + configs = {} + if hasattr(module, "TASKS_TABLE"): + for config in getattr(module, "TASKS_TABLE"): + configs[f"{config.suite[0]}|{config.name}"] = config + return configs - Args: - meta_table: meta_table containing tasks - configurations. If not provided, it will be loaded from TABLE_PATH. + @staticmethod + def _load_from_files(files: list[Path], module_prefix: str) -> dict[str, LightevalTaskConfig]: + configs = {} + for task_file in files: + module_name = task_file.stem + module = importlib.import_module(f"{module_prefix}.{module_name}") + configs.update(Registry._extract_configs(module)) + return configs - Returns: - Dict[str, LightevalTaskConfig]: A dictionary of task names mapped to their corresponding LightevalTaskConfig. - """ - if meta_table is None: - meta_table = [config for config in vars(default_tasks).values() if isinstance(config, LightevalTaskConfig)] + @staticmethod + def _load_from_subdirs(subdirs: list[Path]) -> dict[str, LightevalTaskConfig]: + configs = {} + for task_dir in subdirs: + module_name = task_dir.name + module = importlib.import_module(f"lighteval.tasks.tasks.{module_name}.main") + configs.update(Registry._extract_configs(module)) + return configs - tasks_with_config: dict[str, LightevalTaskConfig] = {} - for config in meta_table: - for suite in config.suite: - if suite in DEFAULT_SUITES: - tasks_with_config[f"{suite}|{config.name}"] = config + @staticmethod + def load_all_task_configs(load_multilingual: bool = False) -> dict[str, LightevalTaskConfig]: + """Load all LightevalTaskConfig objects from all Python files in the tasks/ directory.""" + time_start = time.perf_counter() + # Get the tasks directory + TASKS_DIR = Path(__file__).parent / "tasks" + TASKS_DIR_MULTILINGUAL = Path(__file__).parent / "multilingual" / "tasks" + loaded_configs = {} + + # Get all Python files in the tasks directory (excluding __init__.py) + task_files = [f for f in TASKS_DIR.glob("*.py") if f.name != "__init__.py"] + task_files_multilingual = [f for f in TASKS_DIR_MULTILINGUAL.glob("*.py") if f.name != "__init__.py"] + + # Also get all subdirectories with main.py files + task_subdirs = [d for d in TASKS_DIR.iterdir() if d.is_dir() and (d / "main.py").exists()] + + loaded_configs.update(Registry._load_from_files(task_files, "lighteval.tasks.tasks")) + if load_multilingual: + loaded_configs.update( + Registry._load_from_files(task_files_multilingual, "lighteval.tasks.multilingual.tasks") + ) + loaded_configs.update(Registry._load_from_subdirs(task_subdirs)) - return tasks_with_config + time_end = time.perf_counter() + logger.info(f"Loaded {len(loaded_configs)} task configs in {time_end - time_start:.1f} seconds") + return loaded_configs def print_all_tasks(self, suites: str | None = None): """Print all the tasks in the task registry. From aad136c105203ef2506261efdb6cf2f4ca99d901 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 16:10:26 +0200 Subject: [PATCH 30/43] load-tasks multilingual fix --- src/lighteval/pipeline.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/lighteval/pipeline.py b/src/lighteval/pipeline.py index 6a39c5421..53ed32572 100644 --- a/src/lighteval/pipeline.py +++ b/src/lighteval/pipeline.py @@ -93,6 +93,7 @@ class PipelineParameters: reasoning_tags: str | list[tuple[str, str]] = "[('', '')]" load_responses_from_details_date_id: str | None = None bootstrap_iters: int = 1000 + load_tasks_multilingual: bool = False def __post_init__(self): # noqa C901 if not isinstance(self.reasoning_tags, list): @@ -209,7 +210,7 @@ def _init_tasks_and_requests(self, tasks: str): logger.info("--- LOADING TASKS ---") # The registry contains all the potential tasks - self.registry = Registry(tasks=tasks, load_multilingual=False) + self.registry = Registry(tasks=tasks, load_multilingual=self.pipeline_parameters.load_tasks_multilingual) # load the tasks from the configs and their datasets self.tasks_dict: dict[str, LightevalTask] = self.registry.load_tasks() From 242bc438ecb8b1cb570f77510cfdc3f8e7321081 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 16:18:21 +0200 Subject: [PATCH 31/43] update doc --- docs/source/available-tasks.mdx | 6 +----- docs/source/quicktour.mdx | 10 ++++++++++ 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/docs/source/available-tasks.mdx b/docs/source/available-tasks.mdx index 65c7454cc..450b7ed49 100644 --- a/docs/source/available-tasks.mdx +++ b/docs/source/available-tasks.mdx @@ -1,5 +1,3 @@ -# Available Tasks - + ## Available Commands ### Evaluation Backends From 6806bf882bef6983a7949198681a8d33e6adb9af Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 16:20:46 +0200 Subject: [PATCH 32/43] remove uneeded file --- src/lighteval/main_inspect.py | 103 ---------------------------------- 1 file changed, 103 deletions(-) delete mode 100644 src/lighteval/main_inspect.py diff --git a/src/lighteval/main_inspect.py b/src/lighteval/main_inspect.py deleted file mode 100644 index 4e7f97bc7..000000000 --- a/src/lighteval/main_inspect.py +++ /dev/null @@ -1,103 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - - -from inspect_ai import Epochs, Task, eval, task -from inspect_ai.dataset import hf_dataset -from inspect_ai.solver import generate, system_message - -from lighteval.tasks import default_tasks -from lighteval.tasks.lighteval_task import LightevalTaskConfig_inspect as LightevalTaskConfig - - -@task -def get_task(lighteval_task_config: LightevalTaskConfig): - name = lighteval_task_config.name - sample_fields = lighteval_task_config.prompt_function - - dataset_repo = lighteval_task_config.dataset_repo - dataset_subset = lighteval_task_config.dataset_subset - dataset_split = lighteval_task_config.dataset_split - - system_prompt = lighteval_task_config.system_prompt - - dataset = hf_dataset(dataset_repo, name=dataset_subset, split=dataset_split, sample_fields=sample_fields) - solvers = lighteval_task_config.solvers or [ - system_message(system_prompt), - generate(cache=True), - ] - scorers = lighteval_task_config.scorers - epochs = lighteval_task_config.epochs - epochs_reducer = lighteval_task_config.epochs_reducer - - return Task(dataset=dataset, solver=solvers, scorer=scorers, name=name, epochs=Epochs(epochs, epochs_reducer)) - - -model_args = { - "max-tokens", - "system-message", - "temperature", - "top-p", - "top-k", - "frequence-penalty", - "presence-penalty", - "logit-bias", - "seed", - "stop-seqs", - "num-choices", - "best-of", - "log-probs", - "top-logprobs", - "cache-prompt", - "reasoning-effort", - "reasoning-tokens", - "reasoning-history", - "response-format", - "parallel-tool-calls", - "max-tool-output", - "internal-tools", - "max-retries", - "timeout", -} - - -def