From 61b57deec9f46ed8eaaf5f48950764352de6daea Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Thu, 11 Apr 2024 13:52:59 +0530 Subject: [PATCH 01/69] Add NER model handler and update PretrainedModelForNER class --- .../modelhandler/lmstudio_modelhandler.py | 68 ++++++++++++++++++- 1 file changed, 66 insertions(+), 2 deletions(-) diff --git a/langtest/modelhandler/lmstudio_modelhandler.py b/langtest/modelhandler/lmstudio_modelhandler.py index 33b652477..dca55e3a9 100644 --- a/langtest/modelhandler/lmstudio_modelhandler.py +++ b/langtest/modelhandler/lmstudio_modelhandler.py @@ -1,4 +1,5 @@ from typing import Any, Callable, Union + from .modelhandler import ModelAPI from abc import ABC from functools import lru_cache @@ -6,6 +7,8 @@ from ..errors import Errors from langtest.utils.lib_manager import try_import_lib from ..utils.custom_types.helpers import SimplePromptTemplate +from langtest.utils.custom_types.output import NEROutput +from langtest.utils.custom_types.predictions import NERPrediction from langtest.utils.custom_types.helpers import HashableDict @@ -42,10 +45,13 @@ def chat_completion_api(text: str, url: str, server_prompt: str, **kwargs): input_data_func = kwargs.get("data") data = input_data_func(text) else: - server_prompt = {"role": "assistant", "content": server_prompt} + if isinstance(server_prompt, str): + server_prompt = {"role": "assistant", "content": server_prompt} user_text = {"role": "user", "content": text} data = { - "messages": [server_prompt, user_text], + "messages": [*server_prompt, user_text] + if isinstance(server_prompt, tuple) + else [server_prompt, user_text], "temperature": kwargs.get("temperature", 0.2), "max_tokens": kwargs.get("max_tokens", -1), "stream": kwargs.get("stream", False), @@ -206,6 +212,64 @@ def __call__( ) +class PretrainedModelForNER(PretrainedModel, ModelAPI): + """A class representing a pretrained model for named entity recognition. + + Inherits: + PretrainedModel: The base class for pretrained models. + + Methods + ------- + predict(text: str, *args, **kwargs) + Predicts the answer to a given question based on the pre-trained model. + + Raises + ------ + Exception + If an error occurs during prediction. + """ + + def __call__(self, text: str, *args, **kwargs) -> NEROutput: + """ + Calls the predict method for the given input text. + """ + prompt = HashableDict( + { + "input_variables": ["text"], + "template": "Given the text: {text}, identify the named entities.", + } + ) + + server_prompt = self.kwargs.get( + "system_prompt", "Please identify the named entities in the given text." + ) + + if isinstance(server_prompt, list): + server_prompt = tuple(HashableDict(item) for item in server_prompt) + + text = HashableDict({"text": text}) + + predictions = self.predict(text, prompt, server_prompt, *args, **kwargs) + + if isinstance(predictions, str): + try: + predictions = eval(predictions) + except SyntaxError: + predictions = [] + + return NEROutput( + predictions=[ + NERPrediction.from_span( + entity=prediction.get("entity_group", prediction.get("entity", None)), + word=prediction.get("word", ""), + start=prediction.get("start", -1), + end=prediction.get("end", -1), + ) + for prediction in predictions + ] + ) + + class PretrainedModelForQA(PretrainedModel, ModelAPI): """ A class for handling a pre-trained model for question answering. From b3d16d74ed6a6e9d908013b27d2e468e50decd95 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Thu, 11 Apr 2024 15:21:04 +0530 Subject: [PATCH 02/69] Add default_llm_chat_prompt to helpers.py and Added NER support for CasualLM Models --- .../modelhandler/transformers_modelhandler.py | 159 ++++++++++++++++-- langtest/utils/custom_types/helpers.py | 27 +++ 2 files changed, 173 insertions(+), 13 deletions(-) diff --git a/langtest/modelhandler/transformers_modelhandler.py b/langtest/modelhandler/transformers_modelhandler.py index eff7f77f8..18ddebc82 100644 --- a/langtest/modelhandler/transformers_modelhandler.py +++ b/langtest/modelhandler/transformers_modelhandler.py @@ -1,4 +1,4 @@ -from typing import Dict, List, Tuple, Union +from typing import Any, Dict, List, Tuple, Union import logging import numpy as np from functools import lru_cache @@ -11,7 +11,11 @@ TranslationOutput, ) from ..errors import Errors, Warnings -from ..utils.custom_types.helpers import SimplePromptTemplate, HashableDict +from ..utils.custom_types.helpers import ( + SimplePromptTemplate, + HashableDict, + default_llm_chat_prompt, +) from ..utils.hf_utils import HuggingFacePipeline @@ -22,7 +26,7 @@ class PretrainedModelForNER(ModelAPI): model (transformers.pipeline.Pipeline): Pretrained HuggingFace NER pipeline for predictions. """ - def __init__(self, model): + def __init__(self, model, **kwargs): """Constructor method Args: @@ -33,6 +37,7 @@ def __init__(self, model): ) self.model = model + self.kwargs = kwargs self.predict.cache_clear() @staticmethod @@ -136,7 +141,7 @@ def group_entities(self, entities: List[Dict]) -> List[Dict]: return entity_groups @classmethod - def load_model(cls, path: str) -> "Pipeline": + def load_model(cls, path: Union[str, Any], **kwargs) -> "Pipeline": """Load the NER model into the `model` attribute. Args: @@ -146,9 +151,27 @@ def load_model(cls, path: str) -> "Pipeline": Returns: 'Pipeline': """ - if isinstance(path, str): - return cls(pipeline(model=path, task="ner", ignore_labels=[])) - return cls(path) + # cls.kwargs = kwargs + task = kwargs.get("task", "ner") + device = kwargs.get("device", -1) + + if isinstance(path, str) and task == "ner": + return cls( + pipeline(model=path, task=task, device=device, ignore_labels=[]), **kwargs + ) + elif isinstance(path, str): + import torch + + return cls( + pipeline( + model=path, + task="text-generation", + device=device, + torch_dtype=torch.bfloat16, + ), + **kwargs, + ) + return cls(path, **kwargs) @lru_cache(maxsize=102400) def predict(self, text: str, **kwargs) -> NEROutput: @@ -164,17 +187,38 @@ def predict(self, text: str, **kwargs) -> NEROutput: Returns: NEROutput: A list of named entities recognized in the input text. """ - predictions = self.model(text, **kwargs) - aggregated_words = self._aggregate_words(predictions) - aggregated_predictions = self.group_entities(aggregated_words) + + if self.model.model.__class__.__name__.endswith("ForCausalLM"): + predictions, _ = self.__predict_causalLm(text, **kwargs) + if predictions: + predictions = predictions[-1]["content"] + if not isinstance(predictions, list): + predictions = [predictions] + else: + predictions = [ + { + "entity": "", + "score": -1, + "index": -1, + "word": "", + "start": -1, + "end": -1, + } + ] + + aggregated_predictions = predictions + else: + predictions = self.model(text, **kwargs) + aggregated_words = self._aggregate_words(predictions) + aggregated_predictions = self.group_entities(aggregated_words) return NEROutput( predictions=[ NERPrediction.from_span( entity=prediction.get("entity_group", prediction.get("entity", None)), - word=prediction["word"], - start=prediction["start"], - end=prediction["end"], + word=prediction.get("word", ""), + start=prediction.get("start", -1), + end=prediction.get("end", -1), ) for prediction in aggregated_predictions ] @@ -203,6 +247,95 @@ def __call__(self, text: str, *args, **kwargs) -> NEROutput: """Alias of the 'predict' method""" return self.predict(text=text, **kwargs) + def __predict_causalLm(self, text: str, **kwargs): + import re + import json + + # override default tokenizer parameters with the user-defined parameters + tokenizer_params = { + "tokenize": False, + "add_generation_prompt": False, + "return_dict": True, + **self.kwargs.get("tokenizer_kwargs", {}), + } + + # override default model parameters with the user-defined parameters + model_params = { + "max_new_tokens": 512, + "do_sample": True, + "temperature": 0.2, + "top_k": 10, + "top_p": 0.95, + "batch_size": 1, + "return_full_text": False, + **self.kwargs.get("model_kwargs", {}), + } + + # special characters to stop the sequence generation + role_extract = self.kwargs.get("role_extract", "<\|(.+?)\|>") + stop_char = self.kwargs.get("stop_char", "") + + # process the input text + messages = self.input_process(text) + + # preprocess the messages and generate the prompt for the model + prompt = self.model.tokenizer.apply_chat_template(messages, **tokenizer_params) + + # generate the response from the model + outputs = self.model( + prompt, + **model_params, + ) + + # Extracting the structured responses from the generated text + responses = [] + for message in outputs[0]["generated_text"].split(stop_char): + role_match = re.search(role_extract, message) + if role_match: + role = role_match.group(1) + content = message.replace(role_match.group(0), "").strip() + if role == "assistant": + try: + # extract the JSON content from the generated text + content_json = json.loads(content.replace("'", '"')) + + except json.JSONDecodeError as e: + # print(f"Error decoding JSON content: {e}") + content_json = [ + { + "entity": "", + "score": -1, + "index": -1, + "word": "", + "start": -1, + "end": -1, + } + ] + responses.append({"role": role, "content": content_json}) + + else: + # responses.append({"role": role, "content": content}) + pass + + return responses, outputs + + def input_process(self, input_sen): + """ + Process the input text to be used in the model + """ + + # extract the system prompt from the user-defined parameters + system_prompt = self.kwargs.get("system_prompt", default_llm_chat_prompt) + + if system_prompt: + if isinstance(system_prompt, str): + return f"{system_prompt}\n\n {input_sen}" + elif isinstance(system_prompt, list): + input_sen = {"role": "user", "content": input_sen} + return [*system_prompt, input_sen] + + return default_llm_chat_prompt + class PretrainedModelForTextClassification(ModelAPI): """Transformers pretrained model for text classification tasks diff --git a/langtest/utils/custom_types/helpers.py b/langtest/utils/custom_types/helpers.py index 30bba5858..ab989f23c 100644 --- a/langtest/utils/custom_types/helpers.py +++ b/langtest/utils/custom_types/helpers.py @@ -113,6 +113,33 @@ "pubmedqa": "Context: {context}\nQuestion: {question}\n I've provided a question and context. From here on, I want you to become an intelligent bot that can only answer with one of these three choices: 'yes', 'no', or 'maybe'. If you think the answer to the question is yes, then say 'yes'. If it is no, then say 'no'. If the answer is uncertain or could be either yes or no, say 'maybe'. Do not say anything else other than that.", } +default_llm_chat_prompt = { + "ner": [ + { + "role": "system", + "content": "You are an NER model designed to identify entities in the text. Your task is to classify each identified entity with its type, confidence score, word index, and the start and end positions in the input text. Respond with a list of dictionaries, each containing the keys 'entity', 'score', 'index', 'word', 'start', and 'end'. Note that all keys are mandatory and choose only ORG, and PER enities. Let's start with a few examples.", + }, + {"role": "user", "content": "The sentence is: John is working at Google."}, + { + "role": "assistant", + "content": "[{'entity': 'PER', 'score': 0.99, 'index': 1, 'word': 'John', 'start': 0, 'end': 4}, {'entity': 'ORG', 'score': 0.98, 'index': 5, 'word': 'Google', 'start': 19, 'end': 25}]", + }, + {"role": "user", "content": "The sentence is: Elon Musk founded SpaceX."}, + { + "role": "assistant", + "content": "[{'entity': 'PER', 'score': 0.99, 'index': 1, 'word': 'Elon Musk', 'start': 0, 'end': 9}, {'entity': 'ORG', 'score': 0.97, 'index': 4, 'word': 'SpaceX', 'start': 18, 'end': 24}]", + }, + { + "role": "user", + "content": "The sentence is: Ada Lovelace is considered the first computer programmer.", + }, + { + "role": "assistant", + "content": "[{'entity': 'PER', 'score': 0.98, 'index': 1, 'word': 'Ada Lovelace', 'start': 0, 'end': 12}]", + }, + ] +} + class Span(BaseModel): """Representation of a text's slice""" From 30c20efe68a3e9c2f59bdc2315213e2c45748d27 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Thu, 11 Apr 2024 17:29:48 +0530 Subject: [PATCH 03/69] Fix role_extract regex pattern in PretrainedModelForNER class and lint issues --- langtest/modelhandler/transformers_modelhandler.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/langtest/modelhandler/transformers_modelhandler.py b/langtest/modelhandler/transformers_modelhandler.py index 18ddebc82..6b7099e71 100644 --- a/langtest/modelhandler/transformers_modelhandler.py +++ b/langtest/modelhandler/transformers_modelhandler.py @@ -272,7 +272,7 @@ def __predict_causalLm(self, text: str, **kwargs): } # special characters to stop the sequence generation - role_extract = self.kwargs.get("role_extract", "<\|(.+?)\|>") + role_extract = self.kwargs.get("role_extract", r"<\|(.+?)\|>") stop_char = self.kwargs.get("stop_char", "") # process the input text @@ -299,7 +299,7 @@ def __predict_causalLm(self, text: str, **kwargs): # extract the JSON content from the generated text content_json = json.loads(content.replace("'", '"')) - except json.JSONDecodeError as e: + except json.JSONDecodeError: # print(f"Error decoding JSON content: {e}") content_json = [ { From a46fa39bbf7d5edaf8333cb93118e21a84df8276 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Thu, 11 Apr 2024 19:19:56 +0530 Subject: [PATCH 04/69] Refactor predict method in PretrainedModelForNER class for better performance and error handling --- .../modelhandler/lmstudio_modelhandler.py | 40 +++++++++++++++++-- 1 file changed, 36 insertions(+), 4 deletions(-) diff --git a/langtest/modelhandler/lmstudio_modelhandler.py b/langtest/modelhandler/lmstudio_modelhandler.py index dca55e3a9..5042b29ff 100644 --- a/langtest/modelhandler/lmstudio_modelhandler.py +++ b/langtest/modelhandler/lmstudio_modelhandler.py @@ -229,10 +229,12 @@ class PretrainedModelForNER(PretrainedModel, ModelAPI): If an error occurs during prediction. """ - def __call__(self, text: str, *args, **kwargs) -> NEROutput: - """ - Calls the predict method for the given input text. + @lru_cache(maxsize=102400) + def predict(self, text: str, *args, **kwargs) -> NEROutput: """ + Perform prediction using the pretrained model.""" + + # prompt configuration prompt = HashableDict( { "input_variables": ["text"], @@ -240,20 +242,43 @@ def __call__(self, text: str, *args, **kwargs) -> NEROutput: } ) + # server prompt configuration server_prompt = self.kwargs.get( "system_prompt", "Please identify the named entities in the given text." ) + # check if server prompt is a list and convert it to a tuple if isinstance(server_prompt, list): server_prompt = tuple(HashableDict(item) for item in server_prompt) + # convert text to a dictionary text = HashableDict({"text": text}) - predictions = self.predict(text, prompt, server_prompt, *args, **kwargs) + # result + predictions = None + + try: + prompt_template = SimplePromptTemplate(**prompt) + p = prompt_template.format(**text) + op = chat_completion_api( + text=p, + url=self.model, + server_prompt=server_prompt, + *args, + **self.kwargs, + ) + if self.output_parser: + predictions = self.output_parser(op) + else: + predictions = op["choices"][0]["message"]["content"] + except Exception as e: + raise ValueError(Errors.E089.format(error_message=e)) if isinstance(predictions, str): try: predictions = eval(predictions) + if not isinstance(predictions, list): + predictions = [] except SyntaxError: predictions = [] @@ -269,6 +294,13 @@ def __call__(self, text: str, *args, **kwargs) -> NEROutput: ] ) + def __call__(self, text: str, *args, **kwargs) -> NEROutput: + """ + Calls the predict method for the given input text. + """ + + return self.predict(text, *args, **kwargs) + class PretrainedModelForQA(PretrainedModel, ModelAPI): """ From 85a3b80874ce62f0a929622849475b5c9a732427 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Fri, 12 Apr 2024 12:30:32 +0530 Subject: [PATCH 05/69] Enhancements for user prompt handling for multi-dataset testing in Harness(langtest.py) --- langtest/langtest.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/langtest/langtest.py b/langtest/langtest.py index b3f5b614e..2ca0a6778 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -1572,8 +1572,18 @@ def __multi_datasets_run( ): generated_results = {} + # temp_store_prompt + temp_store_prompt = self._config.get("model_parameters", {}).get( + "user_prompt", None + ) + # Run the testcases for each dataset for dataset_name, samples in testcases.items(): + # update user prompt for each dataset + if temp_store_prompt: + self._config.get("model_parameters", {}).update( + {"user_prompt": temp_store_prompt.get(dataset_name)} + ) # Get the raw data for the dataset if isinstance(self.data, dict): raw_data = self.data.get(dataset_name) @@ -1597,6 +1607,12 @@ def __multi_datasets_run( print(f"{'':-^80}\n") + # resore user prompt + if temp_store_prompt: + self._config.get("model_parameters", {}).update( + {"user_prompt": temp_store_prompt} + ) + if ( self.is_multi_dataset and self._generated_results is None From c9055cfcf40fddf1d5e9ac0389ef0c05b876ac39 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Fri, 12 Apr 2024 14:00:32 +0530 Subject: [PATCH 06/69] Update user prompt handling in Harness for multi-dataset testing --- langtest/langtest.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/langtest/langtest.py b/langtest/langtest.py index 2ca0a6778..b5ba0d90f 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -1580,7 +1580,7 @@ def __multi_datasets_run( # Run the testcases for each dataset for dataset_name, samples in testcases.items(): # update user prompt for each dataset - if temp_store_prompt: + if temp_store_prompt and isinstance(temp_store_prompt, dict): self._config.get("model_parameters", {}).update( {"user_prompt": temp_store_prompt.get(dataset_name)} ) From a32b3d8755cd017a6c633408ef4b9ae3c877b7bc Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 15 Apr 2024 10:49:18 +0530 Subject: [PATCH 07/69] Refactor PretrainedModelForNER class for better performance and error handling --- .../modelhandler/transformers_modelhandler.py | 59 +++++++------------ 1 file changed, 22 insertions(+), 37 deletions(-) diff --git a/langtest/modelhandler/transformers_modelhandler.py b/langtest/modelhandler/transformers_modelhandler.py index 6b7099e71..5ab16b5c5 100644 --- a/langtest/modelhandler/transformers_modelhandler.py +++ b/langtest/modelhandler/transformers_modelhandler.py @@ -191,7 +191,6 @@ def predict(self, text: str, **kwargs) -> NEROutput: if self.model.model.__class__.__name__.endswith("ForCausalLM"): predictions, _ = self.__predict_causalLm(text, **kwargs) if predictions: - predictions = predictions[-1]["content"] if not isinstance(predictions, list): predictions = [predictions] else: @@ -248,13 +247,12 @@ def __call__(self, text: str, *args, **kwargs) -> NEROutput: return self.predict(text=text, **kwargs) def __predict_causalLm(self, text: str, **kwargs): - import re - import json + """Perform predictions on the input text using a causal language model.""" # override default tokenizer parameters with the user-defined parameters tokenizer_params = { "tokenize": False, - "add_generation_prompt": False, + "add_generation_prompt": True, "return_dict": True, **self.kwargs.get("tokenizer_kwargs", {}), } @@ -267,13 +265,20 @@ def __predict_causalLm(self, text: str, **kwargs): "top_k": 10, "top_p": 0.95, "batch_size": 1, - "return_full_text": False, **self.kwargs.get("model_kwargs", {}), } - # special characters to stop the sequence generation - role_extract = self.kwargs.get("role_extract", r"<\|(.+?)\|>") - stop_char = self.kwargs.get("stop_char", "") + # override the default parameters for the tokenizer + tokenizer_params = { + **tokenizer_params, + "add_generation_prompt": True, + } + # override the default parameters for the model + model_params = { + **model_params, + "return_full_text": False, + "handle_long_generation": "hole", + } # process the input text messages = self.input_process(text) @@ -288,36 +293,16 @@ def __predict_causalLm(self, text: str, **kwargs): ) # Extracting the structured responses from the generated text - responses = [] - for message in outputs[0]["generated_text"].split(stop_char): - role_match = re.search(role_extract, message) - if role_match: - role = role_match.group(1) - content = message.replace(role_match.group(0), "").strip() - if role == "assistant": - try: - # extract the JSON content from the generated text - content_json = json.loads(content.replace("'", '"')) - - except json.JSONDecodeError: - # print(f"Error decoding JSON content: {e}") - content_json = [ - { - "entity": "", - "score": -1, - "index": -1, - "word": "", - "start": -1, - "end": -1, - } - ] - responses.append({"role": role, "content": content_json}) - - else: - # responses.append({"role": role, "content": content}) - pass + predictions = [] + if isinstance(outputs[0]["generated_text"], str): + try: + predictions = eval(outputs[0]["generated_text"]) + if not isinstance(predictions, list): + predictions = [] + except SyntaxError: + predictions = [] - return responses, outputs + return predictions, outputs def input_process(self, input_sen): """ From 1c34bd9189efc823bc1d90f22694981d9325bf81 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 15 Apr 2024 11:14:08 +0530 Subject: [PATCH 08/69] Refactor LLMChain and PromptTemplate imports in llm_modelhandler.py --- langtest/modelhandler/llm_modelhandler.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/langtest/modelhandler/llm_modelhandler.py b/langtest/modelhandler/llm_modelhandler.py index 4a9081f33..c256b73b6 100644 --- a/langtest/modelhandler/llm_modelhandler.py +++ b/langtest/modelhandler/llm_modelhandler.py @@ -2,7 +2,8 @@ from typing import Any, Union import langchain.llms as lc import langchain.chat_models as cm -from langchain import LLMChain, PromptTemplate +from langchain.chains import LLMChain +from langchain_core.prompts import PromptTemplate from pydantic import ValidationError from ..modelhandler.modelhandler import ModelAPI, LANGCHAIN_HUBS from ..errors import Errors, Warnings @@ -133,8 +134,8 @@ def predict(self, text: Union[str, dict], prompt: dict, *args, **kwargs): try: prompt_template = PromptTemplate(**prompt) llmchain = LLMChain(prompt=prompt_template, llm=self.model) - output = llmchain.run(**text) - return output + output = llmchain.invoke(text) + return output.get(llmchain.output_key, "") except Exception as e: raise ValueError(Errors.E089.format(error_message=e)) From fc20e0445b64001eead27d71c39a06fc4eab63ac Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 15 Apr 2024 19:38:04 +0530 Subject: [PATCH 09/69] Refactor PretrainedModelForNER class for better performance and error handling --- langtest/modelhandler/llm_modelhandler.py | 95 ++++++++++++++++++++++- 1 file changed, 93 insertions(+), 2 deletions(-) diff --git a/langtest/modelhandler/llm_modelhandler.py b/langtest/modelhandler/llm_modelhandler.py index c256b73b6..a181f27dc 100644 --- a/langtest/modelhandler/llm_modelhandler.py +++ b/langtest/modelhandler/llm_modelhandler.py @@ -1,10 +1,14 @@ import inspect -from typing import Any, Union +from typing import Any, List, Union import langchain.llms as lc import langchain.chat_models as cm from langchain.chains import LLMChain from langchain_core.prompts import PromptTemplate -from pydantic import ValidationError +from langchain_core.exceptions import OutputParserException +from pydantic import Field, ValidationError + +from langtest.utils.custom_types.output import NEROutput +from langtest.utils.custom_types.predictions import NERPrediction from ..modelhandler.modelhandler import ModelAPI, LANGCHAIN_HUBS from ..errors import Errors, Warnings import logging @@ -198,6 +202,93 @@ def __str__(self): return self.message +class PretrainedModelForNER(PretrainedModelForQA, ModelAPI): + """A class representing a pretrained model for named entity recognition. + + Inherits: + PretrainedModelForQA: The base class for pretrained models. + """ + + @lru_cache(maxsize=102400) + def predict(self, text: Union[str, dict], *args, **kwargs) -> NEROutput: + """Perform prediction using the pretrained model. + + Args: + text (Union[str, dict]): The input text or dictionary. + *args: Additional positional arguments. + **kwargs: Additional keyword arguments. + + Returns: + dict: A dictionary containing the prediction result. + - 'result': The prediction result. + """ + try: + prompt = { + "input_variables": ["text"], + "template": "Extract the named entities from the text. \n{format_instructions}\n {text}", + "partial_variables": { + "format_instructions": self.__output_parser().get_format_instructions() + }, + } + prompt_template = PromptTemplate(**prompt) + llmchain = LLMChain( + prompt=prompt_template, + llm=self.model, + output_parser=self.__output_parser(), + ) + result = llmchain.invoke({"text": text}) + result: dict = result.get(llmchain.output_key, {"entities": []}) + + try: + predictions = [] + for entity in result.get("entities", []): + try: + entity = NERPrediction.from_span(**entity) + predictions.append(entity) + except Exception: + pass + + return NEROutput(predictions=predictions) + except Exception: + return NEROutput(predictions=[]) + + except OutputParserException as e: + return NEROutput(predictions=[]) + + except Exception as e: + raise ValueError(Errors.E089.format(error_message=e)) + + def __call__(self, text: str, *args, **kwargs): + return self.predict(text, *args, **kwargs) + + def __output_parser(self): + from langchain_core.output_parsers import JsonOutputParser + from pydantic import BaseModel + + class Word(BaseModel): + """Single word in a named entity recognition prediction""" + + word: str = Field(description="Word in the text") + start: int = Field( + description="Start index of the character in the word from the text" + ) + end: int = Field( + description="End index of the character in the word from the text" + ) + entity: str = Field(description="Named entity type") + score: float = Field(description="Confidence score of the prediction") + pos_tag: str = Field(description="Part of speech tag") + chunk_tag: str = Field(description="Chunk tag") + + class NERParser(BaseModel): + """Named entity recognition prediction parser""" + + entities: List[Word] = Field(description="List of named entities in the text") + + parser = JsonOutputParser(pydantic_object=NERParser) + return parser + + class PretrainedModelForSummarization(PretrainedModelForQA, ModelAPI): """A class representing a pretrained model for summarization. From 01c5473cecb2f8569bd86e92ae39d135e5b91b9f Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 15 Apr 2024 19:49:38 +0530 Subject: [PATCH 10/69] fix linting issues --- langtest/modelhandler/llm_modelhandler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/langtest/modelhandler/llm_modelhandler.py b/langtest/modelhandler/llm_modelhandler.py index a181f27dc..b0e56a56f 100644 --- a/langtest/modelhandler/llm_modelhandler.py +++ b/langtest/modelhandler/llm_modelhandler.py @@ -252,7 +252,7 @@ def predict(self, text: Union[str, dict], *args, **kwargs) -> NEROutput: except Exception: return NEROutput(predictions=[]) - except OutputParserException as e: + except OutputParserException: return NEROutput(predictions=[]) except Exception as e: From 82bbf4a7af7f52057eeed7feb1b4e8aeab68dc9b Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Wed, 17 Apr 2024 15:15:50 +0530 Subject: [PATCH 11/69] New: created the leaderboard and summary classes in utils to track the harness reports over the timestamp. --- langtest/langtest.py | 6 +- langtest/utils/benchmark_utils.py | 244 ++++++++++++++++++++++++++++++ 2 files changed, 249 insertions(+), 1 deletion(-) create mode 100644 langtest/utils/benchmark_utils.py diff --git a/langtest/langtest.py b/langtest/langtest.py index b3f5b614e..17d8d5890 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -1342,7 +1342,11 @@ def __single_dataset_generate(self, dataset: list): return testcases elif str(self.task) in ("question-answering", "summarization"): - if "bias" in tests.keys() and "bias" == self.__data_dict.get("split"): + if ( + "bias" in tests.keys() + and isinstance(self.__data_dict, dict) + and "bias" == self.__data_dict.get("split") + ): if self.__data_dict["data_source"] in ("BoolQ", "XSum"): tests_to_filter = tests["bias"].keys() testcases = DataFactory.filter_curated_bias(tests_to_filter, dataset) diff --git a/langtest/utils/benchmark_utils.py b/langtest/utils/benchmark_utils.py new file mode 100644 index 000000000..79de802cb --- /dev/null +++ b/langtest/utils/benchmark_utils.py @@ -0,0 +1,244 @@ +import os +import pandas as pd + + +class Leaderboard: + """ + Leaderboard class to manage the ranking of the models + + Args: + path (str): The path to the summary file + + + """ + _instance = None + + def __new__(cls, *args, **kwargs): + """ + Singleton pattern to ensure only one instance of the class is created + """ + if not cls._instance: + cls._instance = super().__new__(cls) + return cls._instance + + def __init__(self, path:str, *args, **kwargs) -> None: + """ + Initialize the Leaderboard class with the summary file + """ + self.summary = Summary(path, *args, **kwargs) + + def get_score_board(self): + """ + Get the score board for the models + """ + df = self.summary.summary_df + + # find the timestamp with the highest score + df["timestamp"] = pd.to_datetime(df["timestamp"]) + df = df.sort_values(by="timestamp", ascending=False) + idx = df.groupby( + ["timestamp", "model", "dataset_name", "split", "test_type", "category"] + )["score"].idxmax() + df = df.loc[idx] + pvt_table = df.pivot_table( + index=["model"], columns="dataset_name", values="score" + ) + pvt_table = pvt_table.rename_axis(None, axis=1).reset_index() + pvt_table = pvt_table.fillna("-") + + # mean column + pvt_table.insert(1, "Avg", pvt_table.iloc[:, 1:].mean(axis=1)) + pvt_table = pvt_table.sort_values(by="Avg", ascending=False) + + return pvt_table + + def get_score_board_by_tests(self): + """ + Get the score board for the models by test type + """ + + df = self.summary.summary_df + pvt_table = df.pivot_table( + index=["model", "split"], columns=["dataset_name"], values="score" + ) + # pvt_table.columns = [f"{col[0]}\n{col[1]}" for col in pvt_table.columns] + # pvt_table = pvt_table.rename_axis(None, axis=1).reset_index() + pvt_table = pvt_table.fillna("-") + + return pvt_table + + def get_score_board_by_category(self): + """ + Get the score board for the models by category + """ + df = self.summary.summary_df + pvt_table = df.pivot_table( + index=["model", "category"], columns=["dataset_name"], values="score" + ) + pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) + pvt_table = pvt_table.fillna("-") + pvt_table = pvt_table.rename_axis(None, axis=1).reset_index() + + return pvt_table + + def __repr__(self) -> str: + return self.summary.summary_df.to_markdown() + + +class Summary: + """ + Summary class to manage the summary report + """ + _instance = None + + def __new__(cls, *args, **kwargs) -> None: + """ + Singleton pattern to ensure only one instance of the class is created + """ + if not cls._instance: + cls._instance = super().__new__(cls) + return cls._instance + + def __init__(self, path, *args, **kwargs) -> None: + """ + Initialize the summary + """ + self.file_path = path + self.summary_df: pd.DataFrame = self.load_data_from_file(path, *args, **kwargs) + + def load_data_from_file(self, path: str, *args, **kwargs) -> pd.DataFrame: + """ + Check if file exists + """ + try: + if os.path.exists(path): + return self.__read_from_csv(path, *args, **kwargs) + else: + # Create a new file + df = pd.DataFrame(columns=self.__default_columns()) + df.to_csv(path, index=False) + return df + except FileNotFoundError: + raise FileNotFoundError(f"File not found at {path}") + + def __read_from_csv(self, path: str) -> pd.DataFrame: + """ + Read data from csv file + """ + df = pd.read_csv(path) + return df + + def __default_columns(self): + """ + Default columns for the summary report + """ + cols = [ + "timestamp", + "task", + "model", + "hub", + "category", + "test_type", + "dataset_name", + "split", + "subset", + "total_records", + "success_records", + "failure_records", + "score", + ] + return cols + + def add_report( + self, + generated_results: pd.DataFrame, + ) -> None: + """ + Add a new report to the summary + """ + + from datetime import datetime + + # Filter the dataframe for accuracy, fairness and representation + afr_df = self.__afr(generated_results) + not_afr_df = self.__not_afr(generated_results) + + # concatenate the dataframes + temp_summary_df = pd.concat([afr_df, not_afr_df], axis=0) + + timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + temp_summary_df["timestamp"] = timestamp + + # insert row to the summary df + self.summary_df = pd.concat([self.summary_df, temp_summary_df], ignore_index=True) + + # Save the summary to the file + self.save_summary() + + def save_summary(self) -> None: + """ + Save the summary to the file + """ + self.summary_df.to_csv(self.file_path, index=False) + + def __afr(self, df: pd.DataFrame) -> pd.DataFrame: + """ + Filter the dataframe for accuracy, fairness and representation + to be used in the summary report + """ + df = df[df["category"].isin(["accuracy", "fairness", "representation"])] + df = df[self.__group_by_cols() + ["actual_result"]] + df = df.rename(columns={"actual_result": "score"}) + + return df + + def __not_afr(self, df: pd.DataFrame) -> pd.DataFrame: + """ + Filter the dataframe for non accuracy, fairness and representation + to be used in the summary report + """ + df = df[~df["category"].isin(["accuracy", "fairness", "representation"])] + + grouped = df.groupby(self.__group_by_cols()) + + # Filter the columns + import numpy as np + + total_records = grouped.size().reset_index(name="total_records") + success_records = grouped["pass"].sum().reset_index(name="success_records") + score = grouped["pass"].mean().reset_index(name="score") + failure_records = grouped.apply( + lambda x: np.size(x["pass"]) - np.sum(x["pass"]) + ).reset_index(name="failure_records") + + # concatenate the dataframes + result = pd.concat( + [ + success_records, + failure_records["failure_records"], + total_records["total_records"], + score["score"], + ], + axis=1, + ) + + return result + + def __group_by_cols(self): + """ + Group by columns + """ + return [ + "category", + "dataset_name", + "test_type", + "model", + "hub", + "split", + "subset", + "task", + ] + + @property + def df(self) -> pd.DataFrame: + return self.summary_df From ab450d839e1c3d4a05b01934013f0c0b119428c0 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Wed, 17 Apr 2024 19:13:33 +0530 Subject: [PATCH 12/69] Add benchmarking functionality to Harness class --- langtest/langtest.py | 36 +++++++++++++++++++++++++++++++ langtest/utils/benchmark_utils.py | 7 ++++-- 2 files changed, 41 insertions(+), 2 deletions(-) diff --git a/langtest/langtest.py b/langtest/langtest.py index 17d8d5890..8a5382fb4 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -13,6 +13,7 @@ from pkg_resources import resource_filename + from .tasks import TaskManager from .augmentation import AugmentRobustness, TemplaticAugment from .datahandler.datasource import DataFactory @@ -22,6 +23,7 @@ from .transform.utils import RepresentationOperation +from langtest.utils.benchmark_utils import Summary from langtest.utils.lib_manager import try_import_lib from langtest.utils.custom_types.helpers import TestResultManager from langtest.utils.checkpoints import divide_into_batches, CheckpointManager @@ -92,6 +94,7 @@ def __init__( model: Optional[Union[list, dict]] = None, data: Optional[Union[list, dict]] = None, config: Optional[Union[str, dict]] = None, + benchmarking: dict = None, ): """Initialize the Harness object. @@ -111,6 +114,8 @@ def __init__( self.is_default = False self.__data_dict = data self.__is_multi_model = False + self.__model_info = model + self.__benchmarking = benchmarking # reset classes to default state self.__reset_defaults() @@ -446,6 +451,37 @@ def report( pd.DataFrame: DataFrame containing the results of the tests. """ + + # benchmarking true + if self.__benchmarking: + df = self.generated_results() + + path = self.__benchmarking.get( + os.path.expanduser("save_dir"), + os.path.expanduser("~/.langtest/leaderboard/summary.csv"), + ) + summary = Summary(path) + + # temp dict + temp_dict = {} + if isinstance(self.__data_dict, dict): + temp_dict[self.__data_dict.get("data_source")] = self.__data_dict + else: + for i in self.__data_dict: + temp_dict[i.get("data_source")] = i + + # add the dataset_name column if the data is multi-dataset + df["split"] = df["dataset_name"].apply( + lambda x: temp_dict[x].get("split", "-") + ) + df["model"] = self.__model_info.get("model", "-") + df["hub"] = self.__model_info.get("hub", "-") + df["task"] = str(self.task) + df["subset"] = df["dataset_name"].apply( + lambda x: temp_dict[x].get("subset", "-") + ) + summary.add_report(df) + if self._generated_results is None: raise RuntimeError(Errors.E011) diff --git a/langtest/utils/benchmark_utils.py b/langtest/utils/benchmark_utils.py index 79de802cb..2b101c219 100644 --- a/langtest/utils/benchmark_utils.py +++ b/langtest/utils/benchmark_utils.py @@ -11,6 +11,7 @@ class Leaderboard: """ + _instance = None def __new__(cls, *args, **kwargs): @@ -21,7 +22,7 @@ def __new__(cls, *args, **kwargs): cls._instance = super().__new__(cls) return cls._instance - def __init__(self, path:str, *args, **kwargs) -> None: + def __init__(self, path: str, *args, **kwargs) -> None: """ Initialize the Leaderboard class with the summary file """ @@ -89,6 +90,7 @@ class Summary: """ Summary class to manage the summary report """ + _instance = None def __new__(cls, *args, **kwargs) -> None: @@ -99,7 +101,7 @@ def __new__(cls, *args, **kwargs) -> None: cls._instance = super().__new__(cls) return cls._instance - def __init__(self, path, *args, **kwargs) -> None: + def __init__(self, path: str, *args, **kwargs) -> None: """ Initialize the summary """ @@ -114,6 +116,7 @@ def load_data_from_file(self, path: str, *args, **kwargs) -> pd.DataFrame: if os.path.exists(path): return self.__read_from_csv(path, *args, **kwargs) else: + os.makedirs(os.path.dirname(path), exist_ok=True) # Create a new file df = pd.DataFrame(columns=self.__default_columns()) df.to_csv(path, index=False) From 6801583828c6268da096b80df569d84545944d87 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Fri, 19 Apr 2024 12:25:48 +0530 Subject: [PATCH 13/69] Add benchmarking functionality to Harness class --- langtest/langtest.py | 12 ++++++- langtest/utils/benchmark_utils.py | 53 +++++++++++++++++++++++++++---- 2 files changed, 57 insertions(+), 8 deletions(-) diff --git a/langtest/langtest.py b/langtest/langtest.py index 8a5382fb4..02e64f2a6 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -474,8 +474,18 @@ def report( df["split"] = df["dataset_name"].apply( lambda x: temp_dict[x].get("split", "-") ) - df["model"] = self.__model_info.get("model", "-") df["hub"] = self.__model_info.get("hub", "-") + if self.__model_info.get("hub", "-") == "lm-studio": + import requests as req + + response = req.get( + "http://localhost:1234/v1/models", + ).json() + + model_name = response["data"][0]["id"] + df["model"] = model_name + else: + df["model"] = self.__model_info.get("model", "-") df["task"] = str(self.task) df["subset"] = df["dataset_name"].apply( lambda x: temp_dict[x].get("subset", "-") diff --git a/langtest/utils/benchmark_utils.py b/langtest/utils/benchmark_utils.py index 2b101c219..989b3fc7a 100644 --- a/langtest/utils/benchmark_utils.py +++ b/langtest/utils/benchmark_utils.py @@ -28,7 +28,7 @@ def __init__(self, path: str, *args, **kwargs) -> None: """ self.summary = Summary(path, *args, **kwargs) - def get_score_board(self): + def default(self): """ Get the score board for the models """ @@ -41,34 +41,59 @@ def get_score_board(self): ["timestamp", "model", "dataset_name", "split", "test_type", "category"] )["score"].idxmax() df = df.loc[idx] + + # pivot the table pvt_table = df.pivot_table( index=["model"], columns="dataset_name", values="score" ) + + # mean column + pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) + pvt_table = pvt_table.sort_values(by="Avg", ascending=False) + + # reset the index and fill the NaN values pvt_table = pvt_table.rename_axis(None, axis=1).reset_index() pvt_table = pvt_table.fillna("-") + return pvt_table + + def split_wise(self): + """ + Get the score board for the models by test type + """ + + df = self.summary.summary_df + pvt_table = df.pivot_table( + index=["model", "split"], columns=["dataset_name"], values="score" + ) + # mean column - pvt_table.insert(1, "Avg", pvt_table.iloc[:, 1:].mean(axis=1)) + pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) pvt_table = pvt_table.sort_values(by="Avg", ascending=False) + pvt_table = pvt_table.fillna("-") + return pvt_table - def get_score_board_by_tests(self): + def test_wise(self): """ Get the score board for the models by test type """ df = self.summary.summary_df pvt_table = df.pivot_table( - index=["model", "split"], columns=["dataset_name"], values="score" + index=["model", "test_type"], columns=["dataset_name"], values="score" ) - # pvt_table.columns = [f"{col[0]}\n{col[1]}" for col in pvt_table.columns] - # pvt_table = pvt_table.rename_axis(None, axis=1).reset_index() + + # mean column + pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) + pvt_table = pvt_table.sort_values(by="Avg", ascending=False) + pvt_table = pvt_table.fillna("-") return pvt_table - def get_score_board_by_category(self): + def category_wise(self): """ Get the score board for the models by category """ @@ -82,6 +107,20 @@ def get_score_board_by_category(self): return pvt_table + def custom_wise(self, indices: list, columns: list = []): + """ + Get the score board for the models by custom group + """ + df = self.summary.summary_df + pvt_table = df.pivot_table( + index=["model", *indices], columns=["dataset_name", *columns], values="score" + ) + pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) + pvt_table = pvt_table.fillna("-") + # pvt_table = pvt_table.rename_axis(None, axis=1).reset_index() + + return pvt_table + def __repr__(self) -> str: return self.summary.summary_df.to_markdown() From fc8855a51423d894b088a68304557deaf77948ab Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Fri, 19 Apr 2024 15:18:08 +0530 Subject: [PATCH 14/69] Refactor sorting logic in Leaderboard class to sort by model and average score --- langtest/utils/benchmark_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/langtest/utils/benchmark_utils.py b/langtest/utils/benchmark_utils.py index 989b3fc7a..749cada41 100644 --- a/langtest/utils/benchmark_utils.py +++ b/langtest/utils/benchmark_utils.py @@ -87,7 +87,7 @@ def test_wise(self): # mean column pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) - pvt_table = pvt_table.sort_values(by="Avg", ascending=False) + pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False]) pvt_table = pvt_table.fillna("-") From b6b131c2c42b316942f177db55f741cf05cfe467 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Fri, 19 Apr 2024 19:57:19 +0530 Subject: [PATCH 15/69] Refactor benchmarking logic in Harness class and Leaderboard class --- langtest/langtest.py | 79 ++++++++++++++++--------------- langtest/utils/benchmark_utils.py | 29 ++++++++---- 2 files changed, 63 insertions(+), 45 deletions(-) diff --git a/langtest/langtest.py b/langtest/langtest.py index 02e64f2a6..169fe1cd9 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -454,43 +454,7 @@ def report( # benchmarking true if self.__benchmarking: - df = self.generated_results() - - path = self.__benchmarking.get( - os.path.expanduser("save_dir"), - os.path.expanduser("~/.langtest/leaderboard/summary.csv"), - ) - summary = Summary(path) - - # temp dict - temp_dict = {} - if isinstance(self.__data_dict, dict): - temp_dict[self.__data_dict.get("data_source")] = self.__data_dict - else: - for i in self.__data_dict: - temp_dict[i.get("data_source")] = i - - # add the dataset_name column if the data is multi-dataset - df["split"] = df["dataset_name"].apply( - lambda x: temp_dict[x].get("split", "-") - ) - df["hub"] = self.__model_info.get("hub", "-") - if self.__model_info.get("hub", "-") == "lm-studio": - import requests as req - - response = req.get( - "http://localhost:1234/v1/models", - ).json() - - model_name = response["data"][0]["id"] - df["model"] = model_name - else: - df["model"] = self.__model_info.get("model", "-") - df["task"] = str(self.task) - df["subset"] = df["dataset_name"].apply( - lambda x: temp_dict[x].get("subset", "-") - ) - summary.add_report(df) + self.__tracking() if self._generated_results is None: raise RuntimeError(Errors.E011) @@ -1661,3 +1625,44 @@ def __reset_defaults(self): """Reset the default values.""" model_response = TestResultManager() model_response.clear_data() + + def __tracking(self, *args, **kwargs): + """Track the progress of the testcases.""" + if self.__benchmarking: + df = self.generated_results() + + path = self.__benchmarking.get( + os.path.expanduser("save_dir"), + os.path.expanduser("~/.langtest/leaderboard/"), + ) + summary = Summary(path) + + # temp dict + temp_dict = {} + if isinstance(self.__data_dict, dict): + temp_dict[self.__data_dict.get("data_source")] = self.__data_dict + else: + for i in self.__data_dict: + temp_dict[i.get("data_source")] = i + + # add the dataset_name column if the data is multi-dataset + df["split"] = df["dataset_name"].apply( + lambda x: temp_dict[x].get("split", "-") + ) + df["hub"] = self.__model_info.get("hub", "-") + if self.__model_info.get("hub", "-") == "lm-studio": + import requests as req + + response = req.get( + "http://localhost:1234/v1/models", + ).json() + + model_name = response["data"][0]["id"] + df["model"] = model_name + else: + df["model"] = self.__model_info.get("model", "-") + df["task"] = str(self.task) + df["subset"] = df["dataset_name"].apply( + lambda x: temp_dict[x].get("subset", "-") + ) + summary.add_report(df) diff --git a/langtest/utils/benchmark_utils.py b/langtest/utils/benchmark_utils.py index 749cada41..8240992a4 100644 --- a/langtest/utils/benchmark_utils.py +++ b/langtest/utils/benchmark_utils.py @@ -1,8 +1,10 @@ import os +from typing import TypeVar, Generic import pandas as pd -class Leaderboard: +class Leaderboard(Generic[TypeVar("T", bound="Leaderboard")]): + """ Leaderboard class to manage the ranking of the models @@ -22,7 +24,12 @@ def __new__(cls, *args, **kwargs): cls._instance = super().__new__(cls) return cls._instance - def __init__(self, path: str, *args, **kwargs) -> None: + def __init__( + self, + path: str = os.path.expanduser("~/.langtest/leaderboard/summary.csv"), + *args, + **kwargs, + ) -> None: """ Initialize the Leaderboard class with the summary file """ @@ -49,7 +56,7 @@ def default(self): # mean column pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) - pvt_table = pvt_table.sort_values(by="Avg", ascending=False) + pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False]) # reset the index and fill the NaN values pvt_table = pvt_table.rename_axis(None, axis=1).reset_index() @@ -69,7 +76,7 @@ def split_wise(self): # mean column pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) - pvt_table = pvt_table.sort_values(by="Avg", ascending=False) + pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False]) pvt_table = pvt_table.fillna("-") @@ -102,6 +109,7 @@ def category_wise(self): index=["model", "category"], columns=["dataset_name"], values="score" ) pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) + pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False]) pvt_table = pvt_table.fillna("-") pvt_table = pvt_table.rename_axis(None, axis=1).reset_index() @@ -117,6 +125,7 @@ def custom_wise(self, indices: list, columns: list = []): ) pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) pvt_table = pvt_table.fillna("-") + pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False]) # pvt_table = pvt_table.rename_axis(None, axis=1).reset_index() return pvt_table @@ -125,14 +134,14 @@ def __repr__(self) -> str: return self.summary.summary_df.to_markdown() -class Summary: +class Summary(Generic[TypeVar("T", bound="Summary")]): """ Summary class to manage the summary report """ _instance = None - def __new__(cls, *args, **kwargs) -> None: + def __new__(cls, *args, **kwargs): """ Singleton pattern to ensure only one instance of the class is created """ @@ -144,8 +153,12 @@ def __init__(self, path: str, *args, **kwargs) -> None: """ Initialize the summary """ - self.file_path = path - self.summary_df: pd.DataFrame = self.load_data_from_file(path, *args, **kwargs) + self.save_dir = path + self.file_path = f"{path}summary.csv" + + self.summary_df: pd.DataFrame = self.load_data_from_file( + self.file_path, *args, **kwargs + ) def load_data_from_file(self, path: str, *args, **kwargs) -> pd.DataFrame: """ From d64a6a5e5a395418e7e5b4a2d533372a997378c6 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 22 Apr 2024 09:15:29 +0530 Subject: [PATCH 16/69] Update data source and target column in TestNERDataset class --- tests/test_datasource.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_datasource.py b/tests/test_datasource.py index 55f2c0e29..d83054f7e 100644 --- a/tests/test_datasource.py +++ b/tests/test_datasource.py @@ -83,10 +83,10 @@ def test_load_raw_data(self, dataset, feature_col, target_col): ( HuggingFaceDataset( source_info={ - "data_source": "wikiann", - "subset": "fo", + "data_source": "tner/wikiann", + "subset": "ace", "feature_column": "tokens", - "target_column": "ner_tags", + "target_column": "tags", "split": "test", }, task=TaskManager("ner"), From c9e011e2280f3b1d08824506325becedc45d6dc2 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Tue, 23 Apr 2024 10:44:55 +0530 Subject: [PATCH 17/69] benchmarking logic in Harness and Leaderboard classes, and update leaderboard functionality --- langtest/langtest.py | 38 ++++++++++++++++++++++++++++++- langtest/utils/benchmark_utils.py | 34 +++++++++++++++++++-------- 2 files changed, 61 insertions(+), 11 deletions(-) diff --git a/langtest/langtest.py b/langtest/langtest.py index 169fe1cd9..13becab62 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -23,7 +23,7 @@ from .transform.utils import RepresentationOperation -from langtest.utils.benchmark_utils import Summary +from langtest.utils.benchmark_utils import Leaderboard, Summary from langtest.utils.lib_manager import try_import_lib from langtest.utils.custom_types.helpers import TestResultManager from langtest.utils.checkpoints import divide_into_batches, CheckpointManager @@ -1666,3 +1666,39 @@ def __tracking(self, *args, **kwargs): lambda x: temp_dict[x].get("subset", "-") ) summary.add_report(df) + + def get_leaderboard( + self, + indices=None, + columns=None, + category=False, + split_wise=False, + test_wise=False, + *args, + **kwargs, + ): + """Get the rank of the model on the leaderboard.""" + + if os.path.exists(os.path.expanduser(self.__benchmarking.get("save_dir"))): + path = os.path.expanduser(self.__benchmarking.get("save_dir")) + elif os.path.exists(os.path.expanduser("~/.langtest/leaderboard/")): + path = os.path.expanduser("./.langtest/leaderboard/") + else: + raise FileNotFoundError(f"Summary.csv File is not exists in {path}") + + leaderboard = Leaderboard(path) + + # print(leaderboard.default().to_markdown()) + if indices is not None and columns is not None: + return leaderboard.custom_wise(indices, columns) + + if category: + return leaderboard.category_wise() + + if test_wise: + return leaderboard.test_wise() + + if split_wise: + return leaderboard.split_wise() + + return leaderboard.default() diff --git a/langtest/utils/benchmark_utils.py b/langtest/utils/benchmark_utils.py index 8240992a4..56a440c2c 100644 --- a/langtest/utils/benchmark_utils.py +++ b/langtest/utils/benchmark_utils.py @@ -40,16 +40,7 @@ def default(self): Get the score board for the models """ df = self.summary.summary_df - - # find the timestamp with the highest score - df["timestamp"] = pd.to_datetime(df["timestamp"]) - df = df.sort_values(by="timestamp", ascending=False) - idx = df.groupby( - ["timestamp", "model", "dataset_name", "split", "test_type", "category"] - )["score"].idxmax() - df = df.loc[idx] - - # pivot the table + df = self.__drop_duplicates(df) pvt_table = df.pivot_table( index=["model"], columns="dataset_name", values="score" ) @@ -70,6 +61,7 @@ def split_wise(self): """ df = self.summary.summary_df + df = self.__drop_duplicates(df) pvt_table = df.pivot_table( index=["model", "split"], columns=["dataset_name"], values="score" ) @@ -88,6 +80,7 @@ def test_wise(self): """ df = self.summary.summary_df + df = self.__drop_duplicates(df) pvt_table = df.pivot_table( index=["model", "test_type"], columns=["dataset_name"], values="score" ) @@ -105,6 +98,7 @@ def category_wise(self): Get the score board for the models by category """ df = self.summary.summary_df + df = self.__drop_duplicates(df) pvt_table = df.pivot_table( index=["model", "category"], columns=["dataset_name"], values="score" ) @@ -120,6 +114,7 @@ def custom_wise(self, indices: list, columns: list = []): Get the score board for the models by custom group """ df = self.summary.summary_df + df = self.__drop_duplicates(df) pvt_table = df.pivot_table( index=["model", *indices], columns=["dataset_name", *columns], values="score" ) @@ -130,6 +125,25 @@ def custom_wise(self, indices: list, columns: list = []): return pvt_table + def __drop_duplicates(self, df: pd.DataFrame): + """ + Drop duplicates from the dataframe + """ + # arrange the dataframe by timestamp in descending order + df["timestamp"] = pd.to_datetime(df["timestamp"]) + df = df.sort_values(by="timestamp", ascending=False) + + # remove duplicates + df["timestamp"] = pd.to_datetime(df["timestamp"], format="%Y-%m-%d-%H-%M-%S") + df = df.sort_values(by="timestamp", ascending=False) + unique_records = df.drop_duplicates( + subset=["model", "hub", "dataset_name", "split", "subset", "task"] + ) + + unique_records.reset_index(drop=True, inplace=True) + + return unique_records + def __repr__(self) -> str: return self.summary.summary_df.to_markdown() From a0de4f68f031099946f16024aa54b35175dac2c9 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Tue, 23 Apr 2024 15:05:34 +0530 Subject: [PATCH 18/69] Refactor benchmarking logic in Harness and Leaderboard classes, and update leaderboard functionality --- langtest/langtest.py | 7 +++---- langtest/utils/benchmark_utils.py | 25 +++++++++++++++++++++---- 2 files changed, 24 insertions(+), 8 deletions(-) diff --git a/langtest/langtest.py b/langtest/langtest.py index 13becab62..b4d821c9e 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -1669,8 +1669,8 @@ def __tracking(self, *args, **kwargs): def get_leaderboard( self, - indices=None, - columns=None, + indices=[], + columns=[], category=False, split_wise=False, test_wise=False, @@ -1689,9 +1689,8 @@ def get_leaderboard( leaderboard = Leaderboard(path) # print(leaderboard.default().to_markdown()) - if indices is not None and columns is not None: + if indices or columns: return leaderboard.custom_wise(indices, columns) - if category: return leaderboard.category_wise() diff --git a/langtest/utils/benchmark_utils.py b/langtest/utils/benchmark_utils.py index 56a440c2c..2bbab53de 100644 --- a/langtest/utils/benchmark_utils.py +++ b/langtest/utils/benchmark_utils.py @@ -63,7 +63,9 @@ def split_wise(self): df = self.summary.summary_df df = self.__drop_duplicates(df) pvt_table = df.pivot_table( - index=["model", "split"], columns=["dataset_name"], values="score" + index=["model", "split"], + columns=["dataset_name"], + values="score", ) # mean column @@ -116,7 +118,10 @@ def custom_wise(self, indices: list, columns: list = []): df = self.summary.summary_df df = self.__drop_duplicates(df) pvt_table = df.pivot_table( - index=["model", *indices], columns=["dataset_name", *columns], values="score" + index=["model", *indices], + columns=["dataset_name", *columns], + values="score", + aggfunc="first", ) pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) pvt_table = pvt_table.fillna("-") @@ -136,13 +141,25 @@ def __drop_duplicates(self, df: pd.DataFrame): # remove duplicates df["timestamp"] = pd.to_datetime(df["timestamp"], format="%Y-%m-%d-%H-%M-%S") df = df.sort_values(by="timestamp", ascending=False) + df.reset_index(drop=True, inplace=True) unique_records = df.drop_duplicates( - subset=["model", "hub", "dataset_name", "split", "subset", "task"] + subset=[ + # "timestamp", + "category", + "test_type", + "model", + "hub", + "dataset_name", + "split", + "subset", + "task", + ], + # keep=, ) unique_records.reset_index(drop=True, inplace=True) - return unique_records + return df def __repr__(self) -> str: return self.summary.summary_df.to_markdown() From 214f44b26212c9a5fdcb0d517a9da0df8a34b667 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Tue, 23 Apr 2024 18:19:29 +0530 Subject: [PATCH 19/69] Refactor dataset_name column logic in Harness class to handle single dataset --- langtest/langtest.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/langtest/langtest.py b/langtest/langtest.py index b4d821c9e..b128993bc 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -1645,10 +1645,17 @@ def __tracking(self, *args, **kwargs): for i in self.__data_dict: temp_dict[i.get("data_source")] = i - # add the dataset_name column if the data is multi-dataset + # add the dataset_name column if the data is single dataset + if isinstance(self.__data_dict, dict) and (not self.is_multi_dataset): + df["dataset_name"] = self.__data_dict.get("data_source", "-") + df["split"] = df["dataset_name"].apply( lambda x: temp_dict[x].get("split", "-") ) + df["subset"] = df["dataset_name"].apply( + lambda x: temp_dict[x].get("subset", "-") + ) + df["hub"] = self.__model_info.get("hub", "-") if self.__model_info.get("hub", "-") == "lm-studio": import requests as req @@ -1662,9 +1669,6 @@ def __tracking(self, *args, **kwargs): else: df["model"] = self.__model_info.get("model", "-") df["task"] = str(self.task) - df["subset"] = df["dataset_name"].apply( - lambda x: temp_dict[x].get("subset", "-") - ) summary.add_report(df) def get_leaderboard( From e0f1bfdeed1b8bb699dcd39379b3ead4106bb4f5 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Wed, 24 Apr 2024 12:56:54 +0530 Subject: [PATCH 20/69] Fix comparison operator in SpeedTestSample class --- langtest/utils/custom_types/sample.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/langtest/utils/custom_types/sample.py b/langtest/utils/custom_types/sample.py index 9fc752548..558fecf35 100644 --- a/langtest/utils/custom_types/sample.py +++ b/langtest/utils/custom_types/sample.py @@ -948,7 +948,7 @@ def is_pass(self): expected_unit = self.expected_results.split("/")[1] actual_unit = self.actual_results.split("/")[1] - return (expected_tokens >= actual_tokens) and (expected_unit == actual_unit) + return (expected_tokens <= actual_tokens) and (expected_unit == actual_unit) class TranslationSample(BaseModel): From b7769bd2a775438018199b3d784c51c4015e386f Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Wed, 24 Apr 2024 18:21:41 +0530 Subject: [PATCH 21/69] Implement Augmenter class for data augmentation in langtest --- langtest/augmentation/augmenter.py | 95 ++++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 langtest/augmentation/augmenter.py diff --git a/langtest/augmentation/augmenter.py b/langtest/augmentation/augmenter.py new file mode 100644 index 000000000..8e85c2612 --- /dev/null +++ b/langtest/augmentation/augmenter.py @@ -0,0 +1,95 @@ +import yaml + +from typing import Iterable, Union +from langtest.transform import TestFactory +from langtest.tasks.task import TaskManager +from langtest.utils.custom_types.sample import Sample, SequenceClassificationSample + + +class Augmenter: + def __init__( + self, config: Union[str, dict], task: Union[str, TaskManager], columns_info=None + ) -> None: + self.__config = config + if isinstance(config, str): + self.__config = self.load_config(config) + + self.__tests: dict = self.__config.get("tests", []) + if isinstance(task, str): + task = TaskManager(task) + self.__task = task + self.__testfactory = TestFactory + self.features = columns_info.get("features", []) + self.target = columns_info.get("target", None) + self.__testfactory.is_augment = True + + def load_config(self, config: str) -> dict: + """ + Load the configuration file. + """ + with open(config, "r") as f: + return yaml.safe_load(f) + + def augment(self, data: Iterable) -> str: + """ + Augment the content. + """ + # prepare the data for augmentation + categories = list(self.__tests.keys()) + hash_map = self.prepare_hash_map(data) + + # iterate over the categories + test_types = self.__tests + + testcases = [] + for category in categories: + if category not in hash_map: + continue + R_data = [ + self.__task.create_sample( + row_data=sample, + feature_column=self.features, + target_column=self.target, + ) + for sample in data + ] + test_cases = self.__testfactory.transform(self.__task, R_data, test_types) + testcases.extend(test_cases) + return testcases + + def prepare_hash_map(self, data: Iterable[Sample]) -> dict: + """ + Prepare the data for augmentation. + """ + from collections import defaultdict + + hash_map = defaultdict(lambda: defaultdict(list)) + for category, test_types in self.__tests.items(): + if category == "defaults": + continue + hash_map[category] = {} + for test in test_types: + hash_map[category][test] = [ + SequenceClassificationSample( + original=sample.get("text", "-"), label=sample.get("label", "-") + ) + for sample in data + ] + + return hash_map + + def __or__(self, other: Iterable): + results = self.augment(other) + return results + + def __ror__(self, other: Iterable): + results = self.augment(other) + return results + + +class ContentDataGenerator: + def __init__(self) -> None: + pass + + def generate(self, content: str) -> str: + return content From b50dcfdc56abf93d20caf244609b55b655810ee8 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Wed, 24 Apr 2024 18:25:41 +0530 Subject: [PATCH 22/69] Update data source and target column in TestNERDataset --- tests/test_datasource.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_datasource.py b/tests/test_datasource.py index 55f2c0e29..d83054f7e 100644 --- a/tests/test_datasource.py +++ b/tests/test_datasource.py @@ -83,10 +83,10 @@ def test_load_raw_data(self, dataset, feature_col, target_col): ( HuggingFaceDataset( source_info={ - "data_source": "wikiann", - "subset": "fo", + "data_source": "tner/wikiann", + "subset": "ace", "feature_column": "tokens", - "target_column": "ner_tags", + "target_column": "tags", "split": "test", }, task=TaskManager("ner"), From 08d88fd8e611bd8f9edfb006a71f5e44f05f25fa Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Wed, 24 Apr 2024 21:55:07 +0530 Subject: [PATCH 23/69] Refactor Augmenter class to remove unused code and improve performance --- langtest/augmentation/augmenter.py | 33 +----------------------------- 1 file changed, 1 insertion(+), 32 deletions(-) diff --git a/langtest/augmentation/augmenter.py b/langtest/augmentation/augmenter.py index 8e85c2612..20860e38d 100644 --- a/langtest/augmentation/augmenter.py +++ b/langtest/augmentation/augmenter.py @@ -3,7 +3,6 @@ from typing import Iterable, Union from langtest.transform import TestFactory from langtest.tasks.task import TaskManager -from langtest.utils.custom_types.sample import Sample, SequenceClassificationSample class Augmenter: @@ -36,14 +35,13 @@ def augment(self, data: Iterable) -> str: """ # prepare the data for augmentation categories = list(self.__tests.keys()) - hash_map = self.prepare_hash_map(data) # iterate over the categories test_types = self.__tests testcases = [] for category in categories: - if category not in hash_map: + if category not in ["robustness", "bias"]: continue R_data = [ self.__task.create_sample( @@ -57,27 +55,6 @@ def augment(self, data: Iterable) -> str: testcases.extend(test_cases) return testcases - def prepare_hash_map(self, data: Iterable[Sample]) -> dict: - """ - Prepare the data for augmentation. - """ - from collections import defaultdict - - hash_map = defaultdict(lambda: defaultdict(list)) - for category, test_types in self.__tests.items(): - if category == "defaults": - continue - hash_map[category] = {} - for test in test_types: - hash_map[category][test] = [ - SequenceClassificationSample( - original=sample.get("text", "-"), label=sample.get("label", "-") - ) - for sample in data - ] - - return hash_map - def __or__(self, other: Iterable): results = self.augment(other) return results @@ -85,11 +62,3 @@ def __or__(self, other: Iterable): def __ror__(self, other: Iterable): results = self.augment(other) return results - - -class ContentDataGenerator: - def __init__(self) -> None: - pass - - def generate(self, content: str) -> str: - return content From 042d3d2afd1b29e755fa915737a195563a510b14 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Thu, 25 Apr 2024 19:56:51 +0530 Subject: [PATCH 24/69] updated the nb --- .../misc/PerformanceTest_Notebook.ipynb | 4453 ++++++++++++++++- 1 file changed, 4452 insertions(+), 1 deletion(-) diff --git a/demo/tutorials/misc/PerformanceTest_Notebook.ipynb b/demo/tutorials/misc/PerformanceTest_Notebook.ipynb index 609896cc5..dbfd0f3af 100644 --- a/demo/tutorials/misc/PerformanceTest_Notebook.ipynb +++ b/demo/tutorials/misc/PerformanceTest_Notebook.ipynb @@ -1 +1,4452 @@ -{"cells":[{"cell_type":"markdown","metadata":{"id":"e7PsSmy9sCoR"},"source":["![image.png](data:image/png;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"3o5sAOfwL5qd"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/PerformanceTest_Notebook.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"WJJzt3RWhEc6"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"26qXWhCYhHAt"},"source":["# Getting started with LangTest on John Snow Labs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"azUb114QhOsY"},"outputs":[],"source":["!pip install langtest[transformers]"]},{"cell_type":"markdown","metadata":{"id":"yR6kjOaiheKN"},"source":["# Harness and Its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":925,"status":"ok","timestamp":1692343745209,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"lTzSJpMlhgq5"},"outputs":[],"source":["#Import Harness from the LangTest library\n","from langtest import Harness"]},{"cell_type":"markdown","metadata":{"id":"JFhJ9CcbsKqN"},"source":["# Performance Testing\n","\n","In the testing phase of evaluating Natural Language Processing (NLP) models using LangTest, a dedicated tool for assessing language-related capabilities, the focus is on measuring the time taken to process a given dataset. This metric serves as a key performance indicator, reflecting the efficiency of NLP models in real-world scenarios. The choice of an appropriate dataset is pivotal, ensuring its relevance to the application's context. By comparing the time efficiency of different models, one can identify optimal solutions for specific use cases. Additionally, if processing times are suboptimal, exploration of model architecture adjustments and optimization strategies becomes essential to enhance overall performance and responsiveness, particularly in applications demanding quick and efficient language processing.\n","\n","The formula you provided,\n","\n","$\\ speed $ = $\\frac{number\\ of\\ tokens\\ in\\ given\\ dataset}{time\\ taken}$\n","\n","calculates the speed of processing for an NLP model. Specifically, it represents the number of words processed per unit of time, offering a quantitative measure of the model's efficiency. A higher speed value indicates faster processing, which can be crucial in real-time applications or scenarios where quick language understanding and response are essential. Monitoring and optimizing this speed metric contribute to ensuring the practical utility of NLP models, especially in applications such as chatbots, customer support systems, or any context where rapid language processing is a priority."]},{"cell_type":"markdown","metadata":{"id":"swaYPW-wPlku"},"source":["### Setup and Configure Harness"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":920,"referenced_widgets":["6c5a9f6544e0442ca68098426c146503","e7f4a1278a7e49aba2ed734c228d0c66","0fe42dba7c4b4df2a64ea2002be642cf","9a06564cc5254e89a762426cf3269a9e","3bb1a7ff75c1490db0334e5162aba497","4790617c8b18415d9cafaceabca7022d","2ffcf6981af34b139f304200934abaee","d2d79f3cb2d8444fa36a6851a208e9f4","6b905fe4f7f542c9890f5cfd195beda3","b4d092df96e04c00a943f3c8e3329c39","966ad3248efb4f29a20f19b06c7dfa77","13f8de2b99ff475fbabbfba66b17125e","3f65fa34feac4f00a89cf74bdb8f5a59","d35f7095c5be4de19b625a00a0ea1798","5f050ded4d6d41509dbad6f17284c18c","58ee9406c08144de989c5a26ed5a1ccb","cee5a9b496cf435b9e746424187cad08","36e4580f19164f5d93280c4a06f0879c","1e99e26f69034ec79f52b512a608c4f7","c749fd792c914727b6ff4386b316df57","e24155477021432cb45793c0743eea1b","6fc068b143fc4e3391dd755e8262fcfd","5c12f56844e546c3aaadc192b2583077","c96e0761975e4290be8f4b287e3f6f42","81e8c8d107034c85aa95252a3838b05e","771f704de66e4d0eab0a2cb71dd24d2f","6cf03247c6374a6b89aaffee79998285","501dd4b2d09b4ae993a5bc2f18769ac4","3cbb3222ae2f4bb7b3dfd1a8c54a3503","edc62d1193fc44e98784da4b1a3fa390","5dce3c96154c4dbba25a57052804c82b","d54d2ab9930a447388f0fea290bef2ac","3ce8b14f48a24349b793b75d9350dc95","3f1c56e797cf43588bad099a0783e179","85ff59f9f7ed47b5ab78f767255f5a56","a12cdebdcf8b476f80b906d97a9ea261","a404d4d49f2046bd85ea64cc2de4a734","265a066b8e564034af96902a1e0347fb","d136b83a79034d20971be55510737103","572b68362a1a4292a43d44ebad043042","db4fc546e2d344018f85cd3deeab1115","ef8256e1afce4f268db5bc38a3a7fa86","afa59a1ecaba4b56a596f4cdbd7b6730","3d6af6f687c54a5eb0db38b2c2ef1899","d2906819c5ec4c82b2731eac4afe519d","1db2003c9e124e4f8b6444c157636983","eaae6733b12047ac9edec3adff0ab765","83aa131ac111451495e97bd710631418","f818144e1d304afd8b15900620abfc1d","314d5fcd2f864d8f941f90d76bd0df1b","b3f1bdcee72a47a791c6eaec72fdf136","06797e19a13d40df8c50322bf4b52f90","6689169d7c9447a1bde80313e6e9a7c2","38476374aa9c49f68dcf96e55e520240","6c506299e96344798ac6e36820e275bf","80393d9f400a4e9c8867808c5f2e8b28","eaff3fe3471b4815ab3d27d72142fe22","030f52c161444051b7215c4cf1b4eb27","d2726e7c8ebc4d0c9dbaf7d919bd064b","012930dde07a4a56af962c6993ecbf03","ab2b92ec8670443a9093c015c6084e95","5aee88b7efe54ce2b646b353cc61b26f","f80fde8b46ba4c7fad2f867f2439ef83","1152e3b558814204a94a058f0d506d20","6ee8c33165a946b8a15516a89203f396","9982d7d8bc634c77838aa10ccead8428"]},"executionInfo":{"elapsed":23923,"status":"ok","timestamp":1692343769123,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"JaarBdfe8DQ8","outputId":"9bc19b92-c518-4bcf-f7e6-16f419898566"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6c5a9f6544e0442ca68098426c146503","version_major":2,"version_minor":0},"text/plain":["Downloading (…)lve/main/config.json: 0%| | 0.00/829 [00:00\n","
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categorytest_typeoriginaltest_case
0robustnesslowercaseSOCCER - JAPAN GET LUCKY WIN , CHINA IN SURPRI...soccer - japan get lucky win , china in surpri...
1robustnesslowercaseNadim Ladkinadim ladki
2robustnesslowercaseAL-AIN , United Arab Emirates 1996-12-06al-ain , united arab emirates 1996-12-06
3robustnesslowercaseJapan began the defence of their Asian Cup tit...japan began the defence of their asian cup tit...
4robustnesslowercaseBut China saw their luck desert them in the se...but china saw their luck desert them in the se...
...............
448robustnessuppercaseCRICKET - LARA ENDURES ANOTHER MISERABLE DAY .CRICKET - LARA ENDURES ANOTHER MISERABLE DAY .
449robustnessuppercaseRobert GalvinROBERT GALVIN
450robustnessuppercaseMELBOURNE 1996-12-06MELBOURNE 1996-12-06
451robustnessuppercaseAustralia gave Brian Lara another reason to be...AUSTRALIA GAVE BRIAN LARA ANOTHER REASON TO BE...
452performancespeed--
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453 rows × 4 columns

