Webiste/Notebook Updates

README.md

@@ -84,6 +84,12 @@ Langtest comes with different datasets to test your models, covering a wide rang
 | [**BBQ**](https://arxiv.org/abs/2110.08193)       | Evaluate how your model responds to questions in the presence of social biases against protected classes across various social dimensions. Assess biases in model outputs with both under-informative and adequately informative contexts, aiming to promote fair and unbiased question-answering 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/dataset-notebooks/BBQ_dataset.ipynb)   |
 |[**XSum**](https://aclanthology.org/D18-1206/) | Evaluate your model's ability to generate concise and informative summaries for long articles with the XSum dataset. It consists of articles and corresponding one-sentence summaries, offering a valuable benchmark for text summarization 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/dataset-notebooks/XSum_dataset.ipynb)|
 |[**Real Toxicity Prompts**](https://aclanthology.org/2020.findings-emnlp.301/) | Evaluate your model's accuracy in recognizing and handling toxic language with the Real Toxicity Prompts dataset. It contains real-world prompts from online platforms, ensuring robustness in NLP models to maintain safe environments. | [![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/OpenAI_QA_Testing_Notebook.ipynb)
+|[**LogiQA**](https://aclanthology.org/2020.findings-emnlp.301/) | Evaluate your model's accuracy on Machine Reading Comprehension with Logical Reasoning questions. | [![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/dataset-notebooks/LogiQA_dataset.ipynb)
+|[**BigBench Abstract narrative understanding**](https://arxiv.org/abs/2206.04615) | Evaluate your model's performance in selecting the most relevant proverb for a given narrative. | [![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/dataset-notebooks/Bigbench_dataset.ipynb)
+|[**BigBench Causal Judgment**](https://arxiv.org/abs/2206.04615) | Evaluate your model's performance in measuring the ability to reason about cause and effect. | [![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/dataset-notebooks/Bigbench_dataset.ipynb)
+|[**BigBench DisambiguationQA**](https://arxiv.org/abs/2206.04615) | Evaluate your model's performance on determining the interpretation of sentences containing ambiguous pronoun references.| [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](hhttps://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/dataset-notebooks/Bigbench_dataset.ipynb)
+|[**BigBench DisflQA**](https://arxiv.org/abs/2206.04615) | Evaluate your model's performance in picking the correct answer span from the context given the disfluent question. | [![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/dataset-notebooks/Bigbench_dataset.ipynb)
+|[**ASDiv**](https://arxiv.org/abs/2106.15772) | Evaluate your model's ability answer questions based on Math Word Problems. | [![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/dataset-notebooks/ASDiv_dataset.ipynb)
 
 > **Note**
 > For usage and documentation, head over to [langtest.org](https://langtest.org/docs/pages/docs/data#question-answering)
@@ -95,7 +101,7 @@ You can check out the following langtest blogs:
 
 | Blog | Description |
 |------|-------------|
-| [**Automatically Testing for Demographic Bias in Clinical Treatment Plans Generated by Large Language Models**](https://medium.com/p/ffcf358b6092/edit) | Helps in understanding and testing demographic bias in clinical treatment plans generated by LLM. |
+| [**Automatically Testing for Demographic Bias in Clinical Treatment Plans Generated by Large Language Models**](https://medium.com/john-snow-labs/automatically-testing-for-demographic-bias-in-clinical-treatment-plans-generated-by-large-language-ffcf358b6092) | Helps in understanding and testing demographic bias in clinical treatment plans generated by LLM. |
 | [**LangTest: Unveiling & Fixing Biases with End-to-End NLP Pipelines**](https://www.johnsnowlabs.com/langtest-unveiling-fixing-biases-with-end-to-end-nlp-pipelines/) | The end-to-end language pipeline in LangTest empowers NLP practitioners to tackle biases in language models with a comprehensive, data-driven, and iterative approach. |
 | [**Beyond Accuracy: Robustness Testing of Named Entity Recognition Models with LangTest**](https://medium.com/@prikshit7766/fb046ace7eb9) | While accuracy is undoubtedly crucial, robustness testing takes natural language processing (NLP) models evaluation to the next level by ensuring that models can perform reliably and consistently across a wide array of real-world conditions. |
 