main(): - MODEL = ["openai/gpt-4o"] - all_tasks = [ - # default_tasks.gsm8k_lighteval, - # default_tasks.aime25, - # default_tasks.aime24, - # default_tasks.math_500, - default_tasks.gsm_plus, - # default_tasks.gpqa_diamond, - # default_tasks.gpqa_extended, - # default_tasks.gpqa_main, - ] # default_tasksifeval] - all_tasks = [get_task(task) for task in all_tasks] - - # eval_set(all_tasks, model=MODEL, display="rich", limit=10, max_tasks=3, bundle_dir="./log_static", log_dir="./log_dynamic-1") - - eval(all_tasks, model=MODEL, display="rich", limit=10, max_tasks=3, log_probs=True) - - -if __name__ == "__main__": - main() From e94fa5901a8f6a20a77cb3666804af541f84686a Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 16:22:30 +0200 Subject: [PATCH 33/43] update readme --- README.md | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 503efd75c..1a3fc4cc1 100644 --- a/README.md +++ b/README.md @@ -39,7 +39,14 @@ sample-by-sample results* to debug and see how your models stack-up. ## Available Tasks -Lighteval supports **7,000+ evaluation tasks** across multiple domains and languages. Here's an overview of some *popular benchmarks*: +Lighteval supports **1000+ evaluation tasks** across multiple domains and languages. Here's an overview of some *popular benchmarks*: + + ### 📚 **Knowledge** From 8800d1acff636c7890a58cc4af9fbab0e72cd072 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 16:35:10 +0200 Subject: [PATCH 34/43] update readme --- README.md | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 1a3fc4cc1..0a4c3b417 100644 --- a/README.md +++ b/README.md @@ -39,14 +39,10 @@ sample-by-sample results* to debug and see how your models stack-up. ## Available Tasks -Lighteval supports **1000+ evaluation tasks** across multiple domains and languages. Here's an overview of some *popular benchmarks*: - - +Lighteval supports **1000+ evaluation tasks** across multiple domains and +languages. Use [this +space](https://huggingface.co/spaces/SaylorTwift/benchmark_finder) to find what +you need, or, here's an overview of some *popular benchmarks*: ### 📚 **Knowledge** From 970f33bfcfe11c8043ccaa3b33392e491989f032 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 16:38:23 +0200 Subject: [PATCH 35/43] update readme --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 0a4c3b417..ba5f698b8 100644 --- a/README.md +++ b/README.md @@ -25,6 +25,9 @@ Documentation + + Open Benchmark Index +

--- From b8c26dc2c9ee6919a499c3114e6174dae0918006 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Thu, 16 Oct 2025 17:00:07 +0200 Subject: [PATCH 36/43] fix test --- tests/unit/metrics/test_metric_requests.py | 8 +-- tests/unit/pipeline/test_reasoning_tags.py | 8 +-- tests/unit/tasks/test_registry.py | 59 +++------------------- tests/utils.py | 6 +-- 4 files changed, 14 insertions(+), 67 deletions(-) diff --git a/tests/unit/metrics/test_metric_requests.py b/tests/unit/metrics/test_metric_requests.py index e7f9ee473..7c383c737 100644 --- a/tests/unit/metrics/test_metric_requests.py +++ b/tests/unit/metrics/test_metric_requests.py @@ -25,9 +25,9 @@ from lighteval.metrics.normalizations import LogProbPMINorm from lighteval.metrics.utils.metric_utils import Metric from lighteval.models.model_output import ModelResponse -from lighteval.tasks.default_tasks import xstory_cloze_en_lighteval from lighteval.tasks.lighteval_task import LightevalTask, LightevalTaskConfig from lighteval.tasks.requests import Doc +from lighteval.tasks.tasks.xstory_cloze import xstory_cloze_en from tests.utils import FakeModel, fake_evaluate_task @@ -48,9 +48,9 @@ def get_pmi_task(metrics: list[Metric]): metrics=metrics, suite=["test"], prompt_function=dummy_prompt_fc, - hf_repo=xstory_cloze_en_lighteval.hf_repo, - hf_subset=xstory_cloze_en_lighteval.hf_subset, - evaluation_splits=xstory_cloze_en_lighteval.evaluation_splits, + hf_repo=xstory_cloze_en.hf_repo, + hf_subset=xstory_cloze_en.hf_subset, + evaluation_splits=xstory_cloze_en.evaluation_splits, ) # This is manually edited when updating the config and in the post init function # - we need to get a more homogeneous system for naming... diff --git a/tests/unit/pipeline/test_reasoning_tags.py b/tests/unit/pipeline/test_reasoning_tags.py index f772970c4..df3ba2be9 100644 --- a/tests/unit/pipeline/test_reasoning_tags.py +++ b/tests/unit/pipeline/test_reasoning_tags.py @@ -22,9 +22,7 @@ import tempfile import unittest -from pathlib import Path -from types import ModuleType -from typing import Optional, Union +from typing import Optional from unittest.mock import patch from lighteval.logging.evaluation_tracker import EvaluationTracker @@ -95,9 +93,7 @@ def download_dataset_worker(task) -> None: return task._docs class FakeRegistry(Registry): - def __init__( - self, tasks: Optional[str] = None, custom_tasks: Optional[Union[str, Path, ModuleType]] = None - ): + def __init__(self, tasks: Optional[str] = None, load_multilingual: bool = False): self.tasks_list = [input_task_name] # suite_name, task_name, few_shot = input_task_name.split("|") self.task_to_configs = {input_task_name: [task_config]} diff --git a/tests/unit/tasks/test_registry.py b/tests/unit/tasks/test_registry.py index 377ea7d6c..bbbd32dc8 100644 --- a/tests/unit/tasks/test_registry.py +++ b/tests/unit/tasks/test_registry.py @@ -26,51 +26,6 @@ from lighteval.tasks.registry import Registry -TASKS_TABLE = [ - LightevalTaskConfig( - name="test_task_revision", - # Won't be called, so it can be anything - prompt_function=lambda x: x, # type: ignore - hf_repo="test", - hf_subset="default", - evaluation_splits=["train"], - metrics=[], - ) -] - -TASKS_GROUPS = { - "zero_and_one": "custom|test_task_revision|0,custom|test_task_revision|1", - "all_mmlu": "original|mmlu|3", -} - - -def test_custom_task_groups(): - """ - Tests that task info selector correctly handles custom task groups. - """ - registry = Registry(tasks="zero_and_one", custom_tasks="tests.unit.tasks.test_registry") - - assert set(registry.tasks_list) == {"custom|test_task_revision|0", "custom|test_task_revision|1"} - - assert set(registry.task_to_configs.keys()) == {"custom|test_task_revision"} - - task_info: list[LightevalTaskConfig] = registry.task_to_configs["custom|test_task_revision"] - assert {task_info[0].num_fewshots, task_info[1].num_fewshots} == {0, 1} - - -def test_custom_tasks(): - """ - Tests that task info selector correctly handles custom tasks. - """ - registry = Registry(tasks="custom|test_task_revision|0", custom_tasks="tests.unit.tasks.test_registry") - - assert registry.tasks_list == ["custom|test_task_revision|0"] - assert set(registry.task_to_configs.keys()) == {"custom|test_task_revision"} - - task_info: list[LightevalTaskConfig] = registry.task_to_configs["custom|test_task_revision"] - assert task_info[0].num_fewshots == 0 - - def test_superset_expansion(): """ Tests that task info selector correctly handles supersets. @@ -92,13 +47,13 @@ def test_superset_with_subset_task(): """ Tests that task info selector correctly handles if both superset and one of subset tasks are provided. """ - registry = Registry(tasks="original|mmlu|3,original|mmlu:abstract_algebra|5") + registry = Registry(tasks="lighteval|mmlu|3,lighteval|mmlu:abstract_algebra|5") # We have all mmlu tasks - assert set(registry.tasks_list) == {"original|mmlu|3", "original|mmlu:abstract_algebra|5"} + assert set(registry.tasks_list) == {"lighteval|mmlu|3", "lighteval|mmlu:abstract_algebra|5"} assert len(registry.task_to_configs.keys()) == 57 - task_info: list[LightevalTaskConfig] = registry.task_to_configs["original|mmlu:abstract_algebra"] + task_info: list[LightevalTaskConfig] = registry.task_to_configs["lighteval|mmlu:abstract_algebra"] assert {task_info[0].num_fewshots, task_info[1].num_fewshots} == {3, 5} @@ -133,7 +88,7 @@ def test_task_group_expansion_with_subset_expansion(): """ Tests that task info selector correctly handles a group with task superset is provided. """ - registry = Registry(tasks="all_mmlu", custom_tasks="tests.unit.tasks.test_registry") + registry = Registry(tasks="lighteval|mmlu|0") # We have all mmlu tasks assert len(registry.task_to_configs.keys()) == 57 @@ -151,11 +106,9 @@ def test_task_duplicates(): """ Tests that task info selector correctly handles if duplicate tasks are provided. """ - registry = Registry( - tasks="custom|test_task_revision|0,custom|test_task_revision|0", custom_tasks="tests.unit.tasks.test_registry" - ) + registry = Registry(tasks="lighteval|storycloze:2016|0,lighteval|storycloze:2016|0") - assert list(registry.tasks_list) == ["custom|test_task_revision|0"] + assert list(registry.tasks_list) == ["lighteval|storycloze:2016|0"] def test_task_creation(): diff --git a/tests/utils.py b/tests/utils.py index 3b68dd631..3d33b1c85 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -20,9 +20,7 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from pathlib import Path -from types import ModuleType -from typing import Optional, Union +from typing import Optional from unittest.mock import patch from transformers import AutoTokenizer @@ -108,7 +106,7 @@ def fake_evaluate_task( # Create a mock Registry class class FakeRegistry(Registry): - def __init__(self, tasks: Optional[str], custom_tasks: Optional[Union[str, Path, ModuleType]] = None): + def __init__(self, tasks: Optional[str], load_multilingual: bool = False): self.tasks_list = [task_name_fs] self.task_to_configs = {task_name_fs: [lighteval_task.config]} From 764de725798badccdf3e1961f2796b9b9839e1fc Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Fri, 17 Oct 2025 11:09:27 +0200 Subject: [PATCH 37/43] add back the custom tasks --- examples/custom_tasks_tests.py | 8 ++++---- examples/test_tasks.txt | 12 ++++++------ src/lighteval/cli_args.py | 11 +++++++++++ src/lighteval/main_accelerate.py | 3 +++ src/lighteval/main_baseline.py | 4 +++- src/lighteval/main_custom.py | 3 +++ src/lighteval/main_endpoint.py | 9 +++++++++ src/lighteval/main_nanotron.py | 1 + src/lighteval/main_sglang.py | 3 +++ src/lighteval/main_tasks.py | 8 +++++--- src/lighteval/main_vllm.py | 3 +++ src/lighteval/pipeline.py | 7 ++++++- src/lighteval/tasks/registry.py | 19 ++++++++++++++++--- 13 files changed, 73 insertions(+), 18 deletions(-) diff --git a/examples/custom_tasks_tests.py b/examples/custom_tasks_tests.py index 34c871cd5..1a189c177 100644 --- a/examples/custom_tasks_tests.py +++ b/examples/custom_tasks_tests.py @@ -26,8 +26,8 @@ gsm8k_test = LightevalTaskConfig( - name="gsm8k", - suite=["test"], + name="gsm8k_test", + suite=["lighteval"], prompt_function=prompt.gsm8k, hf_repo="gsm8k", hf_subset="main", @@ -42,8 +42,8 @@ ) gpqa_diamond_test = LightevalTaskConfig( - name="gpqa:diamond", - suite=["test"], + name="gpqa:diamond_test", + suite=["lighteval"], prompt_function=prompt.gpqa_instruct, hf_repo="Idavidrein/gpqa", hf_subset="gpqa_diamond", diff --git a/examples/test_tasks.txt b/examples/test_tasks.txt index 12c8662a9..3b70c7add 100644 --- a/examples/test_tasks.txt +++ b/examples/test_tasks.txt @@ -1,8 +1,8 @@ -leaderboard|arc:challenge|25 -leaderboard|truthfulqa:mc|0 -leaderboard|hellaswag|10 -leaderboard|mmlu:college_chemistry|5 -leaderboard|mmlu:us_foreign_policy|5 +lighteval|arc:challenge|25 +lighteval|truthfulqa:mc|0 +lighteval|hellaswag|10 +lighteval|mmlu:college_chemistry|5 +lighteval|mmlu:us_foreign_policy|5 lighteval|agieval:aqua-rat|0 lighteval|agieval:logiqa-en|0 lighteval|agieval:lsat-ar|0 @@ -24,4 +24,4 @@ lighteval|bigbench:snarks|3 lighteval|bigbench:temporal_sequences|3 lighteval|bigbench:tracking_shuffled_objects_five_objects|3 lighteval|bigbench:tracking_shuffled_objects_seven_objects|3 -test|gsm8k|0 +lighteval|gsm8k_test|0 diff --git a/src/lighteval/cli_args.py b/src/lighteval/cli_args.py index 7a6869c54..a8123218f 100644 --- a/src/lighteval/cli_args.py +++ b/src/lighteval/cli_args.py @@ -58,6 +58,17 @@ class Arg: default=1, ) +custom_tasks = Arg( + type=Annotated[ + Optional[str], + Option( + help="Path to a Python file containing custom task definitions. The file should define a TASKS_TABLE with LightevalTaskConfig objects.", + rich_help_panel=HELP_PANEL_NAME_1, + ), + ], + default=None, +) + num_fewshot_seeds = Arg( type=Annotated[ int, diff --git a/src/lighteval/main_accelerate.py b/src/lighteval/main_accelerate.py index 6e1454353..00fe25676 100644 --- a/src/lighteval/main_accelerate.py +++ b/src/lighteval/main_accelerate.py @@ -27,6 +27,7 @@ from lighteval.cli_args import ( HELP_PANEL_NAME_4, + custom_tasks, dataset_loading_processes, job_id, load_responses_from_details_date_id, @@ -59,6 +60,7 @@ def accelerate( # noqa C901 vision_model: Annotated[ bool, Option(help="Use vision model for evaluation.", rich_help_panel=HELP_PANEL_NAME_4) ] = False, + custom_tasks: custom_tasks.type = custom_tasks.default, load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, @@ -105,6 +107,7 @@ def accelerate( # noqa C901 ) pipeline_params = PipelineParameters( launcher_type=ParallelismManager.ACCELERATE, + custom_tasks_directory=custom_tasks, load_tasks_multilingual=load_tasks_multilingual, job_id=job_id, dataset_loading_processes=dataset_loading_processes, diff --git a/src/lighteval/main_baseline.py b/src/lighteval/main_baseline.py index 57da79c55..2ba82095c 100644 --- a/src/lighteval/main_baseline.py +++ b/src/lighteval/main_baseline.py @@ -22,6 +22,7 @@ from lighteval.cli_args import ( + custom_tasks, dataset_loading_processes, load_tasks_multilingual, max_samples, @@ -34,6 +35,7 @@ def baseline( tasks: tasks.type, load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, + custom_tasks: custom_tasks.type = custom_tasks.default, output_dir: output_dir.type = output_dir.default, max_samples: max_samples.type = max_samples.default, ): @@ -55,7 +57,7 @@ def baseline( from lighteval.tasks.requests import SamplingMethod from lighteval.utils.utils import as_list - registry = Registry(tasks=tasks, load_multilingual=load_tasks_multilingual) + registry = Registry(tasks=tasks, custom_tasks=custom_tasks, load_multilingual=load_tasks_multilingual) tasks_dict: dict[str, LightevalTask] = registry.load_tasks() evaluation_tracker = EvaluationTracker( diff --git a/src/lighteval/main_custom.py b/src/lighteval/main_custom.py index 18f9cdee3..e6124ce62 100644 --- a/src/lighteval/main_custom.py +++ b/src/lighteval/main_custom.py @@ -26,6 +26,7 @@ from typing_extensions import Annotated from lighteval.cli_args import ( + custom_tasks, dataset_loading_processes, job_id, load_tasks_multilingual, @@ -58,6 +59,7 @@ def custom( load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, + custom_tasks: custom_tasks.type = custom_tasks.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, reasoning_tags: reasoning_tags.type = reasoning_tags.default, # === saving === @@ -97,6 +99,7 @@ def custom( launcher_type=parallelism_manager, job_id=job_id, dataset_loading_processes=dataset_loading_processes, + custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, remove_reasoning_tags=remove_reasoning_tags, diff --git a/src/lighteval/main_endpoint.py b/src/lighteval/main_endpoint.py index f21ef8c84..ece2ac430 100644 --- a/src/lighteval/main_endpoint.py +++ b/src/lighteval/main_endpoint.py @@ -27,6 +27,7 @@ from lighteval.cli_args import ( HELP_PANEL_NAME_4, + custom_tasks, dataset_loading_processes, job_id, load_responses_from_details_date_id, @@ -67,6 +68,7 @@ def inference_endpoint( # === Common parameters === load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, + custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, load_responses_from_details_date_id: load_responses_from_details_date_id.type = load_responses_from_details_date_id.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, @@ -115,6 +117,7 @@ def inference_endpoint( launcher_type=parallelism_manager, job_id=job_id, dataset_loading_processes=dataset_loading_processes, + custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, load_responses_from_details_date_id=load_responses_from_details_date_id, @@ -150,6 +153,7 @@ def tgi( # === Common parameters === load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, + custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, load_responses_from_details_date_id: load_responses_from_details_date_id.type = load_responses_from_details_date_id.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, @@ -196,6 +200,7 @@ def tgi( load_tasks_multilingual=load_tasks_multilingual, job_id=job_id, dataset_loading_processes=dataset_loading_processes, + custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, load_responses_from_details_date_id=load_responses_from_details_date_id, @@ -234,6 +239,7 @@ def litellm( load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, + custom_tasks: custom_tasks.type = custom_tasks.default, load_responses_from_details_date_id: load_responses_from_details_date_id.type = load_responses_from_details_date_id.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, reasoning_tags: reasoning_tags.type = reasoning_tags.default, @@ -288,6 +294,7 @@ def litellm( load_tasks_multilingual=load_tasks_multilingual, job_id=job_id, dataset_loading_processes=dataset_loading_processes, + custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, load_responses_from_details_date_id=load_responses_from_details_date_id, @@ -326,6 +333,7 @@ def inference_providers( # === Common parameters === load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, + custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, # === saving === output_dir: output_dir.type = output_dir.default, @@ -376,6 +384,7 @@ def inference_providers( load_tasks_multilingual=load_tasks_multilingual, job_id=job_id, dataset_loading_processes=dataset_loading_processes, + custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, load_responses_from_details_date_id=None, diff --git a/src/lighteval/main_nanotron.py b/src/lighteval/main_nanotron.py index 934b9a737..9399e82cd 100644 --- a/src/lighteval/main_nanotron.py +++ b/src/lighteval/main_nanotron.py @@ -99,6 +99,7 @@ def nanotron( job_id=os.environ.get("SLURM_JOB_ID", 0), nanotron_checkpoint_path=checkpoint_config_path, dataset_loading_processes=lighteval_config.tasks.dataset_loading_processes, + custom_tasks_directory=lighteval_config.tasks.custom_tasks, num_fewshot_seeds=1, max_samples=lighteval_config.tasks.max_samples, remove_reasoning_tags=remove_reasoning_tags, diff --git a/src/lighteval/main_sglang.py b/src/lighteval/main_sglang.py index 65539cb86..ab86349f9 100644 --- a/src/lighteval/main_sglang.py +++ b/src/lighteval/main_sglang.py @@ -21,6 +21,7 @@ # SOFTWARE. from lighteval.cli_args import ( + custom_tasks, dataset_loading_processes, job_id, load_responses_from_details_date_id, @@ -49,6 +50,7 @@ def sglang( # === Common parameters === load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, + custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, load_responses_from_details_date_id: load_responses_from_details_date_id.type = load_responses_from_details_date_id.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, @@ -93,6 +95,7 @@ def sglang( job_id=job_id, load_tasks_multilingual=load_tasks_multilingual, dataset_loading_processes=dataset_loading_processes, + custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, load_responses_from_details_date_id=load_responses_from_details_date_id, diff --git a/src/lighteval/main_tasks.py b/src/lighteval/main_tasks.py index 04679c1c8..230359730 100644 --- a/src/lighteval/main_tasks.py +++ b/src/lighteval/main_tasks.py @@ -25,7 +25,7 @@ from typer import Argument, Option from typing_extensions import Annotated -from lighteval.cli_args import load_tasks_multilingual +from lighteval.cli_args import custom_tasks, load_tasks_multilingual app = typer.Typer() @@ -34,6 +34,7 @@ @app.command() def inspect( tasks: Annotated[str, Argument(help="Id of tasks or path to a text file with a list of tasks")], + custom_tasks: custom_tasks.type = custom_tasks.default, num_samples: Annotated[int, Option(help="Number of samples to display")] = 10, show_config: Annotated[bool, Option(help="Will display the full task config")] = False, ): @@ -45,7 +46,7 @@ def inspect( from lighteval.tasks.registry import Registry - registry = Registry(load_multilingual=True) + registry = Registry(custom_tasks=custom_tasks, load_multilingual=True) # Loading task task_dict = registry.load_tasks() @@ -64,11 +65,12 @@ def inspect( @app.command() def list( load_tasks_multilingual: load_tasks_multilingual.type = load_tasks_multilingual.default, + custom_tasks: custom_tasks.type = custom_tasks.default, ): """List all tasks""" from lighteval.tasks.registry import Registry - registry = Registry(load_multilingual=load_tasks_multilingual) + registry = Registry(custom_tasks=custom_tasks, load_multilingual=load_tasks_multilingual) registry.print_all_tasks() diff --git a/src/lighteval/main_vllm.py b/src/lighteval/main_vllm.py index dc6d1d7d1..fcfb9652a 100644 --- a/src/lighteval/main_vllm.py +++ b/src/lighteval/main_vllm.py @@ -27,6 +27,7 @@ from lighteval.cli_args import ( HELP_PANEL_NAME_4, + custom_tasks, dataset_loading_processes, job_id, load_responses_from_details_date_id, @@ -56,6 +57,7 @@ def vllm( Optional[str], Option(help="Use chain of thought prompt for evaluation.", rich_help_panel=HELP_PANEL_NAME_4) ] = None, dataset_loading_processes: dataset_loading_processes.type = dataset_loading_processes.default, + custom_tasks: custom_tasks.type = custom_tasks.default, num_fewshot_seeds: num_fewshot_seeds.type = num_fewshot_seeds.default, load_responses_from_details_date_id: load_responses_from_details_date_id.type = load_responses_from_details_date_id.default, remove_reasoning_tags: remove_reasoning_tags.type = remove_reasoning_tags.default, @@ -99,6 +101,7 @@ def vllm( launcher_type=ParallelismManager.VLLM, job_id=job_id, dataset_loading_processes=dataset_loading_processes, + custom_tasks_directory=custom_tasks, num_fewshot_seeds=num_fewshot_seeds, max_samples=max_samples, cot_prompt=cot_prompt, diff --git a/src/lighteval/pipeline.py b/src/lighteval/pipeline.py index 53ed32572..2f60ec959 100644 --- a/src/lighteval/pipeline.py +++ b/src/lighteval/pipeline.py @@ -86,6 +86,7 @@ class PipelineParameters: dataset_loading_processes: int = 1 nanotron_checkpoint_path: str | None = None # only for nanotron models # Dataset + custom_tasks_directory: str | None = None num_fewshot_seeds: int = 1 max_samples: int | None = None cot_prompt: str | None = None @@ -210,7 +211,11 @@ def _init_tasks_and_requests(self, tasks: str): logger.info("--- LOADING TASKS ---") # The registry contains all the potential tasks - self.registry = Registry(tasks=tasks, load_multilingual=self.pipeline_parameters.load_tasks_multilingual) + self.registry = Registry( + tasks=tasks, + custom_tasks=self.pipeline_parameters.custom_tasks_directory, + load_multilingual=self.pipeline_parameters.load_tasks_multilingual, + ) # load the tasks from the configs and their datasets self.tasks_dict: dict[str, LightevalTask] = self.registry.load_tasks() diff --git a/src/lighteval/tasks/registry.py b/src/lighteval/tasks/registry.py index f99d2abab..cabde57be 100644 --- a/src/lighteval/tasks/registry.py +++ b/src/lighteval/tasks/registry.py @@ -114,6 +114,7 @@ def __init__( self, tasks: str | Path | None = None, load_multilingual: bool = False, + custom_tasks: str | Path | ModuleType | None = None, ): """ Initialize the Registry class. @@ -126,7 +127,6 @@ def __init__( - A Path object pointing to a custom tasks file - A module object containing custom task configurations - None for default behavior (no custom tasks) - load_community: Whether to load community-contributed tasks. load_multilingual: Whether to load multilingual tasks. Each custom task module should contain a TASKS_TABLE exposing @@ -149,7 +149,9 @@ def __init__( else: self.tasks_list = self._get_full_task_list_from_input_string(tasks) - self._task_registry = Registry.load_all_task_configs(load_multilingual=load_multilingual) + self._task_registry = Registry.load_all_task_configs( + custom_tasks=custom_tasks, load_multilingual=load_multilingual + ) self.task_to_configs = self._update_task_configs() def _get_full_task_list_from_input_string(self, tasks: str | Path) -> list[str]: @@ -328,7 +330,9 @@ def _load_from_subdirs(subdirs: list[Path]) -> dict[str, LightevalTaskConfig]: return configs @staticmethod - def load_all_task_configs(load_multilingual: bool = False) -> dict[str, LightevalTaskConfig]: + def load_all_task_configs( + custom_tasks: str | Path | None = None, load_multilingual: bool = False + ) -> dict[str, LightevalTaskConfig]: """Load all LightevalTaskConfig objects from all Python files in the tasks/ directory.""" time_start = time.perf_counter() # Get the tasks directory @@ -350,6 +354,15 @@ def load_all_task_configs(load_multilingual: bool = False) -> dict[str, Lighteva ) loaded_configs.update(Registry._load_from_subdirs(task_subdirs)) + if custom_tasks is not None: + custom_tasks_module = Registry.create_custom_tasks_module(custom_tasks) + custom_tasks_configs = Registry._extract_configs(custom_tasks_module) + if set(custom_tasks_configs.keys()) & set(loaded_configs.keys()): + raise ValueError( + f"Custom tasks {custom_tasks} conflict with built-in tasks, please use a different name. Conflicting tasks: {set(custom_tasks_configs.keys()) & set(loaded_configs.keys())}" + ) + loaded_configs.update(custom_tasks_configs) + time_end = time.perf_counter() logger.info(f"Loaded {len(loaded_configs)} task configs in {time_end - time_start:.1f} seconds") return loaded_configs From a326ea86408feb729010747211ed9e542e2afde7 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Fri, 17 Oct 2025 11:16:00 +0200 Subject: [PATCH 38/43] add back the custom tasks --- src/lighteval/pipeline.py | 2 +- tests/unit/pipeline/test_reasoning_tags.py | 4 +++- tests/utils.py | 2 +- 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/src/lighteval/pipeline.py b/src/lighteval/pipeline.py index 2f60ec959..1f5da9c14 100644 --- a/src/lighteval/pipeline.py +++ b/src/lighteval/pipeline.py @@ -213,8 +213,8 @@ def _init_tasks_and_requests(self, tasks: str): # The registry contains all the potential tasks self.registry = Registry( tasks=tasks, - custom_tasks=self.pipeline_parameters.custom_tasks_directory, load_multilingual=self.pipeline_parameters.load_tasks_multilingual, + custom_tasks=self.pipeline_parameters.custom_tasks_directory, ) # load the tasks from the configs and their datasets diff --git a/tests/unit/pipeline/test_reasoning_tags.py b/tests/unit/pipeline/test_reasoning_tags.py index df3ba2be9..00fa00d78 100644 --- a/tests/unit/pipeline/test_reasoning_tags.py +++ b/tests/unit/pipeline/test_reasoning_tags.py @@ -93,7 +93,9 @@ def download_dataset_worker(task) -> None: return task._docs class FakeRegistry(Registry): - def __init__(self, tasks: Optional[str] = None, load_multilingual: bool = False): + def __init__( + self, tasks: Optional[str] = None, load_multilingual: bool = False, custom_tasks: Optional[str] = None + ): self.tasks_list = [input_task_name] # suite_name, task_name, few_shot = input_task_name.split("|") self.task_to_configs = {input_task_name: [task_config]} diff --git a/tests/utils.py b/tests/utils.py index 3d33b1c85..b7ba2a042 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -106,7 +106,7 @@ def fake_evaluate_task( # Create a mock Registry class class FakeRegistry(Registry): - def __init__(self, tasks: Optional[str], load_multilingual: bool = False): + def __init__(self, tasks: Optional[str], load_multilingual: bool = False, custom_tasks: Optional[str] = None): self.tasks_list = [task_name_fs] self.task_to_configs = {task_name_fs: [lighteval_task.config]} From 81081cded9ed157f5b29cff579988c9fe5042c85 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Fri, 17 Oct 2025 11:28:36 +0200 Subject: [PATCH 39/43] fix tasks --- .../custom_models/google_translate_model.py | 1 - examples/test_tasks.txt | 28 +++++++++---------- src/lighteval/metrics/imports/bert_scorer.py | 1 - .../metrics/utils/math_comparison.py | 2 +- src/lighteval/utils/cache_management.py | 4 +-- 5 files changed, 16 insertions(+), 20 deletions(-) diff --git a/examples/custom_models/google_translate_model.py b/examples/custom_models/google_translate_model.py index 04493fe35..1fe456900 100644 --- a/examples/custom_models/google_translate_model.py +++ b/examples/custom_models/google_translate_model.py @@ -110,7 +110,6 @@ def greedy_until( Args: requests (list[Request]): list of requests containing the context and ending conditions. - override_bs (int, optional): Override the batch size for generation. Defaults to None. Returns: list[ModelResponse]: list of generated responses. diff --git a/examples/test_tasks.txt b/examples/test_tasks.txt index 3b70c7add..14f847f06 100644 --- a/examples/test_tasks.txt +++ b/examples/test_tasks.txt @@ -10,18 +10,18 @@ lighteval|agieval:lsat-lr|0 lighteval|agieval:lsat-rc|0 lighteval|agieval:sat-en-without-passage|0 lighteval|agieval:sat-en|0 -lighteval|bigbench:causal_judgment|3 -lighteval|bigbench:date_understanding|3 -lighteval|bigbench:disambiguation_qa|3 -lighteval|bigbench:geometric_shapes|3 -lighteval|bigbench:logical_deduction_five_objects|3 -lighteval|bigbench:logical_deduction_seven_objects|3 -lighteval|bigbench:movie_recommendation|3 -lighteval|bigbench:navigate|3 -lighteval|bigbench:ruin_names|3 -lighteval|bigbench:salient_translation_error_detection|3 -lighteval|bigbench:snarks|3 -lighteval|bigbench:temporal_sequences|3 -lighteval|bigbench:tracking_shuffled_objects_five_objects|3 -lighteval|bigbench:tracking_shuffled_objects_seven_objects|3 +lighteval|bigbench_hard:causal_judgment|3 +lighteval|bigbench_hard:date_understanding|3 +lighteval|bigbench_hard:disambiguation_qa|3 +lighteval|bigbench_hard:geometric_shapes|3 +lighteval|bigbench_hard:logical_deduction_five_objects|3 +lighteval|bigbench_hard:logical_deduction_seven_objects|3 +lighteval|bigbench_hard:movie_recommendation|3 +lighteval|bigbench_hard:navigate|3 +lighteval|bigbench_hard:ruin_names|3 +lighteval|bigbench_hard:salient_translation_error_detection|3 +lighteval|bigbench_hard:snarks|3 +lighteval|bigbench_hard:temporal_sequences|3 +lighteval|bigbench_hard:tracking_shuffled_objects_five_objects|3 +lighteval|bigbench_hard:tracking_shuffled_objects_seven_objects|3 lighteval|gsm8k_test|0 diff --git a/src/lighteval/metrics/imports/bert_scorer.py b/src/lighteval/metrics/imports/bert_scorer.py index b8025bf3f..d53c06afb 100644 --- a/src/lighteval/metrics/imports/bert_scorer.py +++ b/src/lighteval/metrics/imports/bert_scorer.py @@ -340,7 +340,6 @@ def __init__( lang (str): Language of the sentences; has to specify at least one of `model_type` or `lang`. `lang` needs to be specified when `rescale_with_baseline` is True. - return_hash (bool): Return hash code of the setting. rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline. baseline_path (str): Customized baseline file. """ diff --git a/src/lighteval/metrics/utils/math_comparison.py b/src/lighteval/metrics/utils/math_comparison.py index 2329acfe0..974d6d2cc 100644 --- a/src/lighteval/metrics/utils/math_comparison.py +++ b/src/lighteval/metrics/utils/math_comparison.py @@ -297,7 +297,7 @@ def is_equation(expr: Basic | MatrixBase) -> bool: Args: expr: The expression to check Returns: - bool: True if expr is an equation, False otherwise + True if expr is an equation, False otherwise """ if isinstance(expr, Eq): return True diff --git a/src/lighteval/utils/cache_management.py b/src/lighteval/utils/cache_management.py index 3e8c0a08a..e5764a04b 100644 --- a/src/lighteval/utils/cache_management.py +++ b/src/lighteval/utils/cache_management.py @@ -79,7 +79,6 @@ def __init__(self, model_config: ModelConfig): Args: model_config: Configuration for the model being cached - cache_dir: Directory to store cache files """ self.model_config = model_config self.model_hash = self.get_model_hash(model_config) @@ -213,7 +212,6 @@ def _load_sample(self, sample: pd.core.series.Series | dict) -> Union[dict, Mode Args: sample: Raw sample data from cache, arrives as a dataframe row - sample_type: Type of sample being loaded Returns: Union[dict, ModelResponse]: Loaded sample in appropriate format for processing @@ -360,7 +358,7 @@ def cached(sampling_method: SamplingMethod = None): # noqa C901 Decorator to cache method results based on Doc inputs. Args: - cache_type_name: Type of cache ("tokenization" or "predictions") + sampling_method: Sampling method to cache Usage: @cached(SamplingMethod.GENERATIVE) From 74b40f62ca5d32a83504571a85e620fe6274ef8e Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Fri, 17 Oct 2025 11:31:41 +0200 Subject: [PATCH 40/43] fix tasks --- src/lighteval/metrics/imports/bert_scorer.