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\n"," \n"],"text/plain":[" category test_type \\\n","0 robustness lowercase \n","1 robustness lowercase \n","2 robustness lowercase \n","3 robustness lowercase \n","4 robustness lowercase \n",".. ... ... \n","448 robustness uppercase \n","449 robustness uppercase \n","450 robustness uppercase \n","451 robustness uppercase \n","452 performance speed \n","\n"," original \\\n","0 SOCCER - JAPAN GET LUCKY WIN , CHINA IN SURPRI... \n","1 Nadim Ladki \n","2 AL-AIN , United Arab Emirates 1996-12-06 \n","3 Japan began the defence of their Asian Cup tit... \n","4 But China saw their luck desert them in the se... \n",".. ... \n","448 CRICKET - LARA ENDURES ANOTHER MISERABLE DAY . \n","449 Robert Galvin \n","450 MELBOURNE 1996-12-06 \n","451 Australia gave Brian Lara another reason to be... \n","452 - \n","\n"," test_case \n","0 soccer - japan get lucky win , china in surpri... \n","1 nadim ladki \n","2 al-ain , united arab emirates 1996-12-06 \n","3 japan began the defence of their asian cup tit... \n","4 but china saw their luck desert them in the se... \n",".. ... \n","448 CRICKET - LARA ENDURES ANOTHER MISERABLE DAY . \n","449 ROBERT GALVIN \n","450 MELBOURNE 1996-12-06 \n","451 AUSTRALIA GAVE BRIAN LARA ANOTHER REASON TO BE... \n","452 - \n","\n","[453 rows x 4 columns]"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["harness.testcases()"]},{"cell_type":"markdown","metadata":{"id":"NOJ8BAU2GGzd"},"source":["harness.testcases() method displays the produced test cases in form of a pandas data frame."]},{"cell_type":"markdown","metadata":{"id":"3CwhQw6hGR9S"},"source":["### Running the tests"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":163600,"status":"ok","timestamp":1692343967668,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"aguX6-aFGOnP","outputId":"66be230c-84f5-4521-a3c5-fb57f91d131a"},"outputs":[{"name":"stderr","output_type":"stream","text":["Running testcases... : 100%|██████████| 453/453 [02:43<00:00, 2.77it/s]\n"]},{"data":{"text/plain":[]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["harness.run()"]},{"cell_type":"markdown","metadata":{"id":"191O2oaUGWrH"},"source":["Called after harness.generate() and is to used to run all the tests. Returns a pass/fail flag for each test."]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":527},"executionInfo":{"elapsed":33,"status":"ok","timestamp":1692343967670,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"XDbd1mpREWR5","outputId":"0375fbee-3ab7-4dca-f10d-eb6c36e23407"},"outputs":[{"data":{"text/html":["\n","
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categorytest_typeoriginaltest_caseexpected_resultactual_resultpass
0robustnesslowercaseSOCCER - JAPAN GET LUCKY WIN , CHINA IN SURPRI...soccer - japan get lucky win , china in surpri...JAPAN: MISC, LUCKY: PER, CHINA: ORGFalse
1robustnesslowercaseNadim Ladkinadim ladkiNadim Ladki: PERFalse
2robustnesslowercaseAL-AIN , United Arab Emirates 1996-12-06al-ain , united arab emirates 1996-12-06AL-AIN: LOC, United Arab Emirates: LOCal-ain: LOCFalse
3robustnesslowercaseJapan began the defence of their Asian Cup tit...japan began the defence of their asian cup tit...Japan: LOC, Asian Cup: MISC, Syria: LOC, Group...japan: ORG, syria: ORGFalse
4robustnesslowercaseBut China saw their luck desert them in the se...but china saw their luck desert them in the se...China: LOC, Uzbekistan: LOCuzbekistan: LOCFalse
........................
448robustnessuppercaseCRICKET - LARA ENDURES ANOTHER MISERABLE DAY .CRICKET - LARA ENDURES ANOTHER MISERABLE DAY .LARA: LOC, MISERABLE: PERLARA: LOC, MISERABLE: PERTrue
449robustnessuppercaseRobert GalvinROBERT GALVINRobert Galvin: PERROBERT: ORG, GALVIN: PERFalse
450robustnessuppercaseMELBOURNE 1996-12-06MELBOURNE 1996-12-06MELBOURNE: LOCMELBOURNE: LOCTrue
451robustnessuppercaseAustralia gave Brian Lara another reason to be...AUSTRALIA GAVE BRIAN LARA ANOTHER REASON TO BE...Australia: LOC, Brian Lara: PER, West Indies: ...AUSTRALIA: LOC, BRIAN LARA: LOC, REASON: PER, ...False
452performancespeed--100 token/sec19.20 token/secTrue
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453 rows × 7 columns

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\n"],"text/plain":[" category test_type \\\n","0 robustness lowercase \n","1 robustness lowercase \n","2 robustness lowercase \n","3 robustness lowercase \n","4 robustness lowercase \n",".. ... ... \n","448 robustness uppercase \n","449 robustness uppercase \n","450 robustness uppercase \n","451 robustness uppercase \n","452 performance speed \n","\n"," original \\\n","0 SOCCER - JAPAN GET LUCKY WIN , CHINA IN SURPRI... \n","1 Nadim Ladki \n","2 AL-AIN , United Arab Emirates 1996-12-06 \n","3 Japan began the defence of their Asian Cup tit... \n","4 But China saw their luck desert them in the se... \n",".. ... \n","448 CRICKET - LARA ENDURES ANOTHER MISERABLE DAY . \n","449 Robert Galvin \n","450 MELBOURNE 1996-12-06 \n","451 Australia gave Brian Lara another reason to be... \n","452 - \n","\n"," test_case \\\n","0 soccer - japan get lucky win , china in surpri... \n","1 nadim ladki \n","2 al-ain , united arab emirates 1996-12-06 \n","3 japan began the defence of their asian cup tit... \n","4 but china saw their luck desert them in the se... \n",".. ... \n","448 CRICKET - LARA ENDURES ANOTHER MISERABLE DAY . \n","449 ROBERT GALVIN \n","450 MELBOURNE 1996-12-06 \n","451 AUSTRALIA GAVE BRIAN LARA ANOTHER REASON TO BE... \n","452 - \n","\n"," expected_result \\\n","0 JAPAN: MISC, LUCKY: PER, CHINA: ORG \n","1 Nadim Ladki: PER \n","2 AL-AIN: LOC, United Arab Emirates: LOC \n","3 Japan: LOC, Asian Cup: MISC, Syria: LOC, Group... \n","4 China: LOC, Uzbekistan: LOC \n",".. ... \n","448 LARA: LOC, MISERABLE: PER \n","449 Robert Galvin: PER \n","450 MELBOURNE: LOC \n","451 Australia: LOC, Brian Lara: PER, West Indies: ... \n","452 100 token/sec \n","\n"," actual_result pass \n","0 False \n","1 False \n","2 al-ain: LOC False \n","3 japan: ORG, syria: ORG False \n","4 uzbekistan: LOC False \n",".. ... ... \n","448 LARA: LOC, MISERABLE: PER True \n","449 ROBERT: ORG, GALVIN: PER False \n","450 MELBOURNE: LOC True \n","451 AUSTRALIA: LOC, BRIAN LARA: LOC, REASON: PER, ... False \n","452 19.20 token/sec True \n","\n","[453 rows x 7 columns]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["harness.generated_results()"]},{"cell_type":"markdown","metadata":{"id":"TKB8Rsr2GZME"},"source":["This method returns the generated results in the form of a pandas dataframe, which provides a convenient and easy-to-use format for working with the test results. You can use this method to quickly identify the test cases that failed and to determine where fixes are needed."]},{"cell_type":"markdown","metadata":{"id":"PBSlpWnUU55G"},"source":["### Final Results"]},{"cell_type":"markdown","metadata":{"id":"umnEgUHM8DRA"},"source":["We can call `.report()` which summarizes the results giving information about pass and fail counts and overall test pass/fail flag.\n","\n","To get time_elapsed for each test we pass parameter `return_runtime=True` in `.report()` method. We can also select the unit for time_elapsed i.e, seconds(s), miliseconds(ms) or microseconds(us) etc."]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"elapsed":30,"status":"ok","timestamp":1692343967672,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"gp57HcF9yxi7","outputId":"8d990f2e-6b4d-480e-e844-95ff9158e126"},"outputs":[{"data":{"text/html":["\n","
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categorytest_typefail_countpass_countpass_rateminimum_pass_ratepass
0robustnesslowercase1824419%66%False
1robustnessuppercase1527433%66%False
2performancespeed01100%100%True
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\n"],"text/plain":[" category test_type fail_count pass_count pass_rate minimum_pass_rate \\\n","0 robustness lowercase 182 44 19% 66% \n","1 robustness uppercase 152 74 33% 66% \n","2 performance speed 0 1 100% 100% \n","\n"," pass \n","0 False \n","1 False \n","2 True "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["harness.report()"]},{"cell_type":"markdown","metadata":{"id":"zg-knds3tq-w"},"source":["# Multiple Models Runtime Testing"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ElMInPJMu3QK"},"outputs":[],"source":["!pip install spacy johnsnowlabs"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":28,"status":"ok","timestamp":1692343967673,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"TnUBvYXptq-w"},"outputs":[],"source":["model_dict=[{\"model\": \"ner.dl\", \"hub\": \"johnsnowlabs\"},\n"," {\"model\": \"en_core_web_sm\", \"hub\": \"spacy\"}]"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1096,"status":"ok","timestamp":1692344027826,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"PmMwW5IIvGav","outputId":"d9e5c932-b286-46e7-d18c-23e23f4cba6f"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-08-18 07:33:45-- https://github.com/JohnSnowLabs/langtest/raw/main/langtest/data/conll/sample.conll\n","Resolving github.com (github.com)... 20.27.177.113\n","Connecting to github.com (github.com)|20.27.177.113|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll [following]\n","--2023-08-18 07:33:45-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 50519 (49K) [text/plain]\n","Saving to: ‘sample.conll’\n","\n","sample.conll 100%[===================>] 49.33K --.-KB/s in 0.01s \n","\n","2023-08-18 07:33:46 (3.77 MB/s) - ‘sample.conll’ saved [50519/50519]\n","\n"]}],"source":["# Load CoNLL\n","!wget https://github.com/JohnSnowLabs/langtest/raw/main/langtest/data/conll/sample.conll"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":233178,"status":"ok","timestamp":1692344334665,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"yey-zVICtq-w","outputId":"d94e8722-e009-4c17-85e5-9f8bcafdaf6a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Warning::Spark Session already created, some configs may not take.\n","recognize_entities_dl download started this may take some time.\n","Approx size to download 159 MB\n","[OK!]\n","Test Configuration : \n"," {\n"," \"tests\": {\n"," \"defaults\": {\n"," \"min_pass_rate\": 1.0\n"," },\n"," \"robustness\": {\n"," \"add_typo\": {\n"," \"min_pass_rate\": 0.7\n"," },\n"," \"american_to_british\": {\n"," \"min_pass_rate\": 0.7\n"," }\n"," },\n"," \"accuracy\": {\n"," \"min_micro_f1_score\": {\n"," \"min_score\": 0.7\n"," }\n"," },\n"," \"bias\": {\n"," \"replace_to_female_pronouns\": {\n"," \"min_pass_rate\": 0.7\n"," },\n"," \"replace_to_low_income_country\": {\n"," \"min_pass_rate\": 0.7\n"," }\n"," },\n"," \"fairness\": {\n"," \"min_gender_f1_score\": {\n"," \"min_score\": 0.6\n"," }\n"," },\n"," \"representation\": {\n"," \"min_label_representation_count\": {\n"," \"min_count\": 50\n"," }\n"," }\n"," }\n","}\n"]}],"source":["harness = Harness(task=\"ner\", model=model_dict, data={\"data_source\":\"sample.conll\"})"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":85,"status":"ok","timestamp":1692344334668,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"JwK7oi7Etq-w","outputId":"11e0d7e4-58f0-497b-d122-07efc38f21cb"},"outputs":[{"data":{"text/plain":["{'tests': {'defaults': {'min_pass_rate': 0.65},\n"," 'robustness': {'uppercase': {'min_pass_rate': 0.66},\n"," 'lowercase': {'min_pass_rate': 0.6}},\n"," 'performance': {'speed': {'min_pass_rate': 100, 'unit': 'tokens/sec'}}}}"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["harness.configure(\n","{\n"," 'tests': {'defaults': {'min_pass_rate': 0.65},\n"," 'robustness': {'uppercase': {'min_pass_rate': 0.66},\n"," 'lowercase': {'min_pass_rate': 0.60},\n"," },\n"," 'performance': {'speed': {'min_pass_rate': 100, 'unit': 'tokens/sec'}\n"," },\n"," }\n"," }\n"," )\n"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":130503,"status":"ok","timestamp":1692344465099,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"vTbPwStvtq-x","outputId":"4b2cbf34-6d9e-4942-b6e5-4bccd7153326"},"outputs":[{"name":"stderr","output_type":"stream","text":["Generating testcases...: 100%|██████████| 2/2 [00:00<00:00, 8256.50it/s]\n","Generating testcases...: 100%|██████████| 2/2 [00:00<00:00, 10094.59it/s]\n","Running testcases... : 100%|██████████| 453/453 [01:30<00:00, 4.99it/s]\n","Running testcases... : 100%|██████████| 453/453 [00:11<00:00, 40.56it/s]\n"]},{"data":{"text/plain":[]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["harness.generate().run()"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"elapsed":63,"status":"ok","timestamp":1692344465100,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"AUZUeCpLtq-x","outputId":"406dea53-1a63-4e17-9ffb-7ed7e2a4531d"},"outputs":[{"data":{"text/html":["\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3o5sAOfwL5qd" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/PerformanceTest_Notebook.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WJJzt3RWhEc6" + }, + "source": [ + "**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n", + "\n", + "Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "26qXWhCYhHAt" + }, + "source": [ + "# Getting started with LangTest on John Snow Labs" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "azUb114QhOsY", + "outputId": "82bc5501-2218-4aed-dd34-d90788761e02", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.40.0)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.4)\n", + "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.20.3)\n", + "Requirement already satisfied: numpy>=1.17 in 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fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.6.0)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.11.0)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\n" + ] + } + ], + "source": [ + "!pip install transformers" + ] + }, + { + "cell_type": "code", + "source": [ + "!git clone -b bug_fix/performance_tests https://github.com/JohnSnowLabs/langtest" + ], + "metadata": { + "id": "cIYrIwMgAJBv", + "outputId": "454ac9c3-5e6e-4cc6-f78b-6b59ad9e9133", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'langtest'...\n", + "remote: Enumerating objects: 33818, done.\u001b[K\n", + "remote: Counting objects: 100% (4461/4461), done.\u001b[K\n", + "remote: Compressing objects: 100% (1402/1402), done.\u001b[K\n", + "remote: Total 33818 (delta 3116), reused 4226 (delta 3008), pack-reused 29357\u001b[K\n", + "Receiving objects: 100% (33818/33818), 162.32 MiB | 23.05 MiB/s, done.\n", + "Resolving deltas: 100% (23191/23191), done.\n", + "Updating files: 100% (1909/1909), done.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install -e ./langtest" + ], + "metadata": { + "id": "RnNrHKtzAqFs", + "outputId": "576cc218-897a-4a2d-b4e2-9e73a841e72d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Obtaining file:///content/langtest\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n", + " Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "Collecting jsonlines<4.0.0,>=3.1.0 (from langtest==2.1.0)\n", + " Downloading jsonlines-3.1.0-py3-none-any.whl (8.6 kB)\n", + "Requirement already satisfied: nest-asyncio<2.0.0,>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from langtest==2.1.0) (1.6.0)\n", + "Requirement already satisfied: pandas<3.0.0,>=2.0.3 in /usr/local/lib/python3.10/dist-packages (from langtest==2.1.0) (2.0.3)\n", + "Collecting pydantic==1.10.8 (from langtest==2.1.0)\n", + " Downloading pydantic-1.10.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: pyyaml<7.0,>=6.0 in /usr/local/lib/python3.10/dist-packages (from langtest==2.1.0) (6.0.1)\n", + "Requirement already satisfied: tenacity<9.0.0,>=8.2.2 in /usr/local/lib/python3.10/dist-packages (from langtest==2.1.0) (8.2.3)\n", + "Requirement already satisfied: tqdm<5.0.0,>=4.65.0 in /usr/local/lib/python3.10/dist-packages (from langtest==2.1.0) (4.66.2)\n", + "Requirement already satisfied: typing-extensions<5.0.0,>=4.10.0 in /usr/local/lib/python3.10/dist-packages (from langtest==2.1.0) (4.11.0)\n", + "Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonlines<4.0.0,>=3.1.0->langtest==2.1.0) (23.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas<3.0.0,>=2.0.3->langtest==2.1.0) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<3.0.0,>=2.0.3->langtest==2.1.0) (2023.4)\n", + "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas<3.0.0,>=2.0.3->langtest==2.1.0) (2024.1)\n", + "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas<3.0.0,>=2.0.3->langtest==2.1.0) (1.25.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas<3.0.0,>=2.0.3->langtest==2.1.0) (1.16.0)\n", + "Building wheels for collected packages: langtest\n", + " Building editable for langtest (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for langtest: filename=langtest-2.1.0-py3-none-any.whl size=11948 sha256=30eaf4ca34c9a56fd42a343ca93be52aff710dde9477306a2fa2ef29c9d5bd37\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-0w9n_2mn/wheels/a2/f4/50/7b4955461b5185fc52d1be1ad40afb2ad10f2610b68e2919a0\n", + "Successfully built langtest\n", + "Installing collected packages: pydantic, jsonlines, langtest\n", + " Attempting uninstall: pydantic\n", + " Found existing installation: pydantic 2.7.0\n", + " Uninstalling pydantic-2.7.0:\n", + " Successfully uninstalled pydantic-2.7.0\n", + "Successfully installed jsonlines-3.1.0 langtest-2.1.0 pydantic-1.10.8\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import sys\n", + "\n", + "sys.path.append('/content/langtest')" + ], + "metadata": { + "id": "JwqpfpiHA87p" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yR6kjOaiheKN" + }, + "source": [ + "# Harness and Its Parameters\n", + "\n", + "The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "lTzSJpMlhgq5" + }, + "outputs": [], + "source": [ + "#Import Harness from the LangTest library\n", + "from langtest import Harness" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JFhJ9CcbsKqN" + }, + "source": [ + "# Performance Testing\n", + "\n", + "In the testing phase of evaluating Natural Language Processing (NLP) models using LangTest, a dedicated tool for assessing language-related capabilities, the focus is on measuring the time taken to process a given dataset. This metric serves as a key performance indicator, reflecting the efficiency of NLP models in real-world scenarios. The choice of an appropriate dataset is pivotal, ensuring its relevance to the application's context. By comparing the time efficiency of different models, one can identify optimal solutions for specific use cases. Additionally, if processing times are suboptimal, exploration of model architecture adjustments and optimization strategies becomes essential to enhance overall performance and responsiveness, particularly in applications demanding quick and efficient language processing.\n", + "\n", + "The formula you provided,\n", + "\n", + "$\\ speed $ = $\\frac{number\\ of\\ tokens\\ in\\ given\\ dataset}{time\\ taken}$\n", + "\n", + "calculates the speed of processing for an NLP model. Specifically, it represents the number of words processed per unit of time, offering a quantitative measure of the model's efficiency. A higher speed value indicates faster processing, which can be crucial in real-time applications or scenarios where quick language understanding and response are essential. Monitoring and optimizing this speed metric contribute to ensuring the practical utility of NLP models, especially in applications such as chatbots, customer support systems, or any context where rapid language processing is a priority." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "swaYPW-wPlku" + }, + "source": [ + "### Setup and Configure Harness" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 990, + "referenced_widgets": [ + "2dfb0cd0b71e4523971ef87c2978ead4", + "9e11e578ef824c5a833e1993e4c37d65", + "5ca9c99b0a2f4298851061725876731b", + "67ef12076e9e49a2bef4bc630f3b4280", + "b82fc8ba2a3c43d89228c6ea299ef0d2", + "ec53df8dbac94e5d90b131473d01a232", + "5ba83daef26c4e34b386d974986bcc5a", + "109fd6ccac294c3e8c690d075bd612e4", + "a0a78418c15b4607854d1da5924d501c", + "7426c97a2b9a48ce888df6aa07a18b92", + "5e496da2c3d34eea89b16f0e243ef0da", + "d852ffbc8eab49d7bf805d130a9e21e9", + "cad2ce042df647f181fb192eb3612bca", + "6761482d010040ee8584d40770c0e7b9", + "5022a84ccefa4c888e7b7283f40ad1f8", + "8843bebcd357479a8225e3956586ce34", + "54e485ca393a4c0cad4e06d80287b4e3", + "6b3b952b5d4e4d3b8d9f64092273016c", + "dcc1386faf57485584383aeda8880d77", + "b8cde32f0b0c44d4a3492211ffcda060", + "6a0378e4bdef468ea9633a41f187c100", + "982e805a22224e7ca21119d6dfe2e661", + "e1a46736d7a145e485c8ebfb6e145e65", + "11843b0f61824383ba8f1477837b372d", + "e5c31b70aa7b437bb6370d6bf8522cb8", + "6b1c659ec6a6418eb446bed941361fc6", + "526a57ea6def48e3bf241c41b8179ddf", + "55496e94dacd473f842c3a061021246d", + "6cb3964ce93a41d0a691eb26eaf260d6", + "3b36a4c564954a4db40f0e755af4227a", + "0767a85207994fd1bf8c60e97b42cecc", + "de8eba29e71e47e5b7f4ec1dfeea28e2", + "93fbd5ae29424a4ba2f46700d9ece4fb", + "7216ca2a83d04b389fa9f6b11d6e00d9", + "675cd83e139749a4b1641e21cabcafee", + "059f8125a73f484cb0b2d4f8a2026624", + "500cebec6e4d46a2ba09e3e0ccdf575c", + "7e4121ebd9de4f55a9e8c3dd432a9e83", + "3b9f0b58affa4afd87cc58ee9c65a078", + "174d07b3bcb245f38fd50216c7b78a1d", + "30396d8addf64e62b9aee6fd458b6147", + "af51a3baa3e94847b557e9f994886a0e", + "07b117e164a44f79bc582fdda270076d", + "9bc44d3e346542daafdf6b708d17b2d4", + "683f3df353e1479e8ae5483df5225dbd", + "d279c6275158449e9ec5f58b391b0069", + "65cb9cefe2934ee7a50ca6d4d70bf8ee", + "1001db8a1bee424385929d7dd5113352", + "de722c2bd03f4e638a877882932cf9eb", + "30849f0661544814870e640f197bc422", + "04fad307273b4f54b5b15646efebb157", + "51b19ae99c7f47d38b0cc7460b2fb8e1", + "7731f14c246043d8a76ff9ea44d0b17a", + "17aa55bf55c7451dbc2a5a8ce5442411", + "e13ed70114e2470e97814679ca3c143b", + "c996405fead84c07aefb48c4e0ed8b58", + "3225b9c982b4486dadbcfda73517ea94", + "499a9cfd951f48a9b93692cb97260dd1", + "52b13a75e2bc4291a6039f96dbccbcd3", + "83694568504a4a26ab4d44b2e50f25a4", + "22c62124e1f24bb092e575890497b3a4", + "954f6183d22a44df87f121077c4c8626", + "f48624c6aa0246228b2aa65fccdf0d51", + "f2a586957ad14110ae3394d50e1b0efd", + "4e6e857f002344ff9a6b342a689f243a", + "1967e05f8bd44132919b9856617d1dda" + ] + }, + "id": "JaarBdfe8DQ8", + "outputId": "baed2de8-d1e6-4c3f-a1f8-4781856c2866" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/829 [00:00\n", + "
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categorytest_typeoriginaltest_case
0robustnesslowercaseSOCCER - JAPAN GET LUCKY WIN , CHINA IN SURPRI...soccer - japan get lucky win , china in surpri...
1robustnesslowercaseNadim Ladkinadim ladki
2robustnesslowercaseAL-AIN , United Arab Emirates 1996-12-06al-ain , united arab emirates 1996-12-06
3robustnesslowercaseJapan began the defence of their Asian Cup tit...japan began the defence of their asian cup tit...
4robustnesslowercaseBut China saw their luck desert them in the se...but china saw their luck desert them in the se...
...............
417robustnessuppercaseGoias 1 Gremio 3GOIAS 1 GREMIO 3
418robustnessuppercasePortuguesa 1 Atletico Mineiro 0PORTUGUESA 1 ATLETICO MINEIRO 0
419robustnessuppercaseRobert GalvinROBERT GALVIN
420robustnessuppercaseAustralia gave Brian Lara another reason to be...AUSTRALIA GAVE BRIAN LARA ANOTHER REASON TO BE...
421performancespeed--
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categorytest_typeoriginaltest_caseexpected_resultactual_resultpass
0robustnesslowercaseSOCCER - JAPAN GET LUCKY WIN , CHINA IN SURPRI...soccer - japan get lucky win , china in surpri...JAPAN: MISC, LUCKY: PER, CHINA: ORGFalse
1robustnesslowercaseNadim Ladkinadim ladkiNadim Ladki: PERFalse
2robustnesslowercaseAL-AIN , United Arab Emirates 1996-12-06al-ain , united arab emirates 1996-12-06AL-AIN: LOC, United Arab Emirates: LOCal-ain: LOCFalse
3robustnesslowercaseJapan began the defence of their Asian Cup tit...japan began the defence of their asian cup tit...Japan: LOC, Asian Cup: MISC, Syria: LOC, Group...japan: ORG, syria: ORGFalse
4robustnesslowercaseBut China saw their luck desert them in the se...but china saw their luck desert them in the se...China: LOC, Uzbekistan: LOCuzbekistan: LOCFalse
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417robustnessuppercaseGoias 1 Gremio 3GOIAS 1 GREMIO 3Goias: ORG, Gremio: ORGGOIAS: ORG, GREMIO: ORGTrue
418robustnessuppercasePortuguesa 1 Atletico Mineiro 0PORTUGUESA 1 ATLETICO MINEIRO 0Portuguesa: ORG, Atletico Mineiro: ORGPORTUGUESA: ORG, ATLETICO MINEIRO: ORGTrue
419robustnessuppercaseRobert GalvinROBERT GALVINRobert Galvin: PERROBERT: ORG, GALVIN: PERFalse
420robustnessuppercaseAustralia gave Brian Lara another reason to be...AUSTRALIA GAVE BRIAN LARA ANOTHER REASON TO BE...Australia: LOC, Brian Lara: PER, West Indies: ...AUSTRALIA: LOC, BRIAN LARA: LOC, REASON: PER, ...False
421performancespeed--100 token/sec156.00 token/secTrue
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3,\n \"samples\": [\n 41,\n 46\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pass_rate\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"18%\",\n \"23%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"minimum_pass_rate\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"100%\",\n \"66%\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pass\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 2,\n \"samples\": [\n true,\n false\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 13 + } + ], + "source": [ + "harness.report()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zg-knds3tq-w" + }, + "source": [ + "# Multiple Models Runtime Testing" + ] + }, + { + "cell_type": "code", + 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pydantic 1.10.8\n", + " Uninstalling pydantic-1.10.8:\n", + " Successfully uninstalled pydantic-1.10.8\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "langtest 2.1.0 requires pydantic==1.10.8, but you have pydantic 1.10.11 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed boto3-1.34.91 botocore-1.34.91 colorama-0.4.6 databricks-api-0.9.0 databricks-cli-0.18.0 dataclasses-0.6 jedi-0.19.1 jmespath-1.0.1 johnsnowlabs-5.3.4 nlu-5.3.0 pydantic-1.10.11 pyspark-3.4.0 s3transfer-0.10.1 spark-nlp-5.3.1 spark-nlp-display-5.0 svgwrite-1.4\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "dataclasses", + "pydantic" + ] + }, + "id": "ce7cb4535bf649f299878016281f4fff" + } + }, + "metadata": {} + } + ], + "source": [ + "!pip install spacy johnsnowlabs" + ] + }, + { + "cell_type": "code", + "source": [ + "from langtest import Harness" + ], + "metadata": { + "id": "Cof4sk98CFU1" + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "TnUBvYXptq-w" + }, + "outputs": [], + "source": [ + "model_dict=[{\"model\": \"ner.dl\", \"hub\": \"johnsnowlabs\"},\n", + " {\"model\": \"en_core_web_sm\", \"hub\": \"spacy\"}]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PmMwW5IIvGav", + "outputId": "444d8d0a-6d29-4d7f-d056-03adc721732e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-04-25 14:19:32-- https://github.com/JohnSnowLabs/langtest/raw/main/langtest/data/conll/sample.conll\n", + "Resolving github.com (github.com)... 20.27.177.113\n", + "Connecting to github.com (github.com)|20.27.177.113|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll [following]\n", + "--2024-04-25 14:19:33-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 50519 (49K) [text/plain]\n", + "Saving to: ‘sample.conll’\n", + "\n", + "sample.conll 100%[===================>] 49.33K --.-KB/s in 0.04s \n", + "\n", + "2024-04-25 14:19:33 (1.27 MB/s) - ‘sample.conll’ saved [50519/50519]\n", + "\n" + ] + } + ], + "source": [ + "# Load CoNLL\n", + "!wget https://github.com/JohnSnowLabs/langtest/raw/main/langtest/data/conll/sample.conll" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yey-zVICtq-w", + "outputId": "961d7aa4-3779-49f6-d46d-ca21821c157b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Warning::Spark Session already created, some configs may not take.\n", + "recognize_entities_dl download started this may take some time.\n", + "Approx size to download 159 MB\n", + "[OK!]\n", + "Test Configuration : \n", + " {\n", + " \"tests\": {\n", + " \"defaults\": {\n", + " \"min_pass_rate\": 1.0\n", + " },\n", + " \"robustness\": {\n", + " \"add_typo\": {\n", + " \"min_pass_rate\": 0.7\n", + " },\n", + " \"american_to_british\": {\n", + " \"min_pass_rate\": 0.7\n", + " }\n", + " },\n", + " \"accuracy\": {\n", + " \"min_micro_f1_score\": {\n", + " \"min_score\": 0.7\n", + " }\n", + " },\n", + " \"bias\": {\n", + " \"replace_to_female_pronouns\": {\n", + " \"min_pass_rate\": 0.7\n", + " },\n", + " \"replace_to_low_income_country\": {\n", + " \"min_pass_rate\": 0.7\n", + " }\n", + " },\n", + " \"fairness\": {\n", + " \"min_gender_f1_score\": {\n", + " \"min_score\": 0.6\n", + " }\n", + " },\n", + " \"representation\": {\n", + " \"min_label_representation_count\": {\n", + " \"min_count\": 50\n", + " }\n", + " }\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "harness = Harness(task=\"ner\", model=model_dict, data={\"data_source\":\"sample.conll\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JwK7oi7Etq-w", + "outputId": "398fd619-92b5-4ddd-ce90-8b544847e9fa" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'tests': {'defaults': {'min_pass_rate': 0.65},\n", + " 'robustness': {'uppercase': {'min_pass_rate': 0.66},\n", + " 'lowercase': {'min_pass_rate': 0.6}},\n", + " 'performance': {'speed': {'min_pass_rate': 100, 'unit': 'tokens/sec'}}}}" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "harness.configure(\n", + "{\n", + " 'tests': {'defaults': {'min_pass_rate': 0.65},\n", + " 'robustness': {'uppercase': {'min_pass_rate': 0.66},\n", + " 'lowercase': {'min_pass_rate': 0.60},\n", + " },\n", + " 'performance': {'speed': {'min_pass_rate': 100, 'unit': 'tokens/sec'}\n", + " },\n", + " }\n", + " }\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vTbPwStvtq-x", + "outputId": "92d29e65-b0bd-4b92-ea12-c4b308083515" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Generating testcases...: 100%|██████████| 2/2 [00:00<00:00, 4236.67it/s]\n", + "WARNING:root:[W009] Removing samples where no transformation has been applied:\n", + "[W010] - Test 'uppercase': 28 samples removed out of 226\n", + "[W010] - Test 'lowercase': 3 samples removed out of 226\n", + "\n", + "Generating testcases...: 100%|██████████| 2/2 [00:00<00:00, 17050.02it/s]\n", + "WARNING:root:[W009] Removing samples where no transformation has been applied:\n", + "[W010] - Test 'uppercase': 28 samples removed out of 226\n", + "[W010] - Test 'lowercase': 3 samples removed out of 226\n", + "\n", + "Running testcases... : 100%|██████████| 422/422 [01:11<00:00, 5.91it/s]\n", + "Running testcases... : 100%|██████████| 422/422 [00:02<00:00, 153.07it/s]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [] + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "harness.generate().run()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "id": "AUZUeCpLtq-x", + "outputId": "c6acea0d-a83f-466a-ebbf-fdb96235d3dd" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
test_typelowercasespeeduppercase
model_name   
en_core_web_sm0.4600001.0000000.220000
ner.dl0.1000000.0000000.830000
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file From 74589d30133e10886200aed1c8597095bb03fc95 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Thu, 25 Apr 2024 21:05:44 +0530 Subject: [PATCH 25/69] Refactor Augmenter class to improve code organization and readability --- langtest/augmentation/augmenter.py | 62 +++++++++++++++++++++--------- 1 file changed, 43 insertions(+), 19 deletions(-) diff --git a/langtest/augmentation/augmenter.py b/langtest/augmentation/augmenter.py index 20860e38d..1f9d7c1ab 100644 --- a/langtest/augmentation/augmenter.py +++ b/langtest/augmentation/augmenter.py @@ -1,14 +1,23 @@ import yaml from typing import Iterable, Union +from langtest.datahandler.datasource import DataFactory from langtest.transform import TestFactory from langtest.tasks.task import TaskManager class Augmenter: - def __init__( - self, config: Union[str, dict], task: Union[str, TaskManager], columns_info=None - ) -> None: + def __init__(self, task: Union[str, TaskManager], config: Union[str, dict]) -> None: + """ + Initialize the Augmenter. + + Args: + config (Union[str, dict]): Configuration file or dictionary. + task (Union[str, TaskManager]): Task Manager. + columns_info ([type], optional): Columns information. Defaults to None. + + """ + self.__config = config if isinstance(config, str): self.__config = self.load_config(config) @@ -17,11 +26,18 @@ def __init__( if isinstance(task, str): task = TaskManager(task) self.__task = task + + # Test Factory and Data Factory self.__testfactory = TestFactory - self.features = columns_info.get("features", []) - self.target = columns_info.get("target", None) + self.__datafactory = DataFactory + self.__testfactory.is_augment = True + # parameters + self.__max_proportion = self.__tests.get("defaults", 0.6).get( + "max_proportion", 0.6 + ) + def load_config(self, config: str) -> dict: """ Load the configuration file. @@ -29,31 +45,39 @@ def load_config(self, config: str) -> dict: with open(config, "r") as f: return yaml.safe_load(f) - def augment(self, data: Iterable) -> str: + def augment(self, data: Union[str, Iterable]) -> str: """ Augment the content. """ + # load the data + if isinstance(data, dict): + self.__datafactory = self.__datafactory(file_path=data, task=self.__task) + data = self.__datafactory.load() # prepare the data for augmentation categories = list(self.__tests.keys()) - # iterate over the categories - test_types = self.__tests - testcases = [] for category in categories: if category not in ["robustness", "bias"]: continue - R_data = [ - self.__task.create_sample( - row_data=sample, - feature_column=self.features, - target_column=self.target, - ) - for sample in data - ] - test_cases = self.__testfactory.transform(self.__task, R_data, test_types) + + test_cases = self.__testfactory.transform(self.__task, data, self.__tests) testcases.extend(test_cases) - return testcases + + self.__augmented_data = testcases + + return self + + def prepare_hash_map(self, data: Union[str, Iterable]) -> str: + hashmap = {index: sample for index, sample in enumerate(data)} + + return hashmap + + def save(self, file_path: str): + """ + Save the augmented data. + """ + self.__datafactory.export(data=self.__augmented_data, output_path=file_path) def __or__(self, other: Iterable): results = self.augment(other) From 702b2b75b0c605321aa55ce96fd44590b11f5bd0 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sat, 27 Apr 2024 06:51:06 +0530 Subject: [PATCH 26/69] update the nb --- .../misc/PerformanceTest_Notebook.ipynb | 1899 +++++++---------- 1 file changed, 822 insertions(+), 1077 deletions(-) diff --git a/demo/tutorials/misc/PerformanceTest_Notebook.ipynb b/demo/tutorials/misc/PerformanceTest_Notebook.ipynb index dbfd0f3af..4d68133e3 100644 --- a/demo/tutorials/misc/PerformanceTest_Notebook.ipynb +++ b/demo/tutorials/misc/PerformanceTest_Notebook.ipynb @@ -40,141 +40,19 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "azUb114QhOsY", - "outputId": "82bc5501-2218-4aed-dd34-d90788761e02", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.40.0)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.4)\n", - "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.20.3)\n", - "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.0)\n", - "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n", - "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.12.25)\n", - "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n", - "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n", - "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n", - "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.2)\n", - "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.6.0)\n", - "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.11.0)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n", - 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"remote: Total 33818 (delta 3116), reused 4226 (delta 3008), pack-reused 29357\u001b[K\n", - "Receiving objects: 100% (33818/33818), 162.32 MiB | 23.05 MiB/s, done.\n", - "Resolving deltas: 100% (23191/23191), done.\n", - "Updating files: 100% (1909/1909), done.\n" - ] - } - ] - }, - { - "cell_type": "code", + "outputs": [], "source": [ - "!pip install -e ./langtest" - ], - "metadata": { - "id": "RnNrHKtzAqFs", - "outputId": "576cc218-897a-4a2d-b4e2-9e73a841e72d", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Obtaining file:///content/langtest\n", - " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", - " Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n", - " Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n", - " Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "Collecting jsonlines<4.0.0,>=3.1.0 (from langtest==2.1.0)\n", - 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"Building wheels for collected packages: langtest\n", - " Building editable for langtest (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for langtest: filename=langtest-2.1.0-py3-none-any.whl size=11948 sha256=30eaf4ca34c9a56fd42a343ca93be52aff710dde9477306a2fa2ef29c9d5bd37\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-0w9n_2mn/wheels/a2/f4/50/7b4955461b5185fc52d1be1ad40afb2ad10f2610b68e2919a0\n", - "Successfully built langtest\n", - "Installing collected packages: pydantic, jsonlines, langtest\n", - " Attempting uninstall: pydantic\n", - " Found existing installation: pydantic 2.7.0\n", - " Uninstalling pydantic-2.7.0:\n", - " Successfully uninstalled pydantic-2.7.0\n", - "Successfully installed jsonlines-3.1.0 langtest-2.1.0 pydantic-1.10.8\n" - ] - } + "!pip install langtest[transformers]" ] }, - { - "cell_type": "code", - "source": [ - "import sys\n", - "\n", - "sys.path.append('/content/langtest')" - ], - "metadata": { - "id": "JwqpfpiHA87p" - }, - 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categorydataset_nametest_typeoriginal_contextoriginal_questionperturbed_contextperturbed_question
0robustnessBoolQuppercase20 euro note -- Until now there has been only ...is the first series 20 euro note still legal t...20 EURO NOTE -- UNTIL NOW THERE HAS BEEN ONLY ...IS THE FIRST SERIES 20 EURO NOTE STILL LEGAL T...
1robustnessBoolQuppercase2018–19 UEFA Champions League -- The final wil...do the champions league winners get automatic ...2018–19 UEFA CHAMPIONS LEAGUE -- THE FINAL WIL...DO THE CHAMPIONS LEAGUE WINNERS GET AUTOMATIC ...
2robustnessBoolQuppercaseBullsnake -- Bullsnakes are very powerful cons...can a bull snake kill a small dogBULLSNAKE -- BULLSNAKES ARE VERY POWERFUL CONS...CAN A BULL SNAKE KILL A SMALL DOG
3robustnessBoolQuppercaseNBA playoffs -- All rounds are best-of-seven s...are all nba playoff games best of 7NBA PLAYOFFS -- ALL ROUNDS ARE BEST-OF-SEVEN S...ARE ALL NBA PLAYOFF GAMES BEST OF 7
4robustnessBoolQuppercaseManchester station group -- The Manchester sta...can i use my train ticket on the tram in manch...MANCHESTER STATION GROUP -- THE MANCHESTER STA...CAN I USE MY TRAIN TICKET ON THE TRAM IN MANCH...
........................
190robustnessNQ-openadd_typo-who has the most followers on the twitter-who has the most followers on tme twitter
191robustnessNQ-openadd_typo-who said it's not what your country can do for...-who said it's not what your country can do for...
192robustnessNQ-openadd_typo-when does lil wayne new album drop 2018-jhen does lil wayne new album drop 2018
193robustnessNQ-openadd_typo-the khajuraho temples are especially well know...-the khajuraho temples are rspecially well know...
194robustnessNQ-openadd_typo-when does the regular nba basketball season start-when does the regular nba basuetball season start
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" + ], + "text/plain": [ + " category dataset_name test_type \\\n", + "0 robustness BoolQ uppercase \n", + "1 robustness BoolQ uppercase \n", + "2 robustness BoolQ uppercase \n", + "3 robustness BoolQ uppercase \n", + "4 robustness BoolQ uppercase \n", + ".. ... ... ... \n", + "190 robustness NQ-open add_typo \n", + "191 robustness NQ-open add_typo \n", + "192 robustness NQ-open add_typo \n", + "193 robustness NQ-open add_typo \n", + "194 robustness NQ-open add_typo \n", + "\n", + " original_context \\\n", + "0 20 euro note -- Until now there has been only ... \n", + "1 2018–19 UEFA Champions League -- The final wil... \n", + "2 Bullsnake -- Bullsnakes are very powerful cons... \n", + "3 NBA playoffs -- All rounds are best-of-seven s... \n", + "4 Manchester station group -- The Manchester sta... \n", + ".. ... \n", + "190 - \n", + "191 - \n", + "192 - \n", + "193 - \n", + "194 - \n", + "\n", + " original_question \\\n", + "0 is the first series 20 euro note still legal t... \n", + "1 do the champions league winners get automatic ... \n", + "2 can a bull snake kill a small dog \n", + "3 are all nba playoff games best of 7 \n", + "4 can i use my train ticket on the tram in manch... \n", + ".. ... \n", + "190 who has the most followers on the twitter \n", + "191 who said it's not what your country can do for... \n", + "192 when does lil wayne new album drop 2018 \n", + "193 the khajuraho temples are especially well know... \n", + "194 when does the regular nba basketball season start \n", + "\n", + " perturbed_context \\\n", + "0 20 EURO NOTE -- UNTIL NOW THERE HAS BEEN ONLY ... \n", + "1 2018–19 UEFA CHAMPIONS LEAGUE -- THE FINAL WIL... \n", + "2 BULLSNAKE -- BULLSNAKES ARE VERY POWERFUL CONS... \n", + "3 NBA PLAYOFFS -- ALL ROUNDS ARE BEST-OF-SEVEN S... \n", + "4 MANCHESTER STATION GROUP -- THE MANCHESTER STA... \n", + ".. ... \n", + "190 - \n", + "191 - \n", + "192 - \n", + "193 - \n", + "194 - \n", + "\n", + " perturbed_question \n", + "0 IS THE FIRST SERIES 20 EURO NOTE STILL LEGAL T... \n", + "1 DO THE CHAMPIONS LEAGUE WINNERS GET AUTOMATIC ... \n", + "2 CAN A BULL SNAKE KILL A SMALL DOG \n", + "3 ARE ALL NBA PLAYOFF GAMES BEST OF 7 \n", + "4 CAN I USE MY TRAIN TICKET ON THE TRAM IN MANCH... \n", + ".. ... \n", + "190 who has the most followers on tme twitter \n", + "191 who said it's not what your country can do for... \n", + "192 jhen does lil wayne new album drop 2018 \n", + "193 the khajuraho temples are rspecially well know... \n", + "194 when does the regular nba basuetball season start \n", + "\n", + "[195 rows x 7 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harness.testcases()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "NOJ8BAU2GGzd" + }, + "source": [ + "harness.testcases() method displays the produced test cases in form of a pandas data frame." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "3CwhQw6hGR9S" + }, + "source": [ + "### Running the tests" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aguX6-aFGOnP", + "outputId": "bb014811-522b-4f07-fa8a-bf3d1c906d7f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + " BoolQ \n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running testcases... : 100%|██████████| 100/100 [01:19<00:00, 1.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "\n", + "================================================================================\n", + " NQ-open \n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running testcases... : 100%|██████████| 95/95 [01:47<00:00, 1.13s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harness.run()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "191O2oaUGWrH" + }, + "source": [ + "Called after harness.generate() and is to used to run all the tests. Returns a pass/fail flag for each test." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 797 + }, + "id": "-cXkdnihGYke", + "outputId": "2aa88caa-5e83-44fe-b3aa-b81b9ae9115a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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categorydataset_nametest_typeoriginal_contextoriginal_questionperturbed_contextperturbed_questionexpected_resultactual_resultpass
0robustnessBoolQuppercase20 euro note -- Until now there has been only ...is the first series 20 euro note still legal t...20 EURO NOTE -- UNTIL NOW THERE HAS BEEN ONLY ...IS THE FIRST SERIES 20 EURO NOTE STILL LEGAL T...\\nFalse\\nFalseTrue
1robustnessBoolQuppercase2018–19 UEFA Champions League -- The final wil...do the champions league winners get automatic ...2018–19 UEFA CHAMPIONS LEAGUE -- THE FINAL WIL...DO THE CHAMPIONS LEAGUE WINNERS GET AUTOMATIC ...\\nTrue\\nTrueTrue
2robustnessBoolQuppercaseBullsnake -- Bullsnakes are very powerful cons...can a bull snake kill a small dogBULLSNAKE -- BULLSNAKES ARE VERY POWERFUL CONS...CAN A BULL SNAKE KILL A SMALL DOG\\nFalse\\nFalseTrue
3robustnessBoolQuppercaseNBA playoffs -- All rounds are best-of-seven s...are all nba playoff games best of 7NBA PLAYOFFS -- ALL ROUNDS ARE BEST-OF-SEVEN S...ARE ALL NBA PLAYOFF GAMES BEST OF 7\\nTrueTrueTrue
4robustnessBoolQuppercaseManchester station group -- The Manchester sta...can i use my train ticket on the tram in manch...MANCHESTER STATION GROUP -- THE MANCHESTER STA...CAN I USE MY TRAIN TICKET ON THE TRAM IN MANCH...\\nTrue\\nFalseFalse
.................................
190robustnessNQ-openadd_typo-who has the most followers on the twitter-who has the most followers on tme twitter?\\n\\nAs of 2021, the person with the most foll...?\\n\\nAs of June 2021, the account with the mos...True
191robustnessNQ-openadd_typo-who said it's not what your country can do for...-who said it's not what your country can do for...?\\n\\nJohn F. Kennedy?\\n\\n\\nJohn F. KennedyTrue
192robustnessNQ-openadd_typo-when does lil wayne new album drop 2018-jhen does lil wayne new album drop 2018\\nLil Wayne's album, \"Tha Carter V,\" was relea...?\\n\\nThere is no official release date for Lil...False
193robustnessNQ-openadd_typo-the khajuraho temples are especially well know...-the khajuraho temples are rspecially well know...\\nerotic sculptures?\\n\\nerotic sculptures.True
194robustnessNQ-openadd_typo-when does the regular nba basketball season start-when does the regular nba basuetball season start?\\nThe regular NBA basketball season typically...?\\n\\nThe regular NBA basketball season typical...True
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195 rows × 10 columns