demo/blogposts/Healthcare_NER_Model_Evaluation_with_LangTest.ipynb

@@ -1313,11 +1313,11 @@
     "\n",
     "\n",
     "\n",
-    "| Parameter     | Description |\n",
-    "| ------------- | ----------- |\n",
-    "| **task**      | Task for which the model is to be evaluated (text-classification or ner) |\n",
-    "| **model**     | Specifies the model(s) to be evaluated. Can be a dictionary or a list of dictionaries. Each dictionary should contain 'model' and 'hub' keys. If a path is specified, the dictionary must contain 'model' and 'hub' keys. |\n",
-    "| **data**      | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys: <ul><li>data_source (mandatory): The source of the data.</li><li>subset (optional): The subset of the data.</li><li>feature_column (optional): The column containing the features.</li><li>target_column (optional): The column containing the target labels.</li><li>split (optional): The data split to be used.</li></ul> |\n",
+    "| Parameter  | Description |  \n",
+    "| - | - | \n",
+    "|**task**     |Task for which the model is to be evaluated (text-classification or 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: <ul><li>model (mandatory): \tPipelineModel or path to a saved model or pretrained pipeline/model from hub.</li><li>hub (mandatory): Hub (library) to use in back-end for loading model from public models hub or from path</li></ul>|\n",
+    "| **data**      | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys: <ul><li>data_source (mandatory): The source of the data.</li><li>subset (optional): The subset of the data.</li><li>feature_column (optional): The column containing the features.</li><li>target_column (optional): The column containing the target labels.</li><li>split (optional): The data split to be used.</li><li>source (optional): Set to 'huggingface' when loading Hugging Face dataset.</li></ul> |\n",
     "| **config**    | Configuration for the tests to be performed, specified in the form of a YAML file. |\n",
     "\n",
     "\n",

demo/tutorials/end-to-end-notebooks/HuggingFace_Real_World_Notebook.ipynb

@@ -112,11 +112,11 @@
         "\n",
         "\n",
         "\n",
-        "| Parameter     | Description |\n",
-        "| ------------- | ----------- |\n",
-        "| **task**      | Task for which the model is to be evaluated (text-classification or ner) |\n",
-        "| **model**     | Specifies the model(s) to be evaluated. Can be a dictionary or a list of dictionaries. Each dictionary should contain 'model' and 'hub' keys. If a path is specified, the dictionary must contain 'model' and 'hub' keys. |\n",
-        "| **data**      | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys: <ul><li>data_source (mandatory): The source of the data.</li><li>subset (optional): The subset of the data.</li><li>feature_column (optional): The column containing the features.</li><li>target_column (optional): The column containing the target labels.</li><li>split (optional): The data split to be used.</li></ul> |\n",
+        "| Parameter  | Description |  \n",
+        "| - | - | \n",
+        "|**task**     |Task for which the model is to be evaluated (text-classification or 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: <ul><li>model (mandatory): \tPipelineModel or path to a saved model or pretrained pipeline/model from hub.</li><li>hub (mandatory): Hub (library) to use in back-end for loading model from public models hub or from path</li></ul>|\n",
+        "| **data**      | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys: <ul><li>data_source (mandatory): The source of the data.</li><li>subset (optional): The subset of the data.</li><li>feature_column (optional): The column containing the features.</li><li>target_column (optional): The column containing the target labels.</li><li>split (optional): The data split to be used.</li><li>source (optional): Set to 'huggingface' when loading Hugging Face dataset.</li></ul> |\n",
         "| **config**    | Configuration for the tests to be performed, specified in the form of a YAML file. |\n",
         "\n",
         "\n",

demo/tutorials/end-to-end-notebooks/JohnSnowLabs_RealWorld_Custom_Pipeline_Notebook.ipynb

@@ -110,11 +110,11 @@
         "\n",
         "\n",
         "\n",
-        "| Parameter     | Description |\n",
-        "| ------------- | ----------- |\n",
-        "| **task**      | Task for which the model is to be evaluated (text-classification or ner) |\n",
-        "| **model**     | Specifies the model(s) to be evaluated. Can be a dictionary or a list of dictionaries. Each dictionary should contain 'model' and 'hub' keys. If a path is specified, the dictionary must contain 'model' and 'hub' keys. |\n",
-        "| **data**      | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys: <ul><li>data_source (mandatory): The source of the data.</li><li>subset (optional): The subset of the data.</li><li>feature_column (optional): The column containing the features.</li><li>target_column (optional): The column containing the target labels.</li><li>split (optional): The data split to be used.</li></ul> |\n",
+        "| Parameter  | Description |  \n",
+        "| - | - | \n",
+        "|**task**     |Task for which the model is to be evaluated (text-classification or 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: <ul><li>model (mandatory): \tPipelineModel or path to a saved model or pretrained pipeline/model from hub.</li><li>hub (mandatory): Hub (library) to use in back-end for loading model from public models hub or from path</li></ul>|\n",
+        "| **data**      | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys: <ul><li>data_source (mandatory): The source of the data.</li><li>subset (optional): The subset of the data.</li><li>feature_column (optional): The column containing the features.</li><li>target_column (optional): The column containing the target labels.</li><li>split (optional): The data split to be used.</li><li>source (optional): Set to 'huggingface' when loading Hugging Face dataset.</li></ul> |\n",
         "| **config**    | Configuration for the tests to be performed, specified in the form of a YAML file. |\n",
         "\n",
         "\n",

demo/tutorials/end-to-end-notebooks/JohnSnowLabs_RealWorld_Notebook.ipynb

@@ -110,11 +110,11 @@
         "\n",
         "\n",
         "\n",
-        "| Parameter     | Description |\n",
-        "| ------------- | ----------- |\n",
-        "| **task**      | Task for which the model is to be evaluated (text-classification or ner) |\n",
-        "| **model**     | Specifies the model(s) to be evaluated. Can be a dictionary or a list of dictionaries. Each dictionary should contain 'model' and 'hub' keys. If a path is specified, the dictionary must contain 'model' and 'hub' keys. |\n",
-        "| **data**      | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys: <ul><li>data_source (mandatory): The source of the data.</li><li>subset (optional): The subset of the data.</li><li>feature_column (optional): The column containing the features.</li><li>target_column (optional): The column containing the target labels.</li><li>split (optional): The data split to be used.</li></ul> |\n",
+        "| Parameter  | Description |  \n",
+        "| - | - | \n",
+        "|**task**     |Task for which the model is to be evaluated (text-classification or 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: <ul><li>model (mandatory): \tPipelineModel or path to a saved model or pretrained pipeline/model from hub.</li><li>hub (mandatory): Hub (library) to use in back-end for loading model from public models hub or from path</li></ul>|\n",
+        "| **data**      | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys: <ul><li>data_source (mandatory): The source of the data.</li><li>subset (optional): The subset of the data.</li><li>feature_column (optional): The column containing the features.</li><li>target_column (optional): The column containing the target labels.</li><li>split (optional): The data split to be used.</li><li>source (optional): Set to 'huggingface' when loading Hugging Face dataset.</li></ul> |\n",
         "| **config**    | Configuration for the tests to be performed, specified in the form of a YAML file. |\n",
         "\n",
         "\n",

demo/tutorials/end-to-end-notebooks/Spacy_Real_World_Notebook.ipynb

@@ -90,11 +90,11 @@
         "\n",
         "\n",
         "\n",
-        "| Parameter     | Description |\n",
-        "| ------------- | ----------- |\n",
-        "| **task**      | Task for which the model is to be evaluated (text-classification or ner) |\n",
-        "| **model**     | Specifies the model(s) to be evaluated. Can be a dictionary or a list of dictionaries. Each dictionary should contain 'model' and 'hub' keys. If a path is specified, the dictionary must contain 'model' and 'hub' keys. |\n",
-        "| **data**      | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys: <ul><li>data_source (mandatory): The source of the data.</li><li>subset (optional): The subset of the data.</li><li>feature_column (optional): The column containing the features.</li><li>target_column (optional): The column containing the target labels.</li><li>split (optional): The data split to be used.</li></ul> |\n",
+        "| Parameter  | Description |  \n",
+        "| - | - | \n",
+        "|**task**     |Task for which the model is to be evaluated (text-classification or 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: <ul><li>model (mandatory): \tPipelineModel or path to a saved model or pretrained pipeline/model from hub.</li><li>hub (mandatory): Hub (library) to use in back-end for loading model from public models hub or from path</li></ul>|\n",
+        "| **data**      | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys: <ul><li>data_source (mandatory): The source of the data.</li><li>subset (optional): The subset of the data.</li><li>feature_column (optional): The column containing the features.</li><li>target_column (optional): The column containing the target labels.</li><li>split (optional): The data split to be used.</li><li>source (optional): Set to 'huggingface' when loading Hugging Face dataset.</li></ul> |\n",
         "| **config**    | Configuration for the tests to be performed, specified in the form of a YAML file. |\n",
         "\n",
         "\n",

demo/tutorials/llm_notebooks/AI21_QA_Summarization_Testing_Notebook.ipynb

@@ -98,8 +98,8 @@
         "| 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. Can be a dictionary or a list of dictionaries. Each dictionary should contain 'model' and 'hub' keys. If a path is specified, the dictionary must contain 'model' and 'hub' keys.|\n",
-        "| **data**      | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys: <ul><li>data_source (mandatory): The source of the data.</li><li>subset (optional): The subset of the data.</li><li>feature_column (optional): The column containing the features.</li><li>target_column (optional): The column containing the target labels.</li><li>split (optional): The data split to be used.</li></ul> |\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: <ul><li>model (mandatory): \tPipelineModel or path to a saved model or pretrained pipeline/model from hub.</li><li>hub (mandatory): Hub (library) to use in back-end for loading model from public models hub or from path</li></ul>|\n",
+        "| **data**      | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys: <ul><li>data_source (mandatory): The source of the data.</li><li>subset (optional): The subset of the data.</li><li>feature_column (optional): The column containing the features.</li><li>target_column (optional): The column containing the target labels.</li><li>split (optional): The data split to be used.</li><li>source (optional): Set to 'huggingface' when loading Hugging Face dataset.</li></ul> |\n",
         "| **config**    | Configuration for the tests to be performed, specified in the form of a YAML file. |\n",
         "\n",
         "<br/>\n",