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/lighteval/metrics/imports/bert_scorer.py b/src/lighteval/metrics/imports/bert_scorer.py index d53c06afb..db9c16c34 100644 --- a/src/lighteval/metrics/imports/bert_scorer.py +++ b/src/lighteval/metrics/imports/bert_scorer.py @@ -110,7 +110,7 @@ def get_bert_embedding( Args: all_sens (list of str): sentences to encode. - model: a BERT model from `pytorch_pretrained_bert`. + model: a BERT model. tokenizer: a BERT tokenizer corresponds to `model`. idf_dict (dict): mapping a word piece index to its inverse document frequency. batch_size (int): batch size for processing, -1 for all sentences. @@ -330,7 +330,6 @@ def __init__( `model_type` or `lang`. num_layers (int): The layer of representation to use. Default using the number of layer tuned on WMT16 correlation data. - verbose (bool): Turn on intermediate status update. idf (bool): A boolean to specify whether to use idf or not (this should be True even if `idf_sents` is given). device (str): On which the contextual embedding model will be allocated on. If this argument is None, the model lives on cuda:0 if cuda is available. From 083fb1b54fa5d35ab4df2ca0f08ce5a38a62bb3c Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Fri, 17 Oct 2025 11:54:23 +0200 Subject: [PATCH 41/43] fix tasks --- src/lighteval/tasks/tasks/truthfulqa.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/src/lighteval/tasks/tasks/truthfulqa.py b/src/lighteval/tasks/tasks/truthfulqa.py index 164183b9a..045c88ffc 100644 --- a/src/lighteval/tasks/tasks/truthfulqa.py +++ b/src/lighteval/tasks/tasks/truthfulqa.py @@ -39,6 +39,23 @@ version=0, ) +truthfulqa_mc = LightevalTaskConfig( + name="truthfulqa:mc", + suite=["lighteval"], + prompt_function=prompt.truthful_qa_multiple_choice, + hf_repo="EleutherAI/truthful_qa_mc", + hf_subset="multiple_choice", + hf_avail_splits=["validation"], + evaluation_splits=["validation"], + few_shots_split=None, + few_shots_select=None, + generation_size=-1, + metrics=[Metrics.truthfulqa_mc_metrics], + stop_sequence=["\n"], + version=0, +) + TASKS_TABLE = [ truthfulqa_gen, + truthfulqa_mc, ] From 2dab2bfda9173bb62586d01aa3dc47fd3c519845 Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Fri, 17 Oct 2025 14:14:17 +0000 Subject: [PATCH 42/43] fix tests --- src/lighteval/tasks/tasks/bigbench_hard.py | 36 ++++++++--------- src/lighteval/tasks/tasks/truthfulqa.py | 4 +- ...enge|25_2025-09-19T14-21-59.670987.parquet | 3 -- ...swag|10_2025-09-19T14-21-59.670987.parquet | 3 -- ...istry|5_2025-09-19T14-21-59.670987.parquet | 3 -- ...olicy|5_2025-09-19T14-21-59.670987.parquet | 3 -- ...qa:mc|0_2025-09-19T14-21-59.670987.parquet | 3 -- ...a-rat|0_2025-09-19T14-21-59.670987.parquet | 3 -- ...qa-en|0_2025-09-19T14-21-59.670987.parquet | 3 -- ...at-ar|0_2025-09-19T14-21-59.670987.parquet | 3 -- ...at-lr|0_2025-09-19T14-21-59.670987.parquet | 3 -- ...at-rc|0_2025-09-19T14-21-59.670987.parquet | 3 -- ...ssage|0_2025-09-19T14-21-59.670987.parquet | 3 -- ...at-en|0_2025-09-19T14-21-59.670987.parquet | 3 -- ...gment|3_2025-09-19T14-21-59.670987.parquet | 3 -- 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tests/reference_details/SmolLM2-1.7B-Instruct-vllm/details_lighteval|truthfulqa:mc|0_2025-10-17T14-03-07.927732.parquet delete mode 100644 tests/reference_details/SmolLM2-1.7B-Instruct-vllm/details_test|gsm8k|0_2025-09-19T14-18-26.717757.parquet create mode 100644 tests/reference_scores/__pycache__/reference_task_scores.cpython-310.pyc create mode 100644 tests/reference_scores/__pycache__/reference_task_scores.cpython-311.pyc create mode 100644 tests/reference_scores/__pycache__/reference_task_scores.cpython-312.pyc create mode 100644 tests/reference_scores/__pycache__/reference_tasks.cpython-310.pyc create mode 100644 tests/reference_scores/__pycache__/reference_tasks.cpython-311.pyc create mode 100644 tests/reference_scores/__pycache__/reference_tasks.cpython-312.pyc diff --git a/src/lighteval/tasks/tasks/bigbench_hard.py b/src/lighteval/tasks/tasks/bigbench_hard.py index 57e0683eb..f17781c2b 100644 --- a/src/lighteval/tasks/tasks/bigbench_hard.py +++ b/src/lighteval/tasks/tasks/bigbench_hard.py @@ -23,7 +23,7 @@ causal_judgment = LightevalTaskConfig( name="bigbench_hard:causal_judgment", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="causal_judgement", hf_avail_splits=["train"], @@ -39,7 +39,7 @@ date_understanding = LightevalTaskConfig( name="bigbench_hard:date_understanding", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="date_understanding", hf_avail_splits=["train"], @@ -55,7 +55,7 @@ disambiguation_qa = LightevalTaskConfig( name="bigbench_hard:disambiguation_qa", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="disambiguation_qa", hf_avail_splits=["train"], @@ -71,7 +71,7 @@ geometric_shapes = LightevalTaskConfig( name="bigbench_hard:geometric_shapes", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="geometric_shapes", hf_avail_splits=["train"], @@ -87,7 +87,7 @@ logical_deduction_five_objects = LightevalTaskConfig( name="bigbench_hard:logical_deduction_five_objects", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="logical_deduction_five_objects", hf_avail_splits=["train"], @@ -103,7 +103,7 @@ logical_deduction_seven_objects = LightevalTaskConfig( name="bigbench_hard:logical_deduction_seven_objects", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="logical_deduction_seven_objects", hf_avail_splits=["train"], @@ -119,7 +119,7 @@ logical_deduction_three_objects = LightevalTaskConfig( name="bigbench_hard:logical_deduction_three_objects", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="logical_deduction_three_objects", hf_avail_splits=["train"], @@ -135,7 +135,7 @@ movie_recommendation = LightevalTaskConfig( name="bigbench_hard:movie_recommendation", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="movie_recommendation", hf_avail_splits=["train"], @@ -151,7 +151,7 @@ navigate = LightevalTaskConfig( name="bigbench_hard:navigate", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="navigate", hf_avail_splits=["train"], @@ -167,7 +167,7 @@ reasoning_about_colored_objects = LightevalTaskConfig( name="bigbench_hard:reasoning_about_colored_objects", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="reasoning_about_colored_objects", hf_avail_splits=["train"], @@ -183,7 +183,7 @@ ruin_names = LightevalTaskConfig( name="bigbench_hard:ruin_names", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="ruin_names", hf_avail_splits=["train"], @@ -199,7 +199,7 @@ salient_translation_error_detection = LightevalTaskConfig( name="bigbench_hard:salient_translation_error_detection", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="salient_translation_error_detection", hf_avail_splits=["train"], @@ -215,7 +215,7 @@ snarks = LightevalTaskConfig( name="bigbench_hard:snarks", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="snarks", hf_avail_splits=["train"], @@ -231,7 +231,7 @@ sports_understanding = LightevalTaskConfig( name="bigbench_hard:sports_understanding", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="sports_understanding", hf_avail_splits=["train"], @@ -247,7 +247,7 @@ temporal_sequences = LightevalTaskConfig( name="bigbench_hard:temporal_sequences", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="temporal_sequences", hf_avail_splits=["train"], @@ -263,7 +263,7 @@ tracking_shuffled_objects_five_objects = LightevalTaskConfig( name="bigbench_hard:tracking_shuffled_objects_five_objects", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="tracking_shuffled_objects_five_objects", hf_avail_splits=["train"], @@ -279,7 +279,7 @@ tracking_shuffled_objects_seven_objects = LightevalTaskConfig( name="bigbench_hard:tracking_shuffled_objects_seven_objects", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="tracking_shuffled_objects_seven_objects", hf_avail_splits=["train"], @@ -295,7 +295,7 @@ tracking_shuffled_objects_three_objects = LightevalTaskConfig( name="bigbench_hard:tracking_shuffled_objects_three_objects", suite=["lighteval"], - prompt_function=prompt.bbh, + prompt_function=prompt.bbh_lighteval, hf_repo="lighteval/bbh", hf_subset="tracking_shuffled_objects_three_objects", hf_avail_splits=["train"], diff --git a/src/lighteval/tasks/tasks/truthfulqa.py b/src/lighteval/tasks/tasks/truthfulqa.py index 045c88ffc..84db92ed6 100644 --- a/src/lighteval/tasks/tasks/truthfulqa.py +++ b/src/lighteval/tasks/tasks/truthfulqa.py @@ -27,7 +27,7 @@ name="truthfulqa:gen", suite=["lighteval"], prompt_function=prompt.truthful_qa_generative, - hf_repo="EleutherAI/truthful_qa_mc", + hf_repo="truthfulqa/truthful_qa", hf_subset="generation", hf_avail_splits=["validation"], evaluation_splits=["validation"], @@ -43,7 +43,7 @@ name="truthfulqa:mc", suite=["lighteval"], prompt_function=prompt.truthful_qa_multiple_choice, - hf_repo="EleutherAI/truthful_qa_mc", + hf_repo="truthfulqa/truthful_qa", hf_subset="multiple_choice", hf_avail_splits=["validation"], evaluation_splits=["validation"], diff --git a/tests/reference_details/SmolLM2-1.7B-Instruct-transformers/details_leaderboard|arc:challenge|25_2025-09-19T14-21-59.670987.parquet b/tests/reference_details/SmolLM2-1.7B-Instruct-transformers/details_leaderboard|arc:challenge|25_2025-09-19T14-21-59.670987.parquet deleted file mode 100644 index df81532e4..000000000 --- a/tests/reference_details/SmolLM2-1.7B-Instruct-transformers/details_leaderboard|arc:challenge|25_2025-09-19T14-21-59.670987.parquet +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d2dce4416d022cb704a77d63dcbacc99e148cb598186f88f33e7b1c5c019335e -size 87199 diff --git a/tests/reference_details/SmolLM2-1.7B-Instruct-transformers/details_leaderboard|hellaswag|10_2025-09-19T14-21-59.670987.parquet 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second smallest, etc. - current_indices = sorted(range(len(current_logprobs)), key=lambda i: current_logprobs[i]) - reference_indices = sorted(range(len(reference_logprobs)), key=lambda i: reference_logprobs[i]) - - return current_indices == reference_indices - - def load_sample_details(details_dir: str): """Load sample-level details from parquet files in the details directory.""" details = {} @@ -115,12 +92,15 @@ def _compare_metrics(current, reference): reference_metrics = reference["metric"] metric_diffs = {} - for metric_name in set(current_metrics.keys()) | set(reference_metrics.keys()): - current_val = current_metrics.get(metric_name) - reference_val = reference_metrics.get(metric_name) + for metric_name in set(current_metrics.keys()) & set(reference_metrics.keys()): + try: + current_val = current_metrics.get(metric_name) + reference_val = reference_metrics.get(metric_name) - if not math.isclose(current_val, reference_val, abs_tol=0.05): - metric_diffs[metric_name] = {"current": current_val, "reference": reference_val} + if not math.isclose(current_val, reference_val, abs_tol=0.05): + metric_diffs[metric_name] = {"current": current_val, "reference": reference_val} + except Exception: + breakpoint() if metric_diffs: sample_diff["metric_differences"] = metric_diffs @@ -175,6 +155,7 @@ def compare_sample_details(current_details, reference_details): for task_name in current_details: if task_name not in reference_details: + breakpoint() differences[task_name] = [{"error": "Task not found in reference results"}] continue From 57ca0e53b0cef3d999dc56c10cc3e9af44b5e59d Mon Sep 17 00:00:00 2001 From: Nathan Habib Date: Fri, 17 Oct 2025 14:24:36 +0000 Subject: [PATCH 43/43] fix tests --- ...qua-rat|0_2025-10-17T14-08-59.659871.parquet | 3 +++ ...giqa-en|0_2025-10-17T14-08-59.659871.parquet | 3 +++ ...lsat-ar|0_2025-10-17T14-08-59.659871.parquet | 3 +++ ...lsat-lr|0_2025-10-17T14-08-59.659871.parquet | 3 +++ ...lsat-rc|0_2025-10-17T14-08-59.659871.parquet | 3 +++ ...passage|0_2025-10-17T14-08-59.659871.parquet | 3 +++ ...:sat-en|0_2025-10-17T14-08-59.659871.parquet | 3 +++ ...llenge|25_2025-10-17T14-08-59.659871.parquet | 3 +++ ...udgment|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...tanding|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...tion_qa|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ..._shapes|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...objects|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...objects|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...ndation|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...avigate|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...n_names|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...tection|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...:snarks|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...quences|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...objects|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...objects|3_2025-10-17T14-08-59.659871.parquet | 3 +++ ...8k_test|0_2025-10-17T14-08-59.659871.parquet | 3 +++ ...laswag|10_2025-10-17T14-08-59.659871.parquet | 3 +++ ...emistry|5_2025-10-17T14-08-59.659871.parquet | 3 +++ ..._policy|5_2025-10-17T14-08-59.659871.parquet | 3 +++ ...ulqa:mc|0_2025-10-17T14-08-59.659871.parquet | 3 +++ .../reference_task_scores.cpython-310.pyc | Bin 8473 -> 0 bytes .../reference_task_scores.cpython-311.pyc | Bin 5958 -> 0 bytes .../reference_task_scores.cpython-312.pyc | Bin 10539 -> 0 bytes .../__pycache__/reference_tasks.cpython-310.pyc | Bin 2888 -> 0 bytes .../__pycache__/reference_tasks.cpython-311.pyc | Bin 3019 -> 0 bytes .../__pycache__/reference_tasks.cpython-312.pyc | Bin 2963 -> 0 bytes 33 files changed, 81 insertions(+) create mode 100644 tests/reference_details/SmolLM2-1.7B-Instruct-transformers/details_lighteval|agieval:aqua-rat|0_2025-10-17T14-08-59.659871.parquet create mode 100644 tests/reference_details/SmolLM2-1.7B-Instruct-transformers/details_lighteval|agieval:logiqa-en|0_2025-10-17T14-08-59.659871.parquet create mode 100644 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