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Benchmarking Results: gpt-3.5-turbo-instruct
fail_countpass_countpass_rateminimum_pass_ratepass
dataset_namecategorytest_type
BoolQrobustnessuppercase143672%80%False
add_typo84284%80%True
NQ-openrobustnessuppercase94182%80%True
add_typo103578%80%False
\n", + "
" + ], + "text/plain": [ + " Benchmarking Results: gpt-3.5-turbo-instruct \\\n", + " fail_count \n", + "dataset_name category test_type \n", + "BoolQ robustness uppercase 14 \n", + " add_typo 8 \n", + "NQ-open robustness uppercase 9 \n", + " add_typo 10 \n", + "\n", + " \\\n", + " pass_count pass_rate minimum_pass_rate \n", + "dataset_name category test_type \n", + "BoolQ robustness uppercase 36 72% 80% \n", + " add_typo 42 84% 80% \n", + "NQ-open robustness uppercase 41 82% 80% \n", + " add_typo 35 78% 80% \n", + "\n", + " \n", + " pass \n", + "dataset_name category test_type \n", + "BoolQ robustness uppercase False \n", + " add_typo True \n", + "NQ-open robustness uppercase True \n", + " add_typo False " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harness.report()" + ] + } + ], + "metadata": { + "accelerator": "TPU", + "colab": { + "machine_shape": "hm", + "provenance": [], + "toc_visible": true + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/langtest/prompts.py b/langtest/prompts.py index 870b451c4..b9df1d28e 100644 --- a/langtest/prompts.py +++ b/langtest/prompts.py @@ -33,12 +33,11 @@ def get_template(self): temp = [] order_less = [] - for field in self.__field_order: - formatted = f"{field.title()}: {{{field}}}" - if field in self.__dict__: - temp.append(formatted) + for field in self.__dict__: + if field in self.__field_order: + temp.append(f"{field.title()}: {{{field}}}") else: - order_less.append(formatted) + order_less.append(f"{field.title()}: {{{field}}}") if order_less: temp.extend(order_less) return "\n" + "\n".join(temp) @@ -175,6 +174,7 @@ def get_prompt(self, hub=None): return self.prompt_style() def get_shot_prompt(self): + print(self.get_examples) return f"{len(self.get_examples)}-shot prompt" def lm_studio_prompt(self): From 36a4426c73cb4f203db8b18d2ff6ac94ffc3ceac Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Fri, 10 May 2024 17:49:41 +0530 Subject: [PATCH 48/69] refactor: improve test case handling in Harness class --- langtest/langtest.py | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/langtest/langtest.py b/langtest/langtest.py index a7a298cb9..0d1846627 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -1018,6 +1018,10 @@ def import_edited_testcases(self, input_path: str, **kwargs): Args: input_path (str): location of the file to load """ + if isinstance(self.data, dict) and not self.__is_multi_model: + self._testcases = {} + elif isinstance(self.data, list): + self._testcases = [] # multi dataset case is handled separately if isinstance(self._testcases, dict) and not self.__is_multi_model: @@ -1047,6 +1051,10 @@ def import_edited_testcases(self, input_path: str, **kwargs): if sample.category not in ["robustness", "bias"] ] + if len(temp_testcases) == 0: + testcases = self.__temp_generate()._testcases + temp_testcases.extend(testcases) + self._testcases = DataFactory( {"data_source": input_path}, task=self.task, is_import=True ).load() @@ -1738,3 +1746,25 @@ def get_leaderboard( return leaderboard.split_wise() return leaderboard.default() + + def __temp_generate(self, *args, **kwargs): + """Temporary function to generate the testcases.""" + + # temp config other than robustness and bias + temp_config = { + "tests": { + k: v + for k, v in self._config.get("tests", {}).items() + if k not in ["robustness", "bias"] + } + } + + temp_harness = self.__class__( + task=str(self.task), + model=self.__model_info, + data=self.__data_dict, + config=temp_config, + ) + temp_harness.generate() + + return temp_harness From 3396282ba59cdb3a9055694140c08817fef12354 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Fri, 10 May 2024 18:02:27 +0530 Subject: [PATCH 49/69] Refactor test case handling in Harness class --- langtest/langtest.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/langtest/langtest.py b/langtest/langtest.py index 0d1846627..f09553d3d 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -1758,7 +1758,9 @@ def __temp_generate(self, *args, **kwargs): if k not in ["robustness", "bias"] } } - + print( + f"{'':=^80}\n{'Adding Testcases Other than Robustness and Bias Categories ':^80}\n{'':=^80}" + ) temp_harness = self.__class__( task=str(self.task), model=self.__model_info, @@ -1766,5 +1768,6 @@ def __temp_generate(self, *args, **kwargs): config=temp_config, ) temp_harness.generate() + print(f"{'':-^80}\n") return temp_harness From 3fc0c59398b901fdd61284e18cfe9ceae38f089f Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Fri, 10 May 2024 20:39:31 +0530 Subject: [PATCH 50/69] improved the `import_edited_testcases()` functionality. and updated dataset in tests --- langtest/datahandler/datasource.py | 4 +- langtest/langtest.py | 33 ++++- langtest/utils/custom_types/sample.py | 2 +- tests/fixtures/test.conll | 184 ++++++++++++++++++++++++++ 4 files changed, 219 insertions(+), 4 deletions(-) diff --git a/langtest/datahandler/datasource.py b/langtest/datahandler/datasource.py index 61ed9dd21..674af8664 100644 --- a/langtest/datahandler/datasource.py +++ b/langtest/datahandler/datasource.py @@ -923,9 +923,11 @@ def _import_data(self, file_name, **kwargs) -> List[Sample]: temp_data = data.groupby("dataset_name") samples = {} for name, df in temp_data: + temp_samples = [] for i in df.to_dict(orient="records"): sample = self.task.get_sample_class(**i) - samples[name] = sample + temp_samples.append(sample) + samples[name] = temp_samples return samples for i in data.to_dict(orient="records"): diff --git a/langtest/langtest.py b/langtest/langtest.py index f09553d3d..d9a241cd2 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -1023,6 +1023,19 @@ def import_edited_testcases(self, input_path: str, **kwargs): elif isinstance(self.data, list): self._testcases = [] + # check the category of the testcases and count the categories + categories_count = list(self._config.get("tests", {})) + + # pop the robustness and bias categories from the categories_count + if "robustness" in categories_count: + categories_count.remove("robustness") + if "bias" in categories_count: + categories_count.remove("bias") + if "defaults" in categories_count: + categories_count.remove("defaults") + + categories_count = len(categories_count) + # multi dataset case is handled separately if isinstance(self._testcases, dict) and not self.__is_multi_model: temp_testcases = { @@ -1034,15 +1047,31 @@ def import_edited_testcases(self, input_path: str, **kwargs): for k, v in self._testcases.items() } + if categories_count > 0: + testcases = self.__temp_generate()._testcases + for k, v in testcases.items(): + if k in temp_testcases: + temp_testcases[k].extend(v) + else: + temp_testcases[k] = v + imported_testcases = DataFactory( {"data_source": input_path}, task=self.task, is_import=True ).load() + # merge the testcases with the imported ones to the temp_testcases for name, list_samples in imported_testcases.items(): if name not in temp_testcases: temp_testcases[name] = list_samples temp_testcases[name].extend(list_samples) + # update the testcases in the harness + for k, v in temp_testcases.items(): + if k in self._testcases: + self._testcases[k].extend(v) + else: + self._testcases[k] = v + # single dataset case elif isinstance(self._testcases, list): temp_testcases = [ @@ -1051,7 +1080,7 @@ def import_edited_testcases(self, input_path: str, **kwargs): if sample.category not in ["robustness", "bias"] ] - if len(temp_testcases) == 0: + if categories_count > 0: testcases = self.__temp_generate()._testcases temp_testcases.extend(testcases) @@ -1759,7 +1788,7 @@ def __temp_generate(self, *args, **kwargs): } } print( - f"{'':=^80}\n{'Adding Testcases Other than Robustness and Bias Categories ':^80}\n{'':=^80}" + f"{'':=^80}\n{'Adding Test Cases Other than Robustness and Bias Categories ':^80}\n{'':=^80}" ) temp_harness = self.__class__( task=str(self.task), diff --git a/langtest/utils/custom_types/sample.py b/langtest/utils/custom_types/sample.py index 558fecf35..3a8d1abf2 100644 --- a/langtest/utils/custom_types/sample.py +++ b/langtest/utils/custom_types/sample.py @@ -373,7 +373,7 @@ class BaseQASample(BaseModel): original_question: str original_context: str - options: str + options: str = None test_type: str = None perturbed_question: str = None perturbed_context: str = None diff --git a/tests/fixtures/test.conll b/tests/fixtures/test.conll index 79bcd5603..577a4a17a 100644 --- a/tests/fixtures/test.conll +++ b/tests/fixtures/test.conll @@ -160,6 +160,71 @@ first JJ I-NP O time NN I-NP O . . O O +A DT B-NP O +London-based JJ I-NP B-MISC +spokesman NN I-NP O +of IN B-PP O +the DT B-NP O +Iraqi JJ I-NP B-ORG +National NNP I-NP I-ORG +Congress NNP B-NP I-ORG +said VBD B-VP O +Iraqi JJ B-NP B-MISC +artillery NN I-NP O +was VBD B-VP O +shelling VBG I-VP O +the DT B-NP O +city NN I-NP O +and CC I-NP O +Iraqi JJ I-NP B-MISC +tanks NNS I-NP O +had VBD B-VP O +advanced VBN I-VP O +to TO B-PP O +within IN B-NP O +10 CD I-NP O +km NN I-NP O +( ( O O +six CD B-NP O +miles NNS I-NP O +) ) O O +of IN B-PP O +Arbil NNP B-NP B-LOC +, , O O +the DT B-NP O +administrative JJ I-NP O +centre NN I-NP O +of IN B-PP O +the DT B-NP O +Kurdish JJ I-NP B-MISC +rebel-controlled JJ I-NP O +region NN I-NP O +of IN B-PP O +northern JJ B-NP O +Iraq NNP I-NP B-LOC +. . O O + +Brokers NNS B-NP O +said VBD B-VP O +blue JJ B-NP O +chips NNS I-NP O +like IN B-PP O +IDLC NNP B-NP B-ORG +, , O O +Bangladesh NNP B-NP B-ORG +Lamps NNP I-NP I-ORG +, , O O +Chittagong NNP B-NP B-ORG +Cement NNP I-NP I-ORG +and CC O O +Atlas NNP B-NP B-ORG +Bangladesh NNP I-NP I-ORG +were VBD B-VP O +expected VBN I-VP O +to TO B-PP O +rise VB B-NP O +. . O O + Despite IN B-PP O winning VBG B-VP O the DT B-NP O @@ -1466,3 +1531,122 @@ China NNP B-NP B-LOC 0 CD I-NP O 2 CD I-NP O 0 CD I-NP O + +A DT B-NP O +London-based JJ I-NP B-MISC +spokesman NN I-NP O +of IN B-PP O +the DT B-NP O +Iraqi JJ I-NP B-ORG +National NNP I-NP I-ORG +Congress NNP B-NP I-ORG +said VBD B-VP O +Iraqi JJ B-NP B-MISC +artillery NN I-NP O +was VBD B-VP O +shelling VBG I-VP O +the DT B-NP O +city NN I-NP O +and CC I-NP O +Iraqi JJ I-NP B-MISC +tanks NNS I-NP O +had VBD B-VP O +advanced VBN I-VP O +to TO B-PP O +within IN B-NP O +10 CD I-NP O +km NN I-NP O +( ( O O +six CD B-NP O +miles NNS I-NP O +) ) O O +of IN B-PP O +Arbil NNP B-NP B-LOC +, , O O +the DT B-NP O +administrative JJ I-NP O +centre NN I-NP O +of IN B-PP O +the DT B-NP O +Kurdish JJ I-NP B-MISC +rebel-controlled JJ I-NP O +region NN I-NP O +of IN B-PP O +northern JJ B-NP O +Iraq NNP I-NP B-LOC +. . O O + +Brokers NNS B-NP O +said VBD B-VP O +blue JJ B-NP O +chips NNS I-NP O +like IN B-PP O +IDLC NNP B-NP B-ORG +, , O O +Bangladesh NNP B-NP B-ORG +Lamps NNP I-NP I-ORG +, , O O +Chittagong NNP B-NP B-ORG +Cement NNP I-NP I-ORG +and CC O O +Atlas NNP B-NP B-ORG +Bangladesh NNP I-NP I-ORG +were VBD B-VP O +expected VBN I-VP O +to TO B-PP O +rise VB B-NP O +. . O O + +They PRP B-NP O +said VBD B-VP O +there EX B-NP O +was VBD B-VP O +still RB I-VP O +demand VB I-VP O +for IN B-PP O +blue JJ B-NP O +chips NNS I-NP O +in IN B-PP O +engineering NN B-NP O +sector NN I-NP O +despite IN B-PP O +their PRP$ B-NP O +persistent JJ I-NP O +rise NN I-NP O +over IN B-PP O +the DT B-NP O +past JJ I-NP O +several JJ I-NP O +sessions NNS I-NP O +. . O O + +The DT B-NP O +DSE NN I-NP B-ORG +all DT B-NP O +share NN I-NP O +price NN I-NP O +index NN I-NP O +closed VBD B-VP O +2.73 CD B-NP O +points NNS I-NP O +or CC O O +0.22 CD B-NP O +percent NN I-NP O +up IN B-PP O +at IN B-PP O +1,196.35 CD B-NP O +on IN B-PP O +a DT B-NP O +turnover NN I-NP O +of IN B-PP O +133.7 CD B-NP O +million CD I-NP O +taka NN I-NP O +on IN B-PP O +Thursday NNP B-NP O +. . O O + +-- : O O +Dhaka NNP B-NP B-ORG +Newsroom NNP I-NP I-ORG +880-2-506363 CD I-NP O \ No newline at end of file From 5d8196dc7ddf0eac2894ab0efab8c9781ff76842 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Fri, 10 May 2024 20:50:10 +0530 Subject: [PATCH 51/69] refactor: remove "robustness" and "bias" categories from categories_count --- langtest/langtest.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/langtest/langtest.py b/langtest/langtest.py index d9a241cd2..53200b567 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -1026,15 +1026,14 @@ def import_edited_testcases(self, input_path: str, **kwargs): # check the category of the testcases and count the categories categories_count = list(self._config.get("tests", {})) - # pop the robustness and bias categories from the categories_count - if "robustness" in categories_count: - categories_count.remove("robustness") - if "bias" in categories_count: - categories_count.remove("bias") - if "defaults" in categories_count: - categories_count.remove("defaults") - - categories_count = len(categories_count) + # Remove the "robustness" and "bias" categories from categories_count + categories_count = len( + [ + category + for category in categories_count + if category not in ["robustness", "bias", "defaults"] + ] + ) # multi dataset case is handled separately if isinstance(self._testcases, dict) and not self.__is_multi_model: From e7e94a219ce68fa4d28b885365b4bb1ac0e610e0 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sat, 11 May 2024 13:13:56 +0530 Subject: [PATCH 52/69] fix lint and format issue --- langtest/langtest.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/langtest/langtest.py b/langtest/langtest.py index 2b9758b05..021d1c77a 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -1665,12 +1665,11 @@ def __reset_defaults(self): """Reset the default values.""" model_response = TestResultManager() model_response.clear_data() - + # Reset the PromptManager prompt_manager = PromptManager() prompt_manager.reset() - def __tracking(self, *args, **kwargs): """Track the progress of the testcases.""" if self.__benchmarking: From 760d9a030a4f819d40a2910d49096636e105531f Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sun, 12 May 2024 13:29:38 +0530 Subject: [PATCH 53/69] Notebook for LLM evaluation in ner task --- .../llm_notebooks/NER Casual LLM.ipynb | 1038 +++++++++++++++++ 1 file changed, 1038 insertions(+) create mode 100644 demo/tutorials/llm_notebooks/NER Casual LLM.ipynb diff --git a/demo/tutorials/llm_notebooks/NER Casual LLM.ipynb b/demo/tutorials/llm_notebooks/NER Casual LLM.ipynb new file mode 100644 index 000000000..b6e6c790d --- /dev/null +++ b/demo/tutorials/llm_notebooks/NER Casual LLM.ipynb @@ -0,0 +1,1038 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + 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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/NER%20Casual%20LLM.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n", + "\n", + "Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting started with LangTest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install \"langtest[evaluate,openai]\" requests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Harness and Its Parameters\n", + "\n", + "The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#Import Harness from the LangTest library\n", + "from langtest import Harness" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It imports the Harness class from within the module, that is designed to provide a blueprint or framework for conducting NLP testing, and that instances of the Harness class can be customized or configured for different testing scenarios or environments.\n", + "\n", + "Here is a list of the different parameters that can be passed to the Harness function:\n", + "\n", + "
\n", + "\n", + "\n", + "| Parameter | Description | \n", + "| - | - | \n", + "|**task** |Task for which the model is to be evaluated (ner)|\n", + "| **model** | Specifies the model(s) to be evaluated. This parameter can be provided as either a dictionary or a list of dictionaries. Each dictionary should contain the following keys:
  • model (mandatory): \tPipelineModel or path to a saved model or pretrained pipeline/model from hub.
  • hub (mandatory): Hub (library) to use in back-end for loading model from public models hub or from path
|\n", + "| **data** | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys:
  • data_source (mandatory): The source of the data.
  • subset (optional): The subset of the data.
  • feature_column (optional): The column containing the features.
  • target_column (optional): The column containing the target labels.
  • split (optional): The data split to be used.
  • source (optional): Set to 'huggingface' when loading Hugging Face dataset.
|\n", + "| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A system prompt, in the context of Language Model (LLM), is a predefined input that guides the model to generate a specific structured output. This is particularly useful in tasks where the output needs to follow a certain format or structure.\n", + "\n", + "For instance, in Named Entity Recognition (NER), a task in Natural Language Processing (NLP), we might want the model to identify and classify named entities in a text into predefined categories like person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.\n", + "\n", + "In such a case, a system prompt could be a sentence with placeholders for the model to fill. The LLM model, upon receiving this prompt, generates an output that fills these placeholders with appropriate entities, thereby producing a structured output.\n", + "\n", + "This approach of using system prompts helps in controlling the output of the LLM models, making them more useful in practical applications where structured outputs are required." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# server prompt for the assistant to generate the response\n", + "system_prompt = [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": \"Named Entity Recognition (NER) involves identifying and categorizing key information in text into predefined categories such as person names (PER) and organizations (ORG). Each identified entity in the input text is represented by a dictionary containing the keys 'entity', 'score', 'index', 'word', 'start', and 'end'. 'entity' denotes the category, 'score' the confidence level (0-1), 'index' the position of the entity's first word in the input, 'word' the actual entity text, and 'start' and 'end' the character positions in the input text. Your task is to analyze the text, classify entities and provide the entity details as requested.\"},\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"The sentence is: John is working at Google.\"\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"[{'entity': 'PER', 'score': 0.99, 'index': 1, 'word': 'John', 'start': 0, 'end': 4}, {'entity': 'ORG', 'score': 0.98, 'index': 5, 'word': 'Google', 'start': 19, 'end': 25}]\"},\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"The sentence is: Elon Musk founded SpaceX.\"\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"[{'entity': 'PER', 'score': 0.99, 'index': 1, 'word': 'Elon Musk', 'start': 0, 'end': 9}, {'entity': 'ORG', 'score': 0.97, 'index': 4, 'word': 'SpaceX', 'start': 18, 'end': 24}]\"},\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"The sentence is: Ada Lovelace is considered the first computer programmer.\"},\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"[{'entity': 'PER', 'score': 0.98, 'index': 1, 'word': 'Ada Lovelace', 'start': 0, 'end': 12}]\"},\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# OpenAI Model Testing For NER\n", + "\n", + "In this section, we dive into testing of OpenAI models in NER task.\n", + "\n", + "LangTest supports robustness and accuracy tests for LLM testing for now." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load CoNLL\n", + "!wget https://github.com/JohnSnowLabs/langtest/raw/main/langtest/data/conll/sample.conll" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup and Configure Harness" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Configuration : \n", + " {\n", + " \"model_parameters\": {\n", + " \"system_prompt\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": \"Named Entity Recognition (NER) involves identifying and categorizing key information in text into predefined categories such as person names (PER) and organizations (ORG). Each identified entity in the input text is represented by a dictionary containing the keys 'entity', 'score', 'index', 'word', 'start', and 'end'. 'entity' denotes the category, 'score' the confidence level (0-1), 'index' the position of the entity's first word in the input, 'word' the actual entity text, and 'start' and 'end' the character positions in the input text. Your task is to analyze the text, classify entities and provide the entity details as requested.\"\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"The sentence is: John is working at Google.\"\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"[{'entity': 'PER', 'score': 0.99, 'index': 1, 'word': 'John', 'start': 0, 'end': 4}, {'entity': 'ORG', 'score': 0.98, 'index': 5, 'word': 'Google', 'start': 19, 'end': 25}]\"\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"The sentence is: Elon Musk founded SpaceX.\"\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"[{'entity': 'PER', 'score': 0.99, 'index': 1, 'word': 'Elon Musk', 'start': 0, 'end': 9}, {'entity': 'ORG', 'score': 0.97, 'index': 4, 'word': 'SpaceX', 'start': 18, 'end': 24}]\"\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"The sentence is: Ada Lovelace is considered the first computer programmer.\"\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"[{'entity': 'PER', 'score': 0.98, 'index': 1, 'word': 'Ada Lovelace', 'start': 0, 'end': 12}]\"\n", + " }\n", + " ]\n", + " },\n", + " \"tests\": {\n", + " \"defaults\": {\n", + " \"min_pass_rate\": 1.0\n", + " },\n", + " \"robustness\": {\n", + " \"lowercase\": {\n", + " \"min_pass_rate\": 0.7\n", + " }\n", + " },\n", + " \"accuracy\": {\n", + " \"min_f1_score\": {\n", + " \"min_score\": 0.7\n", + " }\n", + " }\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "# Create a Harness object\n", + "h = Harness(task=\"ner\",\n", + " model={\n", + " \"model\": \"gpt-3.5-turbo-instruct\",\n", + " \"hub\": \"openai\",},\n", + " data={\n", + " \"data_source\": '../../data/conll03.conll'\n", + " },\n", + " config={\n", + " \"model_parameters\": {\n", + " \"system_prompt\": system_prompt,\n", + " },\n", + " \"tests\": {\n", + " \"defaults\": {\n", + " \"min_pass_rate\": 1.0\n", + " },\n", + " \"robustness\": {\n", + " \"lowercase\": {\n", + " \"min_pass_rate\": 0.7\n", + " }\n", + " },\n", + " \"accuracy\": {\n", + " \"min_f1_score\": {\n", + " \"min_score\": 0.7,\n", + " },\n", + " }\n", + " }\n", + " }\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have specified task as NER, hub as OpenAI and model as GPT-3.5.\n", + "\n", + "For dataset we used default `Conll` dataset " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For tests we used lowercase and uppercase. Other available robustness tests for QA task are:\n", + "* `add_context`\n", + "* `add_contraction`\n", + "* `add_punctuation`\n", + "* `add_typo`\n", + "* `add_ocr_typo`\n", + "* `american_to_british`\n", + "* `british_to_american`\n", + "* `lowercase`\n", + "* `strip_punctuation`\n", + "* `titlecase`\n", + "* `uppercase`\n", + "* `number_to_word`\n", + "* `add_abbreviation`\n", + "* `add_speech_to_text_typo`\n", + "* `add_slangs`\n", + "* `dyslexia_word_swap`\n", + "* `multiple_perturbations`\n", + "* `adjective_synonym_swap`\n", + "* `adjective_antonym_swap`\n", + "* `strip_all_punctuation`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Available Bias tests for QA task are:\n", + "\n", + "* `replace_to_male_pronouns`\n", + "* `replace_to_female_pronouns`\n", + "* `replace_to_neutral_pronouns`\n", + "* `replace_to_high_income_country`\n", + "* `replace_to_low_income_country`\n", + "* `replace_to_upper_middle_income_country`\n", + "* `replace_to_lower_middle_income_country`\n", + "* `replace_to_white_firstnames`\n", + "* `replace_to_black_firstnames`\n", + "* `replace_to_hispanic_firstnames`\n", + "* `replace_to_asian_firstnames`\n", + "* `replace_to_white_lastnames`\n", + "* `replace_to_sikh_names`\n", + "* `replace_to_christian_names`\n", + "* `replace_to_hindu_names`\n", + "* `replace_to_muslim_names`\n", + "* `replace_to_inter_racial_lastnames`\n", + "* `replace_to_native_american_lastnames`\n", + "* `replace_to_asian_lastnames`\n", + "* `replace_to_hispanic_lastnames`\n", + "* `replace_to_black_lastnames`\n", + "* `replace_to_parsi_names`\n", + "* `replace_to_jain_names`\n", + "* `replace_to_buddhist_names`\n", + "\n", + "Available Representation tests for QA task are:\n", + "\n", + "* `min_gender_representation_count`\n", + "* `min_ethnicity_name_representation_count`\n", + "* `min_religion_name_representation_count`\n", + "* `min_country_economic_representation_count`\n", + "* `min_gender_representation_proportion`\n", + "* `min_ethnicity_name_representation_proportion`\n", + "* `min_religion_name_representation_proportion`\n", + "* `min_country_economic_representation_proportion`\n", + "\n", + "\n", + "Available Accuracy tests for QA task are:\n", + "\n", + "* `min_exact_match_score`\n", + "* `min_bleu_score`\n", + "* `min_rouge1_score`\n", + "* `min_rouge2_score`\n", + "* `min_rougeL_score`\n", + "* `min_rougeLsum_score`\n", + "\n", + "\n", + "Available Fairness tests for QA task are:\n", + "\n", + "* `max_gender_rouge1_score`\n", + "* `max_gender_rouge2_score`\n", + "* `max_gender_rougeL_score`\n", + "* `max_gender_rougeLsum_score`\n", + "* `min_gender_rouge1_score`\n", + "* `min_gender_rouge2_score`\n", + "* `min_gender_rougeL_score`\n", + "* `min_gender_rougeLsum_score`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also set prompts and other model parameters in config. Possible parameters are:\n", + "* `user_prompt:` Promt to be given to the model.\n", + "* `temperature:` Temperature of the model.\n", + "* `max_tokens:` Maximum number of output tokens allowed for model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "h.configure({\n", + " \"model_parameters\": {\n", + " \"system_prompt\": system_prompt,\n", + " },\n", + " \"tests\": {\n", + " \"defaults\": {\n", + " \"min_pass_rate\": 1.0\n", + " },\n", + " \"robustness\": {\n", + " \"lowercase\": {\n", + " \"min_pass_rate\": 0.7\n", + " }\n", + " },\n", + " \"accuracy\": {\n", + " \"min_f1_score\": {\n", + " \"min_score\": 0.7,\n", + " },\n", + " }\n", + " }\n", + "})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we have configured the harness to perform two robustness tests (uppercase and lowercase) and defined the minimum pass rate for each test.\n", + "\n", + "➤ You can adjust the level of transformation in the sentence by using the \"`prob`\" parameter, which controls the proportion of words to be changed during robustness tests.\n", + "\n", + "➤ **NOTE** : \"`prob`\" defaults to 1.0, which means all words will be transformed.\n", + "```\n", + "harness.configure(\n", + "{\n", + " 'tests': {\n", + " 'defaults': {'min_pass_rate': 0.65},\n", + " 'robustness': {\n", + " 'lowercase': {'min_pass_rate': 0.66, 'prob': 0.50}, \n", + " 'uppercase':{'min_pass_rate': 0.60, 'prob': 0.70},\n", + " }\n", + " }\n", + "})\n", + "\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import random as rnd \n", + "\n", + "rnd.seed(0)\n", + "\n", + "h.data = rnd.choices(h.data, k=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating the test cases." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generating testcases...: 100%|██████████| 1/1 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
categorytest_typeoriginaltest_caseexpected_result
0robustnesslowercaseResults of Asian Cup group C matches played on...results of asian cup group c matches played on...
1robustnesslowercase5. Ann Battellle ( U.S. ) 23.565. ann battellle ( u.s. ) 23.56Ann Battellle: PER, U.S.: LOC
2robustnesslowercaseIt was the second Syrian defensive blunder in ...it was the second syrian defensive blunder in ...
3robustnesslowercaseROME 1996-12-06rome 1996-12-06
4robustnesslowercaseJapan : 19 - Kenichi Shimokawa , 2 - Hiroshige...japan : 19 - kenichi shimokawa , 2 - hiroshige...
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94robustnesslowercaseScorers : Shkvyrin Igor 78 , Shatskikh Oleg 90scorers : shkvyrin igor 78 , shatskikh oleg 90Shkvyrin Igor: PER, Shatskikh Oleg: PER
95robustnesslowercase9. Ljudmila Dymchenko ( Russia ) 21.599. ljudmila dymchenko ( russia ) 21.59Ljudmila Dymchenko: PER, Russia: LOCATION
96robustnesslowercaseThe former Soviet republic was playing in an A...the former soviet republic was playing in an a...Soviet republic: ORG
97robustnesslowercase3. Ryan Johnson ( Canada ) 24.573. ryan johnson ( canada ) 24.57Ryan Johnson: PER, Canada: LOC
98robustnesslowercaseBitar saved well again from Miura in the 37th ...bitar saved well again from miura in the 37th ...Bitar: PER, Miura: PER
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categorytest_typeoriginaltest_caseexpected_resultactual_resultpass
0robustnesslowercaseResults of Asian Cup group C matches played on...results of asian cup group c matches played on...True
1robustnesslowercase5. Ann Battellle ( U.S. ) 23.565. ann battellle ( u.s. ) 23.56Ann Battellle: PER, U.S.: LOCann battellle: PERFalse
2robustnesslowercaseIt was the second Syrian defensive blunder in ...it was the second syrian defensive blunder in ...True
3robustnesslowercaseROME 1996-12-06rome 1996-12-06True
4robustnesslowercaseJapan : 19 - Kenichi Shimokawa , 2 - Hiroshige...japan : 19 - kenichi shimokawa , 2 - hiroshige...True
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94robustnesslowercaseScorers : Shkvyrin Igor 78 , Shatskikh Oleg 90scorers : shkvyrin igor 78 , shatskikh oleg 90Shkvyrin Igor: PER, Shatskikh Oleg: PERshkvyrin igor: PER, shatskikh oleg: PERFalse
95robustnesslowercase9. Ljudmila Dymchenko ( Russia ) 21.599. ljudmila dymchenko ( russia ) 21.59Ljudmila Dymchenko: PER, Russia: LOCATIONljudmila dymchenko: PER, russia: LOCFalse
96robustnesslowercaseThe former Soviet republic was playing in an A...the former soviet republic was playing in an a...Soviet republic: ORGasian: LOC, soviet republic: ORG, cup finals t...False
97robustnesslowercase3. Ryan Johnson ( Canada ) 24.573. ryan johnson ( canada ) 24.57Ryan Johnson: PER, Canada: LOCryan johnson: PER, canada: LOCFalse
98robustnesslowercaseBitar saved well again from Miura in the 37th ...bitar saved well again from miura in the 37th ...Bitar: PER, Miura: PERFalse
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" + ], + "text/plain": [ + " category test_type original \\\n", + "0 robustness lowercase Results of Asian Cup group C matches played on... \n", + "1 robustness lowercase 5. Ann Battellle ( U.S. ) 23.56 \n", + "2 robustness lowercase It was the second Syrian defensive blunder in ... \n", + "3 robustness lowercase ROME 1996-12-06 \n", + "4 robustness lowercase Japan : 19 - Kenichi Shimokawa , 2 - Hiroshige... \n", + ".. ... ... ... \n", + "94 robustness lowercase Scorers : Shkvyrin Igor 78 , Shatskikh Oleg 90 \n", + "95 robustness lowercase 9. Ljudmila Dymchenko ( Russia ) 21.59 \n", + "96 robustness lowercase The former Soviet republic was playing in an A... \n", + "97 robustness lowercase 3. Ryan Johnson ( Canada ) 24.57 \n", + "98 robustness lowercase Bitar saved well again from Miura in the 37th ... \n", + "\n", + " test_case \\\n", + "0 results of asian cup group c matches played on... \n", + "1 5. ann battellle ( u.s. ) 23.56 \n", + "2 it was the second syrian defensive blunder in ... \n", + "3 rome 1996-12-06 \n", + "4 japan : 19 - kenichi shimokawa , 2 - hiroshige... \n", + ".. ... \n", + "94 scorers : shkvyrin igor 78 , shatskikh oleg 90 \n", + "95 9. ljudmila dymchenko ( russia ) 21.59 \n", + "96 the former soviet republic was playing in an a... \n", + "97 3. ryan johnson ( canada ) 24.57 \n", + "98 bitar saved well again from miura in the 37th ... \n", + "\n", + " expected_result \\\n", + "0 \n", + "1 Ann Battellle: PER, U.S.: LOC \n", + "2 \n", + "3 \n", + "4 \n", + ".. ... \n", + "94 Shkvyrin Igor: PER, Shatskikh Oleg: PER \n", + "95 Ljudmila Dymchenko: PER, Russia: LOCATION \n", + "96 Soviet republic: ORG \n", + "97 Ryan Johnson: PER, Canada: LOC \n", + "98 Bitar: PER, Miura: PER \n", + "\n", + " actual_result pass \n", + "0 True \n", + "1 ann battellle: PER False \n", + "2 True \n", + "3 True \n", + "4 True \n", + ".. ... ... \n", + "94 shkvyrin igor: PER, shatskikh oleg: PER False \n", + "95 ljudmila dymchenko: PER, russia: LOC False \n", + "96 asian: LOC, soviet republic: ORG, cup finals t... False \n", + "97 ryan johnson: PER, canada: LOC False \n", + "98 False \n", + "\n", + "[99 rows x 7 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Final Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can call `.report()` which summarizes the results giving information about pass and fail counts and overall test pass/fail flag." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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categorytest_typefail_countpass_countpass_rateminimum_pass_ratepass
0robustnesslowercase584141%70%False
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" + ], + "text/plain": [ + " category test_type fail_count pass_count pass_rate minimum_pass_rate \\\n", + "0 robustness lowercase 58 41 41% 70% \n", + "\n", + " pass \n", + "0 False " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h.report()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 876e39c49f2a04db20de46f11e5c93d16eaedd14 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sun, 12 May 2024 14:05:12 +0530 Subject: [PATCH 54/69] Data_Augmenter Nb --- demo/tutorials/misc/Data_Augmenter_Notebook.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 demo/tutorials/misc/Data_Augmenter_Notebook.ipynb diff --git a/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb b/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb new file mode 100644 index 000000000..5f29e91d5 --- /dev/null +++ b/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb @@ -0,0 +1 @@ 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)"]},{"cell_type":"markdown","metadata":{"id":"MhgkQYQiEvZt"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"WJJzt3RWhEc6"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"26qXWhCYhHAt"},"source":["# Getting started with LangTest on John Snow Labs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oGIyE43uhTxH"},"outputs":[],"source":["!pip install \"langtest\""]},{"cell_type":"markdown","metadata":{"id":"yR6kjOaiheKN"},"source":["# DataAugmenter and its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["yaml_content = \"\"\"\n","parameters:\n"," type: proportion\n"," style: new\n","tests:\n"," robustness:\n"," uppercase:\n"," max_proportion: 0.2\n"," lowercase:\n"," max_proportion: 0.2\n","\n","\"\"\"\n","\n","with open(\"config.yaml\", \"w\") as f:\n"," f.write(yaml_content)"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1405,"status":"ok","timestamp":1692343652196,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"lTzSJpMlhgq5"},"outputs":[],"source":["from langtest.augmentation.augmenter import Augmenter as DataAugmenter\n","from langtest.tasks.task import TaskManager\n","\n","data_augmenter = DataAugmenter(\n"," task=TaskManager(\"ner\"),\n"," config=\"config.yaml\",\n",")"]},{"cell_type":"markdown","metadata":{"id":"sBcZjwJBhkOw"},"source":["The instance of the `Augmenter` class from the `langtest.augmentation.augmenter` module is to perform the Data augmentation for different tasks from langtest. \n","In this specific instance, the `Augmenter` object is created with the following parameters:\n","\n","
\n","\n","| Parameter | Description |\n","| - | - |\n","| **task** | Task for which the model is to be evaluated (text-classification or ner) |\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","By creating an instance of the `Augmenter` class, you can utilize its methods and functionalities to perform data augmentation on different tasks from langtest specific to the Named Entity Recognition task. The `config.yaml` file contains the specific configuration settings for the tests to be performed, such as the type of augmentation and the maximum proportion of augmentation for different test cases.\n","\n","Overall, the `augment` object represents an instance of the `Augmenter` class that can be used to conduct Data augmentation for the Named Entity Recognition task based on the provided configuration.\n","\n","
\n","
"]},{"cell_type":"markdown","metadata":{"id":"I21Jmq79jgC6"},"source":["#### Load Train and Test CoNLL"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 50519 (49K) [text/plain]\n","Saving to: ‘sample.conll’\n","\n","sample.conll 100%[===================>] 49.33K --.-KB/s in 0.006s \n","\n","2023-08-18 07:27:31 (7.50 MB/s) - ‘sample.conll’ saved [50519/50519]\n","\n","--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 827443 (808K) [text/plain]\n","Saving to: ‘conll03.conll’\n","\n","conll03.conll 100%[===================>] 808.05K --.-KB/s in 0.03s \n","\n","2023-08-18 07:27:31 (30.1 MB/s) - ‘conll03.conll’ saved [827443/827443]\n","\n"]}],"source":["# Load test CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","\n","# Load train CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll"]},{"cell_type":"markdown","metadata":{},"source":["### Augmenting with train data"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.augment(data={\n"," 'data_source': 'conll03.conll'\n","})"]},{"cell_type":"markdown","metadata":{},"source":["### Save the augmentated dataset "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.save(\"augmented.conll\")"]},{"cell_type":"markdown","metadata":{"id":"YPXIxv9D_fR7"},"source":["Essentially it applies perturbations to the input data based on the recommendations from the harness reports. Then this augmented_dataset is used to retrain the original model so as to make the model more robust and improve its performance."]}],"metadata":{"colab":{"machine_shape":"hm","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 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From 4cc11d094459ebc6e1d6f3173318d8bdfaf74140 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Sun, 12 May 2024 16:59:16 +0530 Subject: [PATCH 55/69] Added the MultiPrompt_MultiDataset NB --- demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb diff --git a/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb b/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb new file mode 100644 index 000000000..c0e7c5d76 --- /dev/null +++ b/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb @@ -0,0 +1 @@ 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)"]},{"cell_type":"markdown","metadata":{"id":"Fu8i_qgCBplG"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"IKKgqEEKA3qv"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"JzKpAy4mA5jA"},"source":["# Getting started with LangTest"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jFus50TcGgJA"},"outputs":[],"source":["!pip install \"langtest[openai,transformers,evaluate]\""]},{"cell_type":"markdown","metadata":{"id":"bjK9t-uFBEPw"},"source":["# Harness and Its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":3080,"status":"ok","timestamp":1696324827009,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"9Z2vV7zLBJWz"},"outputs":[],"source":["# Import Harness from the LangTest library\n","from langtest import Harness"]},{"cell_type":"markdown","metadata":{"id":"MW9LVSCyBLoQ"},"source":["It imports the Harness class from within the module, that is designed to provide a blueprint or framework for conducting NLP testing, and that instances of the Harness class can be customized or configured for different testing scenarios or environments.\n","\n","Here is a list of the different parameters that can be passed to the Harness function:\n","\n","
\n","\n","\n","| Parameter | Description | \n","| - | - |\n","|**task** |Task for which the model is to be evaluated (question-answering or summarization)|\n","| **model** | Specifies the model(s) to be evaluated. This parameter can be provided as either a dictionary or a list of dictionaries. Each dictionary should contain the following keys:
  • model (mandatory): \tPipelineModel or path to a saved model or pretrained pipeline/model from hub.
  • hub (mandatory): Hub (library) to use in back-end for loading model from public models hub or from path
|\n","| **data** | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys:
  • data_source (mandatory): The source of the data.
  • subset (optional): The subset of the data.
  • feature_column (optional): The column containing the features.
  • target_column (optional): The column containing the target labels.
  • split (optional): The data split to be used.
  • source (optional): Set to 'huggingface' when loading Hugging Face dataset.
|\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","
\n","
"]},{"cell_type":"markdown","metadata":{"id":"xHwkRUckBw9M"},"source":["# OpenAI Model Testing For Question Answering\n","\n","In this section, we dive into testing of OpenAI models in Question Answering task.\n","\n","LangTest supports robustness tests for LLM testing for now."]},{"cell_type":"markdown","metadata":{"id":"4bgnVoUiBRqU"},"source":["### Set environment for OpenAI"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":17,"status":"ok","timestamp":1696324827010,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"mVYxDu-E_ssg"},"outputs":[],"source":["import os\n","\n","os.environ[\"OPENAI_API_KEY\"] = \"\""]},{"cell_type":"markdown","metadata":{"id":"tCXcKn_9BXEa"},"source":["### Multi Dataset Testing\n","\n","In order to evaluate the model's performance on multiple datasets, we can utilize a Jupyter notebook and provide a list of dictionaries to the `data` parameter. Each dictionary within the list should contain the following keys:\n","\n","```\n","data=[\n"," {\"data_source\": \"BoolQ\", \"split\": \"test-tiny\"},\n"," {\"data_source\": \"NQ-open\", \"split\": \"test-tiny\"},\n"," {\"data_source\": \"MedQA\", \"split\": \"test-tiny\"},\n"," {\"data_source\": \"LogiQA\", \"split\": \"test-tiny\"},\n","],\n","```\n","\n","Here, we specify different data sources and their corresponding splits for testing. This allows for a comprehensive evaluation of the model's performance across diverse datasets. The notebook can then be executed to assess how well the model generalizes to various types of questions and contexts presented in these datasets."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":45,"status":"ok","timestamp":1692371630216,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"ASv9E02sBXrp","outputId":"fb19b9ec-3bd9-416e-f2fc-dc3190b8a861"},"outputs":[{"name":"stdout","output_type":"stream","text":["Test Configuration : \n"," {\n"," \"model_parameters\": {\n"," \"max_tokens\": 64\n"," },\n"," \"tests\": {\n"," \"defaults\": {\n"," \"min_pass_rate\": 1.0\n"," },\n"," \"robustness\": {\n"," \"add_typo\": {\n"," \"min_pass_rate\": 0.7\n"," },\n"," \"lowercase\": {\n"," \"min_pass_rate\": 0.7\n"," }\n"," }\n"," }\n","}\n"]}],"source":["harness = Harness(\n"," task=\"question-answering\",\n"," model={\"model\": \"gpt-3.5-turbo-instruct\", \"hub\": \"openai\"},\n"," data=[\n"," {\"data_source\": \"BoolQ\", \"split\": \"dev-tiny\"},\n"," {\"data_source\": \"NQ-open\", \"split\": \"test-tiny\"}\n"," ],\n",")"]},{"cell_type":"markdown","metadata":{"id":"_wvVHxeSDWLV"},"source":["## Robustness\n","\n","For tests we used uppercase, Dyslexia Word Swap, Add Slangs, Insert Abbreviations and Speech to Text typos . Other available robustness tests for QA task are:\n","* `add_context`\n","* `add_contraction`\n","* `add_punctuation`\n","* `add_typo`\n","* `add_ocr_typo`\n","* `american_to_british`\n","* `british_to_american`\n","* `lowercase`\n","* `strip_punctuation`\n","* `titlecase`\n","* `uppercase`\n","* `number_to_word`\n","* `add_abbreviation`\n","* `add_speech_to_text_typo`\n","* `add_slangs`\n","* `dyslexia_word_swap`\n","* `multiple_perturbations`\n","* `adjective_synonym_swap`\n","* `adjective_antonym_swap`\n","* `strip_all_punctuation`"]},{"cell_type":"markdown","metadata":{"id":"HYExqs-pDbvz"},"source":["You can also set prompts and other model parameters in config. Possible parameters are:\n","* `user_prompt:` Prompt to be given to the model.\n","* `temperature:` Temperature of the model.\n","* `max_tokens:` Maximum number of output tokens allowed for model."]},{"cell_type":"markdown","metadata":{},"source":["To configure prompts for different datasets, you can use the `user_prompt` dictionary. Here's how it works:\n","\n","- Each key in the dictionary represents a dataset name or task (e.g., \"BoolQ\", \"NQ-open\").\n","- The corresponding value is a string template that defines the user prompt for the dataset.\n","- The template can include placeholders:\n"," - `{context}`: This will be replaced with the actual context (passage) relevant to the question from the specific dataset.\n"," - `{question}`: This will be replaced with the actual question from the dataset.\n","- The newline character `\\n` can be used to separate the context and question in the final prompt.\n","\n","Here the example:\n","```python\n","harness.configure(\n"," {\n"," \"model_parameters\": {\n"," \"user_prompt\": {\n"," \"BoolQ\": \"Answer the following question with a True or False. {context}\\nQuestion {question}\",\n"," \"NQ-open\": \"Answer the following question. Question {question}\",\n"," }\n"," },\n"," ....\n"," })\n","```"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":42,"status":"ok","timestamp":1692371630218,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"EzzlV0u4DbN9","outputId":"2a3926cd-9c23-45a6-a0b8-b31b29692be3"},"outputs":[{"data":{"text/plain":["{'model_parameters': {'user_prompt': {'BoolQ': 'Answer the following question with a True or False. {context}\\nQuestion {question}',\n"," 'NQ-open': 'Answer the following question. Question {question}'}},\n"," 'tests': {'defaults': {'min_pass_rate': 0.65},\n"," 'robustness': {'uppercase': {'min_pass_rate': 0.66},\n"," 'dyslexia_word_swap': {'min_pass_rate': 0.6},\n"," 'add_abbreviation': {'min_pass_rate': 0.6},\n"," 'add_slangs': {'min_pass_rate': 0.6},\n"," 'add_speech_to_text_typo': {'min_pass_rate': 0.6}}}}"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["harness.configure(\n"," {\n"," \"model_parameters\": {\n"," \"user_prompt\": {\n"," \"BoolQ\": \"Answer the following question with a True or False. {context}\\nQuestion {question}\",\n"," \"NQ-open\": \"Answer the following question. Question {question}\",\n"," }\n"," },\n"," \"tests\": {\n"," \"defaults\": {\"min_pass_rate\": 0.65},\n"," \"robustness\": {\n"," \"uppercase\": {\"min_pass_rate\": 0.66},\n"," \"dyslexia_word_swap\": {\"min_pass_rate\": 0.60},\n"," \"add_abbreviation\": {\"min_pass_rate\": 0.60},\n"," \"add_slangs\": {\"min_pass_rate\": 0.60},\n"," \"add_speech_to_text_typo\": {\"min_pass_rate\": 0.60},\n"," },\n"," }\n"," }\n",")"]},{"cell_type":"markdown","metadata":{"id":"P7TKPJd3Dft1"},"source":["➤ You can adjust the level of transformation in the sentence by using the \"`prob`\" parameter, which controls the proportion of words to be changed during robustness tests.\n","\n","➤ **NOTE** : \"`prob`\" defaults to 1.0, which means all words will be transformed.\n","```\n","harness.configure(\n","{\n"," 'tests': {\n"," 'defaults': {'min_pass_rate': 0.65},\n"," 'robustness': {\n"," 'uppercase': {'min_pass_rate': 0.66, 'prob': 0.50},\n"," 'dyslexia_word_swap':{'min_pass_rate': 0.60, 'prob': 0.70},\n"," }\n"," }\n","})\n","\n","```"]},{"cell_type":"markdown","metadata":{"id":"SW71UKHfDi2q"},"source":["Here we have configured the harness to perform Five robustness tests and defined the minimum pass rate for each test."]},{"cell_type":"code","execution_count":5,"metadata":{"id":"a9Q8i7-KDgR5"},"outputs":[],"source":["#slice the data\n","harness.data = {k: v[:10] for k, v in harness.data.items()}"]},{"cell_type":"markdown","metadata":{"id":"GlBMu35ODm77"},"source":["### Generating the test cases."]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":58028,"status":"ok","timestamp":1692371688215,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"L1NQcBCHDomc","outputId":"e3df8f16-fadd-4fbb-e479-2f098f07ba5a"},"outputs":[{"name":"stdout","output_type":"stream","text":["================================================================================\n"," BoolQ \n","================================================================================\n"]},{"name":"stderr","output_type":"stream","text":["Generating testcases...: 100%|██████████| 1/1 [00:00\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
categorydataset_nametest_typeoriginal_contextoriginal_questionperturbed_contextperturbed_question
0robustnessBoolQuppercaseAll biomass goes through at least some of thes...does ethanol take more energy make that producesALL BIOMASS GOES THROUGH AT LEAST SOME OF THES...DOES ETHANOL TAKE MORE ENERGY MAKE THAT PRODUCES
1robustnessBoolQuppercaseProperty tax or 'house tax' is a local tax on ...is house tax and property tax are samePROPERTY TAX OR 'HOUSE TAX' IS A LOCAL TAX ON ...IS HOUSE TAX AND PROPERTY TAX ARE SAME
2robustnessBoolQuppercasePhantom pain sensations are described as perce...is pain experienced in a missing body part or ...PHANTOM PAIN SENSATIONS ARE DESCRIBED AS PERCE...IS PAIN EXPERIENCED IN A MISSING BODY PART OR ...
3robustnessBoolQuppercaseHarry Potter and the Escape from Gringotts is ...is harry potter and the escape from gringotts ...HARRY POTTER AND THE ESCAPE FROM GRINGOTTS IS ...IS HARRY POTTER AND THE ESCAPE FROM GRINGOTTS ...
4robustnessBoolQuppercaseHydroxyzine preparations require a doctor's pr...is there a difference between hydroxyzine hcl ...HYDROXYZINE PREPARATIONS REQUIRE A DOCTOR'S PR...IS THERE A DIFFERENCE BETWEEN HYDROXYZINE HCL ...
........................
80robustnessNQ-openadd_speech_to_text_typo-who played grand moff tarkin in rogue one-Hoo played grand moff tarkin in rogue one
81robustnessNQ-openadd_speech_to_text_typo-youngest current member of the house of repres...-youngest current member of the Hause of repres...
82robustnessNQ-openadd_speech_to_text_typo-who wrote the miraculous journey of edward tulane-Houx wrote the miraculous journey of edward tu...
83robustnessNQ-openadd_speech_to_text_typo-when did the night mare before christmas come out-when did the night Mehr before christmas come out
84robustnessNQ-openadd_speech_to_text_typo-when does the green book come out in theaters-when does the green book come out Inn theaters
\n","

85 rows × 7 columns

\n",""],"text/plain":[" category dataset_name test_type \\\n","0 robustness BoolQ uppercase \n","1 robustness BoolQ uppercase \n","2 robustness BoolQ uppercase \n","3 robustness BoolQ uppercase \n","4 robustness BoolQ uppercase \n",".. ... ... ... \n","80 robustness NQ-open add_speech_to_text_typo \n","81 robustness NQ-open add_speech_to_text_typo \n","82 robustness NQ-open add_speech_to_text_typo \n","83 robustness NQ-open add_speech_to_text_typo \n","84 robustness NQ-open add_speech_to_text_typo \n","\n"," original_context \\\n","0 All biomass goes through at least some of thes... \n","1 Property tax or 'house tax' is a local tax on ... \n","2 Phantom pain sensations are described as perce... \n","3 Harry Potter and the Escape from Gringotts is ... \n","4 Hydroxyzine preparations require a doctor's pr... \n",".. ... \n","80 - \n","81 - \n","82 - \n","83 - \n","84 - \n","\n"," original_question \\\n","0 does ethanol take more energy make that produces \n","1 is house tax and property tax are same \n","2 is pain experienced in a missing body part or ... \n","3 is harry potter and the escape from gringotts ... \n","4 is there a difference between hydroxyzine hcl ... \n",".. ... \n","80 who played grand moff tarkin in rogue one \n","81 youngest current member of the house of repres... \n","82 who wrote the miraculous journey of edward tulane \n","83 when did the night mare before christmas come out \n","84 when does the green book come out in theaters \n","\n"," perturbed_context \\\n","0 ALL BIOMASS GOES THROUGH AT LEAST SOME OF THES... \n","1 PROPERTY TAX OR 'HOUSE TAX' IS A LOCAL TAX ON ... \n","2 PHANTOM PAIN SENSATIONS ARE DESCRIBED AS PERCE... \n","3 HARRY POTTER AND THE ESCAPE FROM GRINGOTTS IS ... \n","4 HYDROXYZINE PREPARATIONS REQUIRE A DOCTOR'S PR... \n",".. ... \n","80 - \n","81 - \n","82 - \n","83 - \n","84 - \n","\n"," perturbed_question \n","0 DOES ETHANOL TAKE MORE ENERGY MAKE THAT PRODUCES \n","1 IS HOUSE TAX AND PROPERTY TAX ARE SAME \n","2 IS PAIN EXPERIENCED IN A MISSING BODY PART OR ... \n","3 IS HARRY POTTER AND THE ESCAPE FROM GRINGOTTS ... \n","4 IS THERE A DIFFERENCE BETWEEN HYDROXYZINE HCL ... \n",".. ... \n","80 Hoo played grand moff tarkin in rogue one \n","81 youngest current member of the Hause of repres... \n","82 Houx wrote the miraculous journey of edward tu... \n","83 when did the night Mehr before christmas come out \n","84 when does the green book come out Inn theaters \n","\n","[85 rows x 7 columns]"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["harness.testcases()"]},{"cell_type":"markdown","metadata":{"id":"akSniLOoDxOp"},"source":["harness.generate() method automatically generates the test cases (based on the provided configuration)"]},{"cell_type":"markdown","metadata":{"id":"wk_cgK2BDzcM"},"source":["### Running the tests"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":48720,"status":"ok","timestamp":1692371736914,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"nje7KWD9Dx3Y","outputId":"5ac4304a-0078-49ad-84b0-c5b6c2f58155"},"outputs":[{"name":"stdout","output_type":"stream","text":["================================================================================\n"," BoolQ \n","================================================================================\n"]},{"name":"stderr","output_type":"stream","text":["Running testcases... : 100%|██████████| 48/48 [00:52<00:00, 1.10s/it]\n"]},{"name":"stdout","output_type":"stream","text":["--------------------------------------------------------------------------------\n","\n","================================================================================\n"," NQ-open \n","================================================================================\n"]},{"name":"stderr","output_type":"stream","text":["Running testcases... : 100%|██████████| 37/37 [00:48<00:00, 1.31s/it]"]},{"name":"stdout","output_type":"stream","text":["--------------------------------------------------------------------------------\n","\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"data":{"text/plain":[]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["harness.run()"]},{"cell_type":"markdown","metadata":{"id":"7GnDWiU6D2S4"},"source":["Called after harness.generate() and is to used to run all the tests. Returns a pass/fail flag for each test."]},{"cell_type":"markdown","metadata":{"id":"q17wkdZcD4T8"},"source":["### Generated Results"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":805},"executionInfo":{"elapsed":18550,"status":"ok","timestamp":1692371755410,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"yJta_DvJD3xh","outputId":"91be0a8f-f014-4e04-81bd-8eaa521c84c9"},"outputs":[{"data":{"text/html":["
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categorydataset_nametest_typeoriginal_contextoriginal_questionperturbed_contextperturbed_questionexpected_resultactual_resultpass
0robustnessBoolQuppercaseAll biomass goes through at least some of thes...does ethanol take more energy make that producesALL BIOMASS GOES THROUGH AT LEAST SOME OF THES...DOES ETHANOL TAKE MORE ENERGY MAKE THAT PRODUCES\\n\\n\\nTrueTRUETrue
1robustnessBoolQuppercaseProperty tax or 'house tax' is a local tax on ...is house tax and property tax are samePROPERTY TAX OR 'HOUSE TAX' IS A LOCAL TAX ON ...IS HOUSE TAX AND PROPERTY TAX ARE SAME\\n\\nTrue\\n\\nTrueTrue
2robustnessBoolQuppercasePhantom pain sensations are described as perce...is pain experienced in a missing body part or ...PHANTOM PAIN SENSATIONS ARE DESCRIBED AS PERCE...IS PAIN EXPERIENCED IN A MISSING BODY PART OR ...?\\n\\n\\nTrue\\nTrueTrue
3robustnessBoolQuppercaseHarry Potter and the Escape from Gringotts is ...is harry potter and the escape from gringotts ...HARRY POTTER AND THE ESCAPE FROM GRINGOTTS IS ...IS HARRY POTTER AND THE ESCAPE FROM GRINGOTTS ...\\n\\nTrue?\\n\\nTrueTrue
4robustnessBoolQuppercaseHydroxyzine preparations require a doctor's pr...is there a difference between hydroxyzine hcl ...HYDROXYZINE PREPARATIONS REQUIRE A DOCTOR'S PR...IS THERE A DIFFERENCE BETWEEN HYDROXYZINE HCL ...\\n\\nTrueOATE\\n\\nTrueFalse
.................................
80robustnessNQ-openadd_speech_to_text_typo-who played grand moff tarkin in rogue one-Hoo played grand moff tarkin in rogue one\\n\\nPeter Cushing\\n\\nPeter Cushing played Grand Moff Tarkin in ...True
81robustnessNQ-openadd_speech_to_text_typo-youngest current member of the house of repres...-youngest current member of the Hause of repres...\\n\\nAs of 2021, the youngest current member of...\\n\\nAs of 2021, the youngest current member of...True
82robustnessNQ-openadd_speech_to_text_typo-who wrote the miraculous journey of edward tulane-Houx wrote the miraculous journey of edward tu...\\n\\nThe Miraculous Journey of Edward Tulane wa...\\n\\nWho wrote \"The Miraculous Journey of Edwar...False
83robustnessNQ-openadd_speech_to_text_typo-when did the night mare before christmas come out-when did the night Mehr before christmas come out\\n\\nThe Nightmare Before Christmas was release...\\n\\nThe Nightmare Before Christmas was release...True
84robustnessNQ-openadd_speech_to_text_typo-when does the green book come out in theaters-when does the green book come out Inn theaters\\n\\nThe green book was released in theaters on...\\n\\nThe release date for the green book in the...False
\n","

85 rows × 10 columns

\n","
"],"text/plain":[" category dataset_name test_type \\\n","0 robustness BoolQ uppercase \n","1 robustness BoolQ uppercase \n","2 robustness BoolQ uppercase \n","3 robustness BoolQ uppercase \n","4 robustness BoolQ uppercase \n",".. ... ... ... \n","80 robustness NQ-open add_speech_to_text_typo \n","81 robustness NQ-open add_speech_to_text_typo \n","82 robustness NQ-open add_speech_to_text_typo \n","83 robustness NQ-open add_speech_to_text_typo \n","84 robustness NQ-open add_speech_to_text_typo \n","\n"," original_context \\\n","0 All biomass goes through at least some of thes... \n","1 Property tax or 'house tax' is a local tax on ... \n","2 Phantom pain sensations are described as perce... \n","3 Harry Potter and the Escape from Gringotts is ... \n","4 Hydroxyzine preparations require a doctor's pr... \n",".. ... \n","80 - \n","81 - \n","82 - \n","83 - \n","84 - \n","\n"," original_question \\\n","0 does ethanol take more energy make that produces \n","1 is house tax and property tax are same \n","2 is pain experienced in a missing body part or ... \n","3 is harry potter and the escape from gringotts ... \n","4 is there a difference between hydroxyzine hcl ... \n",".. ... \n","80 who played grand moff tarkin in rogue one \n","81 youngest current member of the house of repres... \n","82 who wrote the miraculous journey of edward tulane \n","83 when did the night mare before christmas come out \n","84 when does the green book come out in theaters \n","\n"," perturbed_context \\\n","0 ALL BIOMASS GOES THROUGH AT LEAST SOME OF THES... \n","1 PROPERTY TAX OR 'HOUSE TAX' IS A LOCAL TAX ON ... \n","2 PHANTOM PAIN SENSATIONS ARE DESCRIBED AS PERCE... \n","3 HARRY POTTER AND THE ESCAPE FROM GRINGOTTS IS ... \n","4 HYDROXYZINE PREPARATIONS REQUIRE A DOCTOR'S PR... \n",".. ... \n","80 - \n","81 - \n","82 - \n","83 - \n","84 - \n","\n"," perturbed_question \\\n","0 DOES ETHANOL TAKE MORE ENERGY MAKE THAT PRODUCES \n","1 IS HOUSE TAX AND PROPERTY TAX ARE SAME \n","2 IS PAIN EXPERIENCED IN A MISSING BODY PART OR ... \n","3 IS HARRY POTTER AND THE ESCAPE FROM GRINGOTTS ... \n","4 IS THERE A DIFFERENCE BETWEEN HYDROXYZINE HCL ... \n",".. ... \n","80 Hoo played grand moff tarkin in rogue one \n","81 youngest current member of the Hause of repres... \n","82 Houx wrote the miraculous journey of edward tu... \n","83 when did the night Mehr before christmas come out \n","84 when does the green book come out Inn theaters \n","\n"," expected_result \\\n","0 \\n\\n\\nTrue \n","1 \\n\\nTrue \n","2 ?\\n\\n\\nTrue \n","3 \\n\\nTrue \n","4 \\n\\nTrue \n",".. ... \n","80 \\n\\nPeter Cushing \n","81 \\n\\nAs of 2021, the youngest current member of... \n","82 \\n\\nThe Miraculous Journey of Edward Tulane wa... \n","83 \\n\\nThe Nightmare Before Christmas was release... \n","84 \\n\\nThe green book was released in theaters on... \n","\n"," actual_result pass \n","0 TRUE True \n","1 \\n\\nTrue True \n","2 \\nTrue True \n","3 ?\\n\\nTrue True \n","4 OATE\\n\\nTrue False \n",".. ... ... \n","80 \\n\\nPeter Cushing played Grand Moff Tarkin in ... True \n","81 \\n\\nAs of 2021, the youngest current member of... True \n","82 \\n\\nWho wrote \"The Miraculous Journey of Edwar... False \n","83 \\n\\nThe Nightmare Before Christmas was release... True \n","84 \\n\\nThe release date for the green book in the... False \n","\n","[85 rows x 10 columns]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["harness.generated_results()"]},{"cell_type":"markdown","metadata":{"id":"Vtv8wGFyD-XR"},"source":["This method returns the generated results in the form of a pandas dataframe, which provides a convenient and easy-to-use format for working with the test results. You can use this method to quickly identify the test cases that failed and to determine where fixes are needed."]},{"cell_type":"markdown","metadata":{"id":"agT9GO6FEC3E"},"source":["### Final Results\n","\n","We can call `.report()` which summarizes the results giving information about pass and fail counts and overall test pass/fail flag."]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":19430,"status":"ok","timestamp":1692371774826,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"qjFtUmbtEA2G","outputId":"62d274a2-8688-491a-f04e-101ebe5a6450"},"outputs":[{"data":{"text/html":["
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Benchmarking Results: gpt-3.5-turbo-instruct
fail_countpass_countpass_rateminimum_pass_ratepass
dataset_namecategorytest_type
BoolQrobustnessuppercase2880%66%True
dyslexia_word_swap2880%60%True
add_abbreviation1990%60%True
add_slangs4450%60%False
add_speech_to_text_typo3770%60%True
NQ-openrobustnessuppercase3770%66%True
dyslexia_word_swap2571%60%True
add_abbreviation5444%60%False
add_slangs1150%60%False
add_speech_to_text_typo5444%60%False
\n","
"],"text/plain":[" Benchmarking Results: gpt-3.5-turbo-instruct \\\n"," fail_count \n","dataset_name category test_type \n","BoolQ robustness uppercase 2 \n"," dyslexia_word_swap 2 \n"," add_abbreviation 1 \n"," add_slangs 4 \n"," add_speech_to_text_typo 3 \n","NQ-open robustness uppercase 3 \n"," dyslexia_word_swap 2 \n"," add_abbreviation 5 \n"," add_slangs 1 \n"," add_speech_to_text_typo 5 \n","\n"," \\\n"," pass_count pass_rate \n","dataset_name category test_type \n","BoolQ robustness uppercase 8 80% \n"," dyslexia_word_swap 8 80% \n"," add_abbreviation 9 90% \n"," add_slangs 4 50% \n"," add_speech_to_text_typo 7 70% \n","NQ-open robustness uppercase 7 70% \n"," dyslexia_word_swap 5 71% \n"," add_abbreviation 4 44% \n"," add_slangs 1 50% \n"," add_speech_to_text_typo 4 44% \n","\n"," \n"," minimum_pass_rate pass \n","dataset_name category test_type \n","BoolQ robustness uppercase 66% True \n"," dyslexia_word_swap 60% True \n"," add_abbreviation 60% True \n"," add_slangs 60% False 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NzXl+5QPcbOo8ERUq2iSJIz3P8n5Nf3DO3176kOXKLPstxOSJNEvPzHQW66Fi9ysb9zmSG6gcLNhj/QDgeN7Ad5wVf6oVquMAMe2b0/23XbbliePHv3eFqE80hw3/y5oSzoO3U7EeJhFqyrU7BaBa55ra15a85Mk01/D6embpRNz/LgZmanl3uDmhsljnQpzrJWMMxq/CRUgMpxvsqh+jO/V/wcS1fAsJu5dRnbychLZf0rypqDDGlOJ5PNwdOMQS57bQ6nnNaR1cPqwrJ8fSMw8/Rncy+ApwgjoPujAbDuez0RMVLHbvdhNJjQeG3l2TOjrX//9pyuVe/+NWe0t7lZkjDTvvxZt4sFcbU9w2f7El39vhJvfNJinNLbR1ZG+uUXrwW6Xb6dWLE+SRLfsWhsNHj0yuH7Dp1bLtvCaRwivuA4WQBY/4jricOhasn/m2vt2fPnL6QFg+HSlnaEh9KuP9i+9Juu5YSty5XUbfCnmPLJN9nuWfSPL0scrleRwXhkp77dS2bQiwy/11FJVVVOxrdsye+3rP7Xz9a998UheZm7higy9/LrruQp0BdssAj3yCPbPlcq926vV3j1JktRnS2vISmURHURzb7XguIuJBpzs4Ne/dmRPMXPtqvN43xddtDtNkuRYs33ZZZt7zz+/foUZ860qputVATz69KEXLxh8ZvDobhsbmz9fe3rWbt2u16x3+XnB5rNBRrZW/cA1lU8+GNGzE5ITM9kyK5UkeuihRQPr19+76pFtevl118urcJaSe2VrW6scuZb0Wat86tFqNT5QqeT9VSr3l2H0cjMbaNJnKqbmCvcc2779vY91GqvOwou3bpPl11TMqIKuV0313oOPVe/aOXX/+8uZ1i6Rbb6Y9cWEVc2iikZZ+OTer3/t93af+so0X/fMnQ3yvj2X4H4NaUMRMdz/jtsvqrP52R2E6ABuq0nTAcRfxyef+wrHV00fjnMmj7Fbffx/kTpRGOWkKm5Riy+IgkzJUJstpqYaTpYUJ4f7nAWq1buOAPedar9WDF2HHzvSdy6NkNImQU50FiVJol/9av+yhfHRm116flHcLgcGkOZNEEAEcVdcUonCgbLKX1+74dN/Ua0e250kSZ0OaB9RALFQvmBwwVvUone523rRkN/iWkjiwm9GpWg7LL4HfusrkEuYW7dlG5Tojzx4DUHVzUTiUW003l+tLvxLM26UEL1PsHUQehGseY754pPRPhi9p1rt2wIc60DqjBhfkUhcPU9HXXbttYMXv+51Q8/kNHZUVydsmzcvW+we/YEIl6q4oYCLikd/0//9F38XLlhe6gn/HuRmcVla1CzNRxZXNfl3HvE3kl2wqVJJdnZikle94Y8HsrGxDaUe/SWMG9xYIKoTGEkeiqcaiR5w2Oos+KvLLttchXqvubwHid6q5PSpuEnQ2C3aWakkV7WPmSSJfvUbFwyW0ujDbtnNiqSIqASNStjDwE3ttFUqj0Rp2LU8ePRRd7+6SZO6mmsoq/EeYBYMsg1z5cVWuYFSOSIdM5BDYE8CUPf9SGMvImuwFOLyJdjoCrj7mbkZeCMs291PI1pNVoTqiB7ETx6j96U6dv4xJKQgkGXzwS7jwgMPkST1001TnL4e5GScczvfRJyWLekcO2m8k/yfJFqtXrA6RPGnIPrP4De4eb+54Vkzxq+BZ3XcU8AjsJUov68S3Zux4M1ffGpJOZfiOp9MMeWxpPZOJXwUZL27q2f1vN+sgWcNwMuOvxENH69U7nvNuBqdaU01KEgZJ0aIVUOs7ksz+A2Nev4Q/Grce90LWpv9muFuKyF8xCj/1k03fXL+bOIR43qtbm7H3a3wSkPLbCD9ov7Rr1YHr9iya+2kJYc7I4rE0JCiGmHEOLEEjZQwX+q22qV0r4j+O5ylbpm25iWPrQTvF5O3u0QfzbKB1ZP7r1TuXRzX7UMq0cfBf9VhgWOYNcav43if7ubmy8F/TSW+5/zz7feGFv70sKg+JSKG5/RhRSygyKpG44LBibdNYpr5MlFdKSqtawORO5dWKpsXTKRvm6mzGMIyEYnHx4AyeE1cpkioM6KIvT4rJIly/3f6gdcXy6AoIjtI64dJXHnx+SHcniCKR4EU95WIrJ05x7oN0wljSaLjtsK0VKHUs5YsNZAU9ypmx3j+sjruu4ii44hAWu8lKr2Z2tjVrL0tym2ns4+rzXecHObzI8aPX9zb1HmpVC9YnRE2icrNbul890wR0yYrLbJFtJ25upu6W+yZXy4e/vC8kcbNUyWacS++uhuOrBb0P7r7cstSLVxammcESB5bKK7uZu7Zmgzf+NBDixbkc+i1PI7eQUxx1KwRu8htKuH95o1lZinuZjjmbX2Cq3umjs8XLb3rByd1PcwmaPv7I0L2zyI6MjHeFXAzRG6MNHzugqGhjZXKp9aQd2rkJocpfTcaYybjBUscxNUtU7N0tbr/IcgVbhYVvNha8yKKgONq1oiRaL2WSu+f2HuirtHHReTd7tni/HwzBVcBXFAR1bbzUMSa46+QEH9w4dDQ73iWPSOqRxAMseJ6ZIjo/FJJV7aGK87RwnJ3W+qeX5e2/QfNGmsLm2lrPlJdhtsCt2J/DNEA5nvghT0zX49JmCsnTb1+MaXyGiw1oEaWfoOFHM+LSVyfYjwOHMctIksHiEpXMbCvb+blpAtMJ4s1+cLi564h6vkAWTqAqqL6NHbyAY4+MAoYFu3A/BmcCDMQ1hJKH+NY/MbChpnHSs6Clok7zCgl/ngwz444x8JtK+snI0kSrVQ2rXDCx1R0vecXILeL5a/nVELphIjsNfc9IcRDImEiE/RMRWWxEG2+9nX3XXLyZKaTw2HGz0noBe/L/1VUo1SQnKG17SqCmmdpFHpeE+L0LUmSqKnXJ3QoqHtWBrnULFuGmZL3aaKKeMs+JCKIiLplkWe2LEjpjmp14eBkp087kiSxSgUT9+2CPi46yd6UF0lWz7I1IcT/u0v0j9dtuO/Prq3c9+bXfnXJsi1b1kaTmWSppOZNHWe80ImD+EoRvcIsNQRVVUSDFT/bhIQrcfWsHrn7r61ff+/VkOhll23uXV8Z/AOV8KtZNtYLFo2fN2IaolGVsB9nt4TosGioC0W/goJFWVbrDaXeD6Csc2cvIupe3C3uphppBs0QGBLy1Etcf8GzbAGeL4ZXVLMy1aAeqOQ25MSqVbRaXdiL+s+6Zf15VpxAca+4yN9Xq0n6Q800ShKF65RM14MMgqRE8X5UHmf32nSciVn9ScZGnyaKQQKIVuixaSs2FCgW4ZMyJZayaPEyNn1rBfftXcnmZ9fw2b03sOQ7mwjRf8fSy9EIgj6O1d/LnWt35IxPjLtW7SPLPkb5vL2okku5cimBv+Wz+/8rn917Awt3D0JVT8UoO8dBdsT0XChx1yLwfE6QnKtyTKeBiT5yz62CrrlDRl+8WQjXFA/nuKoooiaqO71R36QavknGaCb1derhXaJhvVsWk8cwqVlmqqV+Se0DIZTeZ3gqjk728I8nZmrY75buMOe4qi4vJKeBPPOkuZdHZo35SrjuoccW/XUkmRVse1IuRe52EpW6oI+aNQ4gUtYQXeKWXTJZzc+7tyvAlkFy5NRe4Rf3Zb7gc0HjNe4sds90vB6ooI5hWcMQ6ROJ3i6kb45i/+bCRcf/qlod+AJwqOmpbzTESrGk3kZ38yxwN5HIVGSve7bTzU5I0NWIrMOy/lawQ26nVonVqN8CyWPnnffpimjp7WluP8sZjjuCGnAo8+xz5tnfSxSOq9sKcf6tiLzV3fpaHmGP0sbYAkF/CU+HNET1jCxu7w+4qDlfCfDahs0v9ZTWuhvuaZt06nlMs8vP33LL5t4vfvH5WrWKXX2j9pbSsAo3xX2cRvdsGPWvz3wXT4OzYqcb4WX7FuPhKtJ6nKuxjd00xiZ6qe+6aIRNzz6I6M1kYyC6CgmXksie6SvxCGCgcjla2gyhmTgQgffhtpigfWQpwGG88RUyPs6RVROl6MSVIzzEon0fpjzvD2iMrSgkXSPSd5Lpmyj1PsqSpV9G9lQ5fGR/EfIwTbmzM1GxN26EJOETu04ul2dH3+S/IhHuhoQzn37PDAKf+NWxR39/Tc/TZ9zPHKAV4tPGpAQbPHpk0CX+JfD5tN9qriYiJ9wb/3HDhmOPNjfv2rX20JEXXzyo5veAXOHuxUPratYwDfE1sTQuMbfc09tWetidIutEdpqnH80auj2ObbQRxgaiLHqnavR+t6y/RbXg5mgUrQhZulhdzCfFIgKIYwh1N/usRX5P5DIE9ahhsiYS+SOQi/OiGQV7dVPQxYJeDDyZJFPDh5oowmSoVuVLnjUGRMNHRaI+LyQ9mhlJuRqf21CFPjeviMrlaPn69Rs+/alq9dhjlQo0GuDixaJtE9ITTTQC829CfaNQ3yk6r4bbYkPuFA3vxrK+1jUS3DMQW1epbF7gkv0i7oMTcyDERMOwe/qpejn77BNfPj5S/HCgUhnYax56VUu3uzVyVb4ZDKa6yiwbVbeaIHFz3twzcF9dqfzU/GolGSZJrFTZNGDua5quxXH2KCi5mr36e99rLAP2QWKa3dcHvpKiDB5Cs97CHjLfe0axn2cjfiRibPrWKuKe1aR1I4pr1Eef4OjQMZKLWiXDAHTvw2SNEZBeNJSx7A3A508dD6n9aLSu+D9/EIpsXxr1lHweTiD+jwhD42M2+22mG76w6i9Z8u06qncRxVcDZRpjIKEfsVuReAORfpNFS/8W+/W/hOTI5MIas3fStIjPaSharqzE5f0CH0T0g4h/UNo+p9NG9QOi9gF3W3c6FJ17FGxSvJYSLnbzy3MnRpukpaqI/7Xasceq1evG4yIvumh3uviCC3YiPCAhGqG4PXMV1k1hIHO7HogmhDMB4KYhOu6SbQr0fimOXzherRwd/cbDJw6JN+7DssdEI9zb46QwdwZClg20r/Mz3qNDblPXrZbJPVE2dLBaPToK3x95fWXom5h/yt1TL9TUNptqZMgrZjNbuap9dHRkJPoTJ/tdYK+GWIubfeI5NhklmbpZn3t2q0rPPSkL3ghAb/uuzZNonoupB7sbjldh5ESlcnQUjh5Q5L+CPENbFXvH86ElLDUdW6caX+JmOm4eaaq41tiRxvqnN13ZZI5JEat5/DCBexxLc2bbJMrVzfpBBtzTWq5mA1DYFcNSiBZX8pU71Sxbi2XL3QxcwN3cyRMn3Ey1NKAlXdOkO8p8qbstd2tZs91NPfUdUDsx1ck3C5ypCJO4cv93yki4nLS+vAinOU4WHodKEaeZaDOPmedX78PZQVTKGZzZhsK5MzM8HSUdO0ha309aP0BaP0jWOIGIUe6NCAFCWM28+R/B5HMsfnbdxFqStOIan/+fX6KR3oll7ydLdxL1KFFJMQNPe0nTDcTzPkKJTWzad3F+bMtkMdFJMytPdfHMFXMgSorIqED+cUZo+0xoU7RpfSb9PuowKh3X3v7hYrKKXbzv64peJyrz80IWkjNJF3PLhh17II+N22btQc4PPLA7bbhvxX1IhOYDhLtoljV6Bb8cvJ/2cnCOiahmWX3Ig26tVr9br1aTwsaTWLX6vhMmfFk1dApk70uRPjWxKdIjmCg1cftiFA0drFQo+kvSJEksy6wqovtVWyFN7m6ImogOMkskSWK33PJ8bfsjd/1pGuQNZul/EtHdGnpG8WAgaev9InnxCnE1y2K37OJI40/Bomva+2wG0DuF9CiyY/vWux6qVpO0SX+lgp1/vu53T3eIaJ2mKNw80r2XNLrW8pTGCVCNMOVvH3voPUNF8HdxbP7/9q13PYbzpIQSTAjeFVWVsjsHRQPgzegzk1CanyKrxvcN4ToJIXYc1Qjwb6roweZS9OY+X+DSSmWccV+C+4LcOQOCpqLhmEn29Wrl+8OTVwSdHs2XPGcnQY6MDRDF16MaUeqBsZM7iE7sbDk/ig9AIinIA2SZkaVQ6lnOWHrD9J27FXRuh3Ataf3nSMd+lpPRzxHkZ2nUr4lUAr8AACAASURBVOXkS/8HIjuAlNEf9FMq3Uyp9//js/tvnVJkNxEjuT5l6JUHOLzyM8ThtaT1X6Y+9nlK8UE0GGZG/eR8gt5KpA+y6G2Xw8ZxJjnNu8QnqduT2y2IuYGnhtfBUnJ5tPPH2769rQ0pWNGWVPxUl3ASPefAf9SxSyNCfDWiJmBN+5yoIqqHTfwAdPbC+1jPQbf0cBFnaOMrO4orooOO9I+rn+MQBEZcs1pnlVYONetHTiyI45GgEaRtFq6m1wIDHcnwY3n17ok9RlGoC+SFSGWCGwiE0yrc25yHbzx858Ht1aGN4v4rno19VFQeEo0Oi2hK4RgaL3snglmmDstd+DCjcVSYGZjw2hJBjCPFSBPu48sue76myAtISPPzLc5B8nMQZRVu88enq/g2S8F9GtNOPoaITPrdEcFAyiqyF3dEirAmwRR6BVlRrWJr1xLltlyMgkE6uh2V/VLEznrWKLv5RbCkH8Al/KxoZDhWOHNURA+QsTe/dKeTauhn96wkYvREK/BsXe5gQlGG8f71fGbPGyd8Fu99I5959k14I8ZtBFFDxBC/iS27TnEfSUqqdY6uHeWui0Z438tP8K5XHuLoXzzO0OGP4GPvIEv/BNE6acOwdDUiG1my7JKOITxNafKOl9c48ud/g/a9i3r9DtLGnxLFJ9AI6jXQsJhS+WMs3bOqGZI0UcX2JuMZt8xPbY+jzSvj1BCpC1ITpCZyZh+EGlBDfHoJshN959SLPSFPPHZncOJdVgwucjzKQsfAb0isp+fQMHBMVWkvC+wO4tILEkNhMyzGbf2djjKvNfdoUz+104RMYbyGTX64kiTRRqTmkp9H03c/V2+gavWF3SLH/ou4v8fTsd8F+WNURmj6porxRFDPUhC9JoR0DWitKfw0YwUACFNfpM30wsyzurTJSs1XiLur4QvcPPY2ppFL9lkaEXUMiG97kRwZZw5FzwV6Ef8ndxsZZ+aOmmW94K+47JYl5YGBwWU4a1pFkQ1RnkD0ADC+sJ1GpeVZyJYmSaK4r83PurjOKlia7g2hdPA0pr5F55nGQTbVV/cKyCCWKY0xQ/RWouiPCD2fm/iJ/yj/lN6PWx9uSqMGGl/B96KVM4fYOJTHtPOyC9uMw2v2kcUfAdtCFEd5LCSXIvqOZsjYVPrb7J53Lh3lhVXbKcfvx+obCeEQGnImKXI5pu/gwgMxietEFRumMsJTqN2ipDmDo+ZCzdXqLlZ3L75ltm3qAjXwus2kBHSi7xxGII0/jrnEGkkeqNuyXTVvXJd6o6EdCysAVKuYIB0YqBgaVCZyiVlh5uq92Sn3mA06BsmfEZqmgSStVF44uGHDi19qjI1+yN3vEuFA4T0eH89xVKLY1K91UqWI5/TCwTPZMz89/cW3FDpsXso8br2AJrhL0jRk07zkmpCxcRW6SamBO+UU9uCyVzQycTcH3LNYkRXn/yCdLxGXiJb6MENENEsbdXWextLv5jZJDMHcWCoNX/zEE6v6EFbiha3U3VTDCGL/dGYLuZ3FszLOYPQNSGFL1qBEpQFgGSJLO390MSGKgNzuV4oW4375zI4agU5l9NvV96MrhsjsHiwbHY+Qc7uVe3f1zZgt01L/jRUHRvDz/gRr3IOEEUQhrZcpla9mNFsGc/AEpSmIWj2gGJh625uh+aKcZdudVHBcT9MGOUfPcLWKVSpphER9orlHeFzykkLddclVhZz28ZqGDr2lkk3jUUy0Urkwdk72NVlqy/nh6m41F6nLhBqJZ4hxlTLMvN8s0KJzbkX05hxVKsnw0MJlWwaODcVBo4+5Wb9IW9FVHHHWgMduTRUcaIsBPRXG59llvOakC3VEwFrsMZckJY4yZszbdbfzRbStXsr4CGnJ5TBBtnor9lFxjBAPYukCsNeqKJm4iUQK2d5K5ej+rdsu2Ccan3DL+t1dRWxQRFaMjIwckuCL3VtXwtyPoZxe9kzz/Jrc8UxtkPfuvRT8NWSN3K5kthfP9mAetdJrOw3tA2i4FKxMo94P0ev4+D99ie+fGMkXy/r26dHRYq5P80f7dhNK64qCFSuQsJIkyVMaT/UCuf76lOQRWPgzX6As/waXDQgpqsvRxjIS2TdRxT6ddMKNG4tDPBWRmkNNoO5IzZGaS/E5jTbqNReti4fTu4RzJEHmapSWaa7SKC0lU3Nj4xFROdQ+Ty0Hji2uYx09dEkCjdLIgIsvNjOgXfoUHDuheYXjlq3wNJhS59PPOM3whNPs/9Q4VQBztZqkg0d3W+S6WzU6RFtgeZ6P7gAxPiGb5bTombCvkJfTcx8SpD6+zEfBdTVEajbVeVOcSxF9wEpErKm+53lNggjHwWrm2T+4pXVENF9SRUxF+qGxGPe1ZllhRwSQJ5MkMXU9KKJDCCaCOl520VeGYKtVS3mWkGOiQS2r71Orn17udfPkzxYRNxKXI/KMpRouG3n+lb+Enn8bPaXpP0HuIpSeyV9KppTii+ntWwnbjLMNoHbJFwVzz71sQeaf4ohJqBiMHaFeP4Bqmj/O3otob37Krb9nhsjNTWuKmEEuR07Rfjrxu6nPjpF7XSU79xLkxLp/UKmgSZKk69dvWolk42EW446/nA8edOGo5OEhxc+Cu6mIDqpwCbBzciB1ksD6DaxRiRabp4wvN5BXuUnF0n2GRHqGrOicmmDPoP9OZdSa8zxRwk40l9qzMnh5siMwd1n5CYR+0dzHebr0tDQANHegaOruB1TCCcda0qKTB4wrVyVJ8qVOmkClcm+fua+T9vvZx42jB8BHXMMeNfYDa8wzlTy4e74RLhVhZV60Q3C31Mi+AZAGORwsPYSzGjBRAdFV7vYDFaWotI5IhEj69Wr1fSfOrIiwnNnNkiTKsn/fT+Pk68kaoAFE9yAndwDw/JJa5wML5jfwjv301J9Gw7p8jRlbidvFcN0cxDrnWWb5v2ago62c71nWg4t+2vAf1HKeZNY+SR1Y48RMjqntAm2MXyH1fGU6y4qU2BwtBaa1TSe1WxARyzNWbAYJshN9p4/JD0ClklCpJLr1Eb9LVPvNsjw+zwsmaKkiPEua7XMNI7j0uuQ5u7ntSGNxfxvwp8UImveLwoVRaiOvV2WBu1vTGC+CqZaGU8+eELefZ8JbY/bnNc0V4mwtKGf2LCVarS5a7mK3O/5MpXL/1mr1jmm88HDllQN9mcstkqYrEJ9EsIDotwS5zJuhQPlmbb+zZsbE2VEJqWm6C5FDIEvHexHUrAGU3vjwwwvur1SS/fnSxq2eTLhRJVpheXC7FhRansrOznovwyHzuro+jdvaptfZ3frEea2jA4ghqoAcDsiTAFHmQ+bZXtFSxTyFzFXUVpl5LJKNu/TMGmTIGdZXPxsv9kZo7LuEnvJqxk6ChgjsSYLlDq0Z6ywmyvFVIyx69h+Ie9/C2EvzcesnlK/ip1Z8gUsPjHB62eQth9GSvQO4ryJLc6btNkw9O3L65/eDXlwGsbQo2yajICMwOdVwfIXA5k0jrfY0T4umpRTSmqOWhzugrcfcaQmUxcbJAmZ72y0X1CSawYvdib7ZY+3aJB4cXHS1iS/1NN3nrieiKMRbt/pKUb9DVG81y3TcvuS5ucXhYObp0yX1Iy6lRxG/Ec8lcgTFUtMQ3bi+cu//1hjr+X96eg4VMWoLyyYnbw3S83bL0phchcpVJtHIspMHAjxs8PNeLHrkM7C8TpjgZsgdSLTbICevHHk6aB07OyRJYus33Ls60vPuzGxsmVntmfWVz2zH7B9V2Z8GhqJMLAvSGzJfaeLvwv1N7lY4UYq5QcnS2qiKPezwC+30nO55tJ+/4+oi+ywd+6ZoWGd56FbO7NxNlLUhkg/Coru3bHnhcJKQVqsXxnnNR/+ISRp5U5b1XMbVEO03sr+76crjI7t2ra0NHRv6Bwi34pTzQPJ0PrABsd7WlZKdwJE8E+aukfXXf/op1WjY0rQ/L4jhqwVZbtbIox60hFu2uyRHnzytk++E5vM203KsTSSee5Nl6XqcBagaGp2g0djG80PD8MDMYyWJkWxULNpO/eRhRPoRNczWMy9dyrZte1j0zkkHzeKhXvJ8GdffptSzgEbNiGIwHuPFVUdy73el5c2eaclZqkr2skvp6bmYRj1Pa/TsAMYhEtepSy6cUT1IrUsza2Py8ZM16RnahhgK0YTg3kk4i3qQuXTzU72m4VfE7TcJ0Ql1GTUhQhlAQtkss0lDGGAisr3k8QGIR8xH/0IlrMN1QdOp4DmTBJcPx3Hj1akt3HbttYxmLlep6O2epUvBtWlbaxaeyCz9XP1kOtRT1gjBcLS9HuRsMZVlZMW8hDNijNB8lGdPS5IkumULkWSsymx00N0jCdGlAusMUhOGg8mwo6mYlc19UDXEmRW1KNqcHqKKW/b5RoPDUezllg9b8NNw0sCkF4N7/gIJ/ldCuFHUV7lleYiNoG5ZJITbHR+8YHDwi1+r+rGgtVWWydtEdY2bjWsADiaqdcuyh+aVSzvzEKPd6QvbFz0j6BHwFYVwoUBuG3Mxx8zddo6OlIab8/a17faMWXZCkCKHXGKYGHcqKtXqI8k06uypZ2EqNkIyUzTARqCqLBlcisZXktbLedSF7CewO2dC15/aX5CIkTxygMVLHyOetzZP99OVqFxBkuxm0+3ka08V8OKZvo4iYHsjucpaqM6Lvr0Az94KelcRagRuJzC7H6rK4LLL0W/3k922k7suOjI1pKjoKxHj3r2XEOR3SRurwYxo3ijpS9tYYIcY6iRBTodpHDgaxtLM4xqSV0M5mzx4AcMhUzk9G+RpPC31uBzHKQs89zAOoDIghSrtZHnwdrPb3GZlInoos/pfBV48AZDFi/5eG/yChNJveFYvN1W+/CR8vov8RkDfCpK6WX9epqrlnRUXE1V1S78QGPt8Z4/zGbpG5Ix9lB26On0MDv5Ur6Gvxr0XUMtSy/3FROLaj0o/4uNOmMzSybdWKqqK2ZMe/F5ixnn9mUnAHc6jAcdeHHx84cKhTaLh4+QRNCYi6oJC1gv6JhWtAKPu3gfEZqZ5EXsHxDSUEOdxs9q9Dz74nuMA1eojkbL7oIscQFg5ZXwRUwnHzPyfb7nl+RrkNuqr3pDuK9X0gGi0sjBUNZlwbj7FasC2fP8zWXvHARRLI5yL2LT3ZngO/Fe1df81K+Y3289C9DLDWIPIxUVoD2SN3YTy1NUBZ0Jyfcpn9j6IZe/GHUKIsfQm4E8mO+EQYsT72D04zIW/njK6OyJ6Wxn2LiCTdZTC67HoTbgtAIworuPp54nqW7lwRR+mb0PCrdT9m2za8yD+rd2kpUMMMMxL56WE28qk+xZz395LifRdIFdjmVEqK86TpKUt7H5FSlIwtdmZqjo/sHWLLcJriMbkthhMMHVTkyh32bppvq1gPqKFimJKsX+zPwXIZggU74RZPjdJkthrX7u5TMziwnsMnqdw5fbrdkkjV/5D6BnNvPG5gD7ctpzB0A03fOIPGo3yAo3i2y2tNyWaXDV3U3fpQ9wQz+v3FZKPoIiqmttXAvLhavX7w5XKwl6bUUL/yUA+v5+YX4rDxS5mZm0vnPwFpLl0MEntzf/Ns0tCrJ6lzxD8w4svGHzm8IkXFnQebXbocGtYCKndfvvu9IknBv7kpZPyStHwW+T1N1NBiqfBcJMyeWFammuku+dZPSGU1PG9Da+//xtfP76nybSq1W122WVLDp/Xlz4jGq5xyyLaXroI6iIHVdnfnDOAN1yVnPhadeGOoGFDXui3FWCV2yzZL954uv2Y00I+x0paLxNKt1OK3zTrl3CWlUkb/eBQikcYe+kJDi87cdqLcIlvJ02PoNFg7qxhPZv2DY4vP49ofhvI5YSwGWSYWqNOiCKM+USlBZRKg2SNATzLmWpcTmmMfYGGf5yja0+waM9yovJrEF+KyFuJz9uAZ8fRxnFG/BiM1ElLfYQwSFxaSv1kwWR7FPchxkY/xNE1+5vnNlHgG1dX2yeu2e7MhcolTOCkZz7q4qPuPiomNXcZFfOamNda2/Lf3bzmxfb8t3w/cR91l9FsxjjITvTNHqVSvdexQciZFS4mxSdPe5O0CKlINcRDDat/eNEFA/8lL4TQujGvuebEIZEjv25p/ZOi4VirTmOzVqNT2NVM0BTHVCOTEB9yz/6vQPquavU9z7Q7AYq0RcPF2p+pjkGzraMoDMtN+ovtgbT15kvHf5dgrRTCTjjJeICqF7RIUQl4Fo9DVupRkFS1NKIarIitMRFJBTWcPG3O1fJ2HjKjoZRq6DnmWf2PLbLbtq8/+vBFF+1uuw/yfvL9i3Oc1eOpNK9JM60xyyIFuPLK4yPnzcs+hGXvFaI9QeNiPClSIL2Nkef0qqppKJ2wrLElqzdu+Ub1xR2txcEAEnvqqedruD2hWjohzb5a18c8G9sD9XEJrOn1D/A1MwMN7fsX9gd/cmysMTQ5rXLWEPL7BAHL+qifXEy9NrtPkzlqgLQxhPmjpx2ek7hy56uOoeEhQpQ7Yks9g3h6I9Rb9ImmqPQTQoWo52ZKpbcQ4lsJ0QbMLqZRGwSUuHcUZD+1l95Pze7k6CtypqZaJkQpUZybIhq1ftJ0JSJXEKI3EUpvRsONWHYJjbEBRCGeN4LZwzTGfpGjax5vJ7tDPcjJjHBm8axu5BWfFdP8T4H266gdtnVoN3OwZ7JBdqLvtKSvKBL0sKiWTaQPtzJ54QkDqSMyjPsQlu0Usb94tPrbDwM8MMkWXTwQtUrl/g+kfvKL6nabhJ5LgWW49UlegFVB6yI6jNgRS9OnTep/dnxo0WO33747bYZqnH9+ZN//QXZYNX7aMFQL35UEGo2TB0qlUsfsjgaMlDXeIRN0VDFERyRNR4AR1Z4draI2CrghOuI6Ntxxek6GNJSj/aj0mQYTXB1MpaSucqjt3Dvi8eoLB6+5ZvBOVasgvFajaK0QBtyZD152L7SWfC2WuiDH3bMhz+o7UR5UOfbQhmuxR5PEEhK9+sYoVQ0HBN1pmk2gJ5NakW43MaQqSUA0OhZC/DRCLG03mkjpsPjJ0eYSq0mSjFSrfLbuCx8LJreFKGxwD0vzXG0rjpVUJIwAx9zGnvEs+++qjYe2P/q+E52X+YVqlR0i4fEQlZY1tzuYalxv1EYeqX69FarTCpy/d6e7PR6intjVinPNXyBpdvJrPT3DwzOVmpsWlg0T9T4DVj4jI5ijBUNTRr/3GPN69p7u2i7jCPwVIaxFepSe82Cs9mpMHqdU3oPQh3kZiPHm85NnF0GooTJKo3GcNN2PNZ5ArMp7Xr13Qmrh86v3snTPHWR6IyLXEc9bBT6AWR9mEZiimiLRKBKOU39pH7XRv0PCF3jPq4YmO67yJ+uze2+g1LuZdGw5WTadwp3r6I3aX/Kq//W2ZFvFkkTs4986uQLxN6vPQV5b4eixzKvvW3teHmN1775V9ER/i9uaYvW0Dge6EfVAlj3N83922UwXr1K5v5yFk6s9s+UqMmDIAnWPwVLxMOyeHVHVg8C+SuXo6GzVmZtu+uT8kZFohUS+SmCxYX3iquJ+3NWPqLf6hElMJkn0tV/tX1YqlQbaOWFQVxdGouzY/k6LTV150yfnxyO6KgstVScGsiAWsrGDJ08Gi+Ppf69W33dicp+33bYlfv740Apx+jJrHRfU1cZKx77xjTtPmQPcZBqVyr19WQjLQ9YYNNEBy7yfQF4d3RkVYVjdh0APQe+havWOGsWSuW3ZNhEsXJGpz59MTzAZrlbv2teJhqtv3DQY123p1DeLpmPn6/6nvnjnuFzelOB27VobHTl+fJVYusKdpYL3g0YOI2I+BHJo3ryePQ8++JvHTzUHt922JT569IWVmUpvO90A3jN28B8e/A8d+kj06spPrw1ZiJvX7FTXa1b4410D1MMymqnFTWGoUXzP1G7/PxJljCF+75WHzogOgHt39SHzVhIKPpPKML3hEA1bTqO+gCjqwzxGPcI9ArW8iogWoTc+hDeGOLo2v36d1PymY2fZoX7Sl1biuhjxAdA+3CPUR3E5TqZH0Jf28Z6fG5qO3JzbbNqzgZ6+zaS1FTmX7Yj8DdKo/w090duS766oJ4nYJ58bXeaZ3+yEGMfOyktjBqpIJtX3ru3J04U2P7sGjf8WfNW0DNLdKPWAZzt41yt+YeoOE9G+/nG+ZOtLOjT0Xbv9dtL2dZFP19bTYgxJBBcW8/jdZimufK3safucSXWa/phKBW0vedUsk9XcNt3veYzf6fU78zEdeimqgrevTz15/NYa3zP1e/r05BELE49p+3WasI8Wc06SRHftIjp69EJtv4ZF37Ocg6nX9NTzOPGY2V2vU5Exi3VgZoWqwjY7Y+lxCj3NcJxpajlOe9wM+0zYv2CUrf4Vqkwc8+4ZUxJzbrP52Wso9W6mMbYan4FBaqRY+ijiv8Tzq4+TiG1+1hec9Nobxa0X1bP0oBpmmhJk+/f//P88kCSJsenZKwjRF4EFZOn0EmRpHmTpdt698vrZj9fK8ICm6jIXC4ZN7vfHbRGyHxXaM2pgbub63GFittWPN61dzAKniovsACFxZelzl1Cat5n62OXj3qGOfhkB1b1kY7/MC6/eTSJ27y7vS8NL17iEQU5Zx/HUUPfR1OZVhx/gRJKIsXnv2xG9H/N4gkNmAn1uxL2QNv6ad6+8bVYBsF100UUXp0CzWMUwaTact8fTuXJMKExrRqmnHymtgbtJ3PXoEDVTjoh7TfC647Uz/Yh4aipDw0O0ORDCL6AhHndZji9X10afA5aBUtjHZrn+bhdddNHFDMgZZNw4QTZ2pChZNFHymqzSZul84Cou/PU4AZLrJY0bHBHXE47XBK1LpnWh7XPKttcFr5tRH3Pbz7a7cxru/04ZYUPhYe6cqSPFtiyFzJ6d+ynqoosu/rUiZ5CH1p7A2UUUj+YS2jRhMyJKlsbEPeupp2uboVBHh847JioH1b2mntZUqam3fU7ZDjXB63h04OSreo/AxrwOx8n6G9FwMWld8WncP05RXUSOIeSOnblcg7aLLrr4V4vWUonC0+CdY+Pa4Q5ZuhbRm1m4u5ck0eR6SV+M4wOWlo5khLq518y9ZqH4tP/f3m7bniHHYi/tTUQsgTzfslS6sxhzyuJTEyGgYTcuh7r2xy666GKu0JLKgj5NOnaIEGkH70wbXHEvA/8WDVfkbnTX5OVSmzcW71NPjyleV3wio/S2Txtz1NTrkqbH5WR939G1jJK4suSpMpK9EwmvIa3TvnznFIgYuGHZDsbsBFw3RyENXXTRxb92FG5vMf7XoSNktpWoB5gpk4XcIQIr///27ifEruoO4Pj3d869972ZvsQYnTCRYEIYUpmFRBoGXdVAd13ZVpe1QWiKWVYLUkrvUIrYLooUq6YuFARtCy5aKaWbDLRKrS66KLY0dkwlZpKZMB3j+ObNfef+jov73sub/2/GSSPl94FhOMx973Bn8eOce3/n98P5H7L/vapgZR7d6RPS/O++xrRGuaROm1LGIJIUErQQ6fsJWlR/06IUuVxvNqY/Or7vWt7dGWvjXlz2CGW7AVvkcImAS66i5RvMjy2Sn7zpLWONMf8fVi4Vf/HPu3H+LYQM7ZSFiquu7tWHFCWtKaF4lVA8ztzs1W4CZh6jOzhDPSx/spdm0mg5XHSFYxnqaaaFoknQlk+GFubGaeYiSn4ugfuVQ++fILpniXo3ZTtZVeVj1ePRCN4r4v9AaJ3hyl0fbPsAvTHGbGDtXvr5f7+C9w91muC4zXfbUcnqBWX7t8TiKW6Nf+fd8dAfpPJzMeEIyUhzLoER5marPtj5SQnXM+MnYeTBYZyfIKs/g8a7KNsbTLpq/trwAq3mE8wee2GrrHhjjNmO6+Gv+3Lj7L++giQvEXWUUjcPkFW2tuLTgJbvoPpL2vIa82OLOZOdjhAb5CT2H/85cP5OvDyE84+AHKVsb/0cMaIkCSBTEB7mw7FLtno0xuymleEvzx2HH95LO/wY5Nuods4vbkkRgbQ2S2vpjzh+Ra35JqfuWVj3HGg3kD3z/ii++Bo++zqRE8Sy0TvJM8iczjtUH+Ty2GsrvtcYY3bB2kiUR8fBfxwn3fNzQjGBbljdp09nJQmQZAqySFieBvkLTt6mHS+RyiKxdJRxP94fBb5EZILa0CHay/XqxU/cOjjG7vPPuqLlr/mweQpWbuuNMWY3rB8gc1GeO/8NstrPCMVoFSQHLNsdY7Wa9KnDewgBNFR9dKvVaB2fgnMQ2lAG3TSNZ+0EikuA+FdieYqZV3Zem84YYzax/vY3jw75wu9pffIsiEOcDlyUVsQRoyMUyvKSom065wHrIBkxQnsZlpd08ODYPd0TOw165AKqP2UmTG/jXo0xZls2Xhbm0XHLhb0Mhadx8k1Uldh5ntjrM9qp5r3huG+K6+lBdBqUDPD5vjFU5eLTbJ6y/AHt1svMjTdta22MuVE2Xr3lonx05Bqe76O8iEsCzmkv6PWauMsm41U5jL1CE4N+vvsVUq0c01qL0H6C1L3I3G8sOBpjbqitHyzm0THy7gF88jhJ7Vto2IeuetPcW+XJjRgr3iuRi8T4JKfHzu74bo0xZhu2fv6XizI3PovwJGUxSZJdxGdVWbQYtfNWmV7zrN0aRxSRquct7k20/C4Mv3xD/xvGGNNnsLfHuSgzx+bJ0rOE9hkiUyRZwCeuU0OyIn1b452Pq+CbZHRSh14gLJ1hf/t1Zg62dnSXxhizA37gK6cmI/fcqnz8wHka8+dQvQJ6lNrQHlQFYlldGGVNy4beKrFroz7bUqXwJGmLMryDxu8RWs8xO36JuRG1Z47GmP+lwQMkwNRU5H4RFh+4xmO3vcFXH/0dZXsJn9ZIa/Wqx7QH5yIinf1ylPWDo4A4xbkqenrfojZ0haL1JzT8BIk/4jvH3mbiQCA/qUxNbqf5tTHGfGYDZn+vo9eshxRnXwAAALtJREFU+8uOO0aPojIBch/p8HGkPEQobyfGYbzXNdNEdagqIk18chHVC4Tib0TewvNnTn/xam8OSwI3xtwkOw+QcD2Adc9b73+vQcYhXLyDUu9E/GHSZBTxDaJmAGhs4uICoZyB+AGlTEOcxV+7zMzrrV4fW2OMuck+W4Bcrb8Rd34u4fCRhI9Dxp7EsdC5xgfFF8rwcOA/RwK5hF4tSAuMxpjPkd0NkP16W3BYWfJssjPu/LagaIz5nPoUBSp4D1AF9yMAAAAASUVORK5CYII=)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/benchmarks/Benchmarking_with_Harness.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**LangTest**is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy**\n", + "models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook provides a comprehensive overview of benchmarking Language Models (LLMs) in Question-Answering tasks. Explore step-by-step instructions on conducting robustness and accuracy tests to evaluate LLM performance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting started with LangTest" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "0ZVlGWBJyGO8" + }, + "outputs": [], + "source": [ + "yaml_content = \"\"\"\n", + "model_parameters:\n", + " max_tokens: 64\n", + " device: 0\n", + " task: text2text-generation\n", + "tests:\n", + " defaults:\n", + " min_pass_rate: 0.65\n", + " robustness:\n", + " add_typo:\n", + " min_pass_rate: 0.7\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The content stored in the variable `yaml_content` (which should be formatted in valid YAML syntax) is written to the opened file using the `f.write` method." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "zPbGsd-Iydxv" + }, + "outputs": [], + "source": [ + "import yaml\n", + "\n", + "# write a yaml file\n", + "with open('config.yml', 'w') as f:\n", + " f.write(yaml_content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Harness and Its Parameters\n", + "\n", + "The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from langtest import Harness" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It imports the Harness class from within the module, that is designed to provide a blueprint or framework for conducting NLP testing, and that instances of the Harness class can be customized or configured for different testing scenarios or environments.\n", + "\n", + "Here is a list of the different parameters that can be passed to the Harness function:\n", + "\n", + "
\n", + "\n", + "\n", + "| Parameter | Description | \n", + "| - | - |\n", + "|**task** |Task for which the model is to be evaluated (question-answering or summarization)|\n", + "| **model** | Specifies the model(s) to be evaluated. This parameter can be provided as either a dictionary or a list of dictionaries. Each dictionary should contain the following keys:
  • model (mandatory): \tPipelineModel or path to a saved model or pretrained pipeline/model from hub.
  • hub (mandatory): Hub (library) to use in back-end for loading model from public models hub or from path
|\n", + "| **data** | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys:
  • data_source (mandatory): The source of the data.
  • subset (optional): The subset of the data.
  • feature_column (optional): The column containing the features.
  • target_column (optional): The column containing the target labels.
  • split (optional): The data split to be used.
  • source (optional): Set to 'huggingface' when loading Hugging Face dataset.
|\n", + "| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n", + "\n", + "
\n", + "
." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "P3O9AFRlz2y5", + "outputId": "7ce24c8e-d92f-4f52-98ef-a132bf9989c1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Token has not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n", + "Token is valid (permission: write).\n", + "Your token has been saved to C:\\Users\\KALYAN\\.cache\\huggingface\\token\n", + "Login successful\n", + "Test Configuration : \n", + " {\n", + " \"model_parameters\": {\n", + " \"max_tokens\": 64,\n", + " \"device\": 0,\n", + " \"task\": \"text2text-generation\"\n", + " },\n", + " \"tests\": {\n", + " \"defaults\": {\n", + " \"min_pass_rate\": 0.65\n", + " },\n", + " \"robustness\": {\n", + " \"add_typo\": {\n", + " \"min_pass_rate\": 0.7\n", + " }\n", + " }\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "harness = Harness(\n", + " task=\"question-answering\",\n", + " model={\n", + " \"model\": \"google/flan-t5-base\",\n", + " \"hub\": \"huggingface\"\n", + " },\n", + " data=[\n", + " {\n", + " \"data_source\": \"MedMCQA\"\n", + " },\n", + " {\n", + " \"data_source\": \"PubMedQA\"\n", + " },\n", + " {\n", + " \"data_source\": \"MMLU\"\n", + " },\n", + " {\n", + " \"data_source\": \"MedQA\"\n", + " }\n", + " ],\n", + " config=\"config.yml\",\n", + " benchmarking={\n", + " \"save_dir\":\"~/.langtest/leaderboard/\"\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MVm3XwHr-qNa", + "outputId": "7ef92ed4-11d0-45e8-e1a2-0c8be708cb9f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + " MedMCQA \n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generating testcases...: 100%|██████████| 1/1 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
categorydataset_nametest_typeoriginal_contextoriginal_questionperturbed_contextperturbed_questionoptions
0robustnessMedMCQAadd_typo-A patient who was on ventilator and being vent...-A patient who was on ventilator anr being vent...A. Assess the patient, give bag and mask venti...
1robustnessMedMCQAadd_typo-Highest concentration of oxygen is delivered t...-Highest concewtration of oxygen is delivered t...A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ...
2robustnessMedMCQAadd_typo-Steps of intubation - arrange in sequence:- a....-Steps of intubation - arrange in sequence:- a....A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED
3robustnessMedMCQAadd_typo-Tracheal secretions should be suctioned for:-Tracheal secrrtions should be suctioned for:A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds...
4robustnessMedMCQAadd_typo-Which of the following nerve fibre types is le...-Which of the following zerve fibre types is le...A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers
...........................
7373robustnessMedQAadd_typo-A 39-year-old woman presents to the clinic for...-A 39-year-old woman presents to the clinic for...A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro...
7374robustnessMedQAadd_typo-A 38-year-old woman comes to the physician bec...-A 38-year-old woman comes to the physician bec...A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo...
7375robustnessMedQAadd_typo-A 21-year-old college student comes to the phy...-A 21-year-old college student comes to the phy...A. Trial of diphenhydramine\\nB. Complete caffe...
7376robustnessMedQAadd_typo-A 19-year-old man is brought to the physician ...-A 19-year-old man is brought to the physician ...A. Social anxiety disorder\\nB. Avoidant person...
7377robustnessMedQAadd_typo-A 79-year-old man presents to the office due t...-A 79-year-old man presents to the office due t...A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr...
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7378 rows × 8 columns

