Update README.md

README.md

@@ -262,13 +262,35 @@ configs:
     path: truthfulqa/validation-*
 ---
 
+
+
+# ContextualBench - A comprehensive toolkit to evaluate LM on different Contextual datasets
+
+Evaluation Code: [SalesforceAIResearch/SFR-RAG](https://github.com/SalesforceAIResearch/SFR-RAG)
+
+## Description
+
+ContextualBench is a powerful evaluation framework designed to assess the performance of Large Language Models (LLMs) on contextual datasets. It provides a flexible pipeline for evaluating various LLM families across different tasks, with a focus on handling large context inputs.
+
+> Each individual evaluation dataset in ContextualBench is licensed separately and must be adhered by a user.
+
+
+## Features
+
+* Dynamic Retrieval Support: Efficiently handles large context inputs, allowing for comprehensive evaluation of LLMs' contextual understanding capabilities.
+* Extensive Evaluation Dataset: Supports 7 contextual tasks, including: Question Answering (QA), Multi-Hop Question Answering, Classification tasks
+* Multi-LLM Family Support: Compatible with a wide range of LLM families, including: Hugging Face models, Gemma, Mistral, OpenAI, Cohere.
+
+
 The dataset can be loaded using the command
-```
+```python
 task = "hotpotqa" # it can be any other option like triviaqa,popqa,2wiki, MuSiQue, NaturalQuestions etc.
-load_dataset("Salesforce/ContextualBench",task,split="validation")
+load_dataset("Salesforce/ContextualBench", task, split="validation")
 ```
 
-## 2WikiHotpotQA
+## Component Datasets
+
+### 2WikiHotpotQA
 
 This dataset is a multihop question answering task, as proposed in "Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps" by Ho. et. al 
 The folder contains evaluation script and path to dataset on the validation split on around 12k samples. 
@@ -289,7 +311,7 @@ The folder contains evaluation script and path to dataset on the validation spli
 }
 ```
 
-## HotpotQA
+### HotpotQA
 
 HotpotQA is a Wikipedia-based question-answer pairs with the questions require finding and reasoning over multiple supporting documents to answer. We evaluate on 7405 datapoints, on the distractor setting. This dataset was proposed in the below paper 
 ```
@@ -301,7 +323,7 @@ HotpotQA is a Wikipedia-based question-answer pairs with the questions require f
 }
 ```
 
-## MuSiQue
+### MuSiQue
 
 This dataset is a multihop question answering task, that requires 2-4 hop in every questions, making it slightly harder task when compared to other multihop tasks.This dataset was proposed in the below paper 
 
@@ -315,7 +337,7 @@ This dataset is a multihop question answering task, that requires 2-4 hop in eve
 }
 ```
 
-## NaturalQuestions
+### NaturalQuestions
 
 The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question
 
@@ -328,7 +350,7 @@ journal	= {Transactions of the Association of Computational Linguistics}
 }
 ```
 
-## PopQA
+### PopQA
 PopQA is a large-scale open-domain question answering (QA) dataset, the long-tail subset, consisting of 1,399 rare entity queries whose monthly Wikipedia page views are less than 100
 
 Make sure to cite the work
@@ -341,7 +363,7 @@ Make sure to cite the work
 }
 ```
 
-## TriviaQA
+### TriviaQA
 
 TriviaqQA is a reading comprehension dataset containing question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions.
 ```
@@ -358,7 +380,7 @@ archivePrefix = {arXiv},
 }
 ```
 
-## TruthfulQA
+### TruthfulQA
 
 TruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. Questions are crafted so that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts.