Update README.md

README.md

@@ -1,3 +1,75 @@
-# StatPearls
+---
+task_categories:
+- question-answering
+language:
+- en
+tags:
+- medical
+- question answering
+- large language model
+- retrieval-augmented generation
+size_categories:
+- 100K<n<1M
+---
+# The StatPearls Corpus in MedRAG
 
-According to the [privacy policy](https://www.statpearls.com/home/privacypolicy/) of StatPearls, we are not allowed to distribute the StatPearls content. Please download the raw data from [NCBI Bookshelf](https://www.ncbi.nlm.nih.gov/books/NBK430685/) and use [our code](https://github.com/Teddy-XiongGZ/MedRAG/blob/main/src/data/statpearls.py) for the chunking.
+This HF dataset contains the chunked snippets from the StatPearls corpus used in [MedRAG](https://arxiv.org/abs/2402.13178). It can be used for medical Retrieval-Augmented Generation (RAG).
+
+According to the [privacy policy](https://www.statpearls.com/home/privacypolicy/) of StatPearls, **we are not allowed to distribute the StatPearls content**. Please download the raw data from [NCBI Bookshelf](https://www.ncbi.nlm.nih.gov/books/NBK430685/) and use [our code](https://github.com/Teddy-XiongGZ/MedRAG/blob/main/src/data/statpearls.py) for the chunking.
+
+## Dataset Details
+
+### Dataset Descriptions
+
+[StatPearls](https://www.statpearls.com/) is a point-of-the-care clinical decision support tool similar to [UpToDate](https://www.uptodate.com/). We use the 9,330 publicly available StatPearl articles through [NCBI Bookshelf](https://www.ncbi.nlm.nih.gov/books/NBK430685/) to construct the StatPearls corpus. 
+We chunked StatPearls according to the hierarchical structure, treating each paragraph in an article as a snippet and splicing all the relevant hierarchical headings as the corresponding title.
+Our chunked corpus contains 301,202 snippets with an average of 119 tokens.
+
+### Dataset Structure
+Each row is a snippet of StatPearls, which includes the following features:
+
+- id: a unique identifier of the snippet
+- title: the title and subtitles of the StatPearl article from which the snippet is collected
+- content: the content of the snippet
+- contents: a concatenation of 'title' and 'content', which will be used by the [BM25](https://github.com/castorini/pyserini) retriever
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+```shell
+wget https://ftp.ncbi.nlm.nih.gov/pub/litarch/3d/12/statpearls_NBK430685.tar.gz -P ./corpus/statpearls
+tar -xzvf ./corpus/statpearls/statpearls_NBK430685.tar.gz -C ./corpus/statpearls
+python src/data/statpearls.py
+```
+
+### Use in MedRAG
+
+```python
+>> from src.medrag import MedRAG
+
+>> question = "A lesion causing compression of the facial nerve at the stylomastoid foramen will cause ipsilateral"
+>> options = {
+    "A": "paralysis of the facial muscles.",
+    "B": "paralysis of the facial muscles and loss of taste.",
+    "C": "paralysis of the facial muscles, loss of taste and lacrimation.",
+    "D": "paralysis of the facial muscles, loss of taste, lacrimation and decreased salivation."
+}
+
+>> medrag = MedRAG(llm_name="OpenAI/gpt-3.5-turbo-16k", rag=True, retriever_name="MedCPT", corpus_name="StatPearls")
+>> answer, snippets, scores = medrag.answer(question=question, options=options, k=32) # scores are given by the retrieval system
+```
+
+## Citation
+```shell
+@article{xiong2024benchmarking,
+    title={Benchmarking Retrieval-Augmented Generation for Medicine}, 
+    author={Guangzhi Xiong and Qiao Jin and Zhiyong Lu and Aidong Zhang},
+    journal={arXiv preprint arXiv:2402.13178},
+    year={2024}
+}
+```