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Chr... \n", + "\n", + "[7378 rows x 8 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harness.testcases()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lat4hO76ATVr", + "outputId": "c056cc6a-0584-4ddb-ae68-0086faa0a6eb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + " MedMCQA \n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running testcases... : 100%|██████████| 4045/4045 [03:53<00:00, 17.33it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "\n", + "================================================================================\n", + " PubMedQA \n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running testcases... : 100%|██████████| 1000/1000 [01:02<00:00, 15.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "\n", + "================================================================================\n", + " MMLU \n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running testcases... : 100%|██████████| 1054/1054 [00:59<00:00, 17.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "\n", + "================================================================================\n", + " MedQA \n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running testcases... : 100%|██████████| 1279/1279 [01:11<00:00, 17.89it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harness.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "o8tlvlj7IIm3", + "outputId": "5a667aca-3ef9-418d-abf9-4e877874214f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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categorydataset_nametest_typeoriginal_contextoriginal_questionperturbed_contextperturbed_questionoptionsexpected_resultactual_resultpass
0robustnessMedMCQAadd_typo-A patient who was on ventilator and being vent...-A patient who was on ventilator anr being vent...A. Assess the patient, give bag and mask venti...AATrue
1robustnessMedMCQAadd_typo-Highest concentration of oxygen is delivered t...-Highest concewtration of oxygen is delivered t...A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ...AATrue
2robustnessMedMCQAadd_typo-Steps of intubation - arrange in sequence:- a....-Steps of intubation - arrange in sequence:- a....A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAEDABTrue
3robustnessMedMCQAadd_typo-Tracheal secretions should be suctioned for:-Tracheal secrrtions should be suctioned for:A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds...BBTrue
4robustnessMedMCQAadd_typo-Which of the following nerve fibre types is le...-Which of the following zerve fibre types is le...A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibersDDTrue
....................................
7373robustnessMedQAadd_typo-A 39-year-old woman presents to the clinic for...-A 39-year-old woman presents to the clinic for...A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro...DDTrue
7374robustnessMedQAadd_typo-A 38-year-old woman comes to the physician bec...-A 38-year-old woman comes to the physician bec...A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo...DDTrue
7375robustnessMedQAadd_typo-A 21-year-old college student comes to the phy...-A 21-year-old college student comes to the phy...A. Trial of diphenhydramine\\nB. Complete caffe...BBTrue
7376robustnessMedQAadd_typo-A 19-year-old man is brought to the physician ...-A 19-year-old man is brought to the physician ...A. Social anxiety disorder\\nB. Avoidant person...DDTrue
7377robustnessMedQAadd_typo-A 79-year-old man presents to the office due t...-A 79-year-old man presents to the office due t...A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr...CCTrue
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7378 rows × 11 columns

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Benchmarking Results: google/flan-t5-base
fail_countpass_countpass_rateminimum_pass_ratepass
dataset_namecategorytest_type
MedMCQArobustnessadd_typo173387296%70%True
PubMedQArobustnessadd_typo3997100%70%True
MMLUrobustnessadd_typo15103999%70%True
MedQArobustnessadd_typo21277100%70%True
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modelAvgMMLUMedMCQAMedQAPubMedQA
0google/flan-t5-base0.9846090.9857690.9572310.9984360.997
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" + ], + "text/plain": [ + " model Avg MMLU MedMCQA MedQA PubMedQA\n", + "0 google/flan-t5-base 0.984609 0.985769 0.957231 0.998436 0.997" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harness.get_leaderboard()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Benchmarking `google/flan-t5-large` model" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Token has not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n", + "Token is valid (permission: write).\n", + "Your token has been saved to C:\\Users\\KALYAN\\.cache\\huggingface\\token\n", + "Login successful\n", + "Test Configuration : \n", + " {\n", + " \"model_parameters\": {\n", + " \"max_tokens\": 64,\n", + " \"device\": 0,\n", + " \"task\": \"text2text-generation\"\n", + " },\n", + " \"tests\": {\n", + " \"defaults\": {\n", + " \"min_pass_rate\": 0.65\n", + " },\n", + " \"robustness\": {\n", + " \"add_typo\": {\n", + " \"min_pass_rate\": 0.7\n", + " }\n", + " }\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "harness1 = Harness(\n", + " task=\"question-answering\",\n", + " model={\n", + " \"model\": \"google/flan-t5-large\",\n", + " \"hub\": \"huggingface\"\n", + " },\n", + " data=[\n", + " {\n", + " \"data_source\": \"MedMCQA\"\n", + " },\n", + " {\n", + " \"data_source\": \"PubMedQA\"\n", + " },\n", + " {\n", + " \"data_source\": \"MMLU\"\n", + " },\n", + " {\n", + " \"data_source\": \"MedQA\"\n", + " }\n", + " ],\n", + " config=\"config.yml\",\n", + " benchmarking={\n", + " \"save_dir\":\"~/.langtest/leaderboard/\"\n", + " }\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + " MedMCQA \n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generating testcases...: 100%|██████████| 1/1 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
categorydataset_nametest_typeoriginal_contextoriginal_questionperturbed_contextperturbed_questionoptions
0robustnessMedMCQAadd_typo-A patient who was on ventilator and being vent...-A patient who was on ventilator and being vent...A. Assess the patient, give bag and mask venti...
1robustnessMedMCQAadd_typo-Highest concentration of oxygen is delivered t...-Hkghest concentration of oxygen is delivered t...A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ...
2robustnessMedMCQAadd_typo-Steps of intubation - arrange in sequence:- a....-Steps of intubation - arrange in sequence:- a....A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED
3robustnessMedMCQAadd_typo-Tracheal secretions should be suctioned for:-Tracheal secretions should be suctioned ror:A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds...
4robustnessMedMCQAadd_typo-Which of the following nerve fibre types is le...-Ehich of the following nerve fibre types is le...A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers
...........................
7361robustnessMedQAadd_typo-A 39-year-old woman presents to the clinic for...-A 39-year-old woman presents to the clinic for...A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro...
7362robustnessMedQAadd_typo-A 38-year-old woman comes to the physician bec...-A 38-year-old woman comes to the physician bec...A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo...
7363robustnessMedQAadd_typo-A 21-year-old college student comes to the phy...-A 21-year-old college student comes to the phy...A. Trial of diphenhydramine\\nB. Complete caffe...
7364robustnessMedQAadd_typo-A 19-year-old man is brought to the physician ...-A 19-year-old man is brought to the physician ...A. Social anxiety disorder\\nB. Avoidant person...
7365robustnessMedQAadd_typo-A 79-year-old man presents to the office due t...-A 79-year-old man presents to the office due t...A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr...
\n", + "

7366 rows × 8 columns

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Chr... \n", + "\n", + "[7366 rows x 8 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harness1.testcases()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + " MedMCQA \n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running testcases... : 100%|██████████| 4043/4043 [07:10<00:00, 9.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "\n", + "================================================================================\n", + " PubMedQA \n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running testcases... : 100%|██████████| 999/999 [03:02<00:00, 5.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "\n", + "================================================================================\n", + " MMLU \n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running testcases... : 100%|██████████| 1061/1061 [01:55<00:00, 9.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "\n", + "================================================================================\n", + " MedQA \n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running testcases... : 100%|██████████| 1263/1263 [02:19<00:00, 9.07it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harness1.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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categorydataset_nametest_typeoriginal_contextoriginal_questionperturbed_contextperturbed_questionoptionsexpected_resultactual_resultpass
0robustnessMedMCQAadd_typo-A patient who was on ventilator and being vent...-A patient who was on ventilator and being vent...A. Assess the patient, give bag and mask venti...AATrue
1robustnessMedMCQAadd_typo-Highest concentration of oxygen is delivered t...-Hkghest concentration of oxygen is delivered t...A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ...BAFalse
2robustnessMedMCQAadd_typo-Steps of intubation - arrange in sequence:- a....-Steps of intubation - arrange in sequence:- a....A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAEDDDTrue
3robustnessMedMCQAadd_typo-Tracheal secretions should be suctioned for:-Tracheal secretions should be suctioned ror:A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds...AATrue
4robustnessMedMCQAadd_typo-Which of the following nerve fibre types is le...-Ehich of the following nerve fibre types is le...A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibersDDTrue
....................................
7361robustnessMedQAadd_typo-A 39-year-old woman presents to the clinic for...-A 39-year-old woman presents to the clinic for...A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro...EETrue
7362robustnessMedQAadd_typo-A 38-year-old woman comes to the physician bec...-A 38-year-old woman comes to the physician bec...A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo...DDTrue
7363robustnessMedQAadd_typo-A 21-year-old college student comes to the phy...-A 21-year-old college student comes to the phy...A. Trial of diphenhydramine\\nB. Complete caffe...BBTrue
7364robustnessMedQAadd_typo-A 19-year-old man is brought to the physician ...-A 19-year-old man is brought to the physician ...A. Social anxiety disorder\\nB. Avoidant person...DDTrue
7365robustnessMedQAadd_typo-A 79-year-old man presents to the office due t...-A 79-year-old man presents to the office due t...A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr...CCTrue
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7366 rows × 11 columns

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Benchmarking Results: google/flan-t5-large
fail_countpass_countpass_rateminimum_pass_ratepass
dataset_namecategorytest_type
MedMCQArobustnessadd_typo509353487%70%True
PubMedQArobustnessadd_typo8291792%70%True
MMLUrobustnessadd_typo11095190%70%True
MedQArobustnessadd_typo50121396%70%True
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modelAvgMMLUMedMCQAMedQAPubMedQA
0google/flan-t5-base0.9846090.9857690.9572310.9984360.997000
1google/flan-t5-large0.9121890.8963240.8741030.9604120.917918
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" + ], + "text/plain": [ + " model Avg MMLU MedMCQA MedQA PubMedQA\n", + "0 google/flan-t5-base 0.984609 0.985769 0.957231 0.998436 0.997000\n", + "1 google/flan-t5-large 0.912189 0.896324 0.874103 0.960412 0.917918" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "harness1.get_leaderboard()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "A100", + "machine_shape": "hm", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/demo/tutorials/benchmarks/Langtest_Cli_Eval_Command.ipynb b/demo/tutorials/benchmarks/Langtest_Cli_Eval_Command.ipynb index 9b926f95c..50f9a9e7f 100644 --- a/demo/tutorials/benchmarks/Langtest_Cli_Eval_Command.ipynb +++ b/demo/tutorials/benchmarks/Langtest_Cli_Eval_Command.ipynb @@ -46,24 +46,9 @@ "id": "OPPUwGvzyAoV", "outputId": "670c68e7-83fe-418c-8e3e-094590f5b7f2" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.7/19.7 MB\u001b[0m \u001b[31m73.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h\u001b[33mWARNING: langtest 2.1.0rc2 does not provide the extra 'all'\u001b[0m\u001b[33m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.0/13.0 MB\u001b[0m \u001b[31m99.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m105.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m345.4/345.4 kB\u001b[0m \u001b[31m41.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.2.1 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0m" - ] - } - ], + "outputs": [], "source": [ - "!pip install -q langtest[all]==2.1.0rc2" + "!pip install -q langtest[all]" ] }, { From 04d67e47e047ac44920c04d6fb0e5ea6f5bbe149 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 13 May 2024 14:28:30 +0530 Subject: [PATCH 57/69] Refactor Summary class to update summary dataframe and handle file path --- langtest/utils/benchmark_utils.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/langtest/utils/benchmark_utils.py b/langtest/utils/benchmark_utils.py index 2bbab53de..4b07dae65 100644 --- a/langtest/utils/benchmark_utils.py +++ b/langtest/utils/benchmark_utils.py @@ -187,9 +187,7 @@ def __init__(self, path: str, *args, **kwargs) -> None: self.save_dir = path self.file_path = f"{path}summary.csv" - self.summary_df: pd.DataFrame = self.load_data_from_file( - self.file_path, *args, **kwargs - ) + self.summary_df: pd.DataFrame = self.__update_summary_df() def load_data_from_file(self, path: str, *args, **kwargs) -> pd.DataFrame: """ @@ -242,6 +240,8 @@ def add_report( """ Add a new report to the summary """ + # Load and Update the summary dataframe + self.summary_df = self.__update_summary_df() from datetime import datetime @@ -328,3 +328,12 @@ def __group_by_cols(self): @property def df(self) -> pd.DataFrame: return self.summary_df + + def __update_summary_df(self): + """ + Update the summary dataframe + """ + if self.file_path.startswith("~"): + self.file_path = os.path.expanduser(self.file_path) + self.summary_df = self.load_data_from_file(self.file_path) + return self.summary_df From 34dfdf90c31873c30f18fb665963e870a8d1854f Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 13 May 2024 16:30:35 +0530 Subject: [PATCH 58/69] Refactor `Augmenter` class to `DataAugmenter` for improved code organization and readability. --- langtest/augmentation/__init__.py | 618 +---------------------------- langtest/augmentation/augmenter.py | 10 +- langtest/augmentation/base.py | 616 ++++++++++++++++++++++++++++ 3 files changed, 624 insertions(+), 620 deletions(-) create mode 100644 langtest/augmentation/base.py diff --git a/langtest/augmentation/__init__.py b/langtest/augmentation/__init__.py index 26ae43e89..e1c70a2a3 100644 --- a/langtest/augmentation/__init__.py +++ b/langtest/augmentation/__init__.py @@ -1,616 +1,4 @@ -import os -import random -import re -import string -from abc import ABC, abstractmethod -from collections import defaultdict -from copy import deepcopy as copy -from typing import Any, Dict, List, Optional, Union +from .base import BaseAugmentaion, AugmentRobustness, TemplaticAugment +from .augmenter import DataAugmenter -import pandas as pd -import yaml - -from langtest.datahandler.datasource import DataFactory, HuggingFaceDataset -from langtest.transform import TestFactory -from langtest.transform.utils import create_terminology -from langtest.utils.custom_types import Sample -from langtest.utils.custom_types.output import NEROutput -from langtest.utils.custom_types.predictions import NERPrediction, SequenceLabel -from langtest.utils.custom_types.sample import NERSample -from langtest.tasks import TaskManager -from ..utils.lib_manager import try_import_lib -from ..errors import Errors - - -class BaseAugmentaion(ABC): - """Abstract base class for data augmentation techniques. - - Methods: - fix: Abstract method that should be implemented by child classes. - This method should perform the data augmentation operation. - """ - - @abstractmethod - def fix(self, *args, **kwargs): - """Abstract method that should be implemented by child classes. - - This method should perform the data augmentation operation. - - Returns: - NotImplementedError: Raised if the method is not implemented by child classes. - """ - return NotImplementedError - - -class AugmentRobustness(BaseAugmentaion): - """A class for performing a specified task with historical results. - - Attributes: - task (str): A string indicating the task being performed. - config (dict): A dictionary containing configuration parameters for the task. - h_report (pandas.DataFrame): A DataFrame containing a report of historical results for the task. - max_prop (float): The maximum proportion of improvement that can be suggested by the class methods. - Defaults to 0.5. - - Methods: - __init__(self, task, h_report, config, max_prop=0.5) -> None: - Initializes an instance of MyClass with the specified parameters. - - fix(self) -> List[Sample]: - . - - suggestions(self, prop) -> pandas.DataFrame: - Calculates suggestions for improving test performance based on a given report. - """ - - def __init__( - self, - task: TaskManager, - h_report: "pd.DataFrame", - config: Dict, - custom_proportions: Union[Dict, List] = None, - max_prop=0.5, - ) -> None: - """Initializes an instance of MyClass with the specified parameters. - - Args: - task (str): A string indicating the task being performed. - h_report (pandas.DataFrame): A DataFrame containing a report of historical results for the task. - config (dict): A dictionary containing configuration parameters for the task. - custom_proportions - max_prop (float): The maximum proportion of improvement that can be suggested by the class methods. - Defaults to 0.5. - - Returns: - None - - """ - super().__init__() - self.task = task - self.config = config - self.h_report = h_report - self.max_prop = max_prop - self.custom_proportions = custom_proportions - - if isinstance(self.config, str): - with open(self.config) as fread: - self.config = yaml.safe_load(fread) - - def fix( - self, - training_data: dict, - output_path: str, - export_mode: str = "add", - ): - """Applies perturbations to the input data based on the recommendations from harness reports. - - Args: - training_data (dict): A dictionary containing the input data for augmentation. - output_path (str): The path to save the augmented data file. - export_mode (str, optional): Determines how the samples are modified or exported. - - 'inplace': Modifies the list of samples in place. - - 'add': Adds new samples to the input data. - - 'transformed': Exports only the transformed data, excluding untransformed samples. - Defaults to 'add'. - - Returns: - List[Dict[str, Any]]: A list of augmented data samples. - """ - - # if "source" in training_data and training_data["source"] == "huggingface": - # self.df = HuggingFaceDataset(training_data, self.task) - # data = self.df.load_data( - # feature_column=training_data.get("feature_column", "text"), - # target_column=training_data.get("target_column", "label"), - # split=training_data.get("split", "test"), - # subset=training_data.get("subset", None), - # ) - # else: - self.df = DataFactory(training_data, self.task) - data = self.df.load() - TestFactory.is_augment = True - supported_tests = TestFactory.test_scenarios() - suggest: pd.DataFrame = self.suggestions(self.h_report) - sum_propotion = suggest["proportion_increase"].sum() - if suggest.shape[0] <= 0 or suggest.empty: - print("All tests have passed. Augmentation will not be applied in this case.") - return None - - self.config = self._parameters_overrides(self.config, data) - - final_aug_data = [] - hash_map = {k: v for k, v in enumerate(data)} - transformed_data = [] - for proportion in suggest.iterrows(): - cat = proportion[-1]["category"].lower() - if cat not in ["robustness", "bias"]: - continue - test = proportion[-1]["test_type"].lower() - test_type = {cat: {test: self.config.get("tests").get(cat).get(test)}} - if proportion[-1]["test_type"] in supported_tests[cat]: - sample_length = ( - len(data) - * self.max_prop - * (proportion[-1]["proportion_increase"] / sum_propotion) - ) - if export_mode in ("inplace"): - sample_indices = random.sample( - range(0, len(data)), int(sample_length) - ) - for each in sample_indices: - if test == "swap_entities": - test_type["robustness"]["swap_entities"]["parameters"][ - "labels" - ] = [self.label[each]] - res = TestFactory.transform( - self.task, [hash_map[each]], test_type - ) - if len(res) == 0: - continue - hash_map[each] = res[0] - else: - if test == "swap_entities": - sample_data = data[: int(sample_length)] - test_type["robustness"]["swap_entities"]["parameters"][ - "labels" - ] = test_type["robustness"]["swap_entities"]["parameters"][ - "labels" - ][ - : int(sample_length) - ] - else: - sample_data = random.choices(data, k=int(sample_length)) - aug_data = TestFactory.transform(self.task, sample_data, test_type) - final_aug_data.extend(aug_data) - - if export_mode == "transformed": - transformed_data.extend(aug_data) - if "." not in training_data["data_source"]: - if export_mode == "inplace": - final_aug_data = list(hash_map.values()) - self.df.export(final_aug_data, output_path) - elif export_mode == "transformed": - self.df.export(transformed_data, output_path) - else: - data.extend(final_aug_data) - self.df.export(data, output_path) - - TestFactory.is_augment = False - return final_aug_data - - else: - if export_mode == "inplace": - final_aug_data = list(hash_map.values()) - self.df.export(final_aug_data, output_path) - elif export_mode == "transformed": - self.df.export(transformed_data, output_path) - else: - data.extend(final_aug_data) - self.df.export(data, output_path) - - TestFactory.is_augment = False - return final_aug_data - - def suggestions(self, report: "pd.DataFrame") -> "pd.DataFrame": - """Calculates suggestions for improving test performance based on a given report. - - Args: - report (pandas.DataFrame): A DataFrame containing test results by category and test type, - including pass rates and minimum pass rates. - - Returns: - pandas.DataFrame: A DataFrame containing the following columns for each suggestion: - - category: the test category - - test_type: the type of test - - ratio: the pass rate divided by the minimum pass rate for the test - - proportion_increase: a proportion indicating how much the pass rate - should increase to reach the minimum pass rate - """ - report["ratio"] = report["pass_rate"] / report["minimum_pass_rate"] - - if self.custom_proportions and isinstance(self.custom_proportions, dict): - report["proportion_increase"] = report["test_type"].map( - self.custom_proportions - ) - elif self.custom_proportions and isinstance(self.custom_proportions, list): - report["proportion_increase"] = report["ratio"].apply(self._proportion_values) - report = report[report["test_type"].isin(self.custom_proportions)] - else: - report["proportion_increase"] = report["ratio"].apply(self._proportion_values) - - report = report.dropna(subset=["proportion_increase"])[ - ["category", "test_type", "ratio", "proportion_increase"] - ] - return report - - @staticmethod - def _proportion_values(x: float) -> Optional[float]: - """Calculates a proportion indicating how much a pass rate should increase to reach a minimum pass rate. - - Args: - x (float): The ratio of the pass rate to the minimum pass rate for a given test. - - Returns: - float: A proportion indicating how much the pass rate should increase to reach the minimum pass rate. - If the pass rate is greater than or equal to the minimum pass rate, returns None. - If the pass rate is between 0.9 and 1.0 times the minimum pass rate, returns 0.05. - If the pass rate is between 0.8 and 0.9 times the minimum pass rate, returns 0.1. - If the pass rate is between 0.7 and 0.8 times the minimum pass rate, returns 0.2. - If the pass rate is less than 0.7 times the minimum pass rate, returns 0.3. - - """ - if x >= 1: - return None - elif x > 0.9: - return 0.05 - elif x > 0.8: - return 0.1 - elif x > 0.7: - return 0.2 - else: - return 0.3 - - def _parameters_overrides(self, config: dict, data_handler: List[Sample]) -> dict: - tests = config.get("tests", {}).get("robustness", {}) - if "swap_entities" in config.get("tests", {}).get("robustness", {}): - df = pd.DataFrame( - { - "text": [sample.original for sample in data_handler], - "label": [ - [i.entity for i in sample.expected_results.predictions] - for sample in data_handler - ], - } - ) - params = tests["swap_entities"] - params["parameters"] = {} - params["parameters"]["terminology"] = create_terminology(df) - params["parameters"]["labels"] = df.label.tolist() - self.label = ( - self.config.get("tests") - .get("robustness") - .get("swap_entities") - .get("parameters") - .get("labels") - ) - return config - - -class TemplaticAugment(BaseAugmentaion): - """This class is used for templatic augmentation. It is a subclass of the BaseAugmentation class. - - Attributes: - __templates: - A string or a list of strings or samples that represents the templates for the augmentation. - __task: - The task for which the augmentation is being performed. - __generate_templates: - if set to True, generates sample templates from the given ones. - __show_templates: - if set to True, displays the used templates. - - - Methods: - __init__(self, templates: Union[str, List[str]], task: str): - Initializes the TemplaticAugment class. - fix(self, training_data: str, output_path: str, *args, **kwargs): - Performs the templatic augmentation and exports the results to a specified path. - """ - - def __init__( - self, - templates: Union[str, List[str]], - task: TaskManager, - generate_templates=False, - show_templates=False, - ) -> None: - """This constructor for the TemplaticAugment class. - - Args: - templates (Union[str, List[str]]): The templates to be used for the augmentation. - task (str): The task for which the augmentation is being performed. - generate_templates (bool, optional): if set to True, generates sample templates from the given ones. - show_templates (bool, optional): if set to True, displays the used templates. - """ - self.__templates: Union[str, List[str], List[Sample]] = templates - self.__task = task - - if generate_templates: - if try_import_lib("openai"): - import openai - - given_template = self.__templates[:] - for template in given_template: - prompt = f"""Based on the template provided, create 10 new and unique templates that are variations on this theme. Present these as a Python list, with each template as a quoted string. The list should contain only the templates without any additional text or explanation. - - Template: - "{template}" - - Expected Python List Output: - ['Template 1', 'Template 2', 'Template 3', ...] # Replace with actual generated templates - """ - - response = openai.Completion.create( - engine="gpt-3.5-turbo-instruct", - prompt=prompt, - max_tokens=500, - temperature=0, - ) - - generated_response = response.choices[0].text.strip() - # Process the generated response - if generated_response: - # Assuming the response format is a Python-like list in a string - templates_list = generated_response.strip("[]").split('",') - templates_list = [ - template.strip().strip('"') - for template in templates_list - if template.strip() - ] - - # Extend the existing templates list - self.__templates.extend(templates_list) - else: - print("No response or unexpected format.") - - else: - raise RuntimeError(Errors.E084) - - if show_templates: - [print(template) for template in self.__templates] - - if isinstance(self.__templates, str) and os.path.exists(self.__templates): - self.__templates = DataFactory(self.__templates, self.__task).load() - elif isinstance(self.__templates, str): - self.__templates = [self.str_to_sample(self.__templates)] - elif isinstance(self.__templates, list) and isinstance(self.__templates[0], str): - self.__templates = [self.str_to_sample(i) for i in self.__templates] - - def fix( - self, - training_data: Dict[str, Any], - output_path: str, - max_num: int = None, - append_original: bool = False, - *args, - **kwargs, - ) -> bool: - """This method is used to perform the templatic augmentation. - - It takes the input data, performs the augmentation and then saves the augmented data to the output path. - - Args: - training_data (dict): A dictionary containing the input data for augmentation. - output_path (str): The path where the augmented data will be saved. - max_num (int): Maximum number of new samples to generate - append_original (bool, optional): If set to True, appends the original data to the augmented data. Defaults to False. - *args: Variable length argument list. - **kwargs: Arbitrary keyword arguments. - - Returns: - bool: Returns True upon successful completion of the method. - """ - df = DataFactory(training_data, self.__task) - data = df.load() - new_data = ( - data.copy() - if isinstance(data, (pd.DataFrame, pd.Series)) - else copy.deepcopy(data) - if append_original - else [] - ) - self.__search_results = self.search_sample_results(data) - - if not max_num: - max_num = max(len(i) for i in self.__search_results.values()) - - for template in self.__templates: - for _ in range(max_num): - new_sample = self.new_sample(template) - if new_sample: - new_data.append(new_sample) - - df.export(new_data, output_path) - return True - - @staticmethod - def search_sample_results( - samples: List[Sample], - ) -> Dict[str, List[Union[NERPrediction, SequenceLabel]]]: - """This method is used to search the results of the samples for the entities in the templates. - - Args: - samples (List[Sample]): The samples for which the results are to be searched. - - Returns: - Dict[str, List[Union[NERPrediction, SequenceLabel]]]: A dictionary containing the search results. - """ - results_dict = defaultdict(list) - for sample in samples: - chunk = [] - ent_name = "" - for result in sample.expected_results.predictions: - ent = result.entity.split("-")[-1] - if ent != "O" and ent_name == "": - ent_name = ent - if result.entity.endswith(ent_name) and ent != "O": - result.doc_id = 0 - result.doc_name = "" - chunk.append(result) - elif len(chunk) > 0: - results_dict[ent_name].append(tuple(chunk)) - ent_name = "" - chunk = [] - - if chunk: - results_dict[ent_name].append(tuple(chunk)) - return results_dict - - @staticmethod - def extract_variable_names(f_string: str) -> List[str]: - """This method is used to extract the variable names from the templates. - - Args: - f_string (str): The template string. - - Returns: - List[str]: A list of variable names. - """ - pattern = r"{([^{}]*)}" - matches = re.findall(pattern, f_string) - variable_names = [match.strip() for match in matches] - return variable_names - - def new_sample(self, template: Sample): - """This method is used to generate a new sample from a template. - - Args: - template (Sample): The template from which the new sample is to be generated. - - Returns: - Sample: The new sample generated from the template. - """ - template = copy(template) - matches = re.finditer(r"{([^{}]*)}", template.original) - cursor = 0 - other_predictions = [] - if matches: - for match in matches: - entity = match.group(1) - if entity in self.__search_results: - prediction = random.choice(self.__search_results[entity]) - word = " ".join( - i.span.word for i in prediction if isinstance(i, NERPrediction) - ) - - template.original = template.original.replace( - "{" + entity + "}", word, 1 - ) - for result in template.expected_results.predictions[cursor:]: - if prediction[0].entity.endswith(result.entity): - for each_prediction in prediction: - if isinstance(each_prediction, NERPrediction): - each_prediction.chunk_tag = "-X-" - each_prediction.pos_tag = "-X-" - other_predictions.extend(prediction) - cursor += 1 - break - else: - if "{" in result.span.word and "}" in result.span.word: - continue - other_predictions.append(result) - cursor += 1 - else: - continue - template.expected_results.predictions = ( - other_predictions + template.expected_results.predictions[cursor:] - ) - return template - else: - return None - - def str_to_sample(self, template: str): - """This method is used to convert a template string to a Sample object. - - Args: - template (str): The template string to be converted. - - Returns: - Sample: The Sample object generated from the template string. - """ - if self.__task == "ner": - template = self.add_spaces_around_punctuation(template) - sample = NERSample() - sample.original = template - words = template.split() - predictions = [] - cursor = 0 - for word in words: - if "{" in word and "}" in word: - entity = word.replace("{", "").replace("}", "") - else: - entity = "O" - predictions.append( - NERPrediction.from_span( - entity, - word, - cursor, - cursor + len(word), - pos_tag="-X-", - chunk_tag="-X-", - doc_id=0, - doc_name="", - ) - ) - cursor += len(word) + 1 - sample.expected_results = NEROutput(predictions=predictions) - - elif self.__task == "text-classification": - raise NotImplementedError - - return sample - - @property - def templates(self): - """Templates getter""" - return self.__templates - - @templates.setter - def templates(self, templates: Union[str, List[str]]): - self.__init__(templates, self.__task) - - @property - def task(self): - """Task getter""" - return self.__task - - @task.setter - def task(self, task: str): - self.__task = task - - @staticmethod - def add_spaces_around_punctuation(text: str): - """This method is used to add spaces around punctuation in a string. - - Args: - text (str): The string to which spaces are to be added. - - Returns: - str: The string with spaces added around punctuation. - """ - for punct in string.punctuation: - if punct not in ["{", "}", "_"]: - if punct == ".": - # To prevent spaces being added around decimal points - text = re.sub(r"(\d)\.(\d)", r"\1[DOT]\2", text) - - text = text.replace(punct, f" {punct} ") - - if punct == ".": - # Putting back the decimal points to original state - text = text.replace("[DOT]", ".") - - # Removing extra spaces - text = re.sub(r"\s+", " ", text).strip() - - return text +__all__ = ["BaseAugmentaion", "AugmentRobustness", "TemplaticAugment", "DataAugmenter"] diff --git a/langtest/augmentation/augmenter.py b/langtest/augmentation/augmenter.py index 98bbe49b7..96b5125e1 100644 --- a/langtest/augmentation/augmenter.py +++ b/langtest/augmentation/augmenter.py @@ -7,10 +7,10 @@ from langtest.tasks.task import TaskManager -class Augmenter: +class DataAugmenter: def __init__(self, task: Union[str, TaskManager], config: Union[str, dict]) -> None: """ - Initialize the Augmenter. + Initialize the DataAugmenter. Args: config (Union[str, dict]): Configuration file or dictionary. @@ -77,7 +77,7 @@ def augment(self, data: Union[str, Iterable]) -> str: return self - def extend(self, data: Iterable) -> "Augmenter": + def extend(self, data: Iterable) -> "DataAugmenter": """ Extend the content. """ @@ -94,7 +94,7 @@ def extend(self, data: Iterable) -> "Augmenter": return self - def inplace(self, data: Iterable) -> "Augmenter": + def inplace(self, data: Iterable) -> "DataAugmenter": """ Inplace augmentation. """ @@ -117,7 +117,7 @@ def inplace(self, data: Iterable) -> "Augmenter": return self - def new_data(self, data: Iterable) -> "Augmenter": + def new_data(self, data: Iterable) -> "DataAugmenter": """ Create new data. """ diff --git a/langtest/augmentation/base.py b/langtest/augmentation/base.py new file mode 100644 index 000000000..26ae43e89 --- /dev/null +++ b/langtest/augmentation/base.py @@ -0,0 +1,616 @@ +import os +import random +import re +import string +from abc import ABC, abstractmethod +from collections import defaultdict +from copy import deepcopy as copy +from typing import Any, Dict, List, Optional, Union + +import pandas as pd +import yaml + +from langtest.datahandler.datasource import DataFactory, HuggingFaceDataset +from langtest.transform import TestFactory +from langtest.transform.utils import create_terminology +from langtest.utils.custom_types import Sample +from langtest.utils.custom_types.output import NEROutput +from langtest.utils.custom_types.predictions import NERPrediction, SequenceLabel +from langtest.utils.custom_types.sample import NERSample +from langtest.tasks import TaskManager +from ..utils.lib_manager import try_import_lib +from ..errors import Errors + + +class BaseAugmentaion(ABC): + """Abstract base class for data augmentation techniques. + + Methods: + fix: Abstract method that should be implemented by child classes. + This method should perform the data augmentation operation. + """ + + @abstractmethod + def fix(self, *args, **kwargs): + """Abstract method that should be implemented by child classes. + + This method should perform the data augmentation operation. + + Returns: + NotImplementedError: Raised if the method is not implemented by child classes. + """ + return NotImplementedError + + +class AugmentRobustness(BaseAugmentaion): + """A class for performing a specified task with historical results. + + Attributes: + task (str): A string indicating the task being performed. + config (dict): A dictionary containing configuration parameters for the task. + h_report (pandas.DataFrame): A DataFrame containing a report of historical results for the task. + max_prop (float): The maximum proportion of improvement that can be suggested by the class methods. + Defaults to 0.5. + + Methods: + __init__(self, task, h_report, config, max_prop=0.5) -> None: + Initializes an instance of MyClass with the specified parameters. + + fix(self) -> List[Sample]: + . + + suggestions(self, prop) -> pandas.DataFrame: + Calculates suggestions for improving test performance based on a given report. + """ + + def __init__( + self, + task: TaskManager, + h_report: "pd.DataFrame", + config: Dict, + custom_proportions: Union[Dict, List] = None, + max_prop=0.5, + ) -> None: + """Initializes an instance of MyClass with the specified parameters. + + Args: + task (str): A string indicating the task being performed. + h_report (pandas.DataFrame): A DataFrame containing a report of historical results for the task. + config (dict): A dictionary containing configuration parameters for the task. + custom_proportions + max_prop (float): The maximum proportion of improvement that can be suggested by the class methods. + Defaults to 0.5. + + Returns: + None + + """ + super().__init__() + self.task = task + self.config = config + self.h_report = h_report + self.max_prop = max_prop + self.custom_proportions = custom_proportions + + if isinstance(self.config, str): + with open(self.config) as fread: + self.config = yaml.safe_load(fread) + + def fix( + self, + training_data: dict, + output_path: str, + export_mode: str = "add", + ): + """Applies perturbations to the input data based on the recommendations from harness reports. + + Args: + training_data (dict): A dictionary containing the input data for augmentation. + output_path (str): The path to save the augmented data file. + export_mode (str, optional): Determines how the samples are modified or exported. + - 'inplace': Modifies the list of samples in place. + - 'add': Adds new samples to the input data. + - 'transformed': Exports only the transformed data, excluding untransformed samples. + Defaults to 'add'. + + Returns: + List[Dict[str, Any]]: A list of augmented data samples. + """ + + # if "source" in training_data and training_data["source"] == "huggingface": + # self.df = HuggingFaceDataset(training_data, self.task) + # data = self.df.load_data( + # feature_column=training_data.get("feature_column", "text"), + # target_column=training_data.get("target_column", "label"), + # split=training_data.get("split", "test"), + # subset=training_data.get("subset", None), + # ) + # else: + self.df = DataFactory(training_data, self.task) + data = self.df.load() + TestFactory.is_augment = True + supported_tests = TestFactory.test_scenarios() + suggest: pd.DataFrame = self.suggestions(self.h_report) + sum_propotion = suggest["proportion_increase"].sum() + if suggest.shape[0] <= 0 or suggest.empty: + print("All tests have passed. Augmentation will not be applied in this case.") + return None + + self.config = self._parameters_overrides(self.config, data) + + final_aug_data = [] + hash_map = {k: v for k, v in enumerate(data)} + transformed_data = [] + for proportion in suggest.iterrows(): + cat = proportion[-1]["category"].lower() + if cat not in ["robustness", "bias"]: + continue + test = proportion[-1]["test_type"].lower() + test_type = {cat: {test: self.config.get("tests").get(cat).get(test)}} + if proportion[-1]["test_type"] in supported_tests[cat]: + sample_length = ( + len(data) + * self.max_prop + * (proportion[-1]["proportion_increase"] / sum_propotion) + ) + if export_mode in ("inplace"): + sample_indices = random.sample( + range(0, len(data)), int(sample_length) + ) + for each in sample_indices: + if test == "swap_entities": + test_type["robustness"]["swap_entities"]["parameters"][ + "labels" + ] = [self.label[each]] + res = TestFactory.transform( + self.task, [hash_map[each]], test_type + ) + if len(res) == 0: + continue + hash_map[each] = res[0] + else: + if test == "swap_entities": + sample_data = data[: int(sample_length)] + test_type["robustness"]["swap_entities"]["parameters"][ + "labels" + ] = test_type["robustness"]["swap_entities"]["parameters"][ + "labels" + ][ + : int(sample_length) + ] + else: + sample_data = random.choices(data, k=int(sample_length)) + aug_data = TestFactory.transform(self.task, sample_data, test_type) + final_aug_data.extend(aug_data) + + if export_mode == "transformed": + transformed_data.extend(aug_data) + if "." not in training_data["data_source"]: + if export_mode == "inplace": + final_aug_data = list(hash_map.values()) + self.df.export(final_aug_data, output_path) + elif export_mode == "transformed": + self.df.export(transformed_data, output_path) + else: + data.extend(final_aug_data) + self.df.export(data, output_path) + + TestFactory.is_augment = False + return final_aug_data + + else: + if export_mode == "inplace": + final_aug_data = list(hash_map.values()) + self.df.export(final_aug_data, output_path) + elif export_mode == "transformed": + self.df.export(transformed_data, output_path) + else: + data.extend(final_aug_data) + self.df.export(data, output_path) + + TestFactory.is_augment = False + return final_aug_data + + def suggestions(self, report: "pd.DataFrame") -> "pd.DataFrame": + """Calculates suggestions for improving test performance based on a given report. + + Args: + report (pandas.DataFrame): A DataFrame containing test results by category and test type, + including pass rates and minimum pass rates. + + Returns: + pandas.DataFrame: A DataFrame containing the following columns for each suggestion: + - category: the test category + - test_type: the type of test + - ratio: the pass rate divided by the minimum pass rate for the test + - proportion_increase: a proportion indicating how much the pass rate + should increase to reach the minimum pass rate + """ + report["ratio"] = report["pass_rate"] / report["minimum_pass_rate"] + + if self.custom_proportions and isinstance(self.custom_proportions, dict): + report["proportion_increase"] = report["test_type"].map( + self.custom_proportions + ) + elif self.custom_proportions and isinstance(self.custom_proportions, list): + report["proportion_increase"] = report["ratio"].apply(self._proportion_values) + report = report[report["test_type"].isin(self.custom_proportions)] + else: + report["proportion_increase"] = report["ratio"].apply(self._proportion_values) + + report = report.dropna(subset=["proportion_increase"])[ + ["category", "test_type", "ratio", "proportion_increase"] + ] + return report + + @staticmethod + def _proportion_values(x: float) -> Optional[float]: + """Calculates a proportion indicating how much a pass rate should increase to reach a minimum pass rate. + + Args: + x (float): The ratio of the pass rate to the minimum pass rate for a given test. + + Returns: + float: A proportion indicating how much the pass rate should increase to reach the minimum pass rate. + If the pass rate is greater than or equal to the minimum pass rate, returns None. + If the pass rate is between 0.9 and 1.0 times the minimum pass rate, returns 0.05. + If the pass rate is between 0.8 and 0.9 times the minimum pass rate, returns 0.1. + If the pass rate is between 0.7 and 0.8 times the minimum pass rate, returns 0.2. + If the pass rate is less than 0.7 times the minimum pass rate, returns 0.3. + + """ + if x >= 1: + return None + elif x > 0.9: + return 0.05 + elif x > 0.8: + return 0.1 + elif x > 0.7: + return 0.2 + else: + return 0.3 + + def _parameters_overrides(self, config: dict, data_handler: List[Sample]) -> dict: + tests = config.get("tests", {}).get("robustness", {}) + if "swap_entities" in config.get("tests", {}).get("robustness", {}): + df = pd.DataFrame( + { + "text": [sample.original for sample in data_handler], + "label": [ + [i.entity for i in sample.expected_results.predictions] + for sample in data_handler + ], + } + ) + params = tests["swap_entities"] + params["parameters"] = {} + params["parameters"]["terminology"] = create_terminology(df) + params["parameters"]["labels"] = df.label.tolist() + self.label = ( + self.config.get("tests") + .get("robustness") + .get("swap_entities") + .get("parameters") + .get("labels") + ) + return config + + +class TemplaticAugment(BaseAugmentaion): + """This class is used for templatic augmentation. It is a subclass of the BaseAugmentation class. + + Attributes: + __templates: + A string or a list of strings or samples that represents the templates for the augmentation. + __task: + The task for which the augmentation is being performed. + __generate_templates: + if set to True, generates sample templates from the given ones. + __show_templates: + if set to True, displays the used templates. + + + Methods: + __init__(self, templates: Union[str, List[str]], task: str): + Initializes the TemplaticAugment class. + fix(self, training_data: str, output_path: str, *args, **kwargs): + Performs the templatic augmentation and exports the results to a specified path. + """ + + def __init__( + self, + templates: Union[str, List[str]], + task: TaskManager, + generate_templates=False, + show_templates=False, + ) -> None: + """This constructor for the TemplaticAugment class. + + Args: + templates (Union[str, List[str]]): The templates to be used for the augmentation. + task (str): The task for which the augmentation is being performed. + generate_templates (bool, optional): if set to True, generates sample templates from the given ones. + show_templates (bool, optional): if set to True, displays the used templates. + """ + self.__templates: Union[str, List[str], List[Sample]] = templates + self.__task = task + + if generate_templates: + if try_import_lib("openai"): + import openai + + given_template = self.__templates[:] + for template in given_template: + prompt = f"""Based on the template provided, create 10 new and unique templates that are variations on this theme. Present these as a Python list, with each template as a quoted string. The list should contain only the templates without any additional text or explanation. + + Template: + "{template}" + + Expected Python List Output: + ['Template 1', 'Template 2', 'Template 3', ...] # Replace with actual generated templates + """ + + response = openai.Completion.create( + engine="gpt-3.5-turbo-instruct", + prompt=prompt, + max_tokens=500, + temperature=0, + ) + + generated_response = response.choices[0].text.strip() + # Process the generated response + if generated_response: + # Assuming the response format is a Python-like list in a string + templates_list = generated_response.strip("[]").split('",') + templates_list = [ + template.strip().strip('"') + for template in templates_list + if template.strip() + ] + + # Extend the existing templates list + self.__templates.extend(templates_list) + else: + print("No response or unexpected format.") + + else: + raise RuntimeError(Errors.E084) + + if show_templates: + [print(template) for template in self.__templates] + + if isinstance(self.__templates, str) and os.path.exists(self.__templates): + self.__templates = DataFactory(self.__templates, self.__task).load() + elif isinstance(self.__templates, str): + self.__templates = [self.str_to_sample(self.__templates)] + elif isinstance(self.__templates, list) and isinstance(self.__templates[0], str): + self.__templates = [self.str_to_sample(i) for i in self.__templates] + + def fix( + self, + training_data: Dict[str, Any], + output_path: str, + max_num: int = None, + append_original: bool = False, + *args, + **kwargs, + ) -> bool: + """This method is used to perform the templatic augmentation. + + It takes the input data, performs the augmentation and then saves the augmented data to the output path. + + Args: + training_data (dict): A dictionary containing the input data for augmentation. + output_path (str): The path where the augmented data will be saved. + max_num (int): Maximum number of new samples to generate + append_original (bool, optional): If set to True, appends the original data to the augmented data. Defaults to False. + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Returns: + bool: Returns True upon successful completion of the method. + """ + df = DataFactory(training_data, self.__task) + data = df.load() + new_data = ( + data.copy() + if isinstance(data, (pd.DataFrame, pd.Series)) + else copy.deepcopy(data) + if append_original + else [] + ) + self.__search_results = self.search_sample_results(data) + + if not max_num: + max_num = max(len(i) for i in self.__search_results.values()) + + for template in self.__templates: + for _ in range(max_num): + new_sample = self.new_sample(template) + if new_sample: + new_data.append(new_sample) + + df.export(new_data, output_path) + return True + + @staticmethod + def search_sample_results( + samples: List[Sample], + ) -> Dict[str, List[Union[NERPrediction, SequenceLabel]]]: + """This method is used to search the results of the samples for the entities in the templates. + + Args: + samples (List[Sample]): The samples for which the results are to be searched. + + Returns: + Dict[str, List[Union[NERPrediction, SequenceLabel]]]: A dictionary containing the search results. + """ + results_dict = defaultdict(list) + for sample in samples: + chunk = [] + ent_name = "" + for result in sample.expected_results.predictions: + ent = result.entity.split("-")[-1] + if ent != "O" and ent_name == "": + ent_name = ent + if result.entity.endswith(ent_name) and ent != "O": + result.doc_id = 0 + result.doc_name = "" + chunk.append(result) + elif len(chunk) > 0: + results_dict[ent_name].append(tuple(chunk)) + ent_name = "" + chunk = [] + + if chunk: + results_dict[ent_name].append(tuple(chunk)) + return results_dict + + @staticmethod + def extract_variable_names(f_string: str) -> List[str]: + """This method is used to extract the variable names from the templates. + + Args: + f_string (str): The template string. + + Returns: + List[str]: A list of variable names. + """ + pattern = r"{([^{}]*)}" + matches = re.findall(pattern, f_string) + variable_names = [match.strip() for match in matches] + return variable_names + + def new_sample(self, template: Sample): + """This method is used to generate a new sample from a template. + + Args: + template (Sample): The template from which the new sample is to be generated. + + Returns: + Sample: The new sample generated from the template. + """ + template = copy(template) + matches = re.finditer(r"{([^{}]*)}", template.original) + cursor = 0 + other_predictions = [] + if matches: + for match in matches: + entity = match.group(1) + if entity in self.__search_results: + prediction = random.choice(self.__search_results[entity]) + word = " ".join( + i.span.word for i in prediction if isinstance(i, NERPrediction) + ) + + template.original = template.original.replace( + "{" + entity + "}", word, 1 + ) + for result in template.expected_results.predictions[cursor:]: + if prediction[0].entity.endswith(result.entity): + for each_prediction in prediction: + if isinstance(each_prediction, NERPrediction): + each_prediction.chunk_tag = "-X-" + each_prediction.pos_tag = "-X-" + other_predictions.extend(prediction) + cursor += 1 + break + else: + if "{" in result.span.word and "}" in result.span.word: + continue + other_predictions.append(result) + cursor += 1 + else: + continue + template.expected_results.predictions = ( + other_predictions + template.expected_results.predictions[cursor:] + ) + return template + else: + return None + + def str_to_sample(self, template: str): + """This method is used to convert a template string to a Sample object. + + Args: + template (str): The template string to be converted. + + Returns: + Sample: The Sample object generated from the template string. + """ + if self.__task == "ner": + template = self.add_spaces_around_punctuation(template) + sample = NERSample() + sample.original = template + words = template.split() + predictions = [] + cursor = 0 + for word in words: + if "{" in word and "}" in word: + entity = word.replace("{", "").replace("}", "") + else: + entity = "O" + predictions.append( + NERPrediction.from_span( + entity, + word, + cursor, + cursor + len(word), + pos_tag="-X-", + chunk_tag="-X-", + doc_id=0, + doc_name="", + ) + ) + cursor += len(word) + 1 + sample.expected_results = NEROutput(predictions=predictions) + + elif self.__task == "text-classification": + raise NotImplementedError + + return sample + + @property + def templates(self): + """Templates getter""" + return self.__templates + + @templates.setter + def templates(self, templates: Union[str, List[str]]): + self.__init__(templates, self.__task) + + @property + def task(self): + """Task getter""" + return self.__task + + @task.setter + def task(self, task: str): + self.__task = task + + @staticmethod + def add_spaces_around_punctuation(text: str): + """This method is used to add spaces around punctuation in a string. + + Args: + text (str): The string to which spaces are to be added. + + Returns: + str: The string with spaces added around punctuation. + """ + for punct in string.punctuation: + if punct not in ["{", "}", "_"]: + if punct == ".": + # To prevent spaces being added around decimal points + text = re.sub(r"(\d)\.(\d)", r"\1[DOT]\2", text) + + text = text.replace(punct, f" {punct} ") + + if punct == ".": + # Putting back the decimal points to original state + text = text.replace("[DOT]", ".") + + # Removing extra spaces + text = re.sub(r"\s+", " ", text).strip() + + return text From c7b29f7a1b8b1b5bc42d77824992de66eb5eb6e6 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 13 May 2024 16:31:24 +0530 Subject: [PATCH 59/69] resolved: lint issues. --- langtest/augmentation/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/langtest/augmentation/base.py b/langtest/augmentation/base.py index 26ae43e89..5b1036c4b 100644 --- a/langtest/augmentation/base.py +++ b/langtest/augmentation/base.py @@ -10,7 +10,7 @@ import pandas as pd import yaml -from langtest.datahandler.datasource import DataFactory, HuggingFaceDataset +from langtest.datahandler.datasource import DataFactory from langtest.transform import TestFactory from langtest.transform.utils import create_terminology from langtest.utils.custom_types import Sample From cf34e051a1e6c58ef25154025951ad5d5932da58 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 13 May 2024 17:49:41 +0530 Subject: [PATCH 60/69] Updated the Augmeter to DataAugmeter --- demo/tutorials/misc/Data_Augmenter_Notebook.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb b/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb index 5f29e91d5..05ac3ce3a 100644 --- a/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb +++ b/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"markdown","metadata":{"id":"e7PsSmy9sCoR"},"source":["![image.png](data:image/png;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"MhgkQYQiEvZt"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"WJJzt3RWhEc6"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"26qXWhCYhHAt"},"source":["# Getting started with LangTest on John Snow Labs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oGIyE43uhTxH"},"outputs":[],"source":["!pip install \"langtest\""]},{"cell_type":"markdown","metadata":{"id":"yR6kjOaiheKN"},"source":["# DataAugmenter and its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["yaml_content = \"\"\"\n","parameters:\n"," type: proportion\n"," style: new\n","tests:\n"," robustness:\n"," uppercase:\n"," max_proportion: 0.2\n"," lowercase:\n"," max_proportion: 0.2\n","\n","\"\"\"\n","\n","with open(\"config.yaml\", \"w\") as f:\n"," f.write(yaml_content)"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1405,"status":"ok","timestamp":1692343652196,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"lTzSJpMlhgq5"},"outputs":[],"source":["from langtest.augmentation.augmenter import Augmenter as DataAugmenter\n","from langtest.tasks.task import TaskManager\n","\n","data_augmenter = DataAugmenter(\n"," task=TaskManager(\"ner\"),\n"," config=\"config.yaml\",\n",")"]},{"cell_type":"markdown","metadata":{"id":"sBcZjwJBhkOw"},"source":["The instance of the `Augmenter` class from the `langtest.augmentation.augmenter` module is to perform the Data augmentation for different tasks from langtest. \n","In this specific instance, the `Augmenter` object is created with the following parameters:\n","\n","
\n","\n","| Parameter | Description |\n","| - | - |\n","| **task** | Task for which the model is to be evaluated (text-classification or ner) |\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","By creating an instance of the `Augmenter` class, you can utilize its methods and functionalities to perform data augmentation on different tasks from langtest specific to the Named Entity Recognition task. The `config.yaml` file contains the specific configuration settings for the tests to be performed, such as the type of augmentation and the maximum proportion of augmentation for different test cases.\n","\n","Overall, the `augment` object represents an instance of the `Augmenter` class that can be used to conduct Data augmentation for the Named Entity Recognition task based on the provided configuration.\n","\n","
\n","
"]},{"cell_type":"markdown","metadata":{"id":"I21Jmq79jgC6"},"source":["#### Load Train and Test CoNLL"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 50519 (49K) [text/plain]\n","Saving to: ‘sample.conll’\n","\n","sample.conll 100%[===================>] 49.33K --.-KB/s in 0.006s \n","\n","2023-08-18 07:27:31 (7.50 MB/s) - ‘sample.conll’ saved [50519/50519]\n","\n","--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 827443 (808K) [text/plain]\n","Saving to: ‘conll03.conll’\n","\n","conll03.conll 100%[===================>] 808.05K --.-KB/s in 0.03s \n","\n","2023-08-18 07:27:31 (30.1 MB/s) - ‘conll03.conll’ saved [827443/827443]\n","\n"]}],"source":["# Load test CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","\n","# Load train CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll"]},{"cell_type":"markdown","metadata":{},"source":["### Augmenting with train data"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.augment(data={\n"," 'data_source': 'conll03.conll'\n","})"]},{"cell_type":"markdown","metadata":{},"source":["### Save the augmentated dataset "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.save(\"augmented.conll\")"]},{"cell_type":"markdown","metadata":{"id":"YPXIxv9D_fR7"},"source":["Essentially it applies perturbations to the input data based on the recommendations from the harness reports. Then this augmented_dataset is used to retrain the original model so as to make the model more robust and improve its performance."]}],"metadata":{"colab":{"machine_shape":"hm","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 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lDfJYe4yRJ7NprP/Gwp/V3hKh86cyKtqu51zJPv9DosSPAYO5JnkRnRw/73KEps+aUztx/O5NKinbTNzXl+5QPcbOo8ERUq2iSJIz3P8n5Nf3DO3176kOXKLPstxOSJNEvPzHQW66Fi9ysb9zmSG6gcLNhj/QDgeN7Ad5wVf6oVquMAMe2b0/23XbbliePHv3eFqE80hw3/y5oSzoO3U7EeJhFqyrU7BaBa55ra15a85Mk01/D6embpRNz/LgZmanl3uDmhsljnQpzrJWMMxq/CRUgMpxvsqh+jO/V/wcS1fAsJu5dRnbychLZf0rypqDDGlOJ5PNwdOMQS57bQ6nnNaR1cPqwrJ8fSMw8/Rncy+ApwgjoPujAbDuez0RMVLHbvdhNJjQeG3l2TOjrX//9pyuVe/+NWe0t7lZkjDTvvxZt4sFcbU9w2f7El39vhJvfNJinNLbR1ZG+uUXrwW6Xb6dWLE+SRLfsWhsNHj0yuH7Dp1bLtvCaRwivuA4WQBY/4jricOhasn/m2vt2fPnL6QFg+HSlnaEh9KuP9i+9Juu5YSty5XUbfCnmPLJN9nuWfSPL0scrleRwXhkp77dS2bQiwy/11FJVVVOxrdsye+3rP7Xz9a998UheZm7higy9/LrruQp0BdssAj3yCPbPlcq926vV3j1JktRnS2vISmURHURzb7XguIuJBpzs4Ne/dmRPMXPtqvN43xddtDtNkuRYs33ZZZt7zz+/foUZ860qputVATz69KEXLxh8ZvDobhsbmz9fe3rWbt2u16x3+XnB5rNBRrZW/cA1lU8+GNGzE5ITM9kyK5UkeuihRQPr19+76pFtevl118urcJaSe2VrW6scuZb0Wat86tFqNT5QqeT9VSr3l2H0cjMbaNJnKqbmCvcc2779vY91GqvOwou3bpPl11TMqIKuV0313oOPVe/aOXX/+8uZ1i6Rbb6Y9cWEVc2iikZZ+OTer3/t93af+so0X/fMnQ3yvj2X4H4NaUMRMdz/jtsvqrP52R2E6ABuq0nTAcRfxyef+wrHV00fjnMmj7Fbffx/kTpRGOWkKm5Riy+IgkzJUJstpqYaTpYUJ4f7nAWq1buOAPedar9WDF2HHzvSdy6NkNImQU50FiVJol/9av+yhfHRm116flHcLgcGkOZNEEAEcVdcUonCgbLKX1+74dN/Ua0e250kSZ0OaB9RALFQvmBwwVvUone523rRkN/iWkjiwm9GpWg7LL4HfusrkEuYW7dlG5Tojzx4DUHVzUTiUW003l+tLvxLM26UEL1PsHUQehGseY754pPRPhi9p1rt2wIc60DqjBhfkUhcPU9HXXbttYMXv+51Q8/kNHZUVydsmzcvW+we/YEIl6q4oYCLikd/0//9F38XLlhe6gn/HuRmcVla1CzNRxZXNfl3HvE3kl2wqVJJdnZikle94Y8HsrGxDaUe/SWMG9xYIKoTGEkeiqcaiR5w2Oos+KvLLttchXqvubwHid6q5PSpuEnQ2C3aWakkV7WPmSSJfvUbFwyW0ujDbtnNiqSIqASNStjDwE3ttFUqj0Rp2LU8ePRRd7+6SZO6mmsoq/EeYBYMsg1z5cVWuYFSOSIdM5BDYE8CUPf9SGMvImuwFOLyJdjoCrj7mbkZeCMs291PI1pNVoTqiB7ETx6j96U6dv4xJKQgkGXzwS7jwgMPkST1001TnL4e5GScczvfRJyWLekcO2m8k/yfJFqtXrA6RPGnIPrP4De4eb+54Vkzxq+BZ3XcU8AjsJUov68S3Zux4M1ffGpJOZfiOp9MMeWxpPZOJXwUZL27q2f1vN+sgWcNwMuOvxENH69U7nvNuBqdaU01KEgZJ0aIVUOs7ksz+A2Nev4Q/Grce90LWpv9muFuKyF8xCj/1k03fXL+bOIR43qtbm7H3a3wSkPLbCD9ov7Rr1YHr9iya+2kJYc7I4rE0JCiGmHEOLEEjZQwX+q22qV0r4j+O5ylbpm25iWPrQTvF5O3u0QfzbKB1ZP7r1TuXRzX7UMq0cfBf9VhgWOYNcav43if7ubmy8F/TSW+5/zz7feGFv70sKg+JSKG5/RhRSygyKpG44LBibdNYpr5MlFdKSqtawORO5dWKpsXTKRvm6mzGMIyEYnHx4AyeE1cpkioM6KIvT4rJIly/3f6gdcXy6AoIjtI64dJXHnx+SHcniCKR4EU95WIrJ05x7oN0wljSaLjtsK0VKHUs5YsNZAU9ypmx3j+sjruu4ii44hAWu8lKr2Z2tjVrL0tym2ns4+rzXecHObzI8aPX9zb1HmpVC9YnRE2icrNbul890wR0yYrLbJFtJ25upu6W+yZXy4e/vC8kcbNUyWacS++uhuOrBb0P7r7cstSLVxammcESB5bKK7uZu7Zmgzf+NBDixbkc+i1PI7eQUxx1KwRu8htKuH95o1lZinuZjjmbX2Cq3umjs8XLb3rByd1PcwmaPv7I0L2zyI6MjHeFXAzRG6MNHzugqGhjZXKp9aQd2rkJocpfTcaYybjBUscxNUtU7N0tbr/IcgVbhYVvNha8yKKgONq1oiRaL2WSu+f2HuirtHHReTd7tni/HwzBVcBXFAR1bbzUMSa46+QEH9w4dDQ73iWPSOqRxAMseJ6ZIjo/FJJV7aGK87RwnJ3W+qeX5e2/QfNGmsLm2lrPlJdhtsCt2J/DNEA5nvghT0zX49JmCsnTb1+MaXyGiw1oEaWfoOFHM+LSVyfYjwOHMctIksHiEpXMbCvb+blpAtMJ4s1+cLi564h6vkAWTqAqqL6NHbyAY4+MAoYFu3A/BmcCDMQ1hJKH+NY/MbChpnHSs6Clok7zCgl/ngwz444x8JtK+snI0kSrVQ2rXDCx1R0vecXILeL5a/nVELphIjsNfc9IcRDImEiE/RMRWWxEG2+9nX3XXLyZKaTw2HGz0noBe/L/1VUo1SQnKG17SqCmmdpFHpeE+L0LUmSqKnXJ3QoqHtWBrnULFuGmZL3aaKKeMs+JCKIiLplkWe2LEjpjmp14eBkp087kiSxSgUT9+2CPi46yd6UF0lWz7I1IcT/u0v0j9dtuO/Prq3c9+bXfnXJsi1b1kaTmWSppOZNHWe80ImD+EoRvcIsNQRVVUSDFT/bhIQrcfWsHrn7r61ff+/VkOhll23uXV8Z/AOV8KtZNtYLFo2fN2IaolGVsB9nt4TosGioC0W/goJFWVbrDaXeD6Csc2cvIupe3C3uphppBs0QGBLy1Etcf8GzbAGeL4ZXVLMy1aAeqOQ25MSqVbRaXdiL+s+6Zf15VpxAca+4yN9Xq0n6Q800ShKF65RM14MMgqRE8X5UHmf32nSciVn9ScZGnyaKQQKIVuixaSs2FCgW4ZMyJZayaPEyNn1rBfftXcnmZ9fw2b03sOQ7mwjRf8fSy9EIgj6O1d/LnWt35IxPjLtW7SPLPkb5vL2okku5cimBv+Wz+/8rn917Awt3D0JVT8UoO8dBdsT0XChx1yLwfE6QnKtyTKeBiT5yz62CrrlDRl+8WQjXFA/nuKoooiaqO71R36QavknGaCb1derhXaJhvVsWk8cwqVlmqqV+Se0DIZTeZ3gqjk728I8nZmrY75buMOe4qi4vJKeBPPOkuZdHZo35SrjuoccW/XUkmRVse1IuRe52EpW6oI+aNQ4gUtYQXeKWXTJZzc+7tyvAlkFy5NRe4Rf3Zb7gc0HjNe4sds90vB6ooI5hWcMQ6ROJ3i6kb45i/+bCRcf/qlod+AJwqOmpbzTESrGk3kZ38yxwN5HIVGSve7bTzU5I0NWIrMOy/lawQ26nVonVqN8CyWPnnffpimjp7WluP8sZjjuCGnAo8+xz5tnfSxSOq9sKcf6tiLzV3fpaHmGP0sbYAkF/CU+HNET1jCxu7w+4qDlfCfDahs0v9ZTWuhvuaZt06nlMs8vP33LL5t4vfvH5WrWKXX2j9pbSsAo3xX2cRvdsGPWvz3wXT4OzYqcb4WX7FuPhKtJ6nKuxjd00xiZ6qe+6aIRNzz6I6M1kYyC6CgmXksie6SvxCGCgcjla2gyhmTgQgffhtpigfWQpwGG88RUyPs6RVROl6MSVIzzEon0fpjzvD2iMrSgkXSPSd5Lpmyj1PsqSpV9G9lQ5fGR/EfIwTbmzM1GxN26EJOETu04ul2dH3+S/IhHuhoQzn37PDAKf+NWxR39/Tc/TZ9zPHKAV4tPGpAQbPHpk0CX+JfD5tN9qriYiJ9wb/3HDhmOPNjfv2rX20JEXXzyo5veAXOHuxUPratYwDfE1sTQuMbfc09tWetidIutEdpqnH80auj2ObbQRxgaiLHqnavR+t6y/RbXg5mgUrQhZulhdzCfFIgKIYwh1N/usRX5P5DIE9ahhsiYS+SOQi/OiGQV7dVPQxYJeDDyZJFPDh5oowmSoVuVLnjUGRMNHRaI+LyQ9mhlJuRqf21CFPjeviMrlaPn69Rs+/alq9dhjlQo0GuDixaJtE9ITTTQC829CfaNQ3yk6r4bbYkPuFA3vxrK+1jUS3DMQW1epbF7gkv0i7oMTcyDERMOwe/qpejn77BNfPj5S/HCgUhnYax56VUu3uzVyVb4ZDKa6yiwbVbeaIHFz3twzcF9dqfzU/GolGSZJrFTZNGDua5quxXH2KCi5mr36e99rLAP2QWKa3dcHvpKiDB5Cs97CHjLfe0axn2cjfiRibPrWKuKe1aR1I4pr1Eef4OjQMZKLWiXDAHTvw2SNEZBeNJSx7A3A508dD6n9aLSu+D9/EIpsXxr1lHweTiD+jwhD42M2+22mG76w6i9Z8u06qncRxVcDZRpjIKEfsVuReAORfpNFS/8W+/W/hOTI5MIas3fStIjPaSharqzE5f0CH0T0g4h/UNo+p9NG9QOi9gF3W3c6FJ17FGxSvJYSLnbzy3MnRpukpaqI/7Xasceq1evG4yIvumh3uviCC3YiPCAhGqG4PXMV1k1hIHO7HogmhDMB4KYhOu6SbQr0fimOXzherRwd/cbDJw6JN+7DssdEI9zb46QwdwZClg20r/Mz3qNDblPXrZbJPVE2dLBaPToK3x95fWXom5h/yt1TL9TUNptqZMgrZjNbuap9dHRkJPoTJ/tdYK+GWIubfeI5NhklmbpZn3t2q0rPPSkL3ghAb/uuzZNonoupB7sbjldh5ESlcnQUjh5Q5L+CPENbFXvH86ElLDUdW6caX+JmOm4eaaq41tiRxvqnN13ZZI5JEat5/DCBexxLc2bbJMrVzfpBBtzTWq5mA1DYFcNSiBZX8pU71Sxbi2XL3QxcwN3cyRMn3Ey1NKAlXdOkO8p8qbstd2tZs91NPfUdUDsx1ck3C5ypCJO4cv93yki4nLS+vAinOU4WHodKEaeZaDOPmedX78PZQVTKGZzZhsK5MzM8HSUdO0ha309aP0BaP0jWOIGIUe6NCAFCWM28+R/B5HMsfnbdxFqStOIan/+fX6KR3oll7ydLdxL1KFFJMQNPe0nTDcTzPkKJTWzad3F+bMtkMdFJMytPdfHMFXMgSorIqED+cUZo+0xoU7RpfSb9PuowKh3X3v7hYrKKXbzv64peJyrz80IWkjNJF3PLhh17II+N22btQc4PPLA7bbhvxX1IhOYDhLtoljV6Bb8cvJ/2cnCOiahmWX3Ig26tVr9br1aTwsaTWLX6vhMmfFk1dApk70uRPjWxKdIjmCg1cftiFA0drFQo+kvSJEksy6wqovtVWyFN7m6ImogOMkskSWK33PJ8bfsjd/1pGuQNZul/EtHdGnpG8WAgaev9InnxCnE1y2K37OJI40/Bomva+2wG0DuF9CiyY/vWux6qVpO0SX+lgp1/vu53T3eIaJ2mKNw80r2XNLrW8pTGCVCNMOVvH3voPUNF8HdxbP7/9q13PYbzpIQSTAjeFVWVsjsHRQPgzegzk1CanyKrxvcN4ToJIXYc1Qjwb6roweZS9OY+X+DSSmWccV+C+4LcOQOCpqLhmEn29Wrl+8OTVwSdHs2XPGcnQY6MDRDF16MaUeqBsZM7iE7sbDk/ig9AIinIA2SZkaVQ6lnOWHrD9J27FXRuh3Ataf3nSMd+lpPRzxHkZ2nUr4lUAr8AACAASURBVOXkS/8HIjuAlNEf9FMq3Uyp9//js/tvnVJkNxEjuT5l6JUHOLzyM8ThtaT1X6Y+9nlK8UE0GGZG/eR8gt5KpA+y6G2Xw8ZxJjnNu8QnqduT2y2IuYGnhtfBUnJ5tPPH2769rQ0pWNGWVPxUl3ASPefAf9SxSyNCfDWiJmBN+5yoIqqHTfwAdPbC+1jPQbf0cBFnaOMrO4orooOO9I+rn+MQBEZcs1pnlVYONetHTiyI45GgEaRtFq6m1wIDHcnwY3n17ok9RlGoC+SFSGWCGwiE0yrc25yHbzx858Ht1aGN4v4rno19VFQeEo0Oi2hK4RgaL3snglmmDstd+DCjcVSYGZjw2hJBjCPFSBPu48sue76myAtISPPzLc5B8nMQZRVu88enq/g2S8F9GtNOPoaITPrdEcFAyiqyF3dEirAmwRR6BVlRrWJr1xLltlyMgkE6uh2V/VLEznrWKLv5RbCkH8Al/KxoZDhWOHNURA+QsTe/dKeTauhn96wkYvREK/BsXe5gQlGG8f71fGbPGyd8Fu99I5959k14I8ZtBFFDxBC/iS27TnEfSUqqdY6uHeWui0Z438tP8K5XHuLoXzzO0OGP4GPvIEv/BNE6acOwdDUiG1my7JKOITxNafKOl9c48ud/g/a9i3r9DtLGnxLFJ9AI6jXQsJhS+WMs3bOqGZI0UcX2JuMZt8xPbY+jzSvj1BCpC1ITpCZyZh+EGlBDfHoJshN959SLPSFPPHZncOJdVgwucjzKQsfAb0isp+fQMHBMVWkvC+wO4tILEkNhMyzGbf2djjKvNfdoUz+104RMYbyGTX64kiTRRqTmkp9H03c/V2+gavWF3SLH/ou4v8fTsd8F+WNURmj6porxRFDPUhC9JoR0DWitKfw0YwUACFNfpM30wsyzurTJSs1XiLur4QvcPPY2ppFL9lkaEXUMiG97kRwZZw5FzwV6Ef8ndxsZZ+aOmmW94K+47JYl5YGBwWU4a1pFkQ1RnkD0ADC+sJ1GpeVZyJYmSaK4r83PurjOKlia7g2hdPA0pr5F55nGQTbVV/cKyCCWKY0xQ/RWouiPCD2fm/iJ/yj/lN6PWx9uSqMGGl/B96KVM4fYOJTHtPOyC9uMw2v2kcUfAdtCFEd5LCSXIvqOZsjYVPrb7J53Lh3lhVXbKcfvx+obCeEQGnImKXI5pu/gwgMxietEFRumMsJTqN2ipDmDo+ZCzdXqLlZ3L75ltm3qAjXwus2kBHSi7xxGII0/jrnEGkkeqNuyXTVvXJd6o6EdCysAVKuYIB0YqBgaVCZyiVlh5uq92Sn3mA06BsmfEZqmgSStVF44uGHDi19qjI1+yN3vEuFA4T0eH89xVKLY1K91UqWI5/TCwTPZMz89/cW3FDpsXso8br2AJrhL0jRk07zkmpCxcRW6SamBO+UU9uCyVzQycTcH3LNYkRXn/yCdLxGXiJb6MENENEsbdXWextLv5jZJDMHcWCoNX/zEE6v6EFbiha3U3VTDCGL/dGYLuZ3FszLOYPQNSGFL1qBEpQFgGSJLO390MSGKgNzuV4oW4375zI4agU5l9NvV96MrhsjsHiwbHY+Qc7uVe3f1zZgt01L/jRUHRvDz/gRr3IOEEUQhrZcpla9mNFsGc/AEpSmIWj2gGJh625uh+aKcZdudVHBcT9MGOUfPcLWKVSpphER9orlHeFzykkLddclVhZz28ZqGDr2lkk3jUUy0Urkwdk72NVlqy/nh6m41F6nLhBqJZ4hxlTLMvN8s0KJzbkX05hxVKsnw0MJlWwaODcVBo4+5Wb9IW9FVHHHWgMduTRUcaIsBPRXG59llvOakC3VEwFrsMZckJY4yZszbdbfzRbStXsr4CGnJ5TBBtnor9lFxjBAPYukCsNeqKJm4iUQK2d5K5ej+rdsu2Ccan3DL+t1dRWxQRFaMjIwckuCL3VtXwtyPoZxe9kzz/Jrc8UxtkPfuvRT8NWSN3K5kthfP9mAetdJrOw3tA2i4FKxMo94P0ev4+D99ie+fGMkXy/r26dHRYq5P80f7dhNK64qCFSuQsJIkyVMaT/UCuf76lOQRWPgzX6As/waXDQgpqsvRxjIS2TdRxT6ddMKNG4tDPBWRmkNNoO5IzZGaS/E5jTbqNReti4fTu4RzJEHmapSWaa7SKC0lU3Nj4xFROdQ+Ty0Hji2uYx09dEkCjdLIgIsvNjOgXfoUHDuheYXjlq3wNJhS59PPOM3whNPs/9Q4VQBztZqkg0d3W+S6WzU6RFtgeZ6P7gAxPiGb5bTombCvkJfTcx8SpD6+zEfBdTVEajbVeVOcSxF9wEpErKm+53lNggjHwWrm2T+4pXVENF9SRUxF+qGxGPe1ZllhRwSQJ5MkMXU9KKJDCCaCOl520VeGYKtVS3mWkGOiQS2r71Orn17udfPkzxYRNxKXI/KMpRouG3n+lb+Enn8bPaXpP0HuIpSeyV9KppTii+ntWwnbjLMNoHbJFwVzz71sQeaf4ohJqBiMHaFeP4Bqmj/O3otob37Krb9nhsjNTWuKmEEuR07Rfjrxu6nPjpF7XSU79xLkxLp/UKmgSZKk69dvWolk42EW446/nA8edOGo5OEhxc+Cu6mIDqpwCbBzciB1ksD6DaxRiRabp4wvN5BXuUnF0n2GRHqGrOicmmDPoP9OZdSa8zxRwk40l9qzMnh5siMwd1n5CYR+0dzHebr0tDQANHegaOruB1TCCcda0qKTB4wrVyVJ8qVOmkClcm+fua+T9vvZx42jB8BHXMMeNfYDa8wzlTy4e74RLhVhZV60Q3C31Mi+AZAGORwsPYSzGjBRAdFV7vYDFaWotI5IhEj69Wr1fSfOrIiwnNnNkiTKsn/fT+Pk68kaoAFE9yAndwDw/JJa5wML5jfwjv301J9Gw7p8jRlbidvFcN0cxDrnWWb5v2ago62c71nWg4t+2vAf1HKeZNY+SR1Y48RMjqntAm2MXyH1fGU6y4qU2BwtBaa1TSe1WxARyzNWbAYJshN9p4/JD0ClklCpJLr1Eb9LVPvNsjw+zwsmaKkiPEua7XMNI7j0uuQ5u7ntSGNxfxvwp8UImveLwoVRaiOvV2WBu1vTGC+CqZaGU8+eELefZ8JbY/bnNc0V4mwtKGf2LCVarS5a7mK3O/5MpXL/1mr1jmm88HDllQN9mcstkqYrEJ9EsIDotwS5zJuhQPlmbb+zZsbE2VEJqWm6C5FDIEvHexHUrAGU3vjwwwvur1SS/fnSxq2eTLhRJVpheXC7FhRansrOznovwyHzuro+jdvaptfZ3frEea2jA4ghqoAcDsiTAFHmQ+bZXtFSxTyFzFXUVpl5LJKNu/TMGmTIGdZXPxsv9kZo7LuEnvJqxk6ChgjsSYLlDq0Z6ywmyvFVIyx69h+Ie9/C2EvzcesnlK/ip1Z8gUsPjHB62eQth9GSvQO4ryJLc6btNkw9O3L65/eDXlwGsbQo2yajICMwOdVwfIXA5k0jrfY0T4umpRTSmqOWhzugrcfcaQmUxcbJAmZ72y0X1CSawYvdib7ZY+3aJB4cXHS1iS/1NN3nrieiKMRbt/pKUb9DVG81y3TcvuS5ucXhYObp0yX1Iy6lRxG/Ec8lcgTFUtMQ3bi+cu//1hjr+X96eg4VMWoLyyYnbw3S83bL0phchcpVJtHIspMHAjxs8PNeLHrkM7C8TpjgZsgdSLTbICevHHk6aB07OyRJYus33Ls60vPuzGxsmVntmfWVz2zH7B9V2Z8GhqJMLAvSGzJfaeLvwv1N7lY4UYq5QcnS2qiKPezwC+30nO55tJ+/4+oi+ywd+6ZoWGd56FbO7NxNlLUhkg/Coru3bHnhcJKQVqsXxnnNR/+ISRp5U5b1XMbVEO03sr+76crjI7t2ra0NHRv6Bwi34pTzQPJ0PrABsd7WlZKdwJE8E+aukfXXf/op1WjY0rQ/L4jhqwVZbtbIox60hFu2uyRHnzytk++E5vM203KsTSSee5Nl6XqcBagaGp2g0djG80PD8MDMYyWJkWxULNpO/eRhRPoRNczWMy9dyrZte1j0zkkHzeKhXvJ8GdffptSzgEbNiGIwHuPFVUdy73el5c2eaclZqkr2skvp6bmYRj1Pa/TsAMYhEtepSy6cUT1IrUsza2Py8ZM16RnahhgK0YTg3kk4i3qQuXTzU72m4VfE7TcJ0Ql1GTUhQhlAQtkss0lDGGAisr3k8QGIR8xH/0IlrMN1QdOp4DmTBJcPx3Hj1akt3HbttYxmLlep6O2epUvBtWlbaxaeyCz9XP1kOtRT1gjBcLS9HuRsMZVlZMW8hDNijNB8lGdPS5IkumULkWSsymx00N0jCdGlAusMUhOGg8mwo6mYlc19UDXEmRW1KNqcHqKKW/b5RoPDUezllg9b8NNw0sCkF4N7/gIJ/ldCuFHUV7lleYiNoG5ZJITbHR+8YHDwi1+r+rGgtVWWydtEdY2bjWsADiaqdcuyh+aVSzvzEKPd6QvbFz0j6BHwFYVwoUBuG3Mxx8zddo6OlIab8/a17faMWXZCkCKHXGKYGHcqKtXqI8k06uypZ2EqNkIyUzTARqCqLBlcisZXktbLedSF7CewO2dC15/aX5CIkTxygMVLHyOetzZP99OVqFxBkuxm0+3ka08V8OKZvo4iYHsjucpaqM6Lvr0Az94KelcRagRuJzC7H6rK4LLL0W/3k922k7suOjI1pKjoKxHj3r2XEOR3SRurwYxo3ijpS9tYYIcY6iRBTodpHDgaxtLM4xqSV0M5mzx4AcMhUzk9G+RpPC31uBzHKQs89zAOoDIghSrtZHnwdrPb3GZlInoos/pfBV48AZDFi/5eG/yChNJveFYvN1W+/CR8vov8RkDfCpK6WX9epqrlnRUXE1V1S78QGPt8Z4/zGbpG5Ix9lB26On0MDv5Ur6Gvxr0XUMtSy/3FROLaj0o/4uNOmMzSybdWKqqK2ZMe/F5ixnn9mUnAHc6jAcdeHHx84cKhTaLh4+QRNCYi6oJC1gv6JhWtAKPu3gfEZqZ5EXsHxDSUEOdxs9q9Dz74nuMA1eojkbL7oIscQFg5ZXwRUwnHzPyfb7nl+RrkNuqr3pDuK9X0gGi0sjBUNZlwbj7FasC2fP8zWXvHARRLI5yL2LT3ZngO/Fe1df81K+Y3289C9DLDWIPIxUVoD2SN3YTy1NUBZ0Jyfcpn9j6IZe/GHUKIsfQm4E8mO+EQYsT72D04zIW/njK6OyJ6Wxn2LiCTdZTC67HoTbgtAIworuPp54nqW7lwRR+mb0PCrdT9m2za8yD+rd2kpUMMMMxL56WE28qk+xZz395LifRdIFdjmVEqK86TpKUt7H5FSlIwtdmZqjo/sHWLLcJriMbkthhMMHVTkyh32bppvq1gPqKFimJKsX+zPwXIZggU74RZPjdJkthrX7u5TMziwnsMnqdw5fbrdkkjV/5D6BnNvPG5gD7ctpzB0A03fOIPGo3yAo3i2y2tNyWaXDV3U3fpQ9wQz+v3FZKPoIiqmttXAvLhavX7w5XKwl6bUUL/yUA+v5+YX4rDxS5mZm0vnPwFpLl0MEntzf/Ns0tCrJ6lzxD8w4svGHzm8IkXFnQebXbocGtYCKndfvvu9IknBv7kpZPyStHwW+T1N1NBiqfBcJMyeWFammuku+dZPSGU1PG9Da+//xtfP76nybSq1W122WVLDp/Xlz4jGq5xyyLaXroI6iIHVdnfnDOAN1yVnPhadeGOoGFDXui3FWCV2yzZL954uv2Y00I+x0paLxNKt1OK3zTrl3CWlUkb/eBQikcYe+kJDi87cdqLcIlvJ02PoNFg7qxhPZv2DY4vP49ofhvI5YSwGWSYWqNOiCKM+USlBZRKg2SNATzLmWpcTmmMfYGGf5yja0+waM9yovJrEF+KyFuJz9uAZ8fRxnFG/BiM1ElLfYQwSFxaSv1kwWR7FPchxkY/xNE1+5vnNlHgG1dX2yeu2e7MhcolTOCkZz7q4qPuPiomNXcZFfOamNda2/Lf3bzmxfb8t3w/cR91l9FsxjjITvTNHqVSvdexQciZFS4mxSdPe5O0CKlINcRDDat/eNEFA/8lL4TQujGvuebEIZEjv25p/ZOi4VirTmOzVqNT2NVM0BTHVCOTEB9yz/6vQPquavU9z7Q7AYq0RcPF2p+pjkGzraMoDMtN+ovtgbT15kvHf5dgrRTCTjjJeICqF7RIUQl4Fo9DVupRkFS1NKIarIitMRFJBTWcPG3O1fJ2HjKjoZRq6DnmWf2PLbLbtq8/+vBFF+1uuw/yfvL9i3Oc1eOpNK9JM60xyyIFuPLK4yPnzcs+hGXvFaI9QeNiPClSIL2Nkef0qqppKJ2wrLElqzdu+Ub1xR2txcEAEnvqqedruD2hWjohzb5a18c8G9sD9XEJrOn1D/A1MwMN7fsX9gd/cmysMTQ5rXLWEPL7BAHL+qifXEy9NrtPkzlqgLQxhPmjpx2ek7hy56uOoeEhQpQ7Yks9g3h6I9Rb9ImmqPQTQoWo52ZKpbcQ4lsJ0QbMLqZRGwSUuHcUZD+1l95Pze7k6CtypqZaJkQpUZybIhq1ftJ0JSJXEKI3EUpvRsONWHYJjbEBRCGeN4LZwzTGfpGjax5vJ7tDPcjJjHBm8axu5BWfFdP8T4H266gdtnVoN3OwZ7JBdqLvtKSvKBL0sKiWTaQPtzJ54QkDqSMyjPsQlu0Usb94tPrbDwM8MMkWXTwQtUrl/g+kfvKL6nabhJ5LgWW49UlegFVB6yI6jNgRS9OnTep/dnxo0WO33747bYZqnH9+ZN//QXZYNX7aMFQL35UEGo2TB0qlUsfsjgaMlDXeIRN0VDFERyRNR4AR1Z4draI2CrghOuI6Ntxxek6GNJSj/aj0mQYTXB1MpaSucqjt3Dvi8eoLB6+5ZvBOVasgvFajaK0QBtyZD152L7SWfC2WuiDH3bMhz+o7UR5UOfbQhmuxR5PEEhK9+sYoVQ0HBN1pmk2gJ5NakW43MaQqSUA0OhZC/DRCLG03mkjpsPjJ0eYSq0mSjFSrfLbuCx8LJreFKGxwD0vzXG0rjpVUJIwAx9zGnvEs+++qjYe2P/q+E52X+YVqlR0i4fEQlZY1tzuYalxv1EYeqX69FarTCpy/d6e7PR6intjVinPNXyBpdvJrPT3DwzOVmpsWlg0T9T4DVj4jI5ijBUNTRr/3GPN69p7u2i7jCPwVIaxFepSe82Cs9mpMHqdU3oPQh3kZiPHm85NnF0GooTJKo3GcNN2PNZ5ArMp7Xr13Qmrh86v3snTPHWR6IyLXEc9bBT6AWR9mEZiimiLRKBKOU39pH7XRv0PCF3jPq4YmO67yJ+uze2+g1LuZdGw5WTadwp3r6I3aX/Kq//W2ZFvFkkTs4986uQLxN6vPQV5b4eixzKvvW3teHmN1775V9ER/i9uaYvW0Dge6EfVAlj3N83922UwXr1K5v5yFk6s9s+UqMmDIAnWPwVLxMOyeHVHVg8C+SuXo6GzVmZtu+uT8kZFohUS+SmCxYX3iquJ+3NWPqLf6hElMJkn0tV/tX1YqlQbaOWFQVxdGouzY/k6LTV150yfnxyO6KgstVScGsiAWsrGDJ08Gi+Ppf69W33dicp+33bYlfv740Apx+jJrHRfU1cZKx77xjTtPmQPcZBqVyr19WQjLQ9YYNNEBy7yfQF4d3RkVYVjdh0APQe+havWOGsWSuW3ZNhEsXJGpz59MTzAZrlbv2teJhqtv3DQY123p1DeLpmPn6/6nvnjnuFzelOB27VobHTl+fJVYusKdpYL3g0YOI2I+BHJo3ryePQ8++JvHTzUHt922JT569IWVmUpvO90A3jN28B8e/A8d+kj06spPrw1ZiJvX7FTXa1b4410D1MMymqnFTWGoUXzP1G7/PxJljCF+75WHzogOgHt39SHzVhIKPpPKML3hEA1bTqO+gCjqwzxGPcI9ArW8iogWoTc+hDeGOLo2v36d1PymY2fZoX7Sl1biuhjxAdA+3CPUR3E5TqZH0Jf28Z6fG5qO3JzbbNqzgZ6+zaS1FTmX7Yj8DdKo/w090duS766oJ4nYJ58bXeaZ3+yEGMfOyktjBqpIJtX3ru3J04U2P7sGjf8WfNW0DNLdKPWAZzt41yt+YeoOE9G+/nG+ZOtLOjT0Xbv9dtL2dZFP19bTYgxJBBcW8/jdZimufK3safucSXWa/phKBW0vedUsk9XcNt3veYzf6fU78zEdeimqgrevTz15/NYa3zP1e/r05BELE49p+3WasI8Wc06SRHftIjp69EJtv4ZF37Ocg6nX9NTzOPGY2V2vU5Exi3VgZoWqwjY7Y+lxCj3NcJxpajlOe9wM+0zYv2CUrf4Vqkwc8+4ZUxJzbrP52Wso9W6mMbYan4FBaqRY+ijiv8Tzq4+TiG1+1hec9Nobxa0X1bP0oBpmmhJk+/f//P88kCSJsenZKwjRF4EFZOn0EmRpHmTpdt698vrZj9fK8ICm6jIXC4ZN7vfHbRGyHxXaM2pgbub63GFittWPN61dzAKniovsACFxZelzl1Cat5n62OXj3qGOfhkB1b1kY7/MC6/eTSJ27y7vS8NL17iEQU5Zx/HUUPfR1OZVhx/gRJKIsXnv2xG9H/N4gkNmAn1uxL2QNv6ad6+8bVYBsF100UUXp0CzWMUwaTact8fTuXJMKExrRqmnHymtgbtJ3PXoEDVTjoh7TfC647Uz/Yh4aipDw0O0ORDCL6AhHndZji9X10afA5aBUtjHZrn+bhdddNHFDMgZZNw4QTZ2pChZNFHymqzSZul84Cou/PU4AZLrJY0bHBHXE47XBK1LpnWh7XPKttcFr5tRH3Pbz7a7cxru/04ZYUPhYe6cqSPFtiyFzJ6d+ynqoosu/rUiZ5CH1p7A2UUUj+YS2jRhMyJKlsbEPeupp2uboVBHh847JioH1b2mntZUqam3fU7ZDjXB63h04OSreo/AxrwOx8n6G9FwMWld8WncP05RXUSOIeSOnblcg7aLLrr4V4vWUonC0+CdY+Pa4Q5ZuhbRm1m4u5ck0eR6SV+M4wOWlo5khLq518y9ZqH4tP/f3m7bniHHYi/tTUQsgTzfslS6sxhzyuJTEyGgYTcuh7r2xy666GKu0JLKgj5NOnaIEGkH70wbXHEvA/8WDVfkbnTX5OVSmzcW71NPjyleV3wio/S2Txtz1NTrkqbH5WR939G1jJK4suSpMpK9EwmvIa3TvnznFIgYuGHZDsbsBFw3RyENXXTRxb92FG5vMf7XoSNktpWoB5gpk4XcIQIr///27ifEruoO4Pj3d869972ZvsQYnTCRYEIYUpmFRBoGXdVAd13ZVpe1QWiKWVYLUkrvUIrYLooUq6YuFARtCy5aKaWbDLRKrS66KLY0dkwlZpKZMB3j+ObNfef+jov73sub/2/GSSPl94FhOMx973Bn8eOce3/n98P5H7L/vapgZR7d6RPS/O++xrRGuaROm1LGIJIUErQQ6fsJWlR/06IUuVxvNqY/Or7vWt7dGWvjXlz2CGW7AVvkcImAS66i5RvMjy2Sn7zpLWONMf8fVi4Vf/HPu3H+LYQM7ZSFiquu7tWHFCWtKaF4lVA8ztzs1W4CZh6jOzhDPSx/spdm0mg5XHSFYxnqaaaFoknQlk+GFubGaeYiSn4ugfuVQ++fILpniXo3ZTtZVeVj1ePRCN4r4v9AaJ3hyl0fbPsAvTHGbGDtXvr5f7+C9w91muC4zXfbUcnqBWX7t8TiKW6Nf+fd8dAfpPJzMeEIyUhzLoER5marPtj5SQnXM+MnYeTBYZyfIKs/g8a7KNsbTLpq/trwAq3mE8wee2GrrHhjjNmO6+Gv+3Lj7L++giQvEXWUUjcPkFW2tuLTgJbvoPpL2vIa82OLOZOdjhAb5CT2H/85cP5OvDyE84+AHKVsb/0cMaIkCSBTEB7mw7FLtno0xuymleEvzx2HH95LO/wY5Nuods4vbkkRgbQ2S2vpjzh+Ra35JqfuWVj3HGg3kD3z/ii++Bo++zqRE8Sy0TvJM8iczjtUH+Ty2GsrvtcYY3bB2kiUR8fBfxwn3fNzQjGBbljdp09nJQmQZAqySFieBvkLTt6mHS+RyiKxdJRxP94fBb5EZILa0CHay/XqxU/cOjjG7vPPuqLlr/mweQpWbuuNMWY3rB8gc1GeO/8NstrPCMVoFSQHLNsdY7Wa9KnDewgBNFR9dKvVaB2fgnMQ2lAG3TSNZ+0EikuA+FdieYqZV3Zem84YYzax/vY3jw75wu9pffIsiEOcDlyUVsQRoyMUyvKSom065wHrIBkxQnsZlpd08ODYPd0TOw165AKqP2UmTG/jXo0xZls2Xhbm0XHLhb0Mhadx8k1Uldh5ntjrM9qp5r3huG+K6+lBdBqUDPD5vjFU5eLTbJ6y/AHt1svMjTdta22MuVE2Xr3lonx05Bqe76O8iEsCzmkv6PWauMsm41U5jL1CE4N+vvsVUq0c01qL0H6C1L3I3G8sOBpjbqitHyzm0THy7gF88jhJ7Vto2IeuetPcW+XJjRgr3iuRi8T4JKfHzu74bo0xZhu2fv6XizI3PovwJGUxSZJdxGdVWbQYtfNWmV7zrN0aRxSRquct7k20/C4Mv3xD/xvGGNNnsLfHuSgzx+bJ0rOE9hkiUyRZwCeuU0OyIn1b452Pq+CbZHRSh14gLJ1hf/t1Zg62dnSXxhizA37gK6cmI/fcqnz8wHka8+dQvQJ6lNrQHlQFYlldGGVNy4beKrFroz7bUqXwJGmLMryDxu8RWs8xO36JuRG1Z47GmP+lwQMkwNRU5H4RFh+4xmO3vcFXH/0dZXsJn9ZIa/Wqx7QH5yIinf1ylPWDo4A4xbkqenrfojZ0haL1JzT8BIk/4jvH3mbiQCA/qUxNbqf5tTHGfGYDZn+vo9eshxRnXwAAALtJREFU+8uOO0aPojIBch/p8HGkPEQobyfGYbzXNdNEdagqIk18chHVC4Tib0TewvNnTn/xam8OSwI3xtwkOw+QcD2Adc9b73+vQcYhXLyDUu9E/GHSZBTxDaJmAGhs4uICoZyB+AGlTEOcxV+7zMzrrV4fW2OMuck+W4Bcrb8Rd34u4fCRhI9Dxp7EsdC5xgfFF8rwcOA/RwK5hF4tSAuMxpjPkd0NkP16W3BYWfJssjPu/LagaIz5nPoUBSp4D1AF9yMAAAAASUVORK5CYII=)"]},{"cell_type":"markdown","metadata":{"id":"MhgkQYQiEvZt"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"WJJzt3RWhEc6"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"26qXWhCYhHAt"},"source":["# Getting started with LangTest on John Snow Labs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oGIyE43uhTxH"},"outputs":[],"source":["!pip install \"langtest\""]},{"cell_type":"markdown","metadata":{"id":"yR6kjOaiheKN"},"source":["# DataAugmenter and its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["yaml_content = \"\"\"\n","parameters:\n"," type: proportion\n"," style: new\n","tests:\n"," robustness:\n"," uppercase:\n"," max_proportion: 0.2\n"," lowercase:\n"," max_proportion: 0.2\n","\n","\"\"\"\n","\n","with open(\"config.yaml\", \"w\") as f:\n"," f.write(yaml_content)"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1405,"status":"ok","timestamp":1692343652196,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"lTzSJpMlhgq5"},"outputs":[],"source":["from langtest.augmentation import DataAugmenter\n","from langtest.tasks.task import TaskManager\n","\n","data_augmenter = DataAugmenter(\n"," task=TaskManager(\"ner\"),\n"," config=\"config.yaml\",\n",")"]},{"cell_type":"markdown","metadata":{"id":"sBcZjwJBhkOw"},"source":["The instance of the `Augmenter` class from the `langtest.augmentation.augmenter` module is to perform the Data augmentation for different tasks from langtest. \n","In this specific instance, the `Augmenter` object is created with the following parameters:\n","\n","
\n","\n","| Parameter | Description |\n","| - | - |\n","| **task** | Task for which the model is to be evaluated (text-classification or ner) |\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","By creating an instance of the `Augmenter` class, you can utilize its methods and functionalities to perform data augmentation on different tasks from langtest specific to the Named Entity Recognition task. The `config.yaml` file contains the specific configuration settings for the tests to be performed, such as the type of augmentation and the maximum proportion of augmentation for different test cases.\n","\n","Overall, the `augment` object represents an instance of the `Augmenter` class that can be used to conduct Data augmentation for the Named Entity Recognition task based on the provided configuration.\n","\n","
\n","
"]},{"cell_type":"markdown","metadata":{"id":"I21Jmq79jgC6"},"source":["#### Load Train and Test CoNLL"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 50519 (49K) [text/plain]\n","Saving to: ‘sample.conll’\n","\n","sample.conll 100%[===================>] 49.33K --.-KB/s in 0.006s \n","\n","2023-08-18 07:27:31 (7.50 MB/s) - ‘sample.conll’ saved [50519/50519]\n","\n","--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 827443 (808K) [text/plain]\n","Saving to: ‘conll03.conll’\n","\n","conll03.conll 100%[===================>] 808.05K --.-KB/s in 0.03s \n","\n","2023-08-18 07:27:31 (30.1 MB/s) - ‘conll03.conll’ saved [827443/827443]\n","\n"]}],"source":["# Load test CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","\n","# Load train CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll"]},{"cell_type":"markdown","metadata":{},"source":["### Augmenting with train data"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.augment(data={\n"," 'data_source': 'conll03.conll'\n","})"]},{"cell_type":"markdown","metadata":{},"source":["### Save the augmentated dataset "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.save(\"augmented.conll\")"]},{"cell_type":"markdown","metadata":{"id":"YPXIxv9D_fR7"},"source":["Essentially it applies perturbations to the input data based on the recommendations from the harness reports. Then this augmented_dataset is used to retrain the original model so as to make the model more robust and improve its performance."]}],"metadata":{"colab":{"machine_shape":"hm","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 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From 95f12d334cfd7c2d7f012bdfb7c085374b23ebe9 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Mon, 13 May 2024 17:50:58 +0530 Subject: [PATCH 61/69] updated the description in nb --- demo/tutorials/misc/Data_Augmenter_Notebook.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb b/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb index 05ac3ce3a..02dc4438a 100644 --- a/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb +++ b/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb @@ -1 +1 @@ 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)"]},{"cell_type":"markdown","metadata":{"id":"MhgkQYQiEvZt"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"WJJzt3RWhEc6"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"26qXWhCYhHAt"},"source":["# Getting started with LangTest on John Snow Labs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oGIyE43uhTxH"},"outputs":[],"source":["!pip install \"langtest\""]},{"cell_type":"markdown","metadata":{"id":"yR6kjOaiheKN"},"source":["# DataAugmenter and its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["yaml_content = \"\"\"\n","parameters:\n"," type: proportion\n"," style: new\n","tests:\n"," robustness:\n"," uppercase:\n"," max_proportion: 0.2\n"," lowercase:\n"," max_proportion: 0.2\n","\n","\"\"\"\n","\n","with open(\"config.yaml\", \"w\") as f:\n"," f.write(yaml_content)"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1405,"status":"ok","timestamp":1692343652196,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"lTzSJpMlhgq5"},"outputs":[],"source":["from langtest.augmentation import DataAugmenter\n","from langtest.tasks.task import TaskManager\n","\n","data_augmenter = DataAugmenter(\n"," task=TaskManager(\"ner\"),\n"," config=\"config.yaml\",\n",")"]},{"cell_type":"markdown","metadata":{"id":"sBcZjwJBhkOw"},"source":["The instance of the `Augmenter` class from the `langtest.augmentation.augmenter` module is to perform the Data augmentation for different tasks from langtest. \n","In this specific instance, the `Augmenter` object is created with the following parameters:\n","\n","
\n","\n","| Parameter | Description |\n","| - | - |\n","| **task** | Task for which the model is to be evaluated (text-classification or ner) |\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","By creating an instance of the `Augmenter` class, you can utilize its methods and functionalities to perform data augmentation on different tasks from langtest specific to the Named Entity Recognition task. The `config.yaml` file contains the specific configuration settings for the tests to be performed, such as the type of augmentation and the maximum proportion of augmentation for different test cases.\n","\n","Overall, the `augment` object represents an instance of the `Augmenter` class that can be used to conduct Data augmentation for the Named Entity Recognition task based on the provided configuration.\n","\n","
\n","
"]},{"cell_type":"markdown","metadata":{"id":"I21Jmq79jgC6"},"source":["#### Load Train and Test CoNLL"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 50519 (49K) [text/plain]\n","Saving to: ‘sample.conll’\n","\n","sample.conll 100%[===================>] 49.33K --.-KB/s in 0.006s \n","\n","2023-08-18 07:27:31 (7.50 MB/s) - ‘sample.conll’ saved [50519/50519]\n","\n","--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 827443 (808K) [text/plain]\n","Saving to: ‘conll03.conll’\n","\n","conll03.conll 100%[===================>] 808.05K --.-KB/s in 0.03s \n","\n","2023-08-18 07:27:31 (30.1 MB/s) - ‘conll03.conll’ saved [827443/827443]\n","\n"]}],"source":["# Load test CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","\n","# Load train CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll"]},{"cell_type":"markdown","metadata":{},"source":["### Augmenting with train data"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.augment(data={\n"," 'data_source': 'conll03.conll'\n","})"]},{"cell_type":"markdown","metadata":{},"source":["### Save the augmentated dataset "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.save(\"augmented.conll\")"]},{"cell_type":"markdown","metadata":{"id":"YPXIxv9D_fR7"},"source":["Essentially it applies perturbations to the input data based on the recommendations from the harness reports. Then this augmented_dataset is used to retrain the original model so as to make the model more robust and improve its performance."]}],"metadata":{"colab":{"machine_shape":"hm","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 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lDfJYe4yRJ7NprP/Gwp/V3hKh86cyKtqu51zJPv9DosSPAYO5JnkRnRw/73KEps+aUztx/O5NKinbTNzXl+5QPcbOo8ERUq2iSJIz3P8n5Nf3DO3176kOXKLPstxOSJNEvPzHQW66Fi9ysb9zmSG6gcLNhj/QDgeN7Ad5wVf6oVquMAMe2b0/23XbbliePHv3eFqE80hw3/y5oSzoO3U7EeJhFqyrU7BaBa55ra15a85Mk01/D6embpRNz/LgZmanl3uDmhsljnQpzrJWMMxq/CRUgMpxvsqh+jO/V/wcS1fAsJu5dRnbychLZf0rypqDDGlOJ5PNwdOMQS57bQ6nnNaR1cPqwrJ8fSMw8/Rncy+ApwgjoPujAbDuez0RMVLHbvdhNJjQeG3l2TOjrX//9pyuVe/+NWe0t7lZkjDTvvxZt4sFcbU9w2f7El39vhJvfNJinNLbR1ZG+uUXrwW6Xb6dWLE+SRLfsWhsNHj0yuH7Dp1bLtvCaRwivuA4WQBY/4jricOhasn/m2vt2fPnL6QFg+HSlnaEh9KuP9i+9Juu5YSty5XUbfCnmPLJN9nuWfSPL0scrleRwXhkp77dS2bQiwy/11FJVVVOxrdsye+3rP7Xz9a998UheZm7higy9/LrruQp0BdssAj3yCPbPlcq926vV3j1JktRnS2vISmURHURzb7XguIuJBpzs4Ne/dmRPMXPtqvN43xddtDtNkuRYs33ZZZt7zz+/foUZ860qputVATz69KEXLxh8ZvDobhsbmz9fe3rWbt2u16x3+XnB5rNBRrZW/cA1lU8+GNGzE5ITM9kyK5UkeuihRQPr19+76pFtevl118urcJaSe2VrW6scuZb0Wat86tFqNT5QqeT9VSr3l2H0cjMbaNJnKqbmCvcc2779vY91GqvOwou3bpPl11TMqIKuV0313oOPVe/aOXX/+8uZ1i6Rbb6Y9cWEVc2iikZZ+OTer3/t93af+so0X/fMnQ3yvj2X4H4NaUMRMdz/jtsvqrP52R2E6ABuq0nTAcRfxyef+wrHV00fjnMmj7Fbffx/kTpRGOWkKm5Riy+IgkzJUJstpqYaTpYUJ4f7nAWq1buOAPedar9WDF2HHzvSdy6NkNImQU50FiVJol/9av+yhfHRm116flHcLgcGkOZNEEAEcVdcUonCgbLKX1+74dN/Ua0e250kSZ0OaB9RALFQvmBwwVvUone523rRkN/iWkjiwm9GpWg7LL4HfusrkEuYW7dlG5Tojzx4DUHVzUTiUW003l+tLvxLM26UEL1PsHUQehGseY754pPRPhi9p1rt2wIc60DqjBhfkUhcPU9HXXbttYMXv+51Q8/kNHZUVydsmzcvW+we/YEIl6q4oYCLikd/0//9F38XLlhe6gn/HuRmcVla1CzNRxZXNfl3HvE3kl2wqVJJdnZikle94Y8HsrGxDaUe/SWMG9xYIKoTGEkeiqcaiR5w2Oos+KvLLttchXqvubwHid6q5PSpuEnQ2C3aWakkV7WPmSSJfvUbFwyW0ujDbtnNiqSIqASNStjDwE3ttFUqj0Rp2LU8ePRRd7+6SZO6mmsoq/EeYBYMsg1z5cVWuYFSOSIdM5BDYE8CUPf9SGMvImuwFOLyJdjoCrj7mbkZeCMs291PI1pNVoTqiB7ETx6j96U6dv4xJKQgkGXzwS7jwgMPkST1001TnL4e5GScczvfRJyWLekcO2m8k/yfJFqtXrA6RPGnIPrP4De4eb+54Vkzxq+BZ3XcU8AjsJUov68S3Zux4M1ffGpJOZfiOp9MMeWxpPZOJXwUZL27q2f1vN+sgWcNwMuOvxENH69U7nvNuBqdaU01KEgZJ0aIVUOs7ksz+A2Nev4Q/Grce90LWpv9muFuKyF8xCj/1k03fXL+bOIR43qtbm7H3a3wSkPLbCD9ov7Rr1YHr9iya+2kJYc7I4rE0JCiGmHEOLEEjZQwX+q22qV0r4j+O5ylbpm25iWPrQTvF5O3u0QfzbKB1ZP7r1TuXRzX7UMq0cfBf9VhgWOYNcav43if7ubmy8F/TSW+5/zz7feGFv70sKg+JSKG5/RhRSygyKpG44LBibdNYpr5MlFdKSqtawORO5dWKpsXTKRvm6mzGMIyEYnHx4AyeE1cpkioM6KIvT4rJIly/3f6gdcXy6AoIjtI64dJXHnx+SHcniCKR4EU95WIrJ05x7oN0wljSaLjtsK0VKHUs5YsNZAU9ypmx3j+sjruu4ii44hAWu8lKr2Z2tjVrL0tym2ns4+rzXecHObzI8aPX9zb1HmpVC9YnRE2icrNbul890wR0yYrLbJFtJ25upu6W+yZXy4e/vC8kcbNUyWacS++uhuOrBb0P7r7cstSLVxammcESB5bKK7uZu7Zmgzf+NBDixbkc+i1PI7eQUxx1KwRu8htKuH95o1lZinuZjjmbX2Cq3umjs8XLb3rByd1PcwmaPv7I0L2zyI6MjHeFXAzRG6MNHzugqGhjZXKp9aQd2rkJocpfTcaYybjBUscxNUtU7N0tbr/IcgVbhYVvNha8yKKgONq1oiRaL2WSu+f2HuirtHHReTd7tni/HwzBVcBXFAR1bbzUMSa46+QEH9w4dDQ73iWPSOqRxAMseJ6ZIjo/FJJV7aGK87RwnJ3W+qeX5e2/QfNGmsLm2lrPlJdhtsCt2J/DNEA5nvghT0zX49JmCsnTb1+MaXyGiw1oEaWfoOFHM+LSVyfYjwOHMctIksHiEpXMbCvb+blpAtMJ4s1+cLi564h6vkAWTqAqqL6NHbyAY4+MAoYFu3A/BmcCDMQ1hJKH+NY/MbChpnHSs6Clok7zCgl/ngwz444x8JtK+snI0kSrVQ2rXDCx1R0vecXILeL5a/nVELphIjsNfc9IcRDImEiE/RMRWWxEG2+9nX3XXLyZKaTw2HGz0noBe/L/1VUo1SQnKG17SqCmmdpFHpeE+L0LUmSqKnXJ3QoqHtWBrnULFuGmZL3aaKKeMs+JCKIiLplkWe2LEjpjmp14eBkp087kiSxSgUT9+2CPi46yd6UF0lWz7I1IcT/u0v0j9dtuO/Prq3c9+bXfnXJsi1b1kaTmWSppOZNHWe80ImD+EoRvcIsNQRVVUSDFT/bhIQrcfWsHrn7r61ff+/VkOhll23uXV8Z/AOV8KtZNtYLFo2fN2IaolGVsB9nt4TosGioC0W/goJFWVbrDaXeD6Csc2cvIupe3C3uphppBs0QGBLy1Etcf8GzbAGeL4ZXVLMy1aAeqOQ25MSqVbRaXdiL+s+6Zf15VpxAca+4yN9Xq0n6Q800ShKF65RM14MMgqRE8X5UHmf32nSciVn9ScZGnyaKQQKIVuixaSs2FCgW4ZMyJZayaPEyNn1rBfftXcnmZ9fw2b03sOQ7mwjRf8fSy9EIgj6O1d/LnWt35IxPjLtW7SPLPkb5vL2okku5cimBv+Wz+/8rn917Awt3D0JVT8UoO8dBdsT0XChx1yLwfE6QnKtyTKeBiT5yz62CrrlDRl+8WQjXFA/nuKoooiaqO71R36QavknGaCb1derhXaJhvVsWk8cwqVlmqqV+Se0DIZTeZ3gqjk728I8nZmrY75buMOe4qi4vJKeBPPOkuZdHZo35SrjuoccW/XUkmRVse1IuRe52EpW6oI+aNQ4gUtYQXeKWXTJZzc+7tyvAlkFy5NRe4Rf3Zb7gc0HjNe4sds90vB6ooI5hWcMQ6ROJ3i6kb45i/+bCRcf/qlod+AJwqOmpbzTESrGk3kZ38yxwN5HIVGSve7bTzU5I0NWIrMOy/lawQ26nVonVqN8CyWPnnffpimjp7WluP8sZjjuCGnAo8+xz5tnfSxSOq9sKcf6tiLzV3fpaHmGP0sbYAkF/CU+HNET1jCxu7w+4qDlfCfDahs0v9ZTWuhvuaZt06nlMs8vP33LL5t4vfvH5WrWKXX2j9pbSsAo3xX2cRvdsGPWvz3wXT4OzYqcb4WX7FuPhKtJ6nKuxjd00xiZ6qe+6aIRNzz6I6M1kYyC6CgmXksie6SvxCGCgcjla2gyhmTgQgffhtpigfWQpwGG88RUyPs6RVROl6MSVIzzEon0fpjzvD2iMrSgkXSPSd5Lpmyj1PsqSpV9G9lQ5fGR/EfIwTbmzM1GxN26EJOETu04ul2dH3+S/IhHuhoQzn37PDAKf+NWxR39/Tc/TZ9zPHKAV4tPGpAQbPHpk0CX+JfD5tN9qriYiJ9wb/3HDhmOPNjfv2rX20JEXXzyo5veAXOHuxUPratYwDfE1sTQuMbfc09tWetidIutEdpqnH80auj2ObbQRxgaiLHqnavR+t6y/RbXg5mgUrQhZulhdzCfFIgKIYwh1N/usRX5P5DIE9ahhsiYS+SOQi/OiGQV7dVPQxYJeDDyZJFPDh5oowmSoVuVLnjUGRMNHRaI+LyQ9mhlJuRqf21CFPjeviMrlaPn69Rs+/alq9dhjlQo0GuDixaJtE9ITTTQC829CfaNQ3yk6r4bbYkPuFA3vxrK+1jUS3DMQW1epbF7gkv0i7oMTcyDERMOwe/qpejn77BNfPj5S/HCgUhnYax56VUu3uzVyVb4ZDKa6yiwbVbeaIHFz3twzcF9dqfzU/GolGSZJrFTZNGDua5quxXH2KCi5mr36e99rLAP2QWKa3dcHvpKiDB5Cs97CHjLfe0axn2cjfiRibPrWKuKe1aR1I4pr1Eef4OjQMZKLWiXDAHTvw2SNEZBeNJSx7A3A508dD6n9aLSu+D9/EIpsXxr1lHweTiD+jwhD42M2+22mG76w6i9Z8u06qncRxVcDZRpjIKEfsVuReAORfpNFS/8W+/W/hOTI5MIas3fStIjPaSharqzE5f0CH0T0g4h/UNo+p9NG9QOi9gF3W3c6FJ17FGxSvJYSLnbzy3MnRpukpaqI/7Xasceq1evG4yIvumh3uviCC3YiPCAhGqG4PXMV1k1hIHO7HogmhDMB4KYhOu6SbQr0fimOXzherRwd/cbDJw6JN+7DssdEI9zb46QwdwZClg20r/Mz3qNDblPXrZbJPVE2dLBaPToK3x95fWXom5h/yt1TL9TUNptqZMgrZjNbuap9dHRkJPoTJ/tdYK+GWIubfeI5NhklmbpZn3t2q0rPPSkL3ghAb/uuzZNonoupB7sbjldh5ESlcnQUjh5Q5L+CPENbFXvH86ElLDUdW6caX+JmOm4eaaq41tiRxvqnN13ZZI5JEat5/DCBexxLc2bbJMrVzfpBBtzTWq5mA1DYFcNSiBZX8pU71Sxbi2XL3QxcwN3cyRMn3Ey1NKAlXdOkO8p8qbstd2tZs91NPfUdUDsx1ck3C5ypCJO4cv93yki4nLS+vAinOU4WHodKEaeZaDOPmedX78PZQVTKGZzZhsK5MzM8HSUdO0ha309aP0BaP0jWOIGIUe6NCAFCWM28+R/B5HMsfnbdxFqStOIan/+fX6KR3oll7ydLdxL1KFFJMQNPe0nTDcTzPkKJTWzad3F+bMtkMdFJMytPdfHMFXMgSorIqED+cUZo+0xoU7RpfSb9PuowKh3X3v7hYrKKXbzv64peJyrz80IWkjNJF3PLhh17II+N22btQc4PPLA7bbhvxX1IhOYDhLtoljV6Bb8cvJ/2cnCOiahmWX3Ig26tVr9br1aTwsaTWLX6vhMmfFk1dApk70uRPjWxKdIjmCg1cftiFA0drFQo+kvSJEksy6wqovtVWyFN7m6ImogOMkskSWK33PJ8bfsjd/1pGuQNZul/EtHdGnpG8WAgaev9InnxCnE1y2K37OJI40/Bomva+2wG0DuF9CiyY/vWux6qVpO0SX+lgp1/vu53T3eIaJ2mKNw80r2XNLrW8pTGCVCNMOVvH3voPUNF8HdxbP7/9q13PYbzpIQSTAjeFVWVsjsHRQPgzegzk1CanyKrxvcN4ToJIXYc1Qjwb6roweZS9OY+X+DSSmWccV+C+4LcOQOCpqLhmEn29Wrl+8OTVwSdHs2XPGcnQY6MDRDF16MaUeqBsZM7iE7sbDk/ig9AIinIA2SZkaVQ6lnOWHrD9J27FXRuh3Ataf3nSMd+lpPRzxHkZ2nUr4lUAr8AACAASURBVOXkS/8HIjuAlNEf9FMq3Uyp9//js/tvnVJkNxEjuT5l6JUHOLzyM8ThtaT1X6Y+9nlK8UE0GGZG/eR8gt5KpA+y6G2Xw8ZxJjnNu8QnqduT2y2IuYGnhtfBUnJ5tPPH2769rQ0pWNGWVPxUl3ASPefAf9SxSyNCfDWiJmBN+5yoIqqHTfwAdPbC+1jPQbf0cBFnaOMrO4orooOO9I+rn+MQBEZcs1pnlVYONetHTiyI45GgEaRtFq6m1wIDHcnwY3n17ok9RlGoC+SFSGWCGwiE0yrc25yHbzx858Ht1aGN4v4rno19VFQeEo0Oi2hK4RgaL3snglmmDstd+DCjcVSYGZjw2hJBjCPFSBPu48sue76myAtISPPzLc5B8nMQZRVu88enq/g2S8F9GtNOPoaITPrdEcFAyiqyF3dEirAmwRR6BVlRrWJr1xLltlyMgkE6uh2V/VLEznrWKLv5RbCkH8Al/KxoZDhWOHNURA+QsTe/dKeTauhn96wkYvREK/BsXe5gQlGG8f71fGbPGyd8Fu99I5959k14I8ZtBFFDxBC/iS27TnEfSUqqdY6uHeWui0Z438tP8K5XHuLoXzzO0OGP4GPvIEv/BNE6acOwdDUiG1my7JKOITxNafKOl9c48ud/g/a9i3r9DtLGnxLFJ9AI6jXQsJhS+WMs3bOqGZI0UcX2JuMZt8xPbY+jzSvj1BCpC1ITpCZyZh+EGlBDfHoJshN959SLPSFPPHZncOJdVgwucjzKQsfAb0isp+fQMHBMVWkvC+wO4tILEkNhMyzGbf2djjKvNfdoUz+104RMYbyGTX64kiTRRqTmkp9H03c/V2+gavWF3SLH/ou4v8fTsd8F+WNURmj6porxRFDPUhC9JoR0DWitKfw0YwUACFNfpM30wsyzurTJSs1XiLur4QvcPPY2ppFL9lkaEXUMiG97kRwZZw5FzwV6Ef8ndxsZZ+aOmmW94K+47JYl5YGBwWU4a1pFkQ1RnkD0ADC+sJ1GpeVZyJYmSaK4r83PurjOKlia7g2hdPA0pr5F55nGQTbVV/cKyCCWKY0xQ/RWouiPCD2fm/iJ/yj/lN6PWx9uSqMGGl/B96KVM4fYOJTHtPOyC9uMw2v2kcUfAdtCFEd5LCSXIvqOZsjYVPrb7J53Lh3lhVXbKcfvx+obCeEQGnImKXI5pu/gwgMxietEFRumMsJTqN2ipDmDo+ZCzdXqLlZ3L75ltm3qAjXwus2kBHSi7xxGII0/jrnEGkkeqNuyXTVvXJd6o6EdCysAVKuYIB0YqBgaVCZyiVlh5uq92Sn3mA06BsmfEZqmgSStVF44uGHDi19qjI1+yN3vEuFA4T0eH89xVKLY1K91UqWI5/TCwTPZMz89/cW3FDpsXso8br2AJrhL0jRk07zkmpCxcRW6SamBO+UU9uCyVzQycTcH3LNYkRXn/yCdLxGXiJb6MENENEsbdXWextLv5jZJDMHcWCoNX/zEE6v6EFbiha3U3VTDCGL/dGYLuZ3FszLOYPQNSGFL1qBEpQFgGSJLO390MSGKgNzuV4oW4375zI4agU5l9NvV96MrhsjsHiwbHY+Qc7uVe3f1zZgt01L/jRUHRvDz/gRr3IOEEUQhrZcpla9mNFsGc/AEpSmIWj2gGJh625uh+aKcZdudVHBcT9MGOUfPcLWKVSpphER9orlHeFzykkLddclVhZz28ZqGDr2lkk3jUUy0Urkwdk72NVlqy/nh6m41F6nLhBqJZ4hxlTLMvN8s0KJzbkX05hxVKsnw0MJlWwaODcVBo4+5Wb9IW9FVHHHWgMduTRUcaIsBPRXG59llvOakC3VEwFrsMZckJY4yZszbdbfzRbStXsr4CGnJ5TBBtnor9lFxjBAPYukCsNeqKJm4iUQK2d5K5ej+rdsu2Ccan3DL+t1dRWxQRFaMjIwckuCL3VtXwtyPoZxe9kzz/Jrc8UxtkPfuvRT8NWSN3K5kthfP9mAetdJrOw3tA2i4FKxMo94P0ev4+D99ie+fGMkXy/r26dHRYq5P80f7dhNK64qCFSuQsJIkyVMaT/UCuf76lOQRWPgzX6As/waXDQgpqsvRxjIS2TdRxT6ddMKNG4tDPBWRmkNNoO5IzZGaS/E5jTbqNReti4fTu4RzJEHmapSWaa7SKC0lU3Nj4xFROdQ+Ty0Hji2uYx09dEkCjdLIgIsvNjOgXfoUHDuheYXjlq3wNJhS59PPOM3whNPs/9Q4VQBztZqkg0d3W+S6WzU6RFtgeZ6P7gAxPiGb5bTombCvkJfTcx8SpD6+zEfBdTVEajbVeVOcSxF9wEpErKm+53lNggjHwWrm2T+4pXVENF9SRUxF+qGxGPe1ZllhRwSQJ5MkMXU9KKJDCCaCOl520VeGYKtVS3mWkGOiQS2r71Orn17udfPkzxYRNxKXI/KMpRouG3n+lb+Enn8bPaXpP0HuIpSeyV9KppTii+ntWwnbjLMNoHbJFwVzz71sQeaf4ohJqBiMHaFeP4Bqmj/O3otob37Krb9nhsjNTWuKmEEuR07Rfjrxu6nPjpF7XSU79xLkxLp/UKmgSZKk69dvWolk42EW446/nA8edOGo5OEhxc+Cu6mIDqpwCbBzciB1ksD6DaxRiRabp4wvN5BXuUnF0n2GRHqGrOicmmDPoP9OZdSa8zxRwk40l9qzMnh5siMwd1n5CYR+0dzHebr0tDQANHegaOruB1TCCcda0qKTB4wrVyVJ8qVOmkClcm+fua+T9vvZx42jB8BHXMMeNfYDa8wzlTy4e74RLhVhZV60Q3C31Mi+AZAGORwsPYSzGjBRAdFV7vYDFaWotI5IhEj69Wr1fSfOrIiwnNnNkiTKsn/fT+Pk68kaoAFE9yAndwDw/JJa5wML5jfwjv301J9Gw7p8jRlbidvFcN0cxDrnWWb5v2ago62c71nWg4t+2vAf1HKeZNY+SR1Y48RMjqntAm2MXyH1fGU6y4qU2BwtBaa1TSe1WxARyzNWbAYJshN9p4/JD0ClklCpJLr1Eb9LVPvNsjw+zwsmaKkiPEua7XMNI7j0uuQ5u7ntSGNxfxvwp8UImveLwoVRaiOvV2WBu1vTGC+CqZaGU8+eELefZ8JbY/bnNc0V4mwtKGf2LCVarS5a7mK3O/5MpXL/1mr1jmm88HDllQN9mcstkqYrEJ9EsIDotwS5zJuhQPlmbb+zZsbE2VEJqWm6C5FDIEvHexHUrAGU3vjwwwvur1SS/fnSxq2eTLhRJVpheXC7FhRansrOznovwyHzuro+jdvaptfZ3frEea2jA4ghqoAcDsiTAFHmQ+bZXtFSxTyFzFXUVpl5LJKNu/TMGmTIGdZXPxsv9kZo7LuEnvJqxk6ChgjsSYLlDq0Z6ywmyvFVIyx69h+Ie9/C2EvzcesnlK/ip1Z8gUsPjHB62eQth9GSvQO4ryJLc6btNkw9O3L65/eDXlwGsbQo2yajICMwOdVwfIXA5k0jrfY0T4umpRTSmqOWhzugrcfcaQmUxcbJAmZ72y0X1CSawYvdib7ZY+3aJB4cXHS1iS/1NN3nrieiKMRbt/pKUb9DVG81y3TcvuS5ucXhYObp0yX1Iy6lRxG/Ec8lcgTFUtMQ3bi+cu//1hjr+X96eg4VMWoLyyYnbw3S83bL0phchcpVJtHIspMHAjxs8PNeLHrkM7C8TpjgZsgdSLTbICevHHk6aB07OyRJYus33Ls60vPuzGxsmVntmfWVz2zH7B9V2Z8GhqJMLAvSGzJfaeLvwv1N7lY4UYq5QcnS2qiKPezwC+30nO55tJ+/4+oi+ywd+6ZoWGd56FbO7NxNlLUhkg/Coru3bHnhcJKQVqsXxnnNR/+ISRp5U5b1XMbVEO03sr+76crjI7t2ra0NHRv6Bwi34pTzQPJ0PrABsd7WlZKdwJE8E+aukfXXf/op1WjY0rQ/L4jhqwVZbtbIox60hFu2uyRHnzytk++E5vM203KsTSSee5Nl6XqcBagaGp2g0djG80PD8MDMYyWJkWxULNpO/eRhRPoRNczWMy9dyrZte1j0zkkHzeKhXvJ8GdffptSzgEbNiGIwHuPFVUdy73el5c2eaclZqkr2skvp6bmYRj1Pa/TsAMYhEtepSy6cUT1IrUsza2Py8ZM16RnahhgK0YTg3kk4i3qQuXTzU72m4VfE7TcJ0Ql1GTUhQhlAQtkss0lDGGAisr3k8QGIR8xH/0IlrMN1QdOp4DmTBJcPx3Hj1akt3HbttYxmLlep6O2epUvBtWlbaxaeyCz9XP1kOtRT1gjBcLS9HuRsMZVlZMW8hDNijNB8lGdPS5IkumULkWSsymx00N0jCdGlAusMUhOGg8mwo6mYlc19UDXEmRW1KNqcHqKKW/b5RoPDUezllg9b8NNw0sCkF4N7/gIJ/ldCuFHUV7lleYiNoG5ZJITbHR+8YHDwi1+r+rGgtVWWydtEdY2bjWsADiaqdcuyh+aVSzvzEKPd6QvbFz0j6BHwFYVwoUBuG3Mxx8zddo6OlIab8/a17faMWXZCkCKHXGKYGHcqKtXqI8k06uypZ2EqNkIyUzTARqCqLBlcisZXktbLedSF7CewO2dC15/aX5CIkTxygMVLHyOetzZP99OVqFxBkuxm0+3ka08V8OKZvo4iYHsjucpaqM6Lvr0Az94KelcRagRuJzC7H6rK4LLL0W/3k922k7suOjI1pKjoKxHj3r2XEOR3SRurwYxo3ijpS9tYYIcY6iRBTodpHDgaxtLM4xqSV0M5mzx4AcMhUzk9G+RpPC31uBzHKQs89zAOoDIghSrtZHnwdrPb3GZlInoos/pfBV48AZDFi/5eG/yChNJveFYvN1W+/CR8vov8RkDfCpK6WX9epqrlnRUXE1V1S78QGPt8Z4/zGbpG5Ix9lB26On0MDv5Ur6Gvxr0XUMtSy/3FROLaj0o/4uNOmMzSybdWKqqK2ZMe/F5ixnn9mUnAHc6jAcdeHHx84cKhTaLh4+QRNCYi6oJC1gv6JhWtAKPu3gfEZqZ5EXsHxDSUEOdxs9q9Dz74nuMA1eojkbL7oIscQFg5ZXwRUwnHzPyfb7nl+RrkNuqr3pDuK9X0gGi0sjBUNZlwbj7FasC2fP8zWXvHARRLI5yL2LT3ZngO/Fe1df81K+Y3289C9DLDWIPIxUVoD2SN3YTy1NUBZ0Jyfcpn9j6IZe/GHUKIsfQm4E8mO+EQYsT72D04zIW/njK6OyJ6Wxn2LiCTdZTC67HoTbgtAIworuPp54nqW7lwRR+mb0PCrdT9m2za8yD+rd2kpUMMMMxL56WE28qk+xZz395LifRdIFdjmVEqK86TpKUt7H5FSlIwtdmZqjo/sHWLLcJriMbkthhMMHVTkyh32bppvq1gPqKFimJKsX+zPwXIZggU74RZPjdJkthrX7u5TMziwnsMnqdw5fbrdkkjV/5D6BnNvPG5gD7ctpzB0A03fOIPGo3yAo3i2y2tNyWaXDV3U3fpQ9wQz+v3FZKPoIiqmttXAvLhavX7w5XKwl6bUUL/yUA+v5+YX4rDxS5mZm0vnPwFpLl0MEntzf/Ns0tCrJ6lzxD8w4svGHzm8IkXFnQebXbocGtYCKndfvvu9IknBv7kpZPyStHwW+T1N1NBiqfBcJMyeWFammuku+dZPSGU1PG9Da+//xtfP76nybSq1W122WVLDp/Xlz4jGq5xyyLaXroI6iIHVdnfnDOAN1yVnPhadeGOoGFDXui3FWCV2yzZL954uv2Y00I+x0paLxNKt1OK3zTrl3CWlUkb/eBQikcYe+kJDi87cdqLcIlvJ02PoNFg7qxhPZv2DY4vP49ofhvI5YSwGWSYWqNOiCKM+USlBZRKg2SNATzLmWpcTmmMfYGGf5yja0+waM9yovJrEF+KyFuJz9uAZ8fRxnFG/BiM1ElLfYQwSFxaSv1kwWR7FPchxkY/xNE1+5vnNlHgG1dX2yeu2e7MhcolTOCkZz7q4qPuPiomNXcZFfOamNda2/Lf3bzmxfb8t3w/cR91l9FsxjjITvTNHqVSvdexQciZFS4mxSdPe5O0CKlINcRDDat/eNEFA/8lL4TQujGvuebEIZEjv25p/ZOi4VirTmOzVqNT2NVM0BTHVCOTEB9yz/6vQPquavU9z7Q7AYq0RcPF2p+pjkGzraMoDMtN+ovtgbT15kvHf5dgrRTCTjjJeICqF7RIUQl4Fo9DVupRkFS1NKIarIitMRFJBTWcPG3O1fJ2HjKjoZRq6DnmWf2PLbLbtq8/+vBFF+1uuw/yfvL9i3Oc1eOpNK9JM60xyyIFuPLK4yPnzcs+hGXvFaI9QeNiPClSIL2Nkef0qqppKJ2wrLElqzdu+Ub1xR2txcEAEnvqqedruD2hWjohzb5a18c8G9sD9XEJrOn1D/A1MwMN7fsX9gd/cmysMTQ5rXLWEPL7BAHL+qifXEy9NrtPkzlqgLQxhPmjpx2ek7hy56uOoeEhQpQ7Yks9g3h6I9Rb9ImmqPQTQoWo52ZKpbcQ4lsJ0QbMLqZRGwSUuHcUZD+1l95Pze7k6CtypqZaJkQpUZybIhq1ftJ0JSJXEKI3EUpvRsONWHYJjbEBRCGeN4LZwzTGfpGjax5vJ7tDPcjJjHBm8axu5BWfFdP8T4H266gdtnVoN3OwZ7JBdqLvtKSvKBL0sKiWTaQPtzJ54QkDqSMyjPsQlu0Usb94tPrbDwM8MMkWXTwQtUrl/g+kfvKL6nabhJ5LgWW49UlegFVB6yI6jNgRS9OnTep/dnxo0WO33747bYZqnH9+ZN//QXZYNX7aMFQL35UEGo2TB0qlUsfsjgaMlDXeIRN0VDFERyRNR4AR1Z4draI2CrghOuI6Ntxxek6GNJSj/aj0mQYTXB1MpaSucqjt3Dvi8eoLB6+5ZvBOVasgvFajaK0QBtyZD152L7SWfC2WuiDH3bMhz+o7UR5UOfbQhmuxR5PEEhK9+sYoVQ0HBN1pmk2gJ5NakW43MaQqSUA0OhZC/DRCLG03mkjpsPjJ0eYSq0mSjFSrfLbuCx8LJreFKGxwD0vzXG0rjpVUJIwAx9zGnvEs+++qjYe2P/q+E52X+YVqlR0i4fEQlZY1tzuYalxv1EYeqX69FarTCpy/d6e7PR6intjVinPNXyBpdvJrPT3DwzOVmpsWlg0T9T4DVj4jI5ijBUNTRr/3GPN69p7u2i7jCPwVIaxFepSe82Cs9mpMHqdU3oPQh3kZiPHm85NnF0GooTJKo3GcNN2PNZ5ArMp7Xr13Qmrh86v3snTPHWR6IyLXEc9bBT6AWR9mEZiimiLRKBKOU39pH7XRv0PCF3jPq4YmO67yJ+uze2+g1LuZdGw5WTadwp3r6I3aX/Kq//W2ZFvFkkTs4986uQLxN6vPQV5b4eixzKvvW3teHmN1775V9ER/i9uaYvW0Dge6EfVAlj3N83922UwXr1K5v5yFk6s9s+UqMmDIAnWPwVLxMOyeHVHVg8C+SuXo6GzVmZtu+uT8kZFohUS+SmCxYX3iquJ+3NWPqLf6hElMJkn0tV/tX1YqlQbaOWFQVxdGouzY/k6LTV150yfnxyO6KgstVScGsiAWsrGDJ08Gi+Ppf69W33dicp+33bYlfv740Apx+jJrHRfU1cZKx77xjTtPmQPcZBqVyr19WQjLQ9YYNNEBy7yfQF4d3RkVYVjdh0APQe+havWOGsWSuW3ZNhEsXJGpz59MTzAZrlbv2teJhqtv3DQY123p1DeLpmPn6/6nvnjnuFzelOB27VobHTl+fJVYusKdpYL3g0YOI2I+BHJo3ryePQ8++JvHTzUHt922JT569IWVmUpvO90A3jN28B8e/A8d+kj06spPrw1ZiJvX7FTXa1b4410D1MMymqnFTWGoUXzP1G7/PxJljCF+75WHzogOgHt39SHzVhIKPpPKML3hEA1bTqO+gCjqwzxGPcI9ArW8iogWoTc+hDeGOLo2v36d1PymY2fZoX7Sl1biuhjxAdA+3CPUR3E5TqZH0Jf28Z6fG5qO3JzbbNqzgZ6+zaS1FTmX7Yj8DdKo/w090duS766oJ4nYJ58bXeaZ3+yEGMfOyktjBqpIJtX3ru3J04U2P7sGjf8WfNW0DNLdKPWAZzt41yt+YeoOE9G+/nG+ZOtLOjT0Xbv9dtL2dZFP19bTYgxJBBcW8/jdZimufK3safucSXWa/phKBW0vedUsk9XcNt3veYzf6fU78zEdeimqgrevTz15/NYa3zP1e/r05BELE49p+3WasI8Wc06SRHftIjp69EJtv4ZF37Ocg6nX9NTzOPGY2V2vU5Exi3VgZoWqwjY7Y+lxCj3NcJxpajlOe9wM+0zYv2CUrf4Vqkwc8+4ZUxJzbrP52Wso9W6mMbYan4FBaqRY+ijiv8Tzq4+TiG1+1hec9Nobxa0X1bP0oBpmmhJk+/f//P88kCSJsenZKwjRF4EFZOn0EmRpHmTpdt698vrZj9fK8ICm6jIXC4ZN7vfHbRGyHxXaM2pgbub63GFittWPN61dzAKniovsACFxZelzl1Cat5n62OXj3qGOfhkB1b1kY7/MC6/eTSJ27y7vS8NL17iEQU5Zx/HUUPfR1OZVhx/gRJKIsXnv2xG9H/N4gkNmAn1uxL2QNv6ad6+8bVYBsF100UUXp0CzWMUwaTact8fTuXJMKExrRqmnHymtgbtJ3PXoEDVTjoh7TfC647Uz/Yh4aipDw0O0ORDCL6AhHndZji9X10afA5aBUtjHZrn+bhdddNHFDMgZZNw4QTZ2pChZNFHymqzSZul84Cou/PU4AZLrJY0bHBHXE47XBK1LpnWh7XPKttcFr5tRH3Pbz7a7cxru/04ZYUPhYe6cqSPFtiyFzJ6d+ynqoosu/rUiZ5CH1p7A2UUUj+YS2jRhMyJKlsbEPeupp2uboVBHh847JioH1b2mntZUqam3fU7ZDjXB63h04OSreo/AxrwOx8n6G9FwMWld8WncP05RXUSOIeSOnblcg7aLLrr4V4vWUonC0+CdY+Pa4Q5ZuhbRm1m4u5ck0eR6SV+M4wOWlo5khLq518y9ZqH4tP/f3m7bniHHYi/tTUQsgTzfslS6sxhzyuJTEyGgYTcuh7r2xy666GKu0JLKgj5NOnaIEGkH70wbXHEvA/8WDVfkbnTX5OVSmzcW71NPjyleV3wio/S2Txtz1NTrkqbH5WR939G1jJK4suSpMpK9EwmvIa3TvnznFIgYuGHZDsbsBFw3RyENXXTRxb92FG5vMf7XoSNktpWoB5gpk4XcIQIr///27ifEruoO4Pj3d869972ZvsQYnTCRYEIYUpmFRBoGXdVAd13ZVpe1QWiKWVYLUkrvUIrYLooUq6YuFARtCy5aKaWbDLRKrS66KLY0dkwlZpKZMB3j+ObNfef+jov73sub/2/GSSPl94FhOMx973Bn8eOce3/n98P5H7L/vapgZR7d6RPS/O++xrRGuaROm1LGIJIUErQQ6fsJWlR/06IUuVxvNqY/Or7vWt7dGWvjXlz2CGW7AVvkcImAS66i5RvMjy2Sn7zpLWONMf8fVi4Vf/HPu3H+LYQM7ZSFiquu7tWHFCWtKaF4lVA8ztzs1W4CZh6jOzhDPSx/spdm0mg5XHSFYxnqaaaFoknQlk+GFubGaeYiSn4ugfuVQ++fILpniXo3ZTtZVeVj1ePRCN4r4v9AaJ3hyl0fbPsAvTHGbGDtXvr5f7+C9w91muC4zXfbUcnqBWX7t8TiKW6Nf+fd8dAfpPJzMeEIyUhzLoER5marPtj5SQnXM+MnYeTBYZyfIKs/g8a7KNsbTLpq/trwAq3mE8wee2GrrHhjjNmO6+Gv+3Lj7L++giQvEXWUUjcPkFW2tuLTgJbvoPpL2vIa82OLOZOdjhAb5CT2H/85cP5OvDyE84+AHKVsb/0cMaIkCSBTEB7mw7FLtno0xuymleEvzx2HH95LO/wY5Nuods4vbkkRgbQ2S2vpjzh+Ra35JqfuWVj3HGg3kD3z/ii++Bo++zqRE8Sy0TvJM8iczjtUH+Ty2GsrvtcYY3bB2kiUR8fBfxwn3fNzQjGBbljdp09nJQmQZAqySFieBvkLTt6mHS+RyiKxdJRxP94fBb5EZILa0CHay/XqxU/cOjjG7vPPuqLlr/mweQpWbuuNMWY3rB8gc1GeO/8NstrPCMVoFSQHLNsdY7Wa9KnDewgBNFR9dKvVaB2fgnMQ2lAG3TSNZ+0EikuA+FdieYqZV3Zem84YYzax/vY3jw75wu9pffIsiEOcDlyUVsQRoyMUyvKSom065wHrIBkxQnsZlpd08ODYPd0TOw165AKqP2UmTG/jXo0xZls2Xhbm0XHLhb0Mhadx8k1Uldh5ntjrM9qp5r3huG+K6+lBdBqUDPD5vjFU5eLTbJ6y/AHt1svMjTdta22MuVE2Xr3lonx05Bqe76O8iEsCzmkv6PWauMsm41U5jL1CE4N+vvsVUq0c01qL0H6C1L3I3G8sOBpjbqitHyzm0THy7gF88jhJ7Vto2IeuetPcW+XJjRgr3iuRi8T4JKfHzu74bo0xZhu2fv6XizI3PovwJGUxSZJdxGdVWbQYtfNWmV7zrN0aRxSRquct7k20/C4Mv3xD/xvGGNNnsLfHuSgzx+bJ0rOE9hkiUyRZwCeuU0OyIn1b452Pq+CbZHRSh14gLJ1hf/t1Zg62dnSXxhizA37gK6cmI/fcqnz8wHka8+dQvQJ6lNrQHlQFYlldGGVNy4beKrFroz7bUqXwJGmLMryDxu8RWs8xO36JuRG1Z47GmP+lwQMkwNRU5H4RFh+4xmO3vcFXH/0dZXsJn9ZIa/Wqx7QH5yIinf1ylPWDo4A4xbkqenrfojZ0haL1JzT8BIk/4jvH3mbiQCA/qUxNbqf5tTHGfGYDZn+vo9eshxRnXwAAALtJREFU+8uOO0aPojIBch/p8HGkPEQobyfGYbzXNdNEdagqIk18chHVC4Tib0TewvNnTn/xam8OSwI3xtwkOw+QcD2Adc9b73+vQcYhXLyDUu9E/GHSZBTxDaJmAGhs4uICoZyB+AGlTEOcxV+7zMzrrV4fW2OMuck+W4Bcrb8Rd34u4fCRhI9Dxp7EsdC5xgfFF8rwcOA/RwK5hF4tSAuMxpjPkd0NkP16W3BYWfJssjPu/LagaIz5nPoUBSp4D1AF9yMAAAAASUVORK5CYII=)"]},{"cell_type":"markdown","metadata":{"id":"MhgkQYQiEvZt"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"WJJzt3RWhEc6"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"26qXWhCYhHAt"},"source":["# Getting started with LangTest on John Snow Labs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oGIyE43uhTxH"},"outputs":[],"source":["!pip install \"langtest\""]},{"cell_type":"markdown","metadata":{"id":"yR6kjOaiheKN"},"source":["# DataAugmenter and its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["yaml_content = \"\"\"\n","parameters:\n"," type: proportion\n"," style: new\n","tests:\n"," robustness:\n"," uppercase:\n"," max_proportion: 0.2\n"," lowercase:\n"," max_proportion: 0.2\n","\n","\"\"\"\n","\n","with open(\"config.yaml\", \"w\") as f:\n"," f.write(yaml_content)"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1405,"status":"ok","timestamp":1692343652196,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"lTzSJpMlhgq5"},"outputs":[],"source":["from langtest.augmentation import DataAugmenter\n","from langtest.tasks.task import TaskManager\n","\n","data_augmenter = DataAugmenter(\n"," task=TaskManager(\"ner\"),\n"," config=\"config.yaml\",\n",")"]},{"cell_type":"markdown","metadata":{"id":"sBcZjwJBhkOw"},"source":["The instance of the `DataAugmenter` class from the `langtest.augmentation` module is to perform the Data augmentation for different tasks from langtest. \n","In this specific instance, the `DataAugmenter` object is created with the following parameters:\n","\n","
\n","\n","| Parameter | Description |\n","| - | - |\n","| **task** | Task for which the model is to be evaluated (text-classification or ner) |\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","By creating an instance of the `DataAugmenter` class, you can utilize its methods and functionalities to perform data augmentation on different tasks from langtest specific to the Named Entity Recognition task. The `config.yaml` file contains the specific configuration settings for the tests to be performed, such as the type of augmentation and the maximum proportion of augmentation for different test cases.\n","\n","Overall, the `data_augmenter` object represents an instance of the `DataAugmenter` class that can be used to conduct Data augmentation for the Named Entity Recognition task based on the provided configuration.\n","\n","
\n","
"]},{"cell_type":"markdown","metadata":{"id":"I21Jmq79jgC6"},"source":["#### Load Train and Test CoNLL"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 50519 (49K) [text/plain]\n","Saving to: ‘sample.conll’\n","\n","sample.conll 100%[===================>] 49.33K --.-KB/s in 0.006s \n","\n","2023-08-18 07:27:31 (7.50 MB/s) - ‘sample.conll’ saved [50519/50519]\n","\n","--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 827443 (808K) [text/plain]\n","Saving to: ‘conll03.conll’\n","\n","conll03.conll 100%[===================>] 808.05K --.-KB/s in 0.03s \n","\n","2023-08-18 07:27:31 (30.1 MB/s) - ‘conll03.conll’ saved [827443/827443]\n","\n"]}],"source":["# Load test CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","\n","# Load train CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll"]},{"cell_type":"markdown","metadata":{},"source":["### Augmenting with train data"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.augment(data={\n"," 'data_source': 'conll03.conll'\n","})"]},{"cell_type":"markdown","metadata":{},"source":["### Save the augmentated dataset "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.save(\"augmented.conll\")"]},{"cell_type":"markdown","metadata":{"id":"YPXIxv9D_fR7"},"source":["Essentially it applies perturbations to the input data based on the recommendations from the harness reports. Then this augmented_dataset is used to retrain the original model so as to make the model more robust and improve its performance."]}],"metadata":{"colab":{"machine_shape":"hm","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 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From 214ac2ffca2c419ad1e7354db6d2da6977d1e1d3 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Tue, 14 May 2024 12:33:19 +0530 Subject: [PATCH 62/69] updated: ner task on llm --- .../llm_notebooks/NER Casual LLM.ipynb | 543 +++++++++--------- 1 file changed, 261 insertions(+), 282 deletions(-) diff --git a/demo/tutorials/llm_notebooks/NER Casual LLM.ipynb b/demo/tutorials/llm_notebooks/NER Casual LLM.ipynb index b6e6c790d..76e126b62 100644 --- a/demo/tutorials/llm_notebooks/NER Casual LLM.ipynb +++ b/demo/tutorials/llm_notebooks/NER Casual LLM.ipynb @@ -94,48 +94,32 @@ ] }, { - "cell_type": "code", - "execution_count": 5, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# server prompt for the assistant to generate the response\n", - "system_prompt = [\n", - " {\n", - " \"role\": \"system\",\n", - " \"content\": \"Named Entity Recognition (NER) involves identifying and categorizing key information in text into predefined categories such as person names (PER) and organizations (ORG). Each identified entity in the input text is represented by a dictionary containing the keys 'entity', 'score', 'index', 'word', 'start', and 'end'. 'entity' denotes the category, 'score' the confidence level (0-1), 'index' the position of the entity's first word in the input, 'word' the actual entity text, and 'start' and 'end' the character positions in the input text. Your task is to analyze the text, classify entities and provide the entity details as requested.\"},\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"The sentence is: John is working at Google.\"\n", - " },\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"[{'entity': 'PER', 'score': 0.99, 'index': 1, 'word': 'John', 'start': 0, 'end': 4}, {'entity': 'ORG', 'score': 0.98, 'index': 5, 'word': 'Google', 'start': 19, 'end': 25}]\"},\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"The sentence is: Elon Musk founded SpaceX.\"\n", - " },\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"[{'entity': 'PER', 'score': 0.99, 'index': 1, 'word': 'Elon Musk', 'start': 0, 'end': 9}, {'entity': 'ORG', 'score': 0.97, 'index': 4, 'word': 'SpaceX', 'start': 18, 'end': 24}]\"},\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"The sentence is: Ada Lovelace is considered the first computer programmer.\"},\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"[{'entity': 'PER', 'score': 0.98, 'index': 1, 'word': 'Ada Lovelace', 'start': 0, 'end': 12}]\"},\n", - "]" + "# OpenAI Model Testing For NER\n", + "\n", + "In this section, we dive into testing of OpenAI models in NER task.\n", + "\n", + "LangTest supports robustness and accuracy tests for LLM testing for now." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# OpenAI Model Testing For NER\n", - "\n", - "In this section, we dive into testing of OpenAI models in NER task.\n", + "### Set environment for OpenAI" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", "\n", - "LangTest supports robustness and accuracy tests for LLM testing for now." + "os.environ[\"OPENAI_API_KEY\"] = \"\"\n" ] }, { @@ -157,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -167,36 +151,7 @@ "Test Configuration : \n", " {\n", " \"model_parameters\": {\n", - " \"system_prompt\": [\n", - " {\n", - " \"role\": \"system\",\n", - " \"content\": \"Named Entity Recognition (NER) involves identifying and categorizing key information in text into predefined categories such as person names (PER) and organizations (ORG). Each identified entity in the input text is represented by a dictionary containing the keys 'entity', 'score', 'index', 'word', 'start', and 'end'. 'entity' denotes the category, 'score' the confidence level (0-1), 'index' the position of the entity's first word in the input, 'word' the actual entity text, and 'start' and 'end' the character positions in the input text. Your task is to analyze the text, classify entities and provide the entity details as requested.\"\n", - " },\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"The sentence is: John is working at Google.\"\n", - " },\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"[{'entity': 'PER', 'score': 0.99, 'index': 1, 'word': 'John', 'start': 0, 'end': 4}, {'entity': 'ORG', 'score': 0.98, 'index': 5, 'word': 'Google', 'start': 19, 'end': 25}]\"\n", - " },\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"The sentence is: Elon Musk founded SpaceX.\"\n", - " },\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"[{'entity': 'PER', 'score': 0.99, 'index': 1, 'word': 'Elon Musk', 'start': 0, 'end': 9}, {'entity': 'ORG', 'score': 0.97, 'index': 4, 'word': 'SpaceX', 'start': 18, 'end': 24}]\"\n", - " },\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"The sentence is: Ada Lovelace is considered the first computer programmer.\"\n", - " },\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"[{'entity': 'PER', 'score': 0.98, 'index': 1, 'word': 'Ada Lovelace', 'start': 0, 'end': 12}]\"\n", - " }\n", - " ]\n", + " \"temperature\": 0\n", " },\n", " \"tests\": {\n", " \"defaults\": {\n", @@ -228,7 +183,7 @@ " },\n", " config={\n", " \"model_parameters\": {\n", - " \"system_prompt\": system_prompt,\n", + " \"temperature\": 0,\n", " },\n", " \"tests\": {\n", " \"defaults\": {\n", @@ -363,13 +318,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'model_parameters': {'temperature': 0},\n", + " 'tests': {'defaults': {'min_pass_rate': 1.0},\n", + " 'robustness': {'lowercase': {'min_pass_rate': 0.7}},\n", + " 'accuracy': {'min_f1_score': {'min_score': 0.7}}}}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "h.configure({\n", " \"model_parameters\": {\n", - " \"system_prompt\": system_prompt,\n", + " \"temperature\": 0,\n", " },\n", " \"tests\": {\n", " \"defaults\": {\n", @@ -435,24 +404,21 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Generating testcases...: 100%|██████████| 1/1 [00:00test_type\n", " original\n", " test_case\n", - " expected_result\n", " \n", " \n", " \n", @@ -506,41 +471,36 @@ " 0\n", " robustness\n", " lowercase\n", - " Results of Asian Cup group C matches played on...\n", - " results of asian cup group c matches played on...\n", - " \n", + " He won acclaim for the insights that he gave i...\n", + " he won acclaim for the insights that he gave i...\n", " \n", " \n", " 1\n", " robustness\n", " lowercase\n", - " 5. Ann Battellle ( U.S. ) 23.56\n", - " 5. ann battellle ( u.s. ) 23.56\n", - " Ann Battellle: PER, U.S.: LOC\n", + " FLORIDA AT CINCINNATI\n", + " florida at cincinnati\n", " \n", " \n", " 2\n", " robustness\n", " lowercase\n", - " It was the second Syrian defensive blunder in ...\n", - " it was the second syrian defensive blunder in ...\n", - " \n", + " ISSUER : Bay Co Building Authority ST : MI\n", + " issuer : bay co building authority st : mi\n", " \n", " \n", " 3\n", " robustness\n", " lowercase\n", - " ROME 1996-12-06\n", - " rome 1996-12-06\n", - " \n", + " Chernomyrdin said on Thursday after a meeting ...\n", + " chernomyrdin said on thursday after a meeting ...\n", " \n", " \n", " 4\n", " robustness\n", " lowercase\n", - " Japan : 19 - Kenichi Shimokawa , 2 - Hiroshige...\n", - " japan : 19 - kenichi shimokawa , 2 - hiroshige...\n", - " \n", + " Wigan 42 Bradford Bulls 36\n", + " wigan 42 bradford bulls 36\n", " \n", " \n", " ...\n", @@ -548,97 +508,91 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", - " 94\n", - " robustness\n", - " lowercase\n", - " Scorers : Shkvyrin Igor 78 , Shatskikh Oleg 90\n", - " scorers : shkvyrin igor 78 , shatskikh oleg 90\n", - " Shkvyrin Igor: PER, Shatskikh Oleg: PER\n", + " 100\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " ORG\n", " \n", " \n", - " 95\n", - " robustness\n", - " lowercase\n", - " 9. Ljudmila Dymchenko ( Russia ) 21.59\n", - " 9. ljudmila dymchenko ( russia ) 21.59\n", - " Ljudmila Dymchenko: PER, Russia: LOCATION\n", + " 101\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " MISC\n", " \n", " \n", - " 96\n", - " robustness\n", - " lowercase\n", - " The former Soviet republic was playing in an A...\n", - " the former soviet republic was playing in an a...\n", - " Soviet republic: ORG\n", + " 102\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " PER\n", " \n", " \n", - " 97\n", - " robustness\n", - " lowercase\n", - " 3. Ryan Johnson ( Canada ) 24.57\n", - " 3. ryan johnson ( canada ) 24.57\n", - " Ryan Johnson: PER, Canada: LOC\n", + " 103\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " O\n", " \n", " \n", - " 98\n", - " robustness\n", - " lowercase\n", - " Bitar saved well again from Miura in the 37th ...\n", - " bitar saved well again from miura in the 37th ...\n", - " Bitar: PER, Miura: PER\n", + " 104\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " LOC\n", " \n", " \n", "\n", - "

99 rows × 5 columns

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105 rows × 4 columns

\n", "" ], "text/plain": [ - " category test_type original \\\n", - "0 robustness lowercase Results of Asian Cup group C matches played on... \n", - "1 robustness lowercase 5. Ann Battellle ( U.S. ) 23.56 \n", - "2 robustness lowercase It was the second Syrian defensive blunder in ... \n", - "3 robustness lowercase ROME 1996-12-06 \n", - "4 robustness lowercase Japan : 19 - Kenichi Shimokawa , 2 - Hiroshige... \n", - ".. ... ... ... \n", - "94 robustness lowercase Scorers : Shkvyrin Igor 78 , Shatskikh Oleg 90 \n", - "95 robustness lowercase 9. Ljudmila Dymchenko ( Russia ) 21.59 \n", - "96 robustness lowercase The former Soviet republic was playing in an A... \n", - "97 robustness lowercase 3. Ryan Johnson ( Canada ) 24.57 \n", - "98 robustness lowercase Bitar saved well again from Miura in the 37th ... \n", + " category test_type \\\n", + "0 robustness lowercase \n", + "1 robustness lowercase \n", + "2 robustness lowercase \n", + "3 robustness lowercase \n", + "4 robustness lowercase \n", + ".. ... ... \n", + "100 accuracy min_f1_score \n", + "101 accuracy min_f1_score \n", + "102 accuracy min_f1_score \n", + "103 accuracy min_f1_score \n", + "104 accuracy min_f1_score \n", "\n", - " test_case \\\n", - "0 results of asian cup group c matches played on... \n", - "1 5. ann battellle ( u.s. ) 23.56 \n", - "2 it was the second syrian defensive blunder in ... \n", - "3 rome 1996-12-06 \n", - "4 japan : 19 - kenichi shimokawa , 2 - hiroshige... \n", - ".. ... \n", - "94 scorers : shkvyrin igor 78 , shatskikh oleg 90 \n", - "95 9. ljudmila dymchenko ( russia ) 21.59 \n", - "96 the former soviet republic was playing in an a... \n", - "97 3. ryan johnson ( canada ) 24.57 \n", - "98 bitar saved well again from miura in the 37th ... \n", + " original \\\n", + "0 He won acclaim for the insights that he gave i... \n", + "1 FLORIDA AT CINCINNATI \n", + "2 ISSUER : Bay Co Building Authority ST : MI \n", + "3 Chernomyrdin said on Thursday after a meeting ... \n", + "4 Wigan 42 Bradford Bulls 36 \n", + ".. ... \n", + "100 - \n", + "101 - \n", + "102 - \n", + "103 - \n", + "104 - \n", "\n", - " expected_result \n", - "0 \n", - "1 Ann Battellle: PER, U.S.: LOC \n", - "2 \n", - "3 \n", - "4 \n", - ".. ... \n", - "94 Shkvyrin Igor: PER, Shatskikh Oleg: PER \n", - "95 Ljudmila Dymchenko: PER, Russia: LOCATION \n", - "96 Soviet republic: ORG \n", - "97 Ryan Johnson: PER, Canada: LOC \n", - "98 Bitar: PER, Miura: PER \n", + " test_case \n", + "0 he won acclaim for the insights that he gave i... \n", + "1 florida at cincinnati \n", + "2 issuer : bay co building authority st : mi \n", + "3 chernomyrdin said on thursday after a meeting ... \n", + "4 wigan 42 bradford bulls 36 \n", + ".. ... \n", + "100 ORG \n", + "101 MISC \n", + "102 PER \n", + "103 O \n", + "104 LOC \n", "\n", - "[99 rows x 5 columns]" + "[105 rows x 4 columns]" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -670,7 +624,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Running testcases... : 100%|██████████| 99/99 [02:33<00:00, 1.56s/it]\n" + "Running testcases... : 99%|█████████▉| 104/105 [06:20<00:03, 3.66s/it]\n" ] }, { @@ -711,7 +665,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -749,51 +703,51 @@ " 0\n", " robustness\n", " lowercase\n", - " Results of Asian Cup group C matches played on...\n", - " results of asian cup group c matches played on...\n", - " \n", - " \n", - " True\n", + " He won acclaim for the insights that he gave i...\n", + " he won acclaim for the insights that he gave i...\n", + " Europe: Location, Europe: Location, 20th: Date...\n", + " he: PERSON, modern: DATE, europe: LOCATION, eu...\n", + " False\n", " \n", " \n", " 1\n", " robustness\n", " lowercase\n", - " 5. Ann Battellle ( U.S. ) 23.56\n", - " 5. ann battellle ( u.s. ) 23.56\n", - " Ann Battellle: PER, U.S.: LOC\n", - " ann battellle: PER\n", - " False\n", + " FLORIDA AT CINCINNATI\n", + " florida at cincinnati\n", + " FLORIDA: LOCATION, CINCINNATI: LOCATION\n", + " florida: LOCATION, cincinnati: LOCATION\n", + " True\n", " \n", " \n", " 2\n", " robustness\n", " lowercase\n", - " It was the second Syrian defensive blunder in ...\n", - " it was the second syrian defensive blunder in ...\n", - " \n", - " \n", - " True\n", + " ISSUER : Bay Co Building Authority ST : MI\n", + " issuer : bay co building authority st : mi\n", + " Bay Co Building Authority: Organization, ST: L...\n", + " bay: issuer, co: issuer, building: issuer, aut...\n", + " False\n", " \n", " \n", " 3\n", " robustness\n", " lowercase\n", - " ROME 1996-12-06\n", - " rome 1996-12-06\n", - " \n", - " \n", - " True\n", + " Chernomyrdin said on Thursday after a meeting ...\n", + " chernomyrdin said on thursday after a meeting ...\n", + " Chernomyrdin: Person, Thursday: Date, Lebed: P...\n", + " chernomyrdin: PERSON, thursday: DATE, lebed: P...\n", + " False\n", " \n", " \n", " 4\n", " robustness\n", " lowercase\n", - " Japan : 19 - Kenichi Shimokawa , 2 - Hiroshige...\n", - " japan : 19 - kenichi shimokawa , 2 - hiroshige...\n", - " \n", - " \n", - " True\n", + " Wigan 42 Bradford Bulls 36\n", + " wigan 42 bradford bulls 36\n", + " Wigan: Location, 42: Number, Bradford Bulls: O...\n", + " wigan: ORG, 42: CARDINAL, bradford: ORG, bulls...\n", + " False\n", " \n", " \n", " ...\n", @@ -806,117 +760,130 @@ " ...\n", " \n", " \n", - " 94\n", + " 99\n", " robustness\n", " lowercase\n", - " Scorers : Shkvyrin Igor 78 , Shatskikh Oleg 90\n", - " scorers : shkvyrin igor 78 , shatskikh oleg 90\n", - " Shkvyrin Igor: PER, Shatskikh Oleg: PER\n", - " shkvyrin igor: PER, shatskikh oleg: PER\n", + " Indonesian President Suharto has asked busines...\n", + " indonesian president suharto has asked busines...\n", + " Indonesian: Location, President: Title, Suhart...\n", + " indonesian: GPE, president: TITLE, suharto: PE...\n", " False\n", " \n", " \n", - " 95\n", - " robustness\n", - " lowercase\n", - " 9. Ljudmila Dymchenko ( Russia ) 21.59\n", - " 9. ljudmila dymchenko ( russia ) 21.59\n", - " Ljudmila Dymchenko: PER, Russia: LOCATION\n", - " ljudmila dymchenko: PER, russia: LOC\n", + " 100\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " ORG\n", + " 0.7\n", + " 0.0\n", " False\n", " \n", " \n", - " 96\n", - " robustness\n", - " lowercase\n", - " The former Soviet republic was playing in an A...\n", - " the former soviet republic was playing in an a...\n", - " Soviet republic: ORG\n", - " asian: LOC, soviet republic: ORG, cup finals t...\n", - " False\n", + " 101\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " PER\n", + " -\n", + " -\n", + " -\n", " \n", " \n", - " 97\n", - " robustness\n", - " lowercase\n", - " 3. Ryan Johnson ( Canada ) 24.57\n", - " 3. ryan johnson ( canada ) 24.57\n", - " Ryan Johnson: PER, Canada: LOC\n", - " ryan johnson: PER, canada: LOC\n", + " 102\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " O\n", + " 0.7\n", + " 0.4\n", " False\n", " \n", " \n", - " 98\n", - " robustness\n", - " lowercase\n", - " Bitar saved well again from Miura in the 37th ...\n", - " bitar saved well again from miura in the 37th ...\n", - " Bitar: PER, Miura: PER\n", - " \n", + " 103\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " LOC\n", + " 0.7\n", + " 0.0\n", " False\n", " \n", " \n", "\n", - "

99 rows × 7 columns

\n", + "

104 rows × 7 columns

\n", "" ], "text/plain": [ - " category test_type original \\\n", - "0 robustness lowercase Results of Asian Cup group C matches played on... \n", - "1 robustness lowercase 5. Ann Battellle ( U.S. ) 23.56 \n", - "2 robustness lowercase It was the second Syrian defensive blunder in ... \n", - "3 robustness lowercase ROME 1996-12-06 \n", - "4 robustness lowercase Japan : 19 - Kenichi Shimokawa , 2 - Hiroshige... \n", - ".. ... ... ... \n", - "94 robustness lowercase Scorers : Shkvyrin Igor 78 , Shatskikh Oleg 90 \n", - "95 robustness lowercase 9. Ljudmila Dymchenko ( Russia ) 21.59 \n", - "96 robustness lowercase The former Soviet republic was playing in an A... \n", - "97 robustness lowercase 3. Ryan Johnson ( Canada ) 24.57 \n", - "98 robustness lowercase Bitar saved well again from Miura in the 37th ... \n", + " category test_type \\\n", + "0 robustness lowercase \n", + "1 robustness lowercase \n", + "2 robustness lowercase \n", + "3 robustness lowercase \n", + "4 robustness lowercase \n", + ".. ... ... \n", + "99 robustness lowercase \n", + "100 accuracy min_f1_score \n", + "101 accuracy min_f1_score \n", + "102 accuracy min_f1_score \n", + "103 accuracy min_f1_score \n", "\n", - " test_case \\\n", - "0 results of asian cup group c matches played on... \n", - "1 5. ann battellle ( u.s. ) 23.56 \n", - "2 it was the second syrian defensive blunder in ... \n", - "3 rome 1996-12-06 \n", - "4 japan : 19 - kenichi shimokawa , 2 - hiroshige... \n", - ".. ... \n", - "94 scorers : shkvyrin igor 78 , shatskikh oleg 90 \n", - "95 9. ljudmila dymchenko ( russia ) 21.59 \n", - "96 the former soviet republic was playing in an a... \n", - "97 3. ryan johnson ( canada ) 24.57 \n", - "98 bitar saved well again from miura in the 37th ... \n", + " original \\\n", + "0 He won acclaim for the insights that he gave i... \n", + "1 FLORIDA AT CINCINNATI \n", + "2 ISSUER : Bay Co Building Authority ST : MI \n", + "3 Chernomyrdin said on Thursday after a meeting ... \n", + "4 Wigan 42 Bradford Bulls 36 \n", + ".. ... \n", + "99 Indonesian President Suharto has asked busines... \n", + "100 - \n", + "101 - \n", + "102 - \n", + "103 - \n", "\n", - " expected_result \\\n", - "0 \n", - "1 Ann Battellle: PER, U.S.: LOC \n", - "2 \n", - "3 \n", - "4 \n", - ".. ... \n", - "94 Shkvyrin Igor: PER, Shatskikh Oleg: PER \n", - "95 Ljudmila Dymchenko: PER, Russia: LOCATION \n", - "96 Soviet republic: ORG \n", - "97 Ryan Johnson: PER, Canada: LOC \n", - "98 Bitar: PER, Miura: PER \n", + " test_case \\\n", + "0 he won acclaim for the insights that he gave i... \n", + "1 florida at cincinnati \n", + "2 issuer : bay co building authority st : mi \n", + "3 chernomyrdin said on thursday after a meeting ... \n", + "4 wigan 42 bradford bulls 36 \n", + ".. ... \n", + "99 indonesian president suharto has asked busines... \n", + "100 ORG \n", + "101 PER \n", + "102 O \n", + "103 LOC \n", "\n", - " actual_result pass \n", - "0 True \n", - "1 ann battellle: PER False \n", - "2 True \n", - "3 True \n", - "4 True \n", - ".. ... ... \n", - "94 shkvyrin igor: PER, shatskikh oleg: PER False \n", - "95 ljudmila dymchenko: PER, russia: LOC False \n", - "96 asian: LOC, soviet republic: ORG, cup finals t... False \n", - "97 ryan johnson: PER, canada: LOC False \n", - "98 False \n", + " expected_result \\\n", + "0 Europe: Location, Europe: Location, 20th: Date... \n", + "1 FLORIDA: LOCATION, CINCINNATI: LOCATION \n", + "2 Bay Co Building Authority: Organization, ST: L... \n", + "3 Chernomyrdin: Person, Thursday: Date, Lebed: P... \n", + "4 Wigan: Location, 42: Number, Bradford Bulls: O... \n", + ".. ... \n", + "99 Indonesian: Location, President: Title, Suhart... \n", + "100 0.7 \n", + "101 - \n", + "102 0.7 \n", + "103 0.7 \n", "\n", - "[99 rows x 7 columns]" + " actual_result pass \n", + "0 he: PERSON, modern: DATE, europe: LOCATION, eu... False \n", + "1 florida: LOCATION, cincinnati: LOCATION True \n", + "2 bay: issuer, co: issuer, building: issuer, aut... False \n", + "3 chernomyrdin: PERSON, thursday: DATE, lebed: P... False \n", + "4 wigan: ORG, 42: CARDINAL, bradford: ORG, bulls... False \n", + ".. ... ... \n", + "99 indonesian: GPE, president: TITLE, suharto: PE... False \n", + "100 0.0 False \n", + "101 - - \n", + "102 0.4 False \n", + "103 0.0 False \n", + "\n", + "[104 rows x 7 columns]" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -941,7 +908,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -979,25 +946,37 @@ " 0\n", " robustness\n", " lowercase\n", - " 58\n", - " 41\n", - " 41%\n", + " 69\n", + " 31\n", + " 31%\n", " 70%\n", " False\n", " \n", + " \n", + " 1\n", + " accuracy\n", + " min_f1_score\n", + " 4\n", + " 0\n", + " 0%\n", + " 100%\n", + " False\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " category test_type fail_count pass_count pass_rate minimum_pass_rate \\\n", - "0 robustness lowercase 58 41 41% 70% \n", + " category test_type fail_count pass_count pass_rate \\\n", + "0 robustness lowercase 69 31 31% \n", + "1 accuracy min_f1_score 4 0 0% \n", "\n", - " pass \n", - "0 False " + " minimum_pass_rate pass \n", + "0 70% False \n", + "1 100% False " ] }, - "execution_count": 19, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } From 54811092eb54a930c4fdea8b25436d3de978d369 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Tue, 14 May 2024 23:44:42 +0530 Subject: [PATCH 63/69] Refactor leaderboard class to support ranking by different criteria --- langtest/langtest.py | 9 +++-- langtest/utils/benchmark_utils.py | 67 ++++++++++++++++++++++++------- 2 files changed, 57 insertions(+), 19 deletions(-) diff --git a/langtest/langtest.py b/langtest/langtest.py index e30ab002f..f3e1f51d6 100644 --- a/langtest/langtest.py +++ b/langtest/langtest.py @@ -1758,6 +1758,7 @@ def get_leaderboard( category=False, split_wise=False, test_wise=False, + rank_by: Union[str, list] = "Avg", *args, **kwargs, ): @@ -1776,15 +1777,15 @@ def get_leaderboard( if indices or columns: return leaderboard.custom_wise(indices, columns) if category: - return leaderboard.category_wise() + return leaderboard.category_wise(rank_by=rank_by) if test_wise: - return leaderboard.test_wise() + return leaderboard.test_wise(rank_by=rank_by) if split_wise: - return leaderboard.split_wise() + return leaderboard.split_wise(rank_by=rank_by) - return leaderboard.default() + return leaderboard.default(rank_by=rank_by) def __temp_generate(self, *args, **kwargs): """Temporary function to generate the testcases.""" diff --git a/langtest/utils/benchmark_utils.py b/langtest/utils/benchmark_utils.py index 4b07dae65..f3e08f839 100644 --- a/langtest/utils/benchmark_utils.py +++ b/langtest/utils/benchmark_utils.py @@ -1,5 +1,5 @@ import os -from typing import TypeVar, Generic +from typing import TypeVar, Generic, Union import pandas as pd @@ -35,10 +35,16 @@ def __init__( """ self.summary = Summary(path, *args, **kwargs) - def default(self): + def default(self, rank_by: Union[str, list] = "Avg"): """ Get the score board for the models """ + # check if the rank_by is a string + if isinstance(rank_by, str): + rank_by = [rank_by] + + ascending = [False] * len(rank_by) + df = self.summary.summary_df df = self.__drop_duplicates(df) pvt_table = df.pivot_table( @@ -47,7 +53,7 @@ def default(self): # mean column pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) - pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False]) + pvt_table = pvt_table.sort_values(by=rank_by, ascending=ascending) # reset the index and fill the NaN values pvt_table = pvt_table.rename_axis(None, axis=1).reset_index() @@ -55,11 +61,17 @@ def default(self): return pvt_table - def split_wise(self): + def split_wise(self, rank_by: Union[str, list] = "Avg"): """ Get the score board for the models by test type """ + # check if the rank_by is a string + if isinstance(rank_by, str): + rank_by = [rank_by] + + ascending = [False] * len(rank_by) + df = self.summary.summary_df df = self.__drop_duplicates(df) pvt_table = df.pivot_table( @@ -70,51 +82,77 @@ def split_wise(self): # mean column pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) - pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False]) + pvt_table = pvt_table.sort_values(by=rank_by, ascending=ascending) pvt_table = pvt_table.fillna("-") return pvt_table - def test_wise(self): + def test_wise(self, rank_by: Union[str, list] = "Avg"): """ Get the score board for the models by test type """ + # check if the rank_by is a string + if isinstance(rank_by, str): + rank_by = [rank_by] + + # check if the test_type in the rank_by + if "test_type" not in rank_by: + rank_by.insert(0, "test_type") + rank_by.insert(0, "category") + + ascending = [True, True] + [False] * (len(rank_by) - 2) + df = self.summary.summary_df df = self.__drop_duplicates(df) pvt_table = df.pivot_table( - index=["model", "test_type"], columns=["dataset_name"], values="score" + index=["category", "test_type", "model"], + columns=["dataset_name"], + values="score", ) # mean column pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) - pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False]) + pvt_table = pvt_table.sort_values(by=rank_by, ascending=ascending) pvt_table = pvt_table.fillna("-") return pvt_table - def category_wise(self): + def category_wise(self, rank_by: Union[str, list] = "Avg"): """ Get the score board for the models by category """ + # check if the rank_by is a string + if isinstance(rank_by, str): + rank_by = [rank_by] + + ascending = [False] * len(rank_by) + df = self.summary.summary_df df = self.__drop_duplicates(df) pvt_table = df.pivot_table( - index=["model", "category"], columns=["dataset_name"], values="score" + index=["category", "model"], columns=["dataset_name"], values="score" ) pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) - pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False]) + pvt_table = pvt_table.sort_values(by=rank_by, ascending=ascending) pvt_table = pvt_table.fillna("-") - pvt_table = pvt_table.rename_axis(None, axis=1).reset_index() return pvt_table - def custom_wise(self, indices: list, columns: list = []): + def custom_wise( + self, indices: list, columns: list = [], rank_by: Union[str, list] = "Avg" + ): """ Get the score board for the models by custom group """ + # check if the rank_by is a string + if isinstance(rank_by, str): + rank_by = [rank_by] + + ascending = [False] * len(rank_by) + df = self.summary.summary_df df = self.__drop_duplicates(df) pvt_table = df.pivot_table( @@ -125,8 +163,7 @@ def custom_wise(self, indices: list, columns: list = []): ) pvt_table.insert(0, "Avg", pvt_table.mean(axis=1)) pvt_table = pvt_table.fillna("-") - pvt_table = pvt_table.sort_values(by=["model", "Avg"], ascending=[True, False]) - # pvt_table = pvt_table.rename_axis(None, axis=1).reset_index() + pvt_table = pvt_table.sort_values(by=rank_by, ascending=ascending) return pvt_table From 39a01164e1dcb86449c62721599b6763575a8229 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Wed, 15 May 2024 11:27:12 +0530 Subject: [PATCH 64/69] Updated: Benchmarking NB --- .../Benchmarking_with_Harness.ipynb | 1205 ++++++++++------- 1 file changed, 750 insertions(+), 455 deletions(-) diff --git a/demo/tutorials/benchmarks/Benchmarking_with_Harness.ipynb b/demo/tutorials/benchmarks/Benchmarking_with_Harness.ipynb index 43e19d8af..56cc483aa 100644 --- a/demo/tutorials/benchmarks/Benchmarking_with_Harness.ipynb +++ b/demo/tutorials/benchmarks/Benchmarking_with_Harness.ipynb @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 1, "metadata": { "id": "0ZVlGWBJyGO8" }, @@ -62,6 +62,8 @@ " robustness:\n", " add_typo:\n", " min_pass_rate: 0.7\n", + " lowercase:\n", + " min_pass_rate: 0.7\n", "\"\"\"" ] }, @@ -74,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 2, "metadata": { "id": "zPbGsd-Iydxv" }, @@ -98,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -160,6 +162,9 @@ " \"robustness\": {\n", " \"add_typo\": {\n", " \"min_pass_rate\": 0.7\n", + " },\n", + " \"lowercase\": {\n", + " \"min_pass_rate\": 0.7\n", " }\n", " }\n", " }\n", @@ -197,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -221,7 +226,8 @@ "text": [ "Generating testcases...: 100%|██████████| 1/1 [00:00MedMCQA\n", " add_typo\n", " -\n", - " A patient who was on ventilator and being vent...\n", + " Highest concentration of oxygen is delivered t...\n", " -\n", - " A patient who was on ventilator anr being vent...\n", - " A. Assess the patient, give bag and mask venti...\n", + " Highest cpncentration of oxygen is delivered t...\n", + " A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ...\n", " \n", " \n", " 1\n", @@ -360,10 +367,10 @@ " MedMCQA\n", " add_typo\n", " -\n", - " Highest concentration of oxygen is delivered t...\n", + " Steps of intubation - arrange in sequence:- a....\n", " -\n", - " Highest concewtration of oxygen is delivered t...\n", - " A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ...\n", + " Steps of intubation - arrange in sequence:- a....\n", + " A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED\n", " \n", " \n", " 2\n", @@ -371,10 +378,10 @@ " MedMCQA\n", " add_typo\n", " -\n", - " Steps of intubation - arrange in sequence:- a....\n", + " Tracheal secretions should be suctioned for:\n", " -\n", - " Steps of intubation - arrange in sequence:- a....\n", - " A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED\n", + " Ttacheal secretions should be suctioned for:\n", + " A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds...\n", " \n", " \n", " 3\n", @@ -382,10 +389,10 @@ " MedMCQA\n", " add_typo\n", " -\n", - " Tracheal secretions should be suctioned for:\n", + " Which of the following nerve fibre types is le...\n", " -\n", - " Tracheal secrrtions should be suctioned for:\n", - " A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds...\n", + " Which of the following nerve fibre types is le...\n", + " A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers\n", " \n", " \n", " 4\n", @@ -393,10 +400,10 @@ " MedMCQA\n", " add_typo\n", " -\n", - " Which of the following nerve fibre types is le...\n", + " All are true about rapid sequence induction do...\n", " -\n", - " Which of the following zerve fibre types is le...\n", - " A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers\n", + " All are true about rapid sequence induction do...\n", + " A. Inducing agent and neuromuscular relaxant a...\n", " \n", " \n", " ...\n", @@ -410,122 +417,122 @@ " ...\n", " \n", " \n", - " 7373\n", + " 14904\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 39-year-old woman presents to the clinic for...\n", " -\n", - " A 39-year-old woman presents to the clinic for...\n", + " a 39-year-old woman presents to the clinic for...\n", " A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro...\n", " \n", " \n", - " 7374\n", + " 14905\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 38-year-old woman comes to the physician bec...\n", " -\n", - " A 38-year-old woman comes to the physician bec...\n", + " a 38-year-old woman comes to the physician bec...\n", " A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo...\n", " \n", " \n", - " 7375\n", + " 14906\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 21-year-old college student comes to the phy...\n", " -\n", - " A 21-year-old college student comes to the phy...\n", + " a 21-year-old college student comes to the phy...\n", " A. Trial of diphenhydramine\\nB. Complete caffe...\n", " \n", " \n", - " 7376\n", + " 14907\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 19-year-old man is brought to the physician ...\n", " -\n", - " A 19-year-old man is brought to the physician ...\n", + " a 19-year-old man is brought to the physician ...\n", " A. Social anxiety disorder\\nB. Avoidant person...\n", " \n", " \n", - " 7377\n", + " 14908\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 79-year-old man presents to the office due t...\n", " -\n", - " A 79-year-old man presents to the office due t...\n", + " a 79-year-old man presents to the office due t...\n", " A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr...\n", " \n", " \n", "\n", - "

7378 rows × 8 columns

\n", + "

14909 rows × 8 columns

\n", "" ], "text/plain": [ - " category dataset_name test_type original_context \\\n", - "0 robustness MedMCQA add_typo - \n", - "1 robustness MedMCQA add_typo - \n", - "2 robustness MedMCQA add_typo - \n", - "3 robustness MedMCQA add_typo - \n", - "4 robustness MedMCQA add_typo - \n", - "... ... ... ... ... \n", - "7373 robustness MedQA add_typo - \n", - "7374 robustness MedQA add_typo - \n", - "7375 robustness MedQA add_typo - \n", - "7376 robustness MedQA add_typo - \n", - "7377 robustness MedQA add_typo - \n", + " category dataset_name test_type original_context \\\n", + "0 robustness MedMCQA add_typo - \n", + "1 robustness MedMCQA add_typo - \n", + "2 robustness MedMCQA add_typo - \n", + "3 robustness MedMCQA add_typo - \n", + "4 robustness MedMCQA add_typo - \n", + "... ... ... ... ... \n", + "14904 robustness MedQA lowercase - \n", + "14905 robustness MedQA lowercase - \n", + "14906 robustness MedQA lowercase - \n", + "14907 robustness MedQA lowercase - \n", + "14908 robustness MedQA lowercase - \n", "\n", - " original_question perturbed_context \\\n", - "0 A patient who was on ventilator and being vent... - \n", - "1 Highest concentration of oxygen is delivered t... - \n", - "2 Steps of intubation - arrange in sequence:- a.... - \n", - "3 Tracheal secretions should be suctioned for: - \n", - "4 Which of the following nerve fibre types is le... - \n", - "... ... ... \n", - "7373 A 39-year-old woman presents to the clinic for... - \n", - "7374 A 38-year-old woman comes to the physician bec... - \n", - "7375 A 21-year-old college student comes to the phy... - \n", - "7376 A 19-year-old man is brought to the physician ... - \n", - "7377 A 79-year-old man presents to the office due t... - \n", + " original_question perturbed_context \\\n", + "0 Highest concentration of oxygen is delivered t... - \n", + "1 Steps of intubation - arrange in sequence:- a.... - \n", + "2 Tracheal secretions should be suctioned for: - \n", + "3 Which of the following nerve fibre types is le... - \n", + "4 All are true about rapid sequence induction do... - \n", + "... ... ... \n", + "14904 A 39-year-old woman presents to the clinic for... - \n", + "14905 A 38-year-old woman comes to the physician bec... - \n", + "14906 A 21-year-old college student comes to the phy... - \n", + "14907 A 19-year-old man is brought to the physician ... - \n", + "14908 A 79-year-old man presents to the office due t... - \n", "\n", - " perturbed_question \\\n", - "0 A patient who was on ventilator anr being vent... \n", - "1 Highest concewtration of oxygen is delivered t... \n", - "2 Steps of intubation - arrange in sequence:- a.... \n", - "3 Tracheal secrrtions should be suctioned for: \n", - "4 Which of the following zerve fibre types is le... \n", - "... ... \n", - "7373 A 39-year-old woman presents to the clinic for... \n", - "7374 A 38-year-old woman comes to the physician bec... \n", - "7375 A 21-year-old college student comes to the phy... \n", - "7376 A 19-year-old man is brought to the physician ... \n", - "7377 A 79-year-old man presents to the office due t... \n", + " perturbed_question \\\n", + "0 Highest cpncentration of oxygen is delivered t... \n", + "1 Steps of intubation - arrange in sequence:- a.... \n", + "2 Ttacheal secretions should be suctioned for: \n", + "3 Which of the following nerve fibre types is le... \n", + "4 All are true about rapid sequence induction do... \n", + "... ... \n", + "14904 a 39-year-old woman presents to the clinic for... \n", + "14905 a 38-year-old woman comes to the physician bec... \n", + "14906 a 21-year-old college student comes to the phy... \n", + "14907 a 19-year-old man is brought to the physician ... \n", + "14908 a 79-year-old man presents to the office due t... \n", "\n", - " options \n", - "0 A. Assess the patient, give bag and mask venti... \n", - "1 A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ... \n", - "2 A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED \n", - "3 A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds... \n", - "4 A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers \n", - "... ... \n", - "7373 A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro... \n", - "7374 A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo... \n", - "7375 A. Trial of diphenhydramine\\nB. Complete caffe... \n", - "7376 A. Social anxiety disorder\\nB. Avoidant person... \n", - "7377 A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr... \n", + " options \n", + "0 A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ... \n", + "1 A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED \n", + "2 A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds... \n", + "3 A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers \n", + "4 A. Inducing agent and neuromuscular relaxant a... \n", + "... ... \n", + "14904 A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro... \n", + "14905 A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo... \n", + "14906 A. Trial of diphenhydramine\\nB. Complete caffe... \n", + "14907 A. Social anxiety disorder\\nB. Avoidant person... \n", + "14908 A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr... \n", "\n", - "[7378 rows x 8 columns]" + "[14909 rows x 8 columns]" ] }, - "execution_count": 24, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -536,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -558,7 +565,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Running testcases... : 100%|██████████| 4045/4045 [03:53<00:00, 17.33it/s]\n" + "Running testcases... : 100%|██████████| 8178/8178 [05:54<00:00, 23.04it/s]\n" ] }, { @@ -576,7 +583,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Running testcases... : 100%|██████████| 1000/1000 [01:02<00:00, 15.91it/s]\n" + "Running testcases... : 100%|██████████| 2000/2000 [01:34<00:00, 21.24it/s]\n" ] }, { @@ -594,7 +601,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Running testcases... : 100%|██████████| 1054/1054 [00:59<00:00, 17.83it/s]\n" + "Running testcases... : 100%|██████████| 2135/2135 [01:29<00:00, 23.83it/s]\n" ] }, { @@ -612,7 +619,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Running testcases... : 100%|██████████| 1279/1279 [01:11<00:00, 17.89it/s]\n" + "Running testcases... : 100%|██████████| 2596/2596 [01:49<00:00, 23.67it/s] " ] }, { @@ -623,11 +630,18 @@ "\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, { "data": { "text/plain": [] }, - "execution_count": 25, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -638,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -688,10 +702,10 @@ " MedMCQA\n", " add_typo\n", " -\n", - " A patient who was on ventilator and being vent...\n", + " Highest concentration of oxygen is delivered t...\n", " -\n", - " A patient who was on ventilator anr being vent...\n", - " A. Assess the patient, give bag and mask venti...\n", + " Highest cpncentration of oxygen is delivered t...\n", + " A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ...\n", " A\n", " A\n", " True\n", @@ -702,10 +716,10 @@ " MedMCQA\n", " add_typo\n", " -\n", - " Highest concentration of oxygen is delivered t...\n", + " Steps of intubation - arrange in sequence:- a....\n", " -\n", - " Highest concewtration of oxygen is delivered t...\n", - " A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ...\n", + " Steps of intubation - arrange in sequence:- a....\n", + " A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED\n", " A\n", " A\n", " True\n", @@ -716,11 +730,11 @@ " MedMCQA\n", " add_typo\n", " -\n", - " Steps of intubation - arrange in sequence:- a....\n", + " Tracheal secretions should be suctioned for:\n", " -\n", - " Steps of intubation - arrange in sequence:- a....\n", - " A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED\n", - " A\n", + " Ttacheal secretions should be suctioned for:\n", + " A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds...\n", + " B\n", " B\n", " True\n", " \n", @@ -730,12 +744,12 @@ " MedMCQA\n", " add_typo\n", " -\n", - " Tracheal secretions should be suctioned for:\n", + " Which of the following nerve fibre types is le...\n", " -\n", - " Tracheal secrrtions should be suctioned for:\n", - " A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds...\n", - " B\n", - " B\n", + " Which of the following nerve fibre types is le...\n", + " A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers\n", + " D\n", + " A\n", " True\n", " \n", " \n", @@ -744,12 +758,12 @@ " MedMCQA\n", " add_typo\n", " -\n", - " Which of the following nerve fibre types is le...\n", + " All are true about rapid sequence induction do...\n", " -\n", - " Which of the following zerve fibre types is le...\n", - " A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers\n", - " D\n", - " D\n", + " All are true about rapid sequence induction do...\n", + " A. Inducing agent and neuromuscular relaxant a...\n", + " C\n", + " C\n", " True\n", " \n", " \n", @@ -767,70 +781,70 @@ " ...\n", " \n", " \n", - " 7373\n", + " 14904\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 39-year-old woman presents to the clinic for...\n", " -\n", - " A 39-year-old woman presents to the clinic for...\n", + " a 39-year-old woman presents to the clinic for...\n", " A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro...\n", " D\n", " D\n", " True\n", " \n", " \n", - " 7374\n", + " 14905\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 38-year-old woman comes to the physician bec...\n", " -\n", - " A 38-year-old woman comes to the physician bec...\n", + " a 38-year-old woman comes to the physician bec...\n", " A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo...\n", " D\n", " D\n", " True\n", " \n", " \n", - " 7375\n", + " 14906\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 21-year-old college student comes to the phy...\n", " -\n", - " A 21-year-old college student comes to the phy...\n", + " a 21-year-old college student comes to the phy...\n", " A. Trial of diphenhydramine\\nB. Complete caffe...\n", " B\n", " B\n", " True\n", " \n", " \n", - " 7376\n", + " 14907\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 19-year-old man is brought to the physician ...\n", " -\n", - " A 19-year-old man is brought to the physician ...\n", + " a 19-year-old man is brought to the physician ...\n", " A. Social anxiety disorder\\nB. Avoidant person...\n", " D\n", " D\n", " True\n", " \n", " \n", - " 7377\n", + " 14908\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 79-year-old man presents to the office due t...\n", " -\n", - " A 79-year-old man presents to the office due t...\n", + " a 79-year-old man presents to the office due t...\n", " A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr...\n", " C\n", " C\n", @@ -838,79 +852,79 @@ " \n", " \n", "\n", - "

7378 rows × 11 columns

\n", + "

14909 rows × 11 columns

\n", "" ], "text/plain": [ - " category dataset_name test_type original_context \\\n", - "0 robustness MedMCQA add_typo - \n", - "1 robustness MedMCQA add_typo - \n", - "2 robustness MedMCQA add_typo - \n", - "3 robustness MedMCQA add_typo - \n", - "4 robustness MedMCQA add_typo - \n", - "... ... ... ... ... \n", - "7373 robustness MedQA add_typo - \n", - "7374 robustness MedQA add_typo - \n", - "7375 robustness MedQA add_typo - \n", - "7376 robustness MedQA add_typo - \n", - "7377 robustness MedQA add_typo - \n", + " category dataset_name test_type original_context \\\n", + "0 robustness MedMCQA add_typo - \n", + "1 robustness MedMCQA add_typo - \n", + "2 robustness MedMCQA add_typo - \n", + "3 robustness MedMCQA add_typo - \n", + "4 robustness MedMCQA add_typo - \n", + "... ... ... ... ... \n", + "14904 robustness MedQA lowercase - \n", + "14905 robustness MedQA lowercase - \n", + "14906 robustness MedQA lowercase - \n", + "14907 robustness MedQA lowercase - \n", + "14908 robustness MedQA lowercase - \n", "\n", - " original_question perturbed_context \\\n", - "0 A patient who was on ventilator and being vent... - \n", - "1 Highest concentration of oxygen is delivered t... - \n", - "2 Steps of intubation - arrange in sequence:- a.... - \n", - "3 Tracheal secretions should be suctioned for: - \n", - "4 Which of the following nerve fibre types is le... - \n", - "... ... ... \n", - "7373 A 39-year-old woman presents to the clinic for... - \n", - "7374 A 38-year-old woman comes to the physician bec... - \n", - "7375 A 21-year-old college student comes to the phy... - \n", - "7376 A 19-year-old man is brought to the physician ... - \n", - "7377 A 79-year-old man presents to the office due t... - \n", + " original_question perturbed_context \\\n", + "0 Highest concentration of oxygen is delivered t... - \n", + "1 Steps of intubation - arrange in sequence:- a.... - \n", + "2 Tracheal secretions should be suctioned for: - \n", + "3 Which of the following nerve fibre types is le... - \n", + "4 All are true about rapid sequence induction do... - \n", + "... ... ... \n", + "14904 A 39-year-old woman presents to the clinic for... - \n", + "14905 A 38-year-old woman comes to the physician bec... - \n", + "14906 A 21-year-old college student comes to the phy... - \n", + "14907 A 19-year-old man is brought to the physician ... - \n", + "14908 A 79-year-old man presents to the office due t... - \n", "\n", - " perturbed_question \\\n", - "0 A patient who was on ventilator anr being vent... \n", - "1 Highest concewtration of oxygen is delivered t... \n", - "2 Steps of intubation - arrange in sequence:- a.... \n", - "3 Tracheal secrrtions should be suctioned for: \n", - "4 Which of the following zerve fibre types is le... \n", - "... ... \n", - "7373 A 39-year-old woman presents to the clinic for... \n", - "7374 A 38-year-old woman comes to the physician bec... \n", - "7375 A 21-year-old college student comes to the phy... \n", - "7376 A 19-year-old man is brought to the physician ... \n", - "7377 A 79-year-old man presents to the office due t... \n", + " perturbed_question \\\n", + "0 Highest cpncentration of oxygen is delivered t... \n", + "1 Steps of intubation - arrange in sequence:- a.... \n", + "2 Ttacheal secretions should be suctioned for: \n", + "3 Which of the following nerve fibre types is le... \n", + "4 All are true about rapid sequence induction do... \n", + "... ... \n", + "14904 a 39-year-old woman presents to the clinic for... \n", + "14905 a 38-year-old woman comes to the physician bec... \n", + "14906 a 21-year-old college student comes to the phy... \n", + "14907 a 19-year-old man is brought to the physician ... \n", + "14908 a 79-year-old man presents to the office due t... \n", "\n", - " options expected_result \\\n", - "0 A. Assess the patient, give bag and mask venti... A \n", - "1 A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ... A \n", - "2 A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED A \n", - "3 A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds... B \n", - "4 A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers D \n", - "... ... ... \n", - "7373 A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro... D \n", - "7374 A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo... D \n", - "7375 A. Trial of diphenhydramine\\nB. Complete caffe... B \n", - "7376 A. Social anxiety disorder\\nB. Avoidant person... D \n", - "7377 A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr... C \n", + " options expected_result \\\n", + "0 A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ... A \n", + "1 A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED A \n", + "2 A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds... B \n", + "3 A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers D \n", + "4 A. Inducing agent and neuromuscular relaxant a... C \n", + "... ... ... \n", + "14904 A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro... D \n", + "14905 A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo... D \n", + "14906 A. Trial of diphenhydramine\\nB. Complete caffe... B \n", + "14907 A. Social anxiety disorder\\nB. Avoidant person... D \n", + "14908 A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr... C \n", "\n", - " actual_result pass \n", - "0 A True \n", - "1 A True \n", - "2 B True \n", - "3 B True \n", - "4 D True \n", - "... ... ... \n", - "7373 D True \n", - "7374 D True \n", - "7375 B True \n", - "7376 D True \n", - "7377 C True \n", + " actual_result pass \n", + "0 A True \n", + "1 A True \n", + "2 B True \n", + "3 A True \n", + "4 C True \n", + "... ... ... \n", + "14904 D True \n", + "14905 D True \n", + "14906 B True \n", + "14907 D True \n", + "14908 C True \n", "\n", - "[7378 rows x 11 columns]" + "[14909 rows x 11 columns]" ] }, - "execution_count": 26, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -921,7 +935,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -976,45 +990,77 @@ " \n", " \n", " \n", - " MedMCQA\n", - " robustness\n", + " MedMCQA\n", + " robustness\n", " add_typo\n", - " 173\n", - " 3872\n", + " 151\n", + " 3873\n", " 96%\n", " 70%\n", " True\n", " \n", " \n", - " PubMedQA\n", - " robustness\n", + " lowercase\n", + " 103\n", + " 4051\n", + " 98%\n", + " 70%\n", + " True\n", + " \n", + " \n", + " PubMedQA\n", + " robustness\n", " add_typo\n", - " 3\n", - " 997\n", + " 0\n", + " 1000\n", " 100%\n", " 70%\n", " True\n", " \n", " \n", - " MMLU\n", - " robustness\n", - " add_typo\n", - " 15\n", - " 1039\n", + " lowercase\n", + " 6\n", + " 994\n", " 99%\n", " 70%\n", " True\n", " \n", " \n", - " MedQA\n", - " robustness\n", + " MMLU\n", + " robustness\n", + " add_typo\n", + " 19\n", + " 1028\n", + " 98%\n", + " 70%\n", + " True\n", + " \n", + " \n", + " lowercase\n", + " 17\n", + " 1071\n", + " 98%\n", + " 70%\n", + " True\n", + " \n", + " \n", + " MedQA\n", + " robustness\n", " add_typo\n", - " 2\n", - " 1277\n", + " 3\n", + " 1270\n", " 100%\n", " 70%\n", " True\n", " \n", + " \n", + " lowercase\n", + " 8\n", + " 1315\n", + " 99%\n", + " 70%\n", + " True\n", + " \n", " \n", "\n", "" @@ -1023,21 +1069,29 @@ " Benchmarking Results: google/flan-t5-base \\\n", " fail_count \n", "dataset_name category test_type \n", - "MedMCQA robustness add_typo 173 \n", - "PubMedQA robustness add_typo 3 \n", - "MMLU robustness add_typo 15 \n", - "MedQA robustness add_typo 2 \n", + "MedMCQA robustness add_typo 151 \n", + " lowercase 103 \n", + "PubMedQA robustness add_typo 0 \n", + " lowercase 6 \n", + "MMLU robustness add_typo 19 \n", + " lowercase 17 \n", + "MedQA robustness add_typo 3 \n", + " lowercase 8 \n", "\n", " \n", " pass_count pass_rate minimum_pass_rate pass \n", "dataset_name category test_type \n", - "MedMCQA robustness add_typo 3872 96% 70% True \n", - "PubMedQA robustness add_typo 997 100% 70% True \n", - "MMLU robustness add_typo 1039 99% 70% True \n", - "MedQA robustness add_typo 1277 100% 70% True " + "MedMCQA robustness add_typo 3873 96% 70% True \n", + " lowercase 4051 98% 70% True \n", + "PubMedQA robustness add_typo 1000 100% 70% True \n", + " lowercase 994 99% 70% True \n", + "MMLU robustness add_typo 1028 98% 70% True \n", + " lowercase 1071 98% 70% True \n", + "MedQA robustness add_typo 1270 100% 70% True \n", + " lowercase 1315 99% 70% True " ] }, - "execution_count": 27, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1048,7 +1102,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1084,10 +1138,10 @@ " \n", " 0\n", " google/flan-t5-base\n", - " 0.984609\n", - " 0.985769\n", - " 0.957231\n", - " 0.998436\n", + " 0.986188\n", + " 0.983114\n", + " 0.96884\n", + " 0.995798\n", " 0.997\n", " \n", " \n", @@ -1095,11 +1149,11 @@ "" ], "text/plain": [ - " model Avg MMLU MedMCQA MedQA PubMedQA\n", - "0 google/flan-t5-base 0.984609 0.985769 0.957231 0.998436 0.997" + " model Avg MMLU MedMCQA MedQA PubMedQA\n", + "0 google/flan-t5-base 0.986188 0.983114 0.96884 0.995798 0.997" ] }, - "execution_count": 28, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1117,7 +1171,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1142,6 +1196,9 @@ " \"robustness\": {\n", " \"add_typo\": {\n", " \"min_pass_rate\": 0.7\n", + " },\n", + " \"lowercase\": {\n", + " \"min_pass_rate\": 0.7\n", " }\n", " }\n", " }\n", @@ -1179,7 +1236,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1197,7 +1254,8 @@ "text": [ "Generating testcases...: 100%|██████████| 1/1 [00:00-\n", " A patient who was on ventilator and being vent...\n", " -\n", - " A patient who was on ventilator and being vent...\n", + " A patient who was on ventilayor and being vent...\n", " A. Assess the patient, give bag and mask venti...\n", " \n", " \n", @@ -1341,7 +1400,7 @@ " -\n", " Highest concentration of oxygen is delivered t...\n", " -\n", - " Hkghest concentration of oxygen is delivered t...\n", + " Highest concentration of oxygen is deliveted t...\n", " A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ...\n", " \n", " \n", @@ -1361,10 +1420,10 @@ " MedMCQA\n", " add_typo\n", " -\n", - " Tracheal secretions should be suctioned for:\n", + " Which of the following nerve fibre types is le...\n", " -\n", - " Tracheal secretions should be suctioned ror:\n", - " A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds...\n", + " Which of the following nerve fibre types is le...\n", + " A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers\n", " \n", " \n", " 4\n", @@ -1372,10 +1431,10 @@ " MedMCQA\n", " add_typo\n", " -\n", - " Which of the following nerve fibre types is le...\n", + " All are true about rapid sequence induction do...\n", " -\n", - " Ehich of the following nerve fibre types is le...\n", - " A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers\n", + " All are yrue about rapid sequence induction do...\n", + " A. Inducing agent and neuromuscular relaxant a...\n", " \n", " \n", " ...\n", @@ -1389,122 +1448,122 @@ " ...\n", " \n", " \n", - " 7361\n", + " 14930\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 39-year-old woman presents to the clinic for...\n", " -\n", - " A 39-year-old woman presents to the clinic for...\n", + " a 39-year-old woman presents to the clinic for...\n", " A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro...\n", " \n", " \n", - " 7362\n", + " 14931\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 38-year-old woman comes to the physician bec...\n", " -\n", - " A 38-year-old woman comes to the physician bec...\n", + " a 38-year-old woman comes to the physician bec...\n", " A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo...\n", " \n", " \n", - " 7363\n", + " 14932\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 21-year-old college student comes to the phy...\n", " -\n", - " A 21-year-old college student comes to the phy...\n", + " a 21-year-old college student comes to the phy...\n", " A. Trial of diphenhydramine\\nB. Complete caffe...\n", " \n", " \n", - " 7364\n", + " 14933\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 19-year-old man is brought to the physician ...\n", " -\n", - " A 19-year-old man is brought to the physician ...\n", + " a 19-year-old man is brought to the physician ...\n", " A. Social anxiety disorder\\nB. Avoidant person...\n", " \n", " \n", - " 7365\n", + " 14934\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 79-year-old man presents to the office due t...\n", " -\n", - " A 79-year-old man presents to the office due t...\n", + " a 79-year-old man presents to the office due t...\n", " A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr...\n", " \n", " \n", "\n", - "

7366 rows × 8 columns

\n", + "

14935 rows × 8 columns

\n", "" ], "text/plain": [ - " category dataset_name test_type original_context \\\n", - "0 robustness MedMCQA add_typo - \n", - "1 robustness MedMCQA add_typo - \n", - "2 robustness MedMCQA add_typo - \n", - "3 robustness MedMCQA add_typo - \n", - "4 robustness MedMCQA add_typo - \n", - "... ... ... ... ... \n", - "7361 robustness MedQA add_typo - \n", - "7362 robustness MedQA add_typo - \n", - "7363 robustness MedQA add_typo - \n", - "7364 robustness MedQA add_typo - \n", - "7365 robustness MedQA add_typo - \n", + " category dataset_name test_type original_context \\\n", + "0 robustness MedMCQA add_typo - \n", + "1 robustness MedMCQA add_typo - \n", + "2 robustness MedMCQA add_typo - \n", + "3 robustness MedMCQA add_typo - \n", + "4 robustness MedMCQA add_typo - \n", + "... ... ... ... ... \n", + "14930 robustness MedQA lowercase - \n", + "14931 robustness MedQA lowercase - \n", + "14932 robustness MedQA lowercase - \n", + "14933 robustness MedQA lowercase - \n", + "14934 robustness MedQA lowercase - \n", "\n", - " original_question perturbed_context \\\n", - "0 A patient who was on ventilator and being vent... - \n", - "1 Highest concentration of oxygen is delivered t... - \n", - "2 Steps of intubation - arrange in sequence:- a.... - \n", - "3 Tracheal secretions should be suctioned for: - \n", - "4 Which of the following nerve fibre types is le... - \n", - "... ... ... \n", - "7361 A 39-year-old woman presents to the clinic for... - \n", - "7362 A 38-year-old woman comes to the physician bec... - \n", - "7363 A 21-year-old college student comes to the phy... - \n", - "7364 A 19-year-old man is brought to the physician ... - \n", - "7365 A 79-year-old man presents to the office due t... - \n", + " original_question perturbed_context \\\n", + "0 A patient who was on ventilator and being vent... - \n", + "1 Highest concentration of oxygen is delivered t... - \n", + "2 Steps of intubation - arrange in sequence:- a.... - \n", + "3 Which of the following nerve fibre types is le... - \n", + "4 All are true about rapid sequence induction do... - \n", + "... ... ... \n", + "14930 A 39-year-old woman presents to the clinic for... - \n", + "14931 A 38-year-old woman comes to the physician bec... - \n", + "14932 A 21-year-old college student comes to the phy... - \n", + "14933 A 19-year-old man is brought to the physician ... - \n", + "14934 A 79-year-old man presents to the office due t... - \n", "\n", - " perturbed_question \\\n", - "0 A patient who was on ventilator and being vent... \n", - "1 Hkghest concentration of oxygen is delivered t... \n", - "2 Steps of intubation - arrange in sequence:- a.... \n", - "3 Tracheal secretions should be suctioned ror: \n", - "4 Ehich of the following nerve fibre types is le... \n", - "... ... \n", - "7361 A 39-year-old woman presents to the clinic for... \n", - "7362 A 38-year-old woman comes to the physician bec... \n", - "7363 A 21-year-old college student comes to the phy... \n", - "7364 A 19-year-old man is brought to the physician ... \n", - "7365 A 79-year-old man presents to the office due t... \n", + " perturbed_question \\\n", + "0 A patient who was on ventilayor and being vent... \n", + "1 Highest concentration of oxygen is deliveted t... \n", + "2 Steps of intubation - arrange in sequence:- a.... \n", + "3 Which of the following nerve fibre types is le... \n", + "4 All are yrue about rapid sequence induction do... \n", + "... ... \n", + "14930 a 39-year-old woman presents to the clinic for... \n", + "14931 a 38-year-old woman comes to the physician bec... \n", + "14932 a 21-year-old college student comes to the phy... \n", + "14933 a 19-year-old man is brought to the physician ... \n", + "14934 a 79-year-old man presents to the office due t... \n", "\n", - " options \n", - "0 A. Assess the patient, give bag and mask venti... \n", - "1 A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ... \n", - "2 A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED \n", - "3 A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds... \n", - "4 A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers \n", - "... ... \n", - "7361 A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro... \n", - "7362 A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo... \n", - "7363 A. Trial of diphenhydramine\\nB. Complete caffe... \n", - "7364 A. Social anxiety disorder\\nB. Avoidant person... \n", - "7365 A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr... \n", + " options \n", + "0 A. Assess the patient, give bag and mask venti... \n", + "1 A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ... \n", + "2 A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED \n", + "3 A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers \n", + "4 A. Inducing agent and neuromuscular relaxant a... \n", + "... ... \n", + "14930 A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro... \n", + "14931 A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo... \n", + "14932 A. Trial of diphenhydramine\\nB. Complete caffe... \n", + "14933 A. Social anxiety disorder\\nB. Avoidant person... \n", + "14934 A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr... \n", "\n", - "[7366 rows x 8 columns]" + "[14935 rows x 8 columns]" ] }, - "execution_count": 31, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1515,7 +1574,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1531,7 +1590,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Running testcases... : 100%|██████████| 4043/4043 [07:10<00:00, 9.39it/s]\n" + "Running testcases... : 100%|██████████| 8209/8209 [10:31<00:00, 13.01it/s]\n" ] }, { @@ -1549,7 +1608,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Running testcases... : 100%|██████████| 999/999 [03:02<00:00, 5.47it/s]\n" + "Running testcases... : 100%|██████████| 1997/1997 [05:41<00:00, 5.84it/s]\n" ] }, { @@ -1567,7 +1626,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Running testcases... : 100%|██████████| 1061/1061 [01:55<00:00, 9.17it/s]\n" + "Running testcases... : 100%|██████████| 2136/2136 [02:43<00:00, 13.04it/s]\n" ] }, { @@ -1585,7 +1644,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Running testcases... : 100%|██████████| 1263/1263 [02:19<00:00, 9.07it/s]" + "Running testcases... : 100%|██████████| 2593/2593 [03:29<00:00, 12.38it/s] " ] }, { @@ -1607,7 +1666,7 @@ "data": { "text/plain": [] }, - "execution_count": 32, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1618,7 +1677,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1664,7 +1723,7 @@ " -\n", " A patient who was on ventilator and being vent...\n", " -\n", - " A patient who was on ventilator and being vent...\n", + " A patient who was on ventilayor and being vent...\n", " A. Assess the patient, give bag and mask venti...\n", " A\n", " A\n", @@ -1678,11 +1737,11 @@ " -\n", " Highest concentration of oxygen is delivered t...\n", " -\n", - " Hkghest concentration of oxygen is delivered t...\n", + " Highest concentration of oxygen is deliveted t...\n", " A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ...\n", " B\n", - " A\n", - " False\n", + " B\n", + " True\n", " \n", " \n", " 2\n", @@ -1704,12 +1763,12 @@ " MedMCQA\n", " add_typo\n", " -\n", - " Tracheal secretions should be suctioned for:\n", + " Which of the following nerve fibre types is le...\n", " -\n", - " Tracheal secretions should be suctioned ror:\n", - " A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds...\n", - " A\n", - " A\n", + " Which of the following nerve fibre types is le...\n", + " A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers\n", + " D\n", + " D\n", " True\n", " \n", " \n", @@ -1718,12 +1777,12 @@ " MedMCQA\n", " add_typo\n", " -\n", - " Which of the following nerve fibre types is le...\n", + " All are true about rapid sequence induction do...\n", " -\n", - " Ehich of the following nerve fibre types is le...\n", - " A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers\n", - " D\n", - " D\n", + " All are yrue about rapid sequence induction do...\n", + " A. Inducing agent and neuromuscular relaxant a...\n", + " C\n", + " C\n", " True\n", " \n", " \n", @@ -1741,70 +1800,70 @@ " ...\n", " \n", " \n", - " 7361\n", + " 14930\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 39-year-old woman presents to the clinic for...\n", " -\n", - " A 39-year-old woman presents to the clinic for...\n", + " a 39-year-old woman presents to the clinic for...\n", " A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro...\n", " E\n", " E\n", " True\n", " \n", " \n", - " 7362\n", + " 14931\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 38-year-old woman comes to the physician bec...\n", " -\n", - " A 38-year-old woman comes to the physician bec...\n", + " a 38-year-old woman comes to the physician bec...\n", " A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo...\n", " D\n", " D\n", " True\n", " \n", " \n", - " 7363\n", + " 14932\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 21-year-old college student comes to the phy...\n", " -\n", - " A 21-year-old college student comes to the phy...\n", + " a 21-year-old college student comes to the phy...\n", " A. Trial of diphenhydramine\\nB. Complete caffe...\n", " B\n", " B\n", " True\n", " \n", " \n", - " 7364\n", + " 14933\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 19-year-old man is brought to the physician ...\n", " -\n", - " A 19-year-old man is brought to the physician ...\n", + " a 19-year-old man is brought to the physician ...\n", " A. Social anxiety disorder\\nB. Avoidant person...\n", " D\n", " D\n", " True\n", " \n", " \n", - " 7365\n", + " 14934\n", " robustness\n", " MedQA\n", - " add_typo\n", + " lowercase\n", " -\n", " A 79-year-old man presents to the office due t...\n", " -\n", - " A 79-year-old man presents to the office due t...\n", + " a 79-year-old man presents to the office due t...\n", " A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr...\n", " C\n", " C\n", @@ -1812,79 +1871,79 @@ " \n", " \n", "\n", - "

7366 rows × 11 columns

\n", + "

14935 rows × 11 columns

\n", "" ], "text/plain": [ - " category dataset_name test_type original_context \\\n", - "0 robustness MedMCQA add_typo - \n", - "1 robustness MedMCQA add_typo - \n", - "2 robustness MedMCQA add_typo - \n", - "3 robustness MedMCQA add_typo - \n", - "4 robustness MedMCQA add_typo - \n", - "... ... ... ... ... \n", - "7361 robustness MedQA add_typo - \n", - "7362 robustness MedQA add_typo - \n", - "7363 robustness MedQA add_typo - \n", - "7364 robustness MedQA add_typo - \n", - "7365 robustness MedQA add_typo - \n", + " category dataset_name test_type original_context \\\n", + "0 robustness MedMCQA add_typo - \n", + "1 robustness MedMCQA add_typo - \n", + "2 robustness MedMCQA add_typo - \n", + "3 robustness MedMCQA add_typo - \n", + "4 robustness MedMCQA add_typo - \n", + "... ... ... ... ... \n", + "14930 robustness MedQA lowercase - \n", + "14931 robustness MedQA lowercase - \n", + "14932 robustness MedQA lowercase - \n", + "14933 robustness MedQA lowercase - \n", + "14934 robustness MedQA lowercase - \n", "\n", - " original_question perturbed_context \\\n", - "0 A patient who was on ventilator and being vent... - \n", - "1 Highest concentration of oxygen is delivered t... - \n", - "2 Steps of intubation - arrange in sequence:- a.... - \n", - "3 Tracheal secretions should be suctioned for: - \n", - "4 Which of the following nerve fibre types is le... - \n", - "... ... ... \n", - "7361 A 39-year-old woman presents to the clinic for... - \n", - "7362 A 38-year-old woman comes to the physician bec... - \n", - "7363 A 21-year-old college student comes to the phy... - \n", - "7364 A 19-year-old man is brought to the physician ... - \n", - "7365 A 79-year-old man presents to the office due t... - \n", + " original_question perturbed_context \\\n", + "0 A patient who was on ventilator and being vent... - \n", + "1 Highest concentration of oxygen is delivered t... - \n", + "2 Steps of intubation - arrange in sequence:- a.... - \n", + "3 Which of the following nerve fibre types is le... - \n", + "4 All are true about rapid sequence induction do... - \n", + "... ... ... \n", + "14930 A 39-year-old woman presents to the clinic for... - \n", + "14931 A 38-year-old woman comes to the physician bec... - \n", + "14932 A 21-year-old college student comes to the phy... - \n", + "14933 A 19-year-old man is brought to the physician ... - \n", + "14934 A 79-year-old man presents to the office due t... - \n", "\n", - " perturbed_question \\\n", - "0 A patient who was on ventilator and being vent... \n", - "1 Hkghest concentration of oxygen is delivered t... \n", - "2 Steps of intubation - arrange in sequence:- a.... \n", - "3 Tracheal secretions should be suctioned ror: \n", - "4 Ehich of the following nerve fibre types is le... \n", - "... ... \n", - "7361 A 39-year-old woman presents to the clinic for... \n", - "7362 A 38-year-old woman comes to the physician bec... \n", - "7363 A 21-year-old college student comes to the phy... \n", - "7364 A 19-year-old man is brought to the physician ... \n", - "7365 A 79-year-old man presents to the office due t... \n", + " perturbed_question \\\n", + "0 A patient who was on ventilayor and being vent... \n", + "1 Highest concentration of oxygen is deliveted t... \n", + "2 Steps of intubation - arrange in sequence:- a.... \n", + "3 Which of the following nerve fibre types is le... \n", + "4 All are yrue about rapid sequence induction do... \n", + "... ... \n", + "14930 a 39-year-old woman presents to the clinic for... \n", + "14931 a 38-year-old woman comes to the physician bec... \n", + "14932 a 21-year-old college student comes to the phy... \n", + "14933 a 19-year-old man is brought to the physician ... \n", + "14934 a 79-year-old man presents to the office due t... \n", "\n", - " options expected_result \\\n", - "0 A. Assess the patient, give bag and mask venti... A \n", - "1 A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ... B \n", - "2 A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED D \n", - "3 A. 10-15 seconds\\nB. 60 seconds\\nC. 30 seconds... A \n", - "4 A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers D \n", - "... ... ... \n", - "7361 A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro... E \n", - "7362 A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo... D \n", - "7363 A. Trial of diphenhydramine\\nB. Complete caffe... B \n", - "7364 A. Social anxiety disorder\\nB. Avoidant person... D \n", - "7365 A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr... C \n", + " options expected_result \\\n", + "0 A. Assess the patient, give bag and mask venti... A \n", + "1 A. Nasal cannula\\nB. Venturi mask\\nC. Bag and ... B \n", + "2 A. ABCDE\\nB. DBCEA\\nC. ACBED\\nD. CBAED D \n", + "3 A. A beta\\nB. A alpha\\nC. B fibers\\nD. C fibers D \n", + "4 A. Inducing agent and neuromuscular relaxant a... C \n", + "... ... ... \n", + "14930 A. Erythropoietin\\nB. Glucose\\nC. Triiodothyro... E \n", + "14931 A. Celiac disease\\nB. Carcinoid tumor\\nC. VIPo... D \n", + "14932 A. Trial of diphenhydramine\\nB. Complete caffe... B \n", + "14933 A. Social anxiety disorder\\nB. Avoidant person... D \n", + "14934 A. Asthma\\nB. Lymphangioleiomyomatosis\\nC. Chr... C \n", "\n", - " actual_result pass \n", - "0 A True \n", - "1 A False \n", - "2 D True \n", - "3 A True \n", - "4 D True \n", - "... ... ... \n", - "7361 E True \n", - "7362 D True \n", - "7363 B True \n", - "7364 D True \n", - "7365 C True \n", + " actual_result pass \n", + "0 A True \n", + "1 B True \n", + "2 D True \n", + "3 D True \n", + "4 C True \n", + "... ... ... \n", + "14930 E True \n", + "14931 D True \n", + "14932 B True \n", + "14933 D True \n", + "14934 C True \n", "\n", - "[7366 rows x 11 columns]" + "[14935 rows x 11 columns]" ] }, - "execution_count": 33, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1895,7 +1954,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1950,42 +2009,74 @@ " \n", " \n", " \n", - " MedMCQA\n", - " robustness\n", + " MedMCQA\n", + " robustness\n", " add_typo\n", - " 509\n", - " 3534\n", + " 531\n", + " 3524\n", " 87%\n", " 70%\n", " True\n", " \n", " \n", - " PubMedQA\n", - " robustness\n", - " add_typo\n", - " 82\n", - " 917\n", + " lowercase\n", + " 335\n", + " 3819\n", " 92%\n", " 70%\n", " True\n", " \n", " \n", - " MMLU\n", - " robustness\n", + " PubMedQA\n", + " robustness\n", " add_typo\n", - " 110\n", - " 951\n", - " 90%\n", + " 70\n", + " 927\n", + " 93%\n", " 70%\n", " True\n", " \n", " \n", - " MedQA\n", - " robustness\n", + " lowercase\n", + " 300\n", + " 700\n", + " 70%\n", + " 70%\n", + " True\n", + " \n", + " \n", + " MMLU\n", + " robustness\n", " add_typo\n", - " 50\n", - " 1213\n", - " 96%\n", + " 89\n", + " 959\n", + " 92%\n", + " 70%\n", + " True\n", + " \n", + " \n", + " lowercase\n", + " 91\n", + " 997\n", + " 92%\n", + " 70%\n", + " True\n", + " \n", + " \n", + " MedQA\n", + " robustness\n", + " add_typo\n", + " 43\n", + " 1227\n", + " 97%\n", + " 70%\n", + " True\n", + " \n", + " \n", + " lowercase\n", + " 94\n", + " 1229\n", + " 93%\n", " 70%\n", " True\n", " \n", @@ -1997,21 +2088,29 @@ " Benchmarking Results: google/flan-t5-large \\\n", " fail_count \n", "dataset_name category test_type \n", - "MedMCQA robustness add_typo 509 \n", - "PubMedQA robustness add_typo 82 \n", - "MMLU robustness add_typo 110 \n", - "MedQA robustness add_typo 50 \n", + "MedMCQA robustness add_typo 531 \n", + " lowercase 335 \n", + "PubMedQA robustness add_typo 70 \n", + " lowercase 300 \n", + "MMLU robustness add_typo 89 \n", + " lowercase 91 \n", + "MedQA robustness add_typo 43 \n", + " lowercase 94 \n", "\n", " \n", " pass_count pass_rate minimum_pass_rate pass \n", "dataset_name category test_type \n", - "MedMCQA robustness add_typo 3534 87% 70% True \n", - "PubMedQA robustness add_typo 917 92% 70% True \n", - "MMLU robustness add_typo 951 90% 70% True \n", - "MedQA robustness add_typo 1213 96% 70% True " + "MedMCQA robustness add_typo 3524 87% 70% True \n", + " lowercase 3819 92% 70% True \n", + "PubMedQA robustness add_typo 927 93% 70% True \n", + " lowercase 700 70% 70% True \n", + "MMLU robustness add_typo 959 92% 70% True \n", + " lowercase 997 92% 70% True \n", + "MedQA robustness add_typo 1227 97% 70% True \n", + " lowercase 1229 93% 70% True " ] }, - "execution_count": 34, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -2022,7 +2121,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -2058,20 +2157,20 @@ " \n", " 0\n", " google/flan-t5-base\n", - " 0.984609\n", - " 0.985769\n", - " 0.957231\n", - " 0.998436\n", + " 0.986188\n", + " 0.983114\n", + " 0.968840\n", + " 0.995798\n", " 0.997000\n", " \n", " \n", " 1\n", " google/flan-t5-large\n", - " 0.912189\n", - " 0.896324\n", - " 0.874103\n", - " 0.960412\n", - " 0.917918\n", + " 0.893090\n", + " 0.915718\n", + " 0.894203\n", + " 0.947546\n", + " 0.814895\n", " \n", " \n", "\n", @@ -2079,11 +2178,11 @@ ], "text/plain": [ " model Avg MMLU MedMCQA MedQA PubMedQA\n", - "0 google/flan-t5-base 0.984609 0.985769 0.957231 0.998436 0.997000\n", - "1 google/flan-t5-large 0.912189 0.896324 0.874103 0.960412 0.917918" + "0 google/flan-t5-base 0.986188 0.983114 0.968840 0.995798 0.997000\n", + "1 google/flan-t5-large 0.893090 0.915718 0.894203 0.947546 0.814895" ] }, - "execution_count": 35, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2091,6 +2190,202 @@ "source": [ "harness1.get_leaderboard()" ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dataset_nameAvgMMLUMedMCQAMedQAPubMedQA
categorymodel
robustnessgoogle/flan-t5-base0.9861880.9831140.9688400.9957980.997000
google/flan-t5-large0.8930900.9157180.8942030.9475460.814895
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    -## 2.1.0 +## 2.2.0 ------------------ ## 📢 Highlights -John Snow Labs is thrilled to announce the release of LangTest 2.1.0! This update brings exciting new features and improvements designed to streamline your language model testing workflows and provide deeper insights. +John Snow Labs is excited to announce the release of LangTest 2.2.0! This update introduces powerful new features and enhancements to elevate your language model testing experience and deliver even greater insights. -- **🔗 Enhanced API-based LLM Integration:** LangTest now supports testing API-based Large Language Models (LLMs). This allows you to seamlessly integrate diverse LLM models with LangTest and conduct performance evaluations across various datasets. +- 🏆 **Model Ranking & Leaderboard**: LangTest introduces a comprehensive model ranking system. Use harness.get_leaderboard() to rank models based on various test metrics and retain previous rankings for historical comparison. -- **📂 Expanded File Format Support:** LangTest 2.1.0 introduces support for additional file formats, further increasing its flexibility in handling different data structures used in LLM testing. +- 🔍 **Few-Shot Model Evaluation:** Optimize and evaluate your models using few-shot prompt techniques. This feature enables you to assess model performance with minimal data, providing valuable insights into model capabilities with limited examples. -- **📊 Improved Multi-Dataset Handling:** We've made significant improvements in how LangTest manages multiple datasets. This simplifies workflows and allows for more efficient testing across a wider range of data sources. +- 📊 **Evaluating NER in LLMs:** This release extends support for Named Entity Recognition (NER) tasks specifically for Large Language Models (LLMs). Evaluate and benchmark LLMs on their NER performance with ease. -- **🖥️ New Benchmarking Commands**: LangTest now boasts a set of new commands specifically designed for benchmarking language models. These commands provide a structured approach to evaluating model performance and comparing results across different models and datasets. +- 🚀 **Enhanced Data Augmentation:** The new DataAugmenter module allows for streamlined and harness-free data augmentation, making it simpler to enhance your datasets and improve model robustness. + +- 🎯 **Multi-Dataset Prompts:** LangTest now offers optimized prompt handling for multiple datasets, allowing users to add custom prompts for each dataset, enabling seamless integration and efficient testing.
    ## 🔥 Key Enhancements: -### **🔗 Streamlined Integration and Enhanced Functionality for API-Based Large Language Models:** -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/Generic_API-Based_Model_Testing_Demo.ipynb) - -This feature empowers you to seamlessly integrate virtually any language model hosted on an external API platform. Whether you prefer OpenAI, Hugging Face, or even custom vLLM solutions, LangTest now adapts to your workflow. `input_processor` and `output_parser` functions are not required for openai api compatible server. - -#### Key Features: - -- **Effortless API Integration:** Connect to any API system by specifying the API URL, parameters, and a custom function for parsing the returned results. This intuitive approach allows you to leverage your preferred language models with minimal configuration. +### **🏆 Comprehensive Model Ranking & Leaderboard** +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/benchmarks/Benchmarking_with_Harness.ipynb) +The new Model Ranking & Leaderboard system offers a comprehensive way to evaluate and compare model performance based on various metrics across different datasets. This feature allows users to rank models, retain historical rankings, and analyze performance trends. -- **Customizable Parameters:** Define the URL, parameters specific to your chosen API, and a parsing function tailored to extract the desired output. This level of control ensures compatibility with diverse API structures. +**Key Features:** +- **Comprehensive Ranking**: Rank models based on various performance metrics across multiple datasets. +- **Historical Comparison**: Retain and compare previous rankings for consistent performance tracking. +- **Dataset-Specific Insights**: Evaluate model performance on different datasets to gain deeper insights. -- **Unparalleled Flexibility:** Generic API Support removes platform limitations. Now, you can seamlessly integrate language models from various sources, including OpenAI, Hugging Face, and even custom vLLM solutions hosted on private platforms. - -#### How it Works: - -**Parameters:** -Define the `input_processer` function for creating a payload and the `output_parser` function is used to extract the output from the response. - -```python -GOOGLE_API_KEY = "" -model_url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key={GOOGLE_API_KEY}" +**How It Works:** -# headers -headers = { - "Content-Type": "application/json", -} +The following are steps to do model ranking and visualize the leaderboard for `google/flan-t5-base` and `google/flan-t5-large` models. +**1.** Setup and configuration of the Harness are as follows: -# function to create a payload -def input_processor(content): - return {"contents": [ - { - "role": "user", - "parts": [ - { - "text": content - } - ] - } - ]} - -# function to extract output from model response -def output_parser(response): - try: - return response['candidates'][0]['content']['parts'][0]['text'] - except: - return "" +```yaml +# config.yaml +model_parameters: + max_tokens: 64 + device: 0 + task: text2text-generation +tests: + defaults: + min_pass_rate: 0.65 + robustness: + add_typo: + min_pass_rate: 0.7 + lowercase: + min_pass_rate: 0.7 ``` - -To take advantage of this feature, users can utilize the following setup code: - ```python from langtest import Harness -# Initialize Harness with API parameters harness = Harness( task="question-answering", model={ - "model": { - "url": url, - "headers": headers, - "input_processor": input_processor, - "output_parser": output_parser, - }, - "hub": "web", + "model": "google/flan-t5-base", + "hub": "huggingface" }, - data={ - "data_source": "OpenBookQA", - "split": "test-tiny", + data=[ + { + "data_source": "MedMCQA" + }, + { + "data_source": "PubMedQA" + }, + { + "data_source": "MMLU" + }, + { + "data_source": "MedQA" + } + ], + config="config.yml", + benchmarking={ + "save_dir":"~/.langtest/leaderboard/" # required for benchmarking } ) -# Generate, Run and get Report -harness.generate().run().report() ``` -![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/9754c506-e715-4e2c-8b9d-dfd98f0695e5) +**2**. generate the test cases, run on the model, and get the report as follows: +```python +harness.generate().run().report() +``` +![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/d8055592-5501-4139-ad90-55baa4fecbfc) -### 📂 Streamlined Data Handling and Evaluation +**3**. Similarly, do the same steps for the `google/flan-t5-large` model with the same `save_dir` path for benchmarking and the same `config.yaml` -This feature streamlines your testing workflows by enabling LangTest to process a wider range of file formats directly. +**4**. Finally, the leaderboard can show the model rank by calling the below code. +```python +harness.get_leaderboard() +``` +![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/ff741d8e-4fc0-4f94-bcc3-9c67653aaba8) -#### Key Features: +**Conclusion:** +The Model Ranking & Leaderboard system provides a robust and structured method for evaluating and comparing models across multiple datasets, enabling users to make data-driven decisions and continuously improve model performance. -- **Effortless File Format Handling:** LangTest now seamlessly ingests data from various file formats, including pickles (.pkl) in addition to previously supported formats. Simply provide the data source path in your harness configuration, and LangTest takes care of the rest. -- **Simplified Data Source Management**: LangTest intelligently recognizes the file extension and automatically selects the appropriate processing method. This eliminates the need for manual configuration, saving you time and effort. +### **🔍 Efficient Few-Shot Model Evaluation** +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/Fewshot_QA_Notebook.ipynb) +Few-Shot Model Evaluation optimizes and evaluates model performance using minimal data. This feature provides rapid insights into model capabilities, enabling efficient assessment and optimization with limited examples. -- **Enhanced Maintainability**: The underlying code structure is optimized for flexibility. Adding support for new file formats in the future requires minimal effort, ensuring LangTest stays compatible with evolving data storage practices. +**Key Features:** +- **Few-Shot Techniques**: Evaluate models with minimal data to gauge performance quickly. +- **Optimized Performance**: Improve model outputs using targeted few-shot prompts. +- **Efficient Evaluation**: Streamlined process for rapid and effective model assessment. -#### How it works: +**How It Works:** +**1.** Set up few-shot prompts tailored to specific evaluation needs. +```yaml +# config.yaml +prompt_config: + "BoolQ": + instructions: > + You are an intelligent bot and it is your responsibility to make sure + to give a concise answer. Answer should be `true` or `false`. + prompt_type: "instruct" # instruct for completion and chat for conversation(chat models) + examples: + - user: + context: > + The Good Fight -- A second 13-episode season premiered on March 4, 2018. + On May 2, 2018, the series was renewed for a third season. + question: "is there a third series of the good fight?" + ai: + answer: "True" + - user: + context: > + Lost in Space -- The fate of the castaways is never resolved, + as the series was unexpectedly canceled at the end of season 3. + question: "did the robinsons ever get back to earth" + ai: + answer: "True" + "NQ-open": + instructions: > + You are an intelligent bot and it is your responsibility to make sure + to give a short concise answer. + prompt_type: "instruct" # completion + examples: + - user: + question: "where does the electron come from in beta decay?" + ai: + answer: "an atomic nucleus" + - user: + question: "who wrote you're a grand ol flag?" + ai: + answer: "George M. Cohan" + +tests: + defaults: + min_pass_rate: 0.8 + robustness: + uppercase: + min_pass_rate: 0.8 + add_typo: + min_pass_rate: 0.8 +``` +**2.** Initialize the Harness with `config.yaml` file as below code +```python +harness = Harness( + task="question-answering", + model={"model": "gpt-3.5-turbo-instruct","hub":"openai"}, + data=[{"data_source" :"BoolQ", + "split":"test-tiny"}, + {"data_source" :"NQ-open", + "split":"test-tiny"}], + config="config.yaml" + ) +``` +**3.** Generate the test cases, run them on the model, and then generate the report. ```python -from langtest import Harness +harness.generate().run().report() +``` +![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/4bae4008-621c-4d1c-a303-218f9df2700d) -harness = Harness( - task="question-answering", - model={ - "model": "http://localhost:1234/v1/chat/completions", - "hub": "lm-studio", - }, - data={ - "data_source": "path/to/file.pkl", # - }, -) -# generate, run and report +**Conclusion:** +Few-Shot Model Evaluation provides valuable insights into model capabilities with minimal data, allowing for rapid and effective performance optimization. This feature ensures that models can be assessed and improved efficiently, even with limited examples. + + +### **📊 Evaluating NER in LLMs** +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/NER%20Casual%20LLM.ipynb) +Evaluating NER in LLMs enables precise extraction and evaluation of entities using Large Language Models (LLMs). This feature enhances the capability to assess LLM performance on Named Entity Recognition tasks. + +**Key Features:** +- **LLM-Specific Support**: Tailored for evaluating NER tasks using LLMs. +- **Accurate Entity Extraction**: Improved techniques for precise entity extraction. +- **Comprehensive Evaluation**: Detailed assessment of entity extraction performance. + +**How It Works:** +**1.** Set up NER tasks for specific LLM evaluation. +```python +# Create a Harness object +harness = Harness(task="ner", + model={ + "model": "gpt-3.5-turbo-instruct", + "hub": "openai", }, + data={ + "data_source": 'path/to/conll03.conll' + }, + config={ + "model_parameters": { + "temperature": 0, + }, + "tests": { + "defaults": { + "min_pass_rate": 1.0 + }, + "robustness": { + "lowercase": { + "min_pass_rate": 0.7 + } + }, + "accuracy": { + "min_f1_score": { + "min_score": 0.7, + }, + } + } + } + ) +``` +**2.** Generate the test cases based on the configuration in the Harness, run them on the model, and get the report. +```python harness.generate().run().report() ``` -### 📊 Multi-Dataset Handling and Evaluation -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Multiple_dataset.ipynb) +![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/9435fa17-d3f7-4d47-934c-4cd483b11a53) + +Examples: +![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/2ceb3390-9f07-4b17-b9e7-b32504ad1afe) + +**Conclusion:** +Evaluating NER in LLMs allows for accurate entity extraction and performance assessment using LangTest's comprehensive evaluation methods. This feature ensures thorough and reliable evaluation of LLMs on Named Entity Recognition tasks. -This feature empowers you to efficiently benchmark your language models across a wider range of datasets. -#### Key Features: +### **🚀 Enhanced Data Augmentation** +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb) +Enhanced Data Augmentation introduces a new `DataAugmenter` class, enabling streamlined and harness-free data augmentation. This feature simplifies the process of enriching datasets to improve model robustness and performance. -- **Effortless Multi-Dataset Testing:** LangTest now seamlessly integrates and executes tests on multiple datasets within a single harness configuration. This streamlined approach eliminates the need for repetitive setups, saving you time and resources. +**Key Features:** +- **Harness-Free Augmentation**: Perform data augmentation without the need for harness testing. +- **Improved Workflow**: Simplified processes for enhancing datasets efficiently. +- **Robust Models**: Increase model robustness through effective data augmentation techniques. -- **Enhanced Fairness Evaluation**: By testing models across diverse datasets, LangTest helps identify and mitigate potential biases. This ensures your models perform fairly and accurately on a broader spectrum of data, promoting ethical and responsible AI development. +**How It Works:** +The following are steps to import the `DataAugmenter` class from LangTest. +**1.** Create a config.yaml for the data augmentation. +```yaml +# config.yaml +parameters: + type: proportion + style: new +tests: + robustness: + uppercase: + max_proportion: 0.2 + lowercase: + max_proportion: 0.2 + +``` +**2.** Initialize the `DataAugmenter` class and apply various tests for augmentation to your datasets. +```python +from langtest.augmentation import DataAugmenter +from langtest.tasks.task import TaskManager + +data_augmenter = DataAugmenter( + task=TaskManager("ner"), # use the ner, text-classification, question-answering... + config="config.yaml", +) +``` +**3.** Provide the training dataset to `data_augmenter`. +```python +data_augmenter.augment(data={ + 'data_source': 'path/to/conll03.conll' +}) +``` +**4.** Then, save the augmented dataset. +``` +data_augmenter.save("augmented.conll") +``` +**Conclusion:** +Enhanced Data Augmentation capabilities in LangTest ensure that your models are more robust and capable of handling diverse data scenarios. This feature simplifies the augmentation process, leading to improved model performance and reliability. -- **Robust Accuracy Assessment:** Multi-dataset support empowers you to conduct more rigorous accuracy testing. By evaluating models on various datasets, you gain a deeper understanding of their strengths and weaknesses across different data distributions. This comprehensive analysis strengthens your confidence in the model's real-world performance. -#### How it works: +### **🎯Multi-Dataset Prompts** +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb) +Multi-Dataset Prompts streamline the process of integrating and testing various data sources by allowing users to define custom prompts for each dataset. This enhancement ensures efficient prompt handling across multiple datasets, enabling comprehensive performance evaluations. -Initiate the Harness class +**Key Features:** + +- **Custom Prompts:** Add tailored prompts for each dataset to enhance testing accuracy. +- **Seamless Integration:** Easily incorporate multiple datasets into your testing environment. +- **Improved Efficiency:** Simplified workflows for handling diverse data sources. + +**How It Works:** +**1.** Initiate the Harness with `BoolQ` and `NQ-open` datasets. ```python +# Import Harness from the LangTest library +from langtest import Harness + harness = Harness( task="question-answering", model={"model": "gpt-3.5-turbo-instruct", "hub": "openai"}, data=[ - {"data_source": "NQ-open", "split": "test-tiny",}, - {"data_source": "MedQA", "split": "test-tiny"}, - {"data_source": "LogiQA", "split": "test-tiny"}, + {"data_source": "BoolQ", "split": "dev-tiny"}, + {"data_source": "NQ-open", "split": "test-tiny"} ], ) ``` -Configure the accuracy tests in Harness class +**2.** Configure prompts specific to each dataset, allowing tailored evaluations. ```python harness.configure( { + "model_parameters": { + "user_prompt": { + "BoolQ": "Answer the following question with a True or False. {context}\nQuestion {question}", + "NQ-open": "Answer the following question. Question {question}", + } + }, "tests": { "defaults": {"min_pass_rate": 0.65}, - - "accuracy": { - "llm_eval": {"min_score": 0.60}, - "min_rouge1_score": {"min_score": 0.60}, - "min_rouge2_score": {"min_score": 0.60}, - "min_rougeL_score": {"min_score": 0.60}, - "min_rougeLsum_score": {"min_score": 0.60}, + "robustness": { + "uppercase": {"min_pass_rate": 0.66}, + "dyslexia_word_swap": {"min_pass_rate": 0.60}, + "add_abbreviation": {"min_pass_rate": 0.60}, + "add_slangs": {"min_pass_rate": 0.60}, + "add_speech_to_text_typo": {"min_pass_rate": 0.60}, }, } } ) ``` -harness.generate() generates testcases, .run() executes them, and .report() compiles results. +**3.** Generate the test cases, run them on the model, and get the report. ```python harness.generate().run().report() ``` -![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/0d48be2f-e5bc-4971-b0a1-2756a10d3f24) - -### 🖥️ Streamlined Evaluation Workflows with Enhanced CLI Commands -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/benchmarks/Langtest_Cli_Eval_Command.ipynb) - -LangTest's evaluation capabilities, focusing on report management and leaderboards. These enhancements empower you to: - -- **Streamlined Reporting and Tracking:** Effortlessly save and load detailed evaluation reports directly from the command line using `langtest eval`, enabling efficient performance tracking and comparative analysis over time, with manual file review options in the `~/.langtest` or `./.langtest` folder. - -- **Enhanced Leaderboards:** Gain valuable insights with the new langtest show-leaderboard command. This command displays existing leaderboards, providing a centralized view of ranked model performance across evaluations. - -- **Average Model Ranking:** Leaderboard now include the average ranking for each evaluated model. This metric provides a comprehensive understanding of model performance across various datasets and tests. - -### How it works: - -First, create the `parameter.json` or `parameter.yaml` in the working directory - -**JSON Format** -```json -{ - "task": "question-answering", - "model": { - "model": "google/flan-t5-base", - "hub": "huggingface" - }, - "data": [ - { - "data_source": "MedMCQA" - }, - { - "data_source": "PubMedQA" - }, - { - "data_source": "MMLU" - }, - { - "data_source": "MedQA" - } - ], - "config": { - "model_parameters": { - "max_tokens": 64, - "device": 0, - "task": "text2text-generation" - }, - "tests": { - "defaults": { - "min_pass_rate": 0.70 - }, - "robustness": { - "add_typo": { - "min_pass_rate": 0.70 - } - } - } - } -} -``` -**Yaml Format** -```yaml -task: question-answering -model: - model: google/flan-t5-base - hub: huggingface -data: -- data_source: MedMCQA -- data_source: PubMedQA -- data_source: MMLU -- data_source: MedQA -config: - model_parameters: - max_tokens: 64 - device: 0 - task: text2text-generation - tests: - defaults: - min_pass_rate: 0.70 - robustness: - add_typo: - min_pass_rate: 0.7 - -``` -And open the terminal or cmd in your system -```bash -langtest eval --model \ - --hub \ - -c < your configuration file like parameter.json or parameter.yaml> -``` -Finally, we can know the leaderboard and rank of the model. -![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/a405d0c6-5ef1-4efb-924c-0ba8667ebe43) +![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/a961d98d-a229-439e-a9eb-92395dde6f62) ----- - -To visualize the leaderboard anytime using the CLI command -```bash -langtest show-leaderboard -``` -![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/f357c173-e4b1-4dc8-86ad-98438046b89c) +**Conclusion:** +Multi-dataset prompts in LangTest empower users to efficiently manage and test multiple data sources, resulting in more effective and comprehensive language model evaluations. ## 📒 New Notebooks +{:.table2} | Notebooks | Colab Link | |--------------------|-------------| -| Generic API-based Model Testing | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/Generic_API-Based_Model_Testing_Demo.ipynb)| -| Multi-Dataset | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Multiple_dataset.ipynb) | -| Langtest Eval Cli Command | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/benchmarks/Langtest_Cli_Eval_Command.ipynb) | ----------------- +| Model Ranking & Leaderboard | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/benchmarks/Benchmarking_with_Harness.ipynb)| +| Fewshot Model Evaluation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/Fewshot_QA_Notebook.ipynb) | +| Evaluating NER in LLMs | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/NER%20Casual%20LLM.ipynb) | +| Data Augmenter | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb) | +| Multi-Dataset Prompts | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb) | +
    {%- include docs-langtest-pagination.html -%} diff --git a/docs/pages/docs/langtest_versions/release_notes_2_1_0.md b/docs/pages/docs/langtest_versions/release_notes_2_1_0.md new file mode 100644 index 000000000..926aab701 --- /dev/null +++ b/docs/pages/docs/langtest_versions/release_notes_2_1_0.md @@ -0,0 +1,327 @@ +--- +layout: docs +header: true +seotitle: LangTest - Deliver Safe and Effective Language Models | John Snow Labs +title: LangTest Release Notes +permalink: /docs/pages/docs/langtest_versions/release_notes_2_1_0 +key: docs-release-notes +modify_date: 2024-04-02 +--- + +
    + +## 2.1.0 + +## 📢 Highlights + +John Snow Labs is thrilled to announce the release of LangTest 2.1.0! This update brings exciting new features and improvements designed to streamline your language model testing workflows and provide deeper insights. + +- **🔗 Enhanced API-based LLM Integration:** LangTest now supports testing API-based Large Language Models (LLMs). This allows you to seamlessly integrate diverse LLM models with LangTest and conduct performance evaluations across various datasets. + +- **📂 Expanded File Format Support:** LangTest 2.1.0 introduces support for additional file formats, further increasing its flexibility in handling different data structures used in LLM testing. + +- **📊 Improved Multi-Dataset Handling:** We've made significant improvements in how LangTest manages multiple datasets. This simplifies workflows and allows for more efficient testing across a wider range of data sources. + +- **🖥️ New Benchmarking Commands**: LangTest now boasts a set of new commands specifically designed for benchmarking language models. These commands provide a structured approach to evaluating model performance and comparing results across different models and datasets. + +
    + +## 🔥 Key Enhancements: + +### **🔗 Streamlined Integration and Enhanced Functionality for API-Based Large Language Models:** +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/Generic_API-Based_Model_Testing_Demo.ipynb) + +This feature empowers you to seamlessly integrate virtually any language model hosted on an external API platform. Whether you prefer OpenAI, Hugging Face, or even custom vLLM solutions, LangTest now adapts to your workflow. `input_processor` and `output_parser` functions are not required for openai api compatible server. + +#### Key Features: + +- **Effortless API Integration:** Connect to any API system by specifying the API URL, parameters, and a custom function for parsing the returned results. This intuitive approach allows you to leverage your preferred language models with minimal configuration. + +- **Customizable Parameters:** Define the URL, parameters specific to your chosen API, and a parsing function tailored to extract the desired output. This level of control ensures compatibility with diverse API structures. + +- **Unparalleled Flexibility:** Generic API Support removes platform limitations. Now, you can seamlessly integrate language models from various sources, including OpenAI, Hugging Face, and even custom vLLM solutions hosted on private platforms. + +#### How it Works: + +**Parameters:** +Define the `input_processer` function for creating a payload and the `output_parser` function is used to extract the output from the response. + +```python +GOOGLE_API_KEY = "" +model_url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key={GOOGLE_API_KEY}" + +# headers +headers = { + "Content-Type": "application/json", +} + +# function to create a payload +def input_processor(content): + return {"contents": [ + { + "role": "user", + "parts": [ + { + "text": content + } + ] + } + ]} + +# function to extract output from model response +def output_parser(response): + try: + return response['candidates'][0]['content']['parts'][0]['text'] + except: + return "" +``` + +To take advantage of this feature, users can utilize the following setup code: + +```python +from langtest import Harness + +# Initialize Harness with API parameters +harness = Harness( + task="question-answering", + model={ + "model": { + "url": url, + "headers": headers, + "input_processor": input_processor, + "output_parser": output_parser, + }, + "hub": "web", + }, + data={ + "data_source": "OpenBookQA", + "split": "test-tiny", + } +) +# Generate, Run and get Report +harness.generate().run().report() +``` +![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/9754c506-e715-4e2c-8b9d-dfd98f0695e5) + + +### 📂 Streamlined Data Handling and Evaluation + +This feature streamlines your testing workflows by enabling LangTest to process a wider range of file formats directly. + +#### Key Features: + +- **Effortless File Format Handling:** LangTest now seamlessly ingests data from various file formats, including pickles (.pkl) in addition to previously supported formats. Simply provide the data source path in your harness configuration, and LangTest takes care of the rest. + +- **Simplified Data Source Management**: LangTest intelligently recognizes the file extension and automatically selects the appropriate processing method. This eliminates the need for manual configuration, saving you time and effort. + +- **Enhanced Maintainability**: The underlying code structure is optimized for flexibility. Adding support for new file formats in the future requires minimal effort, ensuring LangTest stays compatible with evolving data storage practices. + +#### How it works: + +```python +from langtest import Harness + +harness = Harness( + task="question-answering", + model={ + "model": "http://localhost:1234/v1/chat/completions", + "hub": "lm-studio", + }, + data={ + "data_source": "path/to/file.pkl", # + }, +) +# generate, run and report +harness.generate().run().report() +``` +### 📊 Multi-Dataset Handling and Evaluation +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Multiple_dataset.ipynb) + +This feature empowers you to efficiently benchmark your language models across a wider range of datasets. + +#### Key Features: + +- **Effortless Multi-Dataset Testing:** LangTest now seamlessly integrates and executes tests on multiple datasets within a single harness configuration. This streamlined approach eliminates the need for repetitive setups, saving you time and resources. + +- **Enhanced Fairness Evaluation**: By testing models across diverse datasets, LangTest helps identify and mitigate potential biases. This ensures your models perform fairly and accurately on a broader spectrum of data, promoting ethical and responsible AI development. + +- **Robust Accuracy Assessment:** Multi-dataset support empowers you to conduct more rigorous accuracy testing. By evaluating models on various datasets, you gain a deeper understanding of their strengths and weaknesses across different data distributions. This comprehensive analysis strengthens your confidence in the model's real-world performance. + +#### How it works: + +Initiate the Harness class +```python +harness = Harness( + task="question-answering", + model={"model": "gpt-3.5-turbo-instruct", "hub": "openai"}, + data=[ + {"data_source": "NQ-open", "split": "test-tiny",}, + {"data_source": "MedQA", "split": "test-tiny"}, + {"data_source": "LogiQA", "split": "test-tiny"}, + ], +) +``` +Configure the accuracy tests in Harness class +```python +harness.configure( + { + "tests": { + "defaults": {"min_pass_rate": 0.65}, + + "accuracy": { + "llm_eval": {"min_score": 0.60}, + "min_rouge1_score": {"min_score": 0.60}, + "min_rouge2_score": {"min_score": 0.60}, + "min_rougeL_score": {"min_score": 0.60}, + "min_rougeLsum_score": {"min_score": 0.60}, + }, + } + } +) +``` +harness.generate() generates testcases, .run() executes them, and .report() compiles results. +```python +harness.generate().run().report() +``` +![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/0d48be2f-e5bc-4971-b0a1-2756a10d3f24) + +### 🖥️ Streamlined Evaluation Workflows with Enhanced CLI Commands +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/benchmarks/Langtest_Cli_Eval_Command.ipynb) + +LangTest's evaluation capabilities, focusing on report management and leaderboards. These enhancements empower you to: + +- **Streamlined Reporting and Tracking:** Effortlessly save and load detailed evaluation reports directly from the command line using `langtest eval`, enabling efficient performance tracking and comparative analysis over time, with manual file review options in the `~/.langtest` or `./.langtest` folder. + +- **Enhanced Leaderboards:** Gain valuable insights with the new langtest show-leaderboard command. This command displays existing leaderboards, providing a centralized view of ranked model performance across evaluations. + +- **Average Model Ranking:** Leaderboard now include the average ranking for each evaluated model. This metric provides a comprehensive understanding of model performance across various datasets and tests. + +### How it works: + +First, create the `parameter.json` or `parameter.yaml` in the working directory + +**JSON Format** +```json +{ + "task": "question-answering", + "model": { + "model": "google/flan-t5-base", + "hub": "huggingface" + }, + "data": [ + { + "data_source": "MedMCQA" + }, + { + "data_source": "PubMedQA" + }, + { + "data_source": "MMLU" + }, + { + "data_source": "MedQA" + } + ], + "config": { + "model_parameters": { + "max_tokens": 64, + "device": 0, + "task": "text2text-generation" + }, + "tests": { + "defaults": { + "min_pass_rate": 0.70 + }, + "robustness": { + "add_typo": { + "min_pass_rate": 0.70 + } + } + } + } +} +``` +**Yaml Format** +```yaml +task: question-answering +model: + model: google/flan-t5-base + hub: huggingface +data: +- data_source: MedMCQA +- data_source: PubMedQA +- data_source: MMLU +- data_source: MedQA +config: + model_parameters: + max_tokens: 64 + device: 0 + task: text2text-generation + tests: + defaults: + min_pass_rate: 0.70 + robustness: + add_typo: + min_pass_rate: 0.7 + +``` +And open the terminal or cmd in your system +```bash +langtest eval --model \ + --hub \ + -c < your configuration file like parameter.json or parameter.yaml> +``` +Finally, we can know the leaderboard and rank of the model. +![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/a405d0c6-5ef1-4efb-924c-0ba8667ebe43) + +---- + +To visualize the leaderboard anytime using the CLI command +```bash +langtest show-leaderboard +``` +![image](https://github.com/JohnSnowLabs/langtest/assets/23481244/f357c173-e4b1-4dc8-86ad-98438046b89c) + +## 📒 New Notebooks + +{:.table2} +| Notebooks | Colab Link | +|--------------------|-------------| +| Generic API-based Model Testing | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/Generic_API-Based_Model_Testing_Demo.ipynb)| +| Multi-Dataset | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Multiple_dataset.ipynb) | +| Langtest Eval Cli Command | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/benchmarks/Langtest_Cli_Eval_Command.ipynb) | + + +## 🐛 Fixes + +- Fixed multi-dataset support for accuracy task [#998] +- Fixed bugs in langtest package [#1003][#1004] + + +## ⚡ Enhancements +- Improved the error handling in Harness run method [#990] +- Websites Updates [#1001] +- Updated new version for dependencies [#992] +- Improved the data augmentation for Question-Answering task [#991] + +## What's Changed + +* Feautre/integration with web api by @chakravarthik27 in https://github.com/JohnSnowLabs/langtest/pull/986 +* Refactor TestFactory class to handle exceptions in async tests by @chakravarthik27 in https://github.com/JohnSnowLabs/langtest/pull/990 +* data augmentation support for question-answering task by @chakravarthik27 in https://github.com/JohnSnowLabs/langtest/pull/991 +* Updated dependencies by @chakravarthik27 in https://github.com/JohnSnowLabs/langtest/pull/992 +* Fix/implement the multiple dataset support for accuracy tests by @chakravarthik27 in https://github.com/JohnSnowLabs/langtest/pull/998 +* Feature/add support for other file formats by @chakravarthik27 in https://github.com/JohnSnowLabs/langtest/pull/993 +* Bug Fix: Generated results are none by @chakravarthik27 in https://github.com/JohnSnowLabs/langtest/pull/1000 +* Feature/implement load & save for benchmark reports by @chakravarthik27 in https://github.com/JohnSnowLabs/langtest/pull/999 +* Fix/bug fixes langtest 2 1 0 rc1 by @chakravarthik27 in https://github.com/JohnSnowLabs/langtest/pull/1003 +* website updates by @chakravarthik27 in https://github.com/JohnSnowLabs/langtest/pull/1001 +* Fix/bug fixes langtest 2 1 0 rc1 by @chakravarthik27 in https://github.com/JohnSnowLabs/langtest/pull/1004 +* Release/2.0.1 by @chakravarthik27 in https://github.com/JohnSnowLabs/langtest/pull/1005 + + +**Full Changelog**: https://github.com/JohnSnowLabs/langtest/compare/2.0.0...2.1.0 + +## ⚒️ Previous Versions +
    +{%- include docs-langtest-pagination.html -%} From 3688c5cf588453f9ee43f38680c8301bf9b1ef3a Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Wed, 15 May 2024 13:58:20 +0530 Subject: [PATCH 66/69] Add Fewshot Model Evaluation and Evaluating NER in LLMs tutorials --- .../tutorials/LLM_testing_Notebooks/llm_testing_notebooks.md | 4 +++- .../miscellaneous_notebooks/miscellaneous_notebooks.md | 2 ++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/pages/tutorials/LLM_testing_Notebooks/llm_testing_notebooks.md b/docs/pages/tutorials/LLM_testing_Notebooks/llm_testing_notebooks.md index aa0b67247..dcb763825 100644 --- a/docs/pages/tutorials/LLM_testing_Notebooks/llm_testing_notebooks.md +++ b/docs/pages/tutorials/LLM_testing_Notebooks/llm_testing_notebooks.md @@ -39,4 +39,6 @@ The following table gives an overview of the different tutorial notebooks to tes | [**Sycophancy**](sycophancy) : It is an undesirable behavior in which models tailor their responses to align with a human user's view, even when that view is not objectively correct. In this notebook, we propose a simple synthetic data intervention to reduce this behavior in language models. | OpenAI | Question-Answering | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/Sycophancy.ipynb) | | [**Stereotype**](stereotype): In this tutorial, we assess the model on gender occupational stereotype statements. | OpenAI/AI21 | Question-Answering | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/Wino_Bias_LLM.ipynb) | | [**LM Studio**](lm_studio): Running Hugging Face quantized models through LM-Studio and testing these models for a Question Answering task. | LM Studio | Question-Answering | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/LM-Studio-Demo.ipynb) | -| [**Question Answering Benchmarking**](question_answering_benchmarking): This notebook provides a demo on benchmarking Language Models (LLMs) for Question-Answering tasks. | Hugging Face Inference API | Question-Answering | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/benchmarks/Question-Answering.ipynb) | \ No newline at end of file +| [**Question Answering Benchmarking**](question_answering_benchmarking): This notebook provides a demo on benchmarking Language Models (LLMs) for Question-Answering tasks. | Hugging Face Inference API | Question-Answering | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/benchmarks/Question-Answering.ipynb) | +| **Fewshot Model Evaluation**: This notebook provides a demo on Optimize and evaluate your models using few-shot prompt techniques | OpenAI | Question-Answering | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/Fewshot_QA_Notebook.ipynb) | +| **Evaluating NER in LLMs**:In this tutorial, we assess the support for Named Entity Recognition (NER) tasks specifically for Large Language Models (LLMs) | OpenAI | Question-Answering | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/NER%20Casual%20LLM.ipynb) | \ No newline at end of file diff --git a/docs/pages/tutorials/miscellaneous_notebooks/miscellaneous_notebooks.md b/docs/pages/tutorials/miscellaneous_notebooks/miscellaneous_notebooks.md index 13c26c50e..530ffd633 100644 --- a/docs/pages/tutorials/miscellaneous_notebooks/miscellaneous_notebooks.md +++ b/docs/pages/tutorials/miscellaneous_notebooks/miscellaneous_notebooks.md @@ -39,3 +39,5 @@ The following table gives an overview of the different tutorial notebooks. In th | **LangTestCallback**: In this section, we discussed how to utilize the LangTestCallback funtion while training an Text Classification transformers model. | Hugging Face | Text-Classification | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/HF_Callback_Text_Classification.ipynb) | | **Multiple_dataset**: In this section, we discussed how to evaluate multiple datasets for a particular model. | OpenAI |Question-Answering | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Multiple_dataset.ipynb) | | **Generic API-Based Model**: In this section, we discussed how to test API-based models hosted using Ollama, vLLM, and other tools. | Web |Question-Answering | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/Generic_API-Based_Model_Testing_Demo.ipynb) | +| **Data Augmenter**: In this Notebook, we can allows for streamlined and harness-free data augmentation, making it simpler to enhance your datasets and improve model robustness. | - |NER | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb) | +| **Multi-Dataset Prompt Configs**: In this Notebook, we discussed about optimized prompt handling for multiple datasets, allowing users to add custom prompts for each dataset, enabling seamless integration and efficient testing. | OpenAI |Question-Answering | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb) | From aa76c7f28a38e812bcd3b22cc553603bcff86cd9 Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Wed, 15 May 2024 15:53:45 +0530 Subject: [PATCH 67/69] updated: langtest version in pip --- .../tutorials/benchmarks/Benchmarking_with_Harness.ipynb | 9 +++++++++ demo/tutorials/llm_notebooks/Fewshot_QA_Notebook.ipynb | 2 +- demo/tutorials/llm_notebooks/NER Casual LLM.ipynb | 2 +- demo/tutorials/misc/Data_Augmenter_Notebook.ipynb | 2 +- demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb | 2 +- 5 files changed, 13 insertions(+), 4 deletions(-) diff --git a/demo/tutorials/benchmarks/Benchmarking_with_Harness.ipynb b/demo/tutorials/benchmarks/Benchmarking_with_Harness.ipynb index 56cc483aa..4a0509dd6 100644 --- a/demo/tutorials/benchmarks/Benchmarking_with_Harness.ipynb +++ b/demo/tutorials/benchmarks/Benchmarking_with_Harness.ipynb @@ -36,6 +36,15 @@ "# Getting started with LangTest" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install langtest==2.2.0" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/demo/tutorials/llm_notebooks/Fewshot_QA_Notebook.ipynb b/demo/tutorials/llm_notebooks/Fewshot_QA_Notebook.ipynb index 6b5385f16..268fbad62 100644 --- a/demo/tutorials/llm_notebooks/Fewshot_QA_Notebook.ipynb +++ b/demo/tutorials/llm_notebooks/Fewshot_QA_Notebook.ipynb @@ -48,7 +48,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install \"langtest[evaluate,openai,transformers]\" " + "!pip install \"langtest[evaluate,openai,transformers]==2.2.0\" " ] }, { diff --git a/demo/tutorials/llm_notebooks/NER Casual LLM.ipynb b/demo/tutorials/llm_notebooks/NER Casual LLM.ipynb index 76e126b62..1e4517c6a 100644 --- a/demo/tutorials/llm_notebooks/NER Casual LLM.ipynb +++ b/demo/tutorials/llm_notebooks/NER Casual LLM.ipynb @@ -36,7 +36,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install \"langtest[evaluate,openai]\" requests" + "!pip install \"langtest[evaluate,openai]==2.2.0\" requests" ] }, { diff --git a/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb b/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb index 02dc4438a..fa5d0a296 100644 --- a/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb +++ b/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"markdown","metadata":{"id":"e7PsSmy9sCoR"},"source":["![image.png](data:image/png;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"MhgkQYQiEvZt"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"WJJzt3RWhEc6"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"26qXWhCYhHAt"},"source":["# Getting started with LangTest on John Snow Labs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oGIyE43uhTxH"},"outputs":[],"source":["!pip install \"langtest\""]},{"cell_type":"markdown","metadata":{"id":"yR6kjOaiheKN"},"source":["# DataAugmenter and its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["yaml_content = \"\"\"\n","parameters:\n"," type: proportion\n"," style: new\n","tests:\n"," robustness:\n"," uppercase:\n"," max_proportion: 0.2\n"," lowercase:\n"," max_proportion: 0.2\n","\n","\"\"\"\n","\n","with open(\"config.yaml\", \"w\") as f:\n"," f.write(yaml_content)"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1405,"status":"ok","timestamp":1692343652196,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"lTzSJpMlhgq5"},"outputs":[],"source":["from langtest.augmentation import DataAugmenter\n","from langtest.tasks.task import TaskManager\n","\n","data_augmenter = DataAugmenter(\n"," task=TaskManager(\"ner\"),\n"," config=\"config.yaml\",\n",")"]},{"cell_type":"markdown","metadata":{"id":"sBcZjwJBhkOw"},"source":["The instance of the `DataAugmenter` class from the `langtest.augmentation` module is to perform the Data augmentation for different tasks from langtest. \n","In this specific instance, the `DataAugmenter` object is created with the following parameters:\n","\n","
    \n","\n","| Parameter | Description |\n","| - | - |\n","| **task** | Task for which the model is to be evaluated (text-classification or ner) |\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","By creating an instance of the `DataAugmenter` class, you can utilize its methods and functionalities to perform data augmentation on different tasks from langtest specific to the Named Entity Recognition task. The `config.yaml` file contains the specific configuration settings for the tests to be performed, such as the type of augmentation and the maximum proportion of augmentation for different test cases.\n","\n","Overall, the `data_augmenter` object represents an instance of the `DataAugmenter` class that can be used to conduct Data augmentation for the Named Entity Recognition task based on the provided configuration.\n","\n","
    \n","
    "]},{"cell_type":"markdown","metadata":{"id":"I21Jmq79jgC6"},"source":["#### Load Train and Test CoNLL"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 50519 (49K) [text/plain]\n","Saving to: ‘sample.conll’\n","\n","sample.conll 100%[===================>] 49.33K --.-KB/s in 0.006s \n","\n","2023-08-18 07:27:31 (7.50 MB/s) - ‘sample.conll’ saved [50519/50519]\n","\n","--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 827443 (808K) [text/plain]\n","Saving to: ‘conll03.conll’\n","\n","conll03.conll 100%[===================>] 808.05K --.-KB/s in 0.03s \n","\n","2023-08-18 07:27:31 (30.1 MB/s) - ‘conll03.conll’ saved [827443/827443]\n","\n"]}],"source":["# Load test CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","\n","# Load train CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll"]},{"cell_type":"markdown","metadata":{},"source":["### Augmenting with train data"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.augment(data={\n"," 'data_source': 'conll03.conll'\n","})"]},{"cell_type":"markdown","metadata":{},"source":["### Save the augmentated dataset "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.save(\"augmented.conll\")"]},{"cell_type":"markdown","metadata":{"id":"YPXIxv9D_fR7"},"source":["Essentially it applies perturbations to the input data based on the recommendations from the harness reports. Then this augmented_dataset is used to retrain the original model so as to make the model more robust and improve its performance."]}],"metadata":{"colab":{"machine_shape":"hm","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 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)"]},{"cell_type":"markdown","metadata":{"id":"MhgkQYQiEvZt"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"WJJzt3RWhEc6"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"26qXWhCYhHAt"},"source":["# Getting started with LangTest on John Snow Labs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oGIyE43uhTxH"},"outputs":[],"source":["!pip install \"langtest==2.2.0\""]},{"cell_type":"markdown","metadata":{"id":"yR6kjOaiheKN"},"source":["# DataAugmenter and its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["yaml_content = \"\"\"\n","parameters:\n"," type: proportion\n"," style: new\n","tests:\n"," robustness:\n"," uppercase:\n"," max_proportion: 0.2\n"," lowercase:\n"," max_proportion: 0.2\n","\n","\"\"\"\n","\n","with open(\"config.yaml\", \"w\") as f:\n"," f.write(yaml_content)"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1405,"status":"ok","timestamp":1692343652196,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"lTzSJpMlhgq5"},"outputs":[],"source":["from langtest.augmentation import DataAugmenter\n","from langtest.tasks.task import TaskManager\n","\n","data_augmenter = DataAugmenter(\n"," task=TaskManager(\"ner\"),\n"," config=\"config.yaml\",\n",")"]},{"cell_type":"markdown","metadata":{"id":"sBcZjwJBhkOw"},"source":["The instance of the `DataAugmenter` class from the `langtest.augmentation` module is to perform the Data augmentation for different tasks from langtest. \n","In this specific instance, the `DataAugmenter` object is created with the following parameters:\n","\n","
    \n","\n","| Parameter | Description |\n","| - | - |\n","| **task** | Task for which the model is to be evaluated (text-classification or ner) |\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","By creating an instance of the `DataAugmenter` class, you can utilize its methods and functionalities to perform data augmentation on different tasks from langtest specific to the Named Entity Recognition task. The `config.yaml` file contains the specific configuration settings for the tests to be performed, such as the type of augmentation and the maximum proportion of augmentation for different test cases.\n","\n","Overall, the `data_augmenter` object represents an instance of the `DataAugmenter` class that can be used to conduct Data augmentation for the Named Entity Recognition task based on the provided configuration.\n","\n","
    \n","
    "]},{"cell_type":"markdown","metadata":{"id":"I21Jmq79jgC6"},"source":["#### Load Train and Test CoNLL"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 50519 (49K) [text/plain]\n","Saving to: ‘sample.conll’\n","\n","sample.conll 100%[===================>] 49.33K --.-KB/s in 0.006s \n","\n","2023-08-18 07:27:31 (7.50 MB/s) - ‘sample.conll’ saved [50519/50519]\n","\n","--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 827443 (808K) [text/plain]\n","Saving to: ‘conll03.conll’\n","\n","conll03.conll 100%[===================>] 808.05K --.-KB/s in 0.03s \n","\n","2023-08-18 07:27:31 (30.1 MB/s) - ‘conll03.conll’ saved [827443/827443]\n","\n"]}],"source":["# Load test CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","\n","# Load train CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll"]},{"cell_type":"markdown","metadata":{},"source":["### Augmenting with train data"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.augment(data={\n"," 'data_source': 'conll03.conll'\n","})"]},{"cell_type":"markdown","metadata":{},"source":["### Save the augmentated dataset "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.save(\"augmented.conll\")"]},{"cell_type":"markdown","metadata":{"id":"YPXIxv9D_fR7"},"source":["Essentially it applies perturbations to the input data based on the recommendations from the harness reports. Then this augmented_dataset is used to retrain the original model so as to make the model more robust and improve its performance."]}],"metadata":{"colab":{"machine_shape":"hm","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 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diff --git a/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb b/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb index c0e7c5d76..27812169c 100644 --- a/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb +++ b/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb @@ -1 +1 @@ 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)"]},{"cell_type":"markdown","metadata":{"id":"Fu8i_qgCBplG"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"IKKgqEEKA3qv"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"JzKpAy4mA5jA"},"source":["# Getting started with LangTest"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jFus50TcGgJA"},"outputs":[],"source":["!pip install \"langtest[openai,transformers,evaluate]\""]},{"cell_type":"markdown","metadata":{"id":"bjK9t-uFBEPw"},"source":["# Harness and Its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":3080,"status":"ok","timestamp":1696324827009,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"9Z2vV7zLBJWz"},"outputs":[],"source":["# Import Harness from the LangTest library\n","from langtest import Harness"]},{"cell_type":"markdown","metadata":{"id":"MW9LVSCyBLoQ"},"source":["It imports the Harness class from within the module, that is designed to provide a blueprint or framework for conducting NLP testing, and that instances of the Harness class can be customized or configured for different testing scenarios or environments.\n","\n","Here is a list of the different parameters that can be passed to the Harness function:\n","\n","
    \n","\n","\n","| Parameter | Description | \n","| - | - |\n","|**task** |Task for which the model is to be evaluated (question-answering or summarization)|\n","| **model** | Specifies the model(s) to be evaluated. This parameter can be provided as either a dictionary or a list of dictionaries. Each dictionary should contain the following keys:
    • model (mandatory): \tPipelineModel or path to a saved model or pretrained pipeline/model from hub.
    • hub (mandatory): Hub (library) to use in back-end for loading model from public models hub or from path
    |\n","| **data** | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys:
    • data_source (mandatory): The source of the data.
    • subset (optional): The subset of the data.
    • feature_column (optional): The column containing the features.
    • target_column (optional): The column containing the target labels.
    • split (optional): The data split to be used.
    • source (optional): Set to 'huggingface' when loading Hugging Face dataset.
    |\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","
    \n","
    "]},{"cell_type":"markdown","metadata":{"id":"xHwkRUckBw9M"},"source":["# OpenAI Model Testing For Question Answering\n","\n","In this section, we dive into testing of OpenAI models in Question Answering task.\n","\n","LangTest supports robustness tests for LLM testing for now."]},{"cell_type":"markdown","metadata":{"id":"4bgnVoUiBRqU"},"source":["### Set environment for OpenAI"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":17,"status":"ok","timestamp":1696324827010,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"mVYxDu-E_ssg"},"outputs":[],"source":["import os\n","\n","os.environ[\"OPENAI_API_KEY\"] = \"\""]},{"cell_type":"markdown","metadata":{"id":"tCXcKn_9BXEa"},"source":["### Multi Dataset Testing\n","\n","In order to evaluate the model's performance on multiple datasets, we can utilize a Jupyter notebook and provide a list of dictionaries to the `data` parameter. Each dictionary within the list should contain the following keys:\n","\n","```\n","data=[\n"," {\"data_source\": \"BoolQ\", \"split\": \"test-tiny\"},\n"," {\"data_source\": \"NQ-open\", \"split\": \"test-tiny\"},\n"," {\"data_source\": \"MedQA\", \"split\": \"test-tiny\"},\n"," {\"data_source\": \"LogiQA\", \"split\": \"test-tiny\"},\n","],\n","```\n","\n","Here, we specify different data sources and their corresponding splits for testing. This allows for a comprehensive evaluation of the model's performance across diverse datasets. The notebook can then be executed to assess how well the model generalizes to various types of questions and contexts presented in these datasets."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":45,"status":"ok","timestamp":1692371630216,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"ASv9E02sBXrp","outputId":"fb19b9ec-3bd9-416e-f2fc-dc3190b8a861"},"outputs":[{"name":"stdout","output_type":"stream","text":["Test Configuration : \n"," {\n"," \"model_parameters\": {\n"," \"max_tokens\": 64\n"," },\n"," \"tests\": {\n"," \"defaults\": {\n"," \"min_pass_rate\": 1.0\n"," },\n"," \"robustness\": {\n"," \"add_typo\": {\n"," \"min_pass_rate\": 0.7\n"," },\n"," \"lowercase\": {\n"," \"min_pass_rate\": 0.7\n"," }\n"," }\n"," }\n","}\n"]}],"source":["harness = Harness(\n"," task=\"question-answering\",\n"," model={\"model\": \"gpt-3.5-turbo-instruct\", \"hub\": \"openai\"},\n"," data=[\n"," {\"data_source\": \"BoolQ\", \"split\": \"dev-tiny\"},\n"," {\"data_source\": \"NQ-open\", \"split\": \"test-tiny\"}\n"," ],\n",")"]},{"cell_type":"markdown","metadata":{"id":"_wvVHxeSDWLV"},"source":["## Robustness\n","\n","For tests we used uppercase, Dyslexia Word Swap, Add Slangs, Insert Abbreviations and Speech to Text typos . Other available robustness tests for QA task are:\n","* `add_context`\n","* `add_contraction`\n","* `add_punctuation`\n","* `add_typo`\n","* `add_ocr_typo`\n","* `american_to_british`\n","* `british_to_american`\n","* `lowercase`\n","* `strip_punctuation`\n","* `titlecase`\n","* `uppercase`\n","* `number_to_word`\n","* `add_abbreviation`\n","* `add_speech_to_text_typo`\n","* `add_slangs`\n","* `dyslexia_word_swap`\n","* `multiple_perturbations`\n","* `adjective_synonym_swap`\n","* `adjective_antonym_swap`\n","* `strip_all_punctuation`"]},{"cell_type":"markdown","metadata":{"id":"HYExqs-pDbvz"},"source":["You can also set prompts and other model parameters in config. Possible parameters are:\n","* `user_prompt:` Prompt to be given to the model.\n","* `temperature:` Temperature of the model.\n","* `max_tokens:` Maximum number of output tokens allowed for model."]},{"cell_type":"markdown","metadata":{},"source":["To configure prompts for different datasets, you can use the `user_prompt` dictionary. Here's how it works:\n","\n","- Each key in the dictionary represents a dataset name or task (e.g., \"BoolQ\", \"NQ-open\").\n","- The corresponding value is a string template that defines the user prompt for the dataset.\n","- The template can include placeholders:\n"," - `{context}`: This will be replaced with the actual context (passage) relevant to the question from the specific dataset.\n"," - `{question}`: This will be replaced with the actual question from the dataset.\n","- The newline character `\\n` can be used to separate the context and question in the final prompt.\n","\n","Here the example:\n","```python\n","harness.configure(\n"," {\n"," \"model_parameters\": {\n"," \"user_prompt\": {\n"," \"BoolQ\": \"Answer the following question with a True or False. {context}\\nQuestion {question}\",\n"," \"NQ-open\": \"Answer the following question. Question {question}\",\n"," }\n"," },\n"," ....\n"," })\n","```"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":42,"status":"ok","timestamp":1692371630218,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"EzzlV0u4DbN9","outputId":"2a3926cd-9c23-45a6-a0b8-b31b29692be3"},"outputs":[{"data":{"text/plain":["{'model_parameters': {'user_prompt': {'BoolQ': 'Answer the following question with a True or False. {context}\\nQuestion {question}',\n"," 'NQ-open': 'Answer the following question. Question {question}'}},\n"," 'tests': {'defaults': {'min_pass_rate': 0.65},\n"," 'robustness': {'uppercase': {'min_pass_rate': 0.66},\n"," 'dyslexia_word_swap': {'min_pass_rate': 0.6},\n"," 'add_abbreviation': {'min_pass_rate': 0.6},\n"," 'add_slangs': {'min_pass_rate': 0.6},\n"," 'add_speech_to_text_typo': {'min_pass_rate': 0.6}}}}"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["harness.configure(\n"," {\n"," \"model_parameters\": {\n"," \"user_prompt\": {\n"," \"BoolQ\": \"Answer the following question with a True or False. {context}\\nQuestion {question}\",\n"," \"NQ-open\": \"Answer the following question. Question {question}\",\n"," }\n"," },\n"," \"tests\": {\n"," \"defaults\": {\"min_pass_rate\": 0.65},\n"," \"robustness\": {\n"," \"uppercase\": {\"min_pass_rate\": 0.66},\n"," \"dyslexia_word_swap\": {\"min_pass_rate\": 0.60},\n"," \"add_abbreviation\": {\"min_pass_rate\": 0.60},\n"," \"add_slangs\": {\"min_pass_rate\": 0.60},\n"," \"add_speech_to_text_typo\": {\"min_pass_rate\": 0.60},\n"," },\n"," }\n"," }\n",")"]},{"cell_type":"markdown","metadata":{"id":"P7TKPJd3Dft1"},"source":["➤ You can adjust the level of transformation in the sentence by using the \"`prob`\" parameter, which controls the proportion of words to be changed during robustness tests.\n","\n","➤ **NOTE** : \"`prob`\" defaults to 1.0, which means all words will be transformed.\n","```\n","harness.configure(\n","{\n"," 'tests': {\n"," 'defaults': {'min_pass_rate': 0.65},\n"," 'robustness': {\n"," 'uppercase': {'min_pass_rate': 0.66, 'prob': 0.50},\n"," 'dyslexia_word_swap':{'min_pass_rate': 0.60, 'prob': 0.70},\n"," }\n"," }\n","})\n","\n","```"]},{"cell_type":"markdown","metadata":{"id":"SW71UKHfDi2q"},"source":["Here we have configured the harness to perform Five robustness tests and defined the minimum pass rate for each test."]},{"cell_type":"code","execution_count":5,"metadata":{"id":"a9Q8i7-KDgR5"},"outputs":[],"source":["#slice the data\n","harness.data = {k: v[:10] for k, v in harness.data.items()}"]},{"cell_type":"markdown","metadata":{"id":"GlBMu35ODm77"},"source":["### Generating the test cases."]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":58028,"status":"ok","timestamp":1692371688215,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"L1NQcBCHDomc","outputId":"e3df8f16-fadd-4fbb-e479-2f098f07ba5a"},"outputs":[{"name":"stdout","output_type":"stream","text":["================================================================================\n"," BoolQ \n","================================================================================\n"]},{"name":"stderr","output_type":"stream","text":["Generating testcases...: 100%|██████████| 1/1 [00:00\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
    categorydataset_nametest_typeoriginal_contextoriginal_questionperturbed_contextperturbed_question
    0robustnessBoolQuppercaseAll biomass goes through at least some of thes...does ethanol take more energy make that producesALL BIOMASS GOES THROUGH AT LEAST SOME OF THES...DOES ETHANOL TAKE MORE ENERGY MAKE THAT PRODUCES
    1robustnessBoolQuppercaseProperty tax or 'house tax' is a local tax on ...is house tax and property tax are samePROPERTY TAX OR 'HOUSE TAX' IS A LOCAL TAX ON ...IS HOUSE TAX AND PROPERTY TAX ARE SAME
    2robustnessBoolQuppercasePhantom pain sensations are described as perce...is pain experienced in a missing body part or ...PHANTOM PAIN SENSATIONS ARE DESCRIBED AS PERCE...IS PAIN EXPERIENCED IN A MISSING BODY PART OR ...
    3robustnessBoolQuppercaseHarry Potter and the Escape from Gringotts is ...is harry potter and the escape from gringotts ...HARRY POTTER AND THE ESCAPE FROM GRINGOTTS IS ...IS HARRY POTTER AND THE ESCAPE FROM GRINGOTTS ...
    4robustnessBoolQuppercaseHydroxyzine preparations require a doctor's pr...is there a difference between hydroxyzine hcl ...HYDROXYZINE PREPARATIONS REQUIRE A DOCTOR'S PR...IS THERE A DIFFERENCE BETWEEN HYDROXYZINE HCL ...
    ........................
    80robustnessNQ-openadd_speech_to_text_typo-who played grand moff tarkin in rogue one-Hoo played grand moff tarkin in rogue one
    81robustnessNQ-openadd_speech_to_text_typo-youngest current member of the house of repres...-youngest current member of the Hause of repres...
    82robustnessNQ-openadd_speech_to_text_typo-who wrote the miraculous journey of edward tulane-Houx wrote the miraculous journey of edward tu...
    83robustnessNQ-openadd_speech_to_text_typo-when did the night mare before christmas come out-when did the night Mehr before christmas come out
    84robustnessNQ-openadd_speech_to_text_typo-when does the green book come out in theaters-when does the green book come out Inn theaters
    \n","

    85 rows × 7 columns

    \n",""],"text/plain":[" category dataset_name test_type \\\n","0 robustness BoolQ uppercase \n","1 robustness BoolQ uppercase \n","2 robustness BoolQ uppercase \n","3 robustness BoolQ uppercase \n","4 robustness BoolQ uppercase \n",".. ... ... ... \n","80 robustness NQ-open add_speech_to_text_typo \n","81 robustness NQ-open add_speech_to_text_typo \n","82 robustness NQ-open add_speech_to_text_typo \n","83 robustness NQ-open add_speech_to_text_typo \n","84 robustness NQ-open add_speech_to_text_typo \n","\n"," original_context \\\n","0 All biomass goes through at least some of thes... \n","1 Property tax or 'house tax' is a local tax on ... \n","2 Phantom pain sensations are described as perce... \n","3 Harry Potter and the Escape from Gringotts is ... \n","4 Hydroxyzine preparations require a doctor's pr... \n",".. ... \n","80 - \n","81 - \n","82 - \n","83 - \n","84 - \n","\n"," original_question \\\n","0 does ethanol take more energy make that produces \n","1 is house tax and property tax are same \n","2 is pain experienced in a missing body part or ... \n","3 is harry potter and the escape from gringotts ... \n","4 is there a difference between hydroxyzine hcl ... \n",".. ... \n","80 who played grand moff tarkin in rogue one \n","81 youngest current member of the house of repres... \n","82 who wrote the miraculous journey of edward tulane \n","83 when did the night mare before christmas come out \n","84 when does the green book come out in theaters \n","\n"," perturbed_context \\\n","0 ALL BIOMASS GOES THROUGH AT LEAST SOME OF THES... \n","1 PROPERTY TAX OR 'HOUSE TAX' IS A LOCAL TAX ON ... \n","2 PHANTOM PAIN SENSATIONS ARE DESCRIBED AS PERCE... \n","3 HARRY POTTER AND THE ESCAPE FROM GRINGOTTS IS ... \n","4 HYDROXYZINE PREPARATIONS REQUIRE A DOCTOR'S PR... \n",".. ... \n","80 - \n","81 - \n","82 - \n","83 - \n","84 - \n","\n"," perturbed_question \n","0 DOES ETHANOL TAKE MORE ENERGY MAKE THAT PRODUCES \n","1 IS HOUSE TAX AND PROPERTY TAX ARE SAME \n","2 IS PAIN EXPERIENCED IN A MISSING BODY PART OR ... \n","3 IS HARRY POTTER AND THE ESCAPE FROM GRINGOTTS ... \n","4 IS THERE A DIFFERENCE BETWEEN HYDROXYZINE HCL ... \n",".. ... \n","80 Hoo played grand moff tarkin in rogue one \n","81 youngest current member of the Hause of repres... \n","82 Houx wrote the miraculous journey of edward tu... \n","83 when did the night Mehr before christmas come out \n","84 when does the green book come out Inn theaters \n","\n","[85 rows x 7 columns]"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["harness.testcases()"]},{"cell_type":"markdown","metadata":{"id":"akSniLOoDxOp"},"source":["harness.generate() method automatically generates the test cases (based on the provided configuration)"]},{"cell_type":"markdown","metadata":{"id":"wk_cgK2BDzcM"},"source":["### Running the tests"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":48720,"status":"ok","timestamp":1692371736914,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"nje7KWD9Dx3Y","outputId":"5ac4304a-0078-49ad-84b0-c5b6c2f58155"},"outputs":[{"name":"stdout","output_type":"stream","text":["================================================================================\n"," BoolQ \n","================================================================================\n"]},{"name":"stderr","output_type":"stream","text":["Running testcases... : 100%|██████████| 48/48 [00:52<00:00, 1.10s/it]\n"]},{"name":"stdout","output_type":"stream","text":["--------------------------------------------------------------------------------\n","\n","================================================================================\n"," NQ-open \n","================================================================================\n"]},{"name":"stderr","output_type":"stream","text":["Running testcases... : 100%|██████████| 37/37 [00:48<00:00, 1.31s/it]"]},{"name":"stdout","output_type":"stream","text":["--------------------------------------------------------------------------------\n","\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"data":{"text/plain":[]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["harness.run()"]},{"cell_type":"markdown","metadata":{"id":"7GnDWiU6D2S4"},"source":["Called after harness.generate() and is to used to run all the tests. Returns a pass/fail flag for each test."]},{"cell_type":"markdown","metadata":{"id":"q17wkdZcD4T8"},"source":["### Generated Results"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":805},"executionInfo":{"elapsed":18550,"status":"ok","timestamp":1692371755410,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"yJta_DvJD3xh","outputId":"91be0a8f-f014-4e04-81bd-8eaa521c84c9"},"outputs":[{"data":{"text/html":["
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    categorydataset_nametest_typeoriginal_contextoriginal_questionperturbed_contextperturbed_questionexpected_resultactual_resultpass
    0robustnessBoolQuppercaseAll biomass goes through at least some of thes...does ethanol take more energy make that producesALL BIOMASS GOES THROUGH AT LEAST SOME OF THES...DOES ETHANOL TAKE MORE ENERGY MAKE THAT PRODUCES\\n\\n\\nTrueTRUETrue
    1robustnessBoolQuppercaseProperty tax or 'house tax' is a local tax on ...is house tax and property tax are samePROPERTY TAX OR 'HOUSE TAX' IS A LOCAL TAX ON ...IS HOUSE TAX AND PROPERTY TAX ARE SAME\\n\\nTrue\\n\\nTrueTrue
    2robustnessBoolQuppercasePhantom pain sensations are described as perce...is pain experienced in a missing body part or ...PHANTOM PAIN SENSATIONS ARE DESCRIBED AS PERCE...IS PAIN EXPERIENCED IN A MISSING BODY PART OR ...?\\n\\n\\nTrue\\nTrueTrue
    3robustnessBoolQuppercaseHarry Potter and the Escape from Gringotts is ...is harry potter and the escape from gringotts ...HARRY POTTER AND THE ESCAPE FROM GRINGOTTS IS ...IS HARRY POTTER AND THE ESCAPE FROM GRINGOTTS ...\\n\\nTrue?\\n\\nTrueTrue
    4robustnessBoolQuppercaseHydroxyzine preparations require a doctor's pr...is there a difference between hydroxyzine hcl ...HYDROXYZINE PREPARATIONS REQUIRE A DOCTOR'S PR...IS THERE A DIFFERENCE BETWEEN HYDROXYZINE HCL ...\\n\\nTrueOATE\\n\\nTrueFalse
    .................................
    80robustnessNQ-openadd_speech_to_text_typo-who played grand moff tarkin in rogue one-Hoo played grand moff tarkin in rogue one\\n\\nPeter Cushing\\n\\nPeter Cushing played Grand Moff Tarkin in ...True
    81robustnessNQ-openadd_speech_to_text_typo-youngest current member of the house of repres...-youngest current member of the Hause of repres...\\n\\nAs of 2021, the youngest current member of...\\n\\nAs of 2021, the youngest current member of...True
    82robustnessNQ-openadd_speech_to_text_typo-who wrote the miraculous journey of edward tulane-Houx wrote the miraculous journey of edward tu...\\n\\nThe Miraculous Journey of Edward Tulane wa...\\n\\nWho wrote \"The Miraculous Journey of Edwar...False
    83robustnessNQ-openadd_speech_to_text_typo-when did the night mare before christmas come out-when did the night Mehr before christmas come out\\n\\nThe Nightmare Before Christmas was release...\\n\\nThe Nightmare Before Christmas was release...True
    84robustnessNQ-openadd_speech_to_text_typo-when does the green book come out in theaters-when does the green book come out Inn theaters\\n\\nThe green book was released in theaters on...\\n\\nThe release date for the green book in the...False
    \n","

    85 rows × 10 columns

    \n","
    "],"text/plain":[" category dataset_name test_type \\\n","0 robustness BoolQ uppercase \n","1 robustness BoolQ uppercase \n","2 robustness BoolQ uppercase \n","3 robustness BoolQ uppercase \n","4 robustness BoolQ uppercase \n",".. ... ... ... \n","80 robustness NQ-open add_speech_to_text_typo \n","81 robustness NQ-open add_speech_to_text_typo \n","82 robustness NQ-open add_speech_to_text_typo \n","83 robustness NQ-open add_speech_to_text_typo \n","84 robustness NQ-open add_speech_to_text_typo \n","\n"," original_context \\\n","0 All biomass goes through at least some of thes... \n","1 Property tax or 'house tax' is a local tax on ... \n","2 Phantom pain sensations are described as perce... \n","3 Harry Potter and the Escape from Gringotts is ... \n","4 Hydroxyzine preparations require a doctor's pr... \n",".. ... \n","80 - \n","81 - \n","82 - \n","83 - \n","84 - \n","\n"," original_question \\\n","0 does ethanol take more energy make that produces \n","1 is house tax and property tax are same \n","2 is pain experienced in a missing body part or ... \n","3 is harry potter and the escape from gringotts ... \n","4 is there a difference between hydroxyzine hcl ... \n",".. ... \n","80 who played grand moff tarkin in rogue one \n","81 youngest current member of the house of repres... \n","82 who wrote the miraculous journey of edward tulane \n","83 when did the night mare before christmas come out \n","84 when does the green book come out in theaters \n","\n"," perturbed_context \\\n","0 ALL BIOMASS GOES THROUGH AT LEAST SOME OF THES... \n","1 PROPERTY TAX OR 'HOUSE TAX' IS A LOCAL TAX ON ... \n","2 PHANTOM PAIN SENSATIONS ARE DESCRIBED AS PERCE... \n","3 HARRY POTTER AND THE ESCAPE FROM GRINGOTTS IS ... \n","4 HYDROXYZINE PREPARATIONS REQUIRE A DOCTOR'S PR... \n",".. ... \n","80 - \n","81 - \n","82 - \n","83 - \n","84 - \n","\n"," perturbed_question \\\n","0 DOES ETHANOL TAKE MORE ENERGY MAKE THAT PRODUCES \n","1 IS HOUSE TAX AND PROPERTY TAX ARE SAME \n","2 IS PAIN EXPERIENCED IN A MISSING BODY PART OR ... \n","3 IS HARRY POTTER AND THE ESCAPE FROM GRINGOTTS ... \n","4 IS THERE A DIFFERENCE BETWEEN HYDROXYZINE HCL ... \n",".. ... \n","80 Hoo played grand moff tarkin in rogue one \n","81 youngest current member of the Hause of repres... \n","82 Houx wrote the miraculous journey of edward tu... \n","83 when did the night Mehr before christmas come out \n","84 when does the green book come out Inn theaters \n","\n"," expected_result \\\n","0 \\n\\n\\nTrue \n","1 \\n\\nTrue \n","2 ?\\n\\n\\nTrue \n","3 \\n\\nTrue \n","4 \\n\\nTrue \n",".. ... \n","80 \\n\\nPeter Cushing \n","81 \\n\\nAs of 2021, the youngest current member of... \n","82 \\n\\nThe Miraculous Journey of Edward Tulane wa... \n","83 \\n\\nThe Nightmare Before Christmas was release... \n","84 \\n\\nThe green book was released in theaters on... \n","\n"," actual_result pass \n","0 TRUE True \n","1 \\n\\nTrue True \n","2 \\nTrue True \n","3 ?\\n\\nTrue True \n","4 OATE\\n\\nTrue False \n",".. ... ... \n","80 \\n\\nPeter Cushing played Grand Moff Tarkin in ... True \n","81 \\n\\nAs of 2021, the youngest current member of... True \n","82 \\n\\nWho wrote \"The Miraculous Journey of Edwar... False \n","83 \\n\\nThe Nightmare Before Christmas was release... True \n","84 \\n\\nThe release date for the green book in the... False \n","\n","[85 rows x 10 columns]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["harness.generated_results()"]},{"cell_type":"markdown","metadata":{"id":"Vtv8wGFyD-XR"},"source":["This method returns the generated results in the form of a pandas dataframe, which provides a convenient and easy-to-use format for working with the test results. You can use this method to quickly identify the test cases that failed and to determine where fixes are needed."]},{"cell_type":"markdown","metadata":{"id":"agT9GO6FEC3E"},"source":["### Final Results\n","\n","We can call `.report()` which summarizes the results giving information about pass and fail counts and overall test pass/fail flag."]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":19430,"status":"ok","timestamp":1692371774826,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"qjFtUmbtEA2G","outputId":"62d274a2-8688-491a-f04e-101ebe5a6450"},"outputs":[{"data":{"text/html":["
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    Benchmarking Results: gpt-3.5-turbo-instruct
    fail_countpass_countpass_rateminimum_pass_ratepass
    dataset_namecategorytest_type
    BoolQrobustnessuppercase2880%66%True
    dyslexia_word_swap2880%60%True
    add_abbreviation1990%60%True
    add_slangs4450%60%False
    add_speech_to_text_typo3770%60%True
    NQ-openrobustnessuppercase3770%66%True
    dyslexia_word_swap2571%60%True
    add_abbreviation5444%60%False
    add_slangs1150%60%False
    add_speech_to_text_typo5444%60%False
    \n","
    "],"text/plain":[" Benchmarking Results: gpt-3.5-turbo-instruct \\\n"," fail_count \n","dataset_name category test_type \n","BoolQ robustness uppercase 2 \n"," dyslexia_word_swap 2 \n"," add_abbreviation 1 \n"," add_slangs 4 \n"," add_speech_to_text_typo 3 \n","NQ-open robustness uppercase 3 \n"," dyslexia_word_swap 2 \n"," add_abbreviation 5 \n"," add_slangs 1 \n"," add_speech_to_text_typo 5 \n","\n"," \\\n"," pass_count pass_rate \n","dataset_name category test_type \n","BoolQ robustness uppercase 8 80% \n"," dyslexia_word_swap 8 80% \n"," add_abbreviation 9 90% \n"," add_slangs 4 50% \n"," add_speech_to_text_typo 7 70% \n","NQ-open robustness uppercase 7 70% \n"," dyslexia_word_swap 5 71% \n"," add_abbreviation 4 44% \n"," add_slangs 1 50% \n"," add_speech_to_text_typo 4 44% \n","\n"," \n"," minimum_pass_rate pass \n","dataset_name category test_type \n","BoolQ robustness uppercase 66% True \n"," dyslexia_word_swap 60% True \n"," add_abbreviation 60% True \n"," add_slangs 60% 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)"]},{"cell_type":"markdown","metadata":{"id":"Fu8i_qgCBplG"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/MultiPrompt_MultiDataset.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"IKKgqEEKA3qv"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"JzKpAy4mA5jA"},"source":["# Getting started with LangTest"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jFus50TcGgJA"},"outputs":[],"source":["!pip install \"langtest[openai,transformers,evaluate]==2.2.0\""]},{"cell_type":"markdown","metadata":{"id":"bjK9t-uFBEPw"},"source":["# Harness and Its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":3080,"status":"ok","timestamp":1696324827009,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"9Z2vV7zLBJWz"},"outputs":[],"source":["# Import Harness from the LangTest library\n","from langtest import Harness"]},{"cell_type":"markdown","metadata":{"id":"MW9LVSCyBLoQ"},"source":["It imports the Harness class from within the module, that is designed to provide a blueprint or framework for conducting NLP testing, and that instances of the Harness class can be customized or configured for different testing scenarios or environments.\n","\n","Here is a list of the different parameters that can be passed to the Harness function:\n","\n","
    \n","\n","\n","| Parameter | Description | \n","| - | - |\n","|**task** |Task for which the model is to be evaluated (question-answering or summarization)|\n","| **model** | Specifies the model(s) to be evaluated. This parameter can be provided as either a dictionary or a list of dictionaries. Each dictionary should contain the following keys:
    • model (mandatory): \tPipelineModel or path to a saved model or pretrained pipeline/model from hub.
    • hub (mandatory): Hub (library) to use in back-end for loading model from public models hub or from path
    |\n","| **data** | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys:
    • data_source (mandatory): The source of the data.
    • subset (optional): The subset of the data.
    • feature_column (optional): The column containing the features.
    • target_column (optional): The column containing the target labels.
    • split (optional): The data split to be used.
    • source (optional): Set to 'huggingface' when loading Hugging Face dataset.
    |\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","
    \n","
    "]},{"cell_type":"markdown","metadata":{"id":"xHwkRUckBw9M"},"source":["# OpenAI Model Testing For Question Answering\n","\n","In this section, we dive into testing of OpenAI models in Question Answering task.\n","\n","LangTest supports robustness tests for LLM testing for now."]},{"cell_type":"markdown","metadata":{"id":"4bgnVoUiBRqU"},"source":["### Set environment for OpenAI"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":17,"status":"ok","timestamp":1696324827010,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"mVYxDu-E_ssg"},"outputs":[],"source":["import os\n","\n","os.environ[\"OPENAI_API_KEY\"] = \"\""]},{"cell_type":"markdown","metadata":{"id":"tCXcKn_9BXEa"},"source":["### Multi Dataset Testing\n","\n","In order to evaluate the model's performance on multiple datasets, we can utilize a Jupyter notebook and provide a list of dictionaries to the `data` parameter. Each dictionary within the list should contain the following keys:\n","\n","```\n","data=[\n"," {\"data_source\": \"BoolQ\", \"split\": \"test-tiny\"},\n"," {\"data_source\": \"NQ-open\", \"split\": \"test-tiny\"},\n"," {\"data_source\": \"MedQA\", \"split\": \"test-tiny\"},\n"," {\"data_source\": \"LogiQA\", \"split\": \"test-tiny\"},\n","],\n","```\n","\n","Here, we specify different data sources and their corresponding splits for testing. This allows for a comprehensive evaluation of the model's performance across diverse datasets. The notebook can then be executed to assess how well the model generalizes to various types of questions and contexts presented in these datasets."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":45,"status":"ok","timestamp":1692371630216,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"ASv9E02sBXrp","outputId":"fb19b9ec-3bd9-416e-f2fc-dc3190b8a861"},"outputs":[{"name":"stdout","output_type":"stream","text":["Test Configuration : \n"," {\n"," \"model_parameters\": {\n"," \"max_tokens\": 64\n"," },\n"," \"tests\": {\n"," \"defaults\": {\n"," \"min_pass_rate\": 1.0\n"," },\n"," \"robustness\": {\n"," \"add_typo\": {\n"," \"min_pass_rate\": 0.7\n"," },\n"," \"lowercase\": {\n"," \"min_pass_rate\": 0.7\n"," }\n"," }\n"," }\n","}\n"]}],"source":["harness = Harness(\n"," task=\"question-answering\",\n"," model={\"model\": \"gpt-3.5-turbo-instruct\", \"hub\": \"openai\"},\n"," data=[\n"," {\"data_source\": \"BoolQ\", \"split\": \"dev-tiny\"},\n"," {\"data_source\": \"NQ-open\", \"split\": \"test-tiny\"}\n"," ],\n",")"]},{"cell_type":"markdown","metadata":{"id":"_wvVHxeSDWLV"},"source":["## Robustness\n","\n","For tests we used uppercase, Dyslexia Word Swap, Add Slangs, Insert Abbreviations and Speech to Text typos . Other available robustness tests for QA task are:\n","* `add_context`\n","* `add_contraction`\n","* `add_punctuation`\n","* `add_typo`\n","* `add_ocr_typo`\n","* `american_to_british`\n","* `british_to_american`\n","* `lowercase`\n","* `strip_punctuation`\n","* `titlecase`\n","* `uppercase`\n","* `number_to_word`\n","* `add_abbreviation`\n","* `add_speech_to_text_typo`\n","* `add_slangs`\n","* `dyslexia_word_swap`\n","* `multiple_perturbations`\n","* `adjective_synonym_swap`\n","* `adjective_antonym_swap`\n","* `strip_all_punctuation`"]},{"cell_type":"markdown","metadata":{"id":"HYExqs-pDbvz"},"source":["You can also set prompts and other model parameters in config. Possible parameters are:\n","* `user_prompt:` Prompt to be given to the model.\n","* `temperature:` Temperature of the model.\n","* `max_tokens:` Maximum number of output tokens allowed for model."]},{"cell_type":"markdown","metadata":{},"source":["To configure prompts for different datasets, you can use the `user_prompt` dictionary. Here's how it works:\n","\n","- Each key in the dictionary represents a dataset name or task (e.g., \"BoolQ\", \"NQ-open\").\n","- The corresponding value is a string template that defines the user prompt for the dataset.\n","- The template can include placeholders:\n"," - `{context}`: This will be replaced with the actual context (passage) relevant to the question from the specific dataset.\n"," - `{question}`: This will be replaced with the actual question from the dataset.\n","- The newline character `\\n` can be used to separate the context and question in the final prompt.\n","\n","Here the example:\n","```python\n","harness.configure(\n"," {\n"," \"model_parameters\": {\n"," \"user_prompt\": {\n"," \"BoolQ\": \"Answer the following question with a True or False. {context}\\nQuestion {question}\",\n"," \"NQ-open\": \"Answer the following question. Question {question}\",\n"," }\n"," },\n"," ....\n"," })\n","```"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":42,"status":"ok","timestamp":1692371630218,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"EzzlV0u4DbN9","outputId":"2a3926cd-9c23-45a6-a0b8-b31b29692be3"},"outputs":[{"data":{"text/plain":["{'model_parameters': {'user_prompt': {'BoolQ': 'Answer the following question with a True or False. {context}\\nQuestion {question}',\n"," 'NQ-open': 'Answer the following question. Question {question}'}},\n"," 'tests': {'defaults': {'min_pass_rate': 0.65},\n"," 'robustness': {'uppercase': {'min_pass_rate': 0.66},\n"," 'dyslexia_word_swap': {'min_pass_rate': 0.6},\n"," 'add_abbreviation': {'min_pass_rate': 0.6},\n"," 'add_slangs': {'min_pass_rate': 0.6},\n"," 'add_speech_to_text_typo': {'min_pass_rate': 0.6}}}}"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["harness.configure(\n"," {\n"," \"model_parameters\": {\n"," \"user_prompt\": {\n"," \"BoolQ\": \"Answer the following question with a True or False. {context}\\nQuestion {question}\",\n"," \"NQ-open\": \"Answer the following question. Question {question}\",\n"," }\n"," },\n"," \"tests\": {\n"," \"defaults\": {\"min_pass_rate\": 0.65},\n"," \"robustness\": {\n"," \"uppercase\": {\"min_pass_rate\": 0.66},\n"," \"dyslexia_word_swap\": {\"min_pass_rate\": 0.60},\n"," \"add_abbreviation\": {\"min_pass_rate\": 0.60},\n"," \"add_slangs\": {\"min_pass_rate\": 0.60},\n"," \"add_speech_to_text_typo\": {\"min_pass_rate\": 0.60},\n"," },\n"," }\n"," }\n",")"]},{"cell_type":"markdown","metadata":{"id":"P7TKPJd3Dft1"},"source":["➤ You can adjust the level of transformation in the sentence by using the \"`prob`\" parameter, which controls the proportion of words to be changed during robustness tests.\n","\n","➤ **NOTE** : \"`prob`\" defaults to 1.0, which means all words will be transformed.\n","```\n","harness.configure(\n","{\n"," 'tests': {\n"," 'defaults': {'min_pass_rate': 0.65},\n"," 'robustness': {\n"," 'uppercase': {'min_pass_rate': 0.66, 'prob': 0.50},\n"," 'dyslexia_word_swap':{'min_pass_rate': 0.60, 'prob': 0.70},\n"," }\n"," }\n","})\n","\n","```"]},{"cell_type":"markdown","metadata":{"id":"SW71UKHfDi2q"},"source":["Here we have configured the harness to perform Five robustness tests and defined the minimum pass rate for each test."]},{"cell_type":"code","execution_count":5,"metadata":{"id":"a9Q8i7-KDgR5"},"outputs":[],"source":["#slice the data\n","harness.data = {k: v[:10] for k, v in harness.data.items()}"]},{"cell_type":"markdown","metadata":{"id":"GlBMu35ODm77"},"source":["### Generating the test cases."]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":58028,"status":"ok","timestamp":1692371688215,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"L1NQcBCHDomc","outputId":"e3df8f16-fadd-4fbb-e479-2f098f07ba5a"},"outputs":[{"name":"stdout","output_type":"stream","text":["================================================================================\n"," BoolQ \n","================================================================================\n"]},{"name":"stderr","output_type":"stream","text":["Generating testcases...: 100%|██████████| 1/1 [00:00\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
    categorydataset_nametest_typeoriginal_contextoriginal_questionperturbed_contextperturbed_question
    0robustnessBoolQuppercaseAll biomass goes through at least some of thes...does ethanol take more energy make that producesALL BIOMASS GOES THROUGH AT LEAST SOME OF THES...DOES ETHANOL TAKE MORE ENERGY MAKE THAT PRODUCES
    1robustnessBoolQuppercaseProperty tax or 'house tax' is a local tax on ...is house tax and property tax are samePROPERTY TAX OR 'HOUSE TAX' IS A LOCAL TAX ON ...IS HOUSE TAX AND PROPERTY TAX ARE SAME
    2robustnessBoolQuppercasePhantom pain sensations are described as perce...is pain experienced in a missing body part or ...PHANTOM PAIN SENSATIONS ARE DESCRIBED AS PERCE...IS PAIN EXPERIENCED IN A MISSING BODY PART OR ...
    3robustnessBoolQuppercaseHarry Potter and the Escape from Gringotts is ...is harry potter and the escape from gringotts ...HARRY POTTER AND THE ESCAPE FROM GRINGOTTS IS ...IS HARRY POTTER AND THE ESCAPE FROM GRINGOTTS ...
    4robustnessBoolQuppercaseHydroxyzine preparations require a doctor's pr...is there a difference between hydroxyzine hcl ...HYDROXYZINE PREPARATIONS REQUIRE A DOCTOR'S PR...IS THERE A DIFFERENCE BETWEEN HYDROXYZINE HCL ...
    ........................
    80robustnessNQ-openadd_speech_to_text_typo-who played grand moff tarkin in rogue one-Hoo played grand moff tarkin in rogue one
    81robustnessNQ-openadd_speech_to_text_typo-youngest current member of the house of repres...-youngest current member of the Hause of repres...
    82robustnessNQ-openadd_speech_to_text_typo-who wrote the miraculous journey of edward tulane-Houx wrote the miraculous journey of edward tu...
    83robustnessNQ-openadd_speech_to_text_typo-when did the night mare before christmas come out-when did the night Mehr before christmas come out
    84robustnessNQ-openadd_speech_to_text_typo-when does the green book come out in theaters-when does the green book come out Inn theaters
    \n","

    85 rows × 7 columns

    \n",""],"text/plain":[" category dataset_name test_type \\\n","0 robustness BoolQ uppercase \n","1 robustness BoolQ uppercase \n","2 robustness BoolQ uppercase \n","3 robustness BoolQ uppercase \n","4 robustness BoolQ uppercase \n",".. ... ... ... \n","80 robustness NQ-open add_speech_to_text_typo \n","81 robustness NQ-open add_speech_to_text_typo \n","82 robustness NQ-open add_speech_to_text_typo \n","83 robustness NQ-open add_speech_to_text_typo \n","84 robustness NQ-open add_speech_to_text_typo \n","\n"," original_context \\\n","0 All biomass goes through at least some of thes... \n","1 Property tax or 'house tax' is a local tax on ... \n","2 Phantom pain sensations are described as perce... \n","3 Harry Potter and the Escape from Gringotts is ... \n","4 Hydroxyzine preparations require a doctor's pr... \n",".. ... \n","80 - \n","81 - \n","82 - \n","83 - \n","84 - \n","\n"," original_question \\\n","0 does ethanol take more energy make that produces \n","1 is house tax and property tax are same \n","2 is pain experienced in a missing body part or ... \n","3 is harry potter and the escape from gringotts ... \n","4 is there a difference between hydroxyzine hcl ... \n",".. ... \n","80 who played grand moff tarkin in rogue one \n","81 youngest current member of the house of repres... \n","82 who wrote the miraculous journey of edward tulane \n","83 when did the night mare before christmas come out \n","84 when does the green book come out in theaters \n","\n"," perturbed_context \\\n","0 ALL BIOMASS GOES THROUGH AT LEAST SOME OF THES... \n","1 PROPERTY TAX OR 'HOUSE TAX' IS A LOCAL TAX ON ... \n","2 PHANTOM PAIN SENSATIONS ARE DESCRIBED AS PERCE... \n","3 HARRY POTTER AND THE ESCAPE FROM GRINGOTTS IS ... \n","4 HYDROXYZINE PREPARATIONS REQUIRE A DOCTOR'S PR... \n",".. ... \n","80 - \n","81 - \n","82 - \n","83 - \n","84 - \n","\n"," perturbed_question \n","0 DOES ETHANOL TAKE MORE ENERGY MAKE THAT PRODUCES \n","1 IS HOUSE TAX AND PROPERTY TAX ARE SAME \n","2 IS PAIN EXPERIENCED IN A MISSING BODY PART OR ... \n","3 IS HARRY POTTER AND THE ESCAPE FROM GRINGOTTS ... \n","4 IS THERE A DIFFERENCE BETWEEN HYDROXYZINE HCL ... \n",".. ... \n","80 Hoo played grand moff tarkin in rogue one \n","81 youngest current member of the Hause of repres... \n","82 Houx wrote the miraculous journey of edward tu... \n","83 when did the night Mehr before christmas come out \n","84 when does the green book come out Inn theaters \n","\n","[85 rows x 7 columns]"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["harness.testcases()"]},{"cell_type":"markdown","metadata":{"id":"akSniLOoDxOp"},"source":["harness.generate() method automatically generates the test cases (based on the provided configuration)"]},{"cell_type":"markdown","metadata":{"id":"wk_cgK2BDzcM"},"source":["### Running the tests"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":48720,"status":"ok","timestamp":1692371736914,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"nje7KWD9Dx3Y","outputId":"5ac4304a-0078-49ad-84b0-c5b6c2f58155"},"outputs":[{"name":"stdout","output_type":"stream","text":["================================================================================\n"," BoolQ \n","================================================================================\n"]},{"name":"stderr","output_type":"stream","text":["Running testcases... : 100%|██████████| 48/48 [00:52<00:00, 1.10s/it]\n"]},{"name":"stdout","output_type":"stream","text":["--------------------------------------------------------------------------------\n","\n","================================================================================\n"," NQ-open \n","================================================================================\n"]},{"name":"stderr","output_type":"stream","text":["Running testcases... : 100%|██████████| 37/37 [00:48<00:00, 1.31s/it]"]},{"name":"stdout","output_type":"stream","text":["--------------------------------------------------------------------------------\n","\n"]},{"name":"stderr","output_type":"stream","text":["\n"]},{"data":{"text/plain":[]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["harness.run()"]},{"cell_type":"markdown","metadata":{"id":"7GnDWiU6D2S4"},"source":["Called after harness.generate() and is to used to run all the tests. Returns a pass/fail flag for each test."]},{"cell_type":"markdown","metadata":{"id":"q17wkdZcD4T8"},"source":["### Generated Results"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":805},"executionInfo":{"elapsed":18550,"status":"ok","timestamp":1692371755410,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"yJta_DvJD3xh","outputId":"91be0a8f-f014-4e04-81bd-8eaa521c84c9"},"outputs":[{"data":{"text/html":["
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    categorydataset_nametest_typeoriginal_contextoriginal_questionperturbed_contextperturbed_questionexpected_resultactual_resultpass
    0robustnessBoolQuppercaseAll biomass goes through at least some of thes...does ethanol take more energy make that producesALL BIOMASS GOES THROUGH AT LEAST SOME OF THES...DOES ETHANOL TAKE MORE ENERGY MAKE THAT PRODUCES\\n\\n\\nTrueTRUETrue
    1robustnessBoolQuppercaseProperty tax or 'house tax' is a local tax on ...is house tax and property tax are samePROPERTY TAX OR 'HOUSE TAX' IS A LOCAL TAX ON ...IS HOUSE TAX AND PROPERTY TAX ARE SAME\\n\\nTrue\\n\\nTrueTrue
    2robustnessBoolQuppercasePhantom pain sensations are described as perce...is pain experienced in a missing body part or ...PHANTOM PAIN SENSATIONS ARE DESCRIBED AS PERCE...IS PAIN EXPERIENCED IN A MISSING BODY PART OR ...?\\n\\n\\nTrue\\nTrueTrue
    3robustnessBoolQuppercaseHarry Potter and the Escape from Gringotts is ...is harry potter and the escape from gringotts ...HARRY POTTER AND THE ESCAPE FROM GRINGOTTS IS ...IS HARRY POTTER AND THE ESCAPE FROM GRINGOTTS ...\\n\\nTrue?\\n\\nTrueTrue
    4robustnessBoolQuppercaseHydroxyzine preparations require a doctor's pr...is there a difference between hydroxyzine hcl ...HYDROXYZINE PREPARATIONS REQUIRE A DOCTOR'S PR...IS THERE A DIFFERENCE BETWEEN HYDROXYZINE HCL ...\\n\\nTrueOATE\\n\\nTrueFalse
    .................................
    80robustnessNQ-openadd_speech_to_text_typo-who played grand moff tarkin in rogue one-Hoo played grand moff tarkin in rogue one\\n\\nPeter Cushing\\n\\nPeter Cushing played Grand Moff Tarkin in ...True
    81robustnessNQ-openadd_speech_to_text_typo-youngest current member of the house of repres...-youngest current member of the Hause of repres...\\n\\nAs of 2021, the youngest current member of...\\n\\nAs of 2021, the youngest current member of...True
    82robustnessNQ-openadd_speech_to_text_typo-who wrote the miraculous journey of edward tulane-Houx wrote the miraculous journey of edward tu...\\n\\nThe Miraculous Journey of Edward Tulane wa...\\n\\nWho wrote \"The Miraculous Journey of Edwar...False
    83robustnessNQ-openadd_speech_to_text_typo-when did the night mare before christmas come out-when did the night Mehr before christmas come out\\n\\nThe Nightmare Before Christmas was release...\\n\\nThe Nightmare Before Christmas was release...True
    84robustnessNQ-openadd_speech_to_text_typo-when does the green book come out in theaters-when does the green book come out Inn theaters\\n\\nThe green book was released in theaters on...\\n\\nThe release date for the green book in the...False
    \n","

    85 rows × 10 columns

    \n","
    "],"text/plain":[" category dataset_name test_type \\\n","0 robustness BoolQ uppercase \n","1 robustness BoolQ uppercase \n","2 robustness BoolQ uppercase \n","3 robustness BoolQ uppercase \n","4 robustness BoolQ uppercase \n",".. ... ... ... \n","80 robustness NQ-open add_speech_to_text_typo \n","81 robustness NQ-open add_speech_to_text_typo \n","82 robustness NQ-open add_speech_to_text_typo \n","83 robustness NQ-open add_speech_to_text_typo \n","84 robustness NQ-open add_speech_to_text_typo \n","\n"," original_context \\\n","0 All biomass goes through at least some of thes... \n","1 Property tax or 'house tax' is a local tax on ... \n","2 Phantom pain sensations are described as perce... \n","3 Harry Potter and the Escape from Gringotts is ... \n","4 Hydroxyzine preparations require a doctor's pr... \n",".. ... \n","80 - \n","81 - \n","82 - \n","83 - \n","84 - \n","\n"," original_question \\\n","0 does ethanol take more energy make that produces \n","1 is house tax and property tax are same \n","2 is pain experienced in a missing body part or ... \n","3 is harry potter and the escape from gringotts ... \n","4 is there a difference between hydroxyzine hcl ... \n",".. ... \n","80 who played grand moff tarkin in rogue one \n","81 youngest current member of the house of repres... \n","82 who wrote the miraculous journey of edward tulane \n","83 when did the night mare before christmas come out \n","84 when does the green book come out in theaters \n","\n"," perturbed_context \\\n","0 ALL BIOMASS GOES THROUGH AT LEAST SOME OF THES... \n","1 PROPERTY TAX OR 'HOUSE TAX' IS A LOCAL TAX ON ... \n","2 PHANTOM PAIN SENSATIONS ARE DESCRIBED AS PERCE... \n","3 HARRY POTTER AND THE ESCAPE FROM GRINGOTTS IS ... \n","4 HYDROXYZINE PREPARATIONS REQUIRE A DOCTOR'S PR... \n",".. ... \n","80 - \n","81 - \n","82 - \n","83 - \n","84 - \n","\n"," perturbed_question \\\n","0 DOES ETHANOL TAKE MORE ENERGY MAKE THAT PRODUCES \n","1 IS HOUSE TAX AND PROPERTY TAX ARE SAME \n","2 IS PAIN EXPERIENCED IN A MISSING BODY PART OR ... \n","3 IS HARRY POTTER AND THE ESCAPE FROM GRINGOTTS ... \n","4 IS THERE A DIFFERENCE BETWEEN HYDROXYZINE HCL ... \n",".. ... \n","80 Hoo played grand moff tarkin in rogue one \n","81 youngest current member of the Hause of repres... \n","82 Houx wrote the miraculous journey of edward tu... \n","83 when did the night Mehr before christmas come out \n","84 when does the green book come out Inn theaters \n","\n"," expected_result \\\n","0 \\n\\n\\nTrue \n","1 \\n\\nTrue \n","2 ?\\n\\n\\nTrue \n","3 \\n\\nTrue \n","4 \\n\\nTrue \n",".. ... \n","80 \\n\\nPeter Cushing \n","81 \\n\\nAs of 2021, the youngest current member of... \n","82 \\n\\nThe Miraculous Journey of Edward Tulane wa... \n","83 \\n\\nThe Nightmare Before Christmas was release... \n","84 \\n\\nThe green book was released in theaters on... \n","\n"," actual_result pass \n","0 TRUE True \n","1 \\n\\nTrue True \n","2 \\nTrue True \n","3 ?\\n\\nTrue True \n","4 OATE\\n\\nTrue False \n",".. ... ... \n","80 \\n\\nPeter Cushing played Grand Moff Tarkin in ... True \n","81 \\n\\nAs of 2021, the youngest current member of... True \n","82 \\n\\nWho wrote \"The Miraculous Journey of Edwar... False \n","83 \\n\\nThe Nightmare Before Christmas was release... True \n","84 \\n\\nThe release date for the green book in the... False \n","\n","[85 rows x 10 columns]"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["harness.generated_results()"]},{"cell_type":"markdown","metadata":{"id":"Vtv8wGFyD-XR"},"source":["This method returns the generated results in the form of a pandas dataframe, which provides a convenient and easy-to-use format for working with the test results. You can use this method to quickly identify the test cases that failed and to determine where fixes are needed."]},{"cell_type":"markdown","metadata":{"id":"agT9GO6FEC3E"},"source":["### Final Results\n","\n","We can call `.report()` which summarizes the results giving information about pass and fail counts and overall test pass/fail flag."]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":19430,"status":"ok","timestamp":1692371774826,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"qjFtUmbtEA2G","outputId":"62d274a2-8688-491a-f04e-101ebe5a6450"},"outputs":[{"data":{"text/html":["
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    Benchmarking Results: gpt-3.5-turbo-instruct
    fail_countpass_countpass_rateminimum_pass_ratepass
    dataset_namecategorytest_type
    BoolQrobustnessuppercase2880%66%True
    dyslexia_word_swap2880%60%True
    add_abbreviation1990%60%True
    add_slangs4450%60%False
    add_speech_to_text_typo3770%60%True
    NQ-openrobustnessuppercase3770%66%True
    dyslexia_word_swap2571%60%True
    add_abbreviation5444%60%False
    add_slangs1150%60%False
    add_speech_to_text_typo5444%60%False
    \n","
    "],"text/plain":[" Benchmarking Results: gpt-3.5-turbo-instruct \\\n"," fail_count \n","dataset_name category test_type \n","BoolQ robustness uppercase 2 \n"," dyslexia_word_swap 2 \n"," add_abbreviation 1 \n"," add_slangs 4 \n"," add_speech_to_text_typo 3 \n","NQ-open robustness uppercase 3 \n"," dyslexia_word_swap 2 \n"," add_abbreviation 5 \n"," add_slangs 1 \n"," add_speech_to_text_typo 5 \n","\n"," \\\n"," pass_count pass_rate \n","dataset_name category test_type \n","BoolQ robustness uppercase 8 80% \n"," dyslexia_word_swap 8 80% \n"," add_abbreviation 9 90% \n"," add_slangs 4 50% \n"," add_speech_to_text_typo 7 70% \n","NQ-open robustness uppercase 7 70% \n"," dyslexia_word_swap 5 71% \n"," add_abbreviation 4 44% \n"," add_slangs 1 50% \n"," add_speech_to_text_typo 4 44% \n","\n"," \n"," minimum_pass_rate pass \n","dataset_name category test_type \n","BoolQ robustness uppercase 66% True \n"," dyslexia_word_swap 60% True \n"," add_abbreviation 60% True \n"," add_slangs 60% 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)"]},{"cell_type":"markdown","metadata":{"id":"MhgkQYQiEvZt"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"WJJzt3RWhEc6"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"26qXWhCYhHAt"},"source":["# Getting started with LangTest on John Snow Labs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oGIyE43uhTxH"},"outputs":[],"source":["!pip install \"langtest==2.2.0\""]},{"cell_type":"markdown","metadata":{"id":"yR6kjOaiheKN"},"source":["# DataAugmenter and its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["yaml_content = \"\"\"\n","parameters:\n"," type: proportion\n"," style: new\n","tests:\n"," robustness:\n"," uppercase:\n"," max_proportion: 0.2\n"," lowercase:\n"," max_proportion: 0.2\n","\n","\"\"\"\n","\n","with open(\"config.yaml\", \"w\") as f:\n"," f.write(yaml_content)"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1405,"status":"ok","timestamp":1692343652196,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"lTzSJpMlhgq5"},"outputs":[],"source":["from langtest.augmentation import DataAugmenter\n","from langtest.tasks.task import TaskManager\n","\n","data_augmenter = DataAugmenter(\n"," task=TaskManager(\"ner\"),\n"," config=\"config.yaml\",\n",")"]},{"cell_type":"markdown","metadata":{"id":"sBcZjwJBhkOw"},"source":["The instance of the `DataAugmenter` class from the `langtest.augmentation` module is to perform the Data augmentation for different tasks from langtest. \n","In this specific instance, the `DataAugmenter` object is created with the following parameters:\n","\n","
    \n","\n","| Parameter | Description |\n","| - | - |\n","| **task** | Task for which the model is to be evaluated (text-classification or ner) |\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","By creating an instance of the `DataAugmenter` class, you can utilize its methods and functionalities to perform data augmentation on different tasks from langtest specific to the Named Entity Recognition task. The `config.yaml` file contains the specific configuration settings for the tests to be performed, such as the type of augmentation and the maximum proportion of augmentation for different test cases.\n","\n","Overall, the `data_augmenter` object represents an instance of the `DataAugmenter` class that can be used to conduct Data augmentation for the Named Entity Recognition task based on the provided configuration.\n","\n","
    \n","
    "]},{"cell_type":"markdown","metadata":{"id":"I21Jmq79jgC6"},"source":["#### Load Train and Test CoNLL"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 50519 (49K) [text/plain]\n","Saving to: ‘sample.conll’\n","\n","sample.conll 100%[===================>] 49.33K --.-KB/s in 0.006s \n","\n","2023-08-18 07:27:31 (7.50 MB/s) - ‘sample.conll’ saved [50519/50519]\n","\n","--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 827443 (808K) [text/plain]\n","Saving to: ‘conll03.conll’\n","\n","conll03.conll 100%[===================>] 808.05K --.-KB/s in 0.03s \n","\n","2023-08-18 07:27:31 (30.1 MB/s) - ‘conll03.conll’ saved [827443/827443]\n","\n"]}],"source":["# Load test CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","\n","# Load train CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll"]},{"cell_type":"markdown","metadata":{},"source":["### Augmenting with train data"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.augment(data={\n"," 'data_source': 'conll03.conll'\n","})"]},{"cell_type":"markdown","metadata":{},"source":["### Save the augmentated dataset "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.save(\"augmented.conll\")"]},{"cell_type":"markdown","metadata":{"id":"YPXIxv9D_fR7"},"source":["Essentially it applies perturbations to the input data based on the recommendations from the harness reports. Then this augmented_dataset is used to retrain the original model so as to make the model more robust and improve its performance."]}],"metadata":{"colab":{"machine_shape":"hm","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 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)"]},{"cell_type":"markdown","metadata":{"id":"MhgkQYQiEvZt"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Data_Augmenter_Notebook.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"WJJzt3RWhEc6"},"source":["**LangTest** is an open-source python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using **John Snow Labs, Hugging Face, Spacy** models or **OpenAI, Cohere, AI21, Hugging Face Inference API and Azure-OpenAI** based LLMs, it has got you covered. You can test any Named Entity Recognition (NER), Text Classification, fill-mask, Translation model using the library. We also support testing LLMS for Question-Answering, Summarization and text-generation tasks on benchmark datasets. The library supports 60+ out of the box tests. For a complete list of supported test categories, please refer to the [documentation](http://langtest.org/docs/pages/docs/test_categories).\n","\n","Metrics are calculated by comparing the model's extractions in the original list of sentences against the extractions carried out in the noisy list of sentences. The original annotated labels are not used at any point, we are simply comparing the model against itself in a 2 settings."]},{"cell_type":"markdown","metadata":{"id":"26qXWhCYhHAt"},"source":["# Getting started with LangTest on John Snow Labs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oGIyE43uhTxH"},"outputs":[],"source":["!pip install langtest==2.2.0"]},{"cell_type":"markdown","metadata":{"id":"yR6kjOaiheKN"},"source":["# DataAugmenter and its Parameters\n","\n","The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way."]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["yaml_content = \"\"\"\n","parameters:\n"," type: proportion\n"," style: new\n","tests:\n"," robustness:\n"," uppercase:\n"," max_proportion: 0.2\n"," lowercase:\n"," max_proportion: 0.2\n","\n","\"\"\"\n","\n","with open(\"config.yaml\", \"w\") as f:\n"," f.write(yaml_content)"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":1405,"status":"ok","timestamp":1692343652196,"user":{"displayName":"Prikshit sharma","userId":"07819241395213139913"},"user_tz":-330},"id":"lTzSJpMlhgq5"},"outputs":[],"source":["from langtest.augmentation import DataAugmenter\n","from langtest.tasks.task import TaskManager\n","\n","data_augmenter = DataAugmenter(\n"," task=TaskManager(\"ner\"),\n"," config=\"config.yaml\",\n",")"]},{"cell_type":"markdown","metadata":{"id":"sBcZjwJBhkOw"},"source":["The instance of the `DataAugmenter` class from the `langtest.augmentation` module is to perform the Data augmentation for different tasks from langtest. \n","In this specific instance, the `DataAugmenter` object is created with the following parameters:\n","\n","
    \n","\n","| Parameter | Description |\n","| - | - |\n","| **task** | Task for which the model is to be evaluated (text-classification or ner) |\n","| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n","\n","By creating an instance of the `DataAugmenter` class, you can utilize its methods and functionalities to perform data augmentation on different tasks from langtest specific to the Named Entity Recognition task. The `config.yaml` file contains the specific configuration settings for the tests to be performed, such as the type of augmentation and the maximum proportion of augmentation for different test cases.\n","\n","Overall, the `data_augmenter` object represents an instance of the `DataAugmenter` class that can be used to conduct Data augmentation for the Named Entity Recognition task based on the provided configuration.\n","\n","
    \n","
    "]},{"cell_type":"markdown","metadata":{"id":"I21Jmq79jgC6"},"source":["#### Load Train and Test CoNLL"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 50519 (49K) [text/plain]\n","Saving to: ‘sample.conll’\n","\n","sample.conll 100%[===================>] 49.33K --.-KB/s in 0.006s \n","\n","2023-08-18 07:27:31 (7.50 MB/s) - ‘sample.conll’ saved [50519/50519]\n","\n","--2023-08-18 07:27:31-- https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 827443 (808K) [text/plain]\n","Saving to: ‘conll03.conll’\n","\n","conll03.conll 100%[===================>] 808.05K --.-KB/s in 0.03s \n","\n","2023-08-18 07:27:31 (30.1 MB/s) - ‘conll03.conll’ saved [827443/827443]\n","\n"]}],"source":["# Load test CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/langtest/data/conll/sample.conll\n","\n","# Load train CoNLL\n","!wget https://raw.githubusercontent.com/JohnSnowLabs/langtest/main/demo/data/conll03.conll"]},{"cell_type":"markdown","metadata":{},"source":["### Augmenting with train data"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.augment(data={\n"," 'data_source': 'conll03.conll'\n","})"]},{"cell_type":"markdown","metadata":{},"source":["### Save the augmentated dataset "]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["data_augmenter.save(\"augmented.conll\")"]},{"cell_type":"markdown","metadata":{"id":"YPXIxv9D_fR7"},"source":["Essentially it applies perturbations to the input data based on the recommendations from the harness reports. Then this augmented_dataset is used to retrain the original model so as to make the model more robust and improve its performance."]}],"metadata":{"colab":{"machine_shape":"hm","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 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From 7b9fb210fa23cdd57f9db72c0718b36b1bc5b13f Mon Sep 17 00:00:00 2001 From: Kalyan Chakravarthy Date: Wed, 15 May 2024 18:26:36 +0530 Subject: [PATCH 69/69] chore: update langtest version to 2.2.0 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 9ce884bf9..21806b1b5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langtest" -version = "2.1.0" +version = "2.2.0" description = "John Snow Labs provides a library for delivering safe & effective NLP models." authors = ["John Snow Labs "] readme = "README.md"