Create README.md

README.md

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+---
+task_categories:
+- question-answering
+language:
+- en
+tags:
+- medical
+- question answering
+- large language model
+- retrieval-augmented generation
+size_categories:
+- 10M<n<100M
+---
+# The PubMed Corpus in MedRAG
+
+This HF dataset contains the snippets from the PubMed corpus used in [MedRAG](https://arxiv.org/abs/2402.13178). It can be used for medical Retrieval-Augmented Generation (RAG).
+
+## Dataset Details
+
+### Dataset Descriptions
+
+[PubMed](https://pubmed.ncbi.nlm.nih.gov/) is the most widely used literature resource, containing over 36 million biomedical articles. 
+For MedRAG, we use a PubMed subset of 23.9 million articles with valid titles and abstracts.
+This HF dataset contains our ready-to-use snippets for the PubMed corpus, including 23,898,701 snippets with an average of 296 tokens.
+
+### Dataset Structure
+Each row is a snippet of PubMed, which includes the following features:
+
+- id: a unique identifier of the snippet
+- title: the title of the PubMed article from which the snippet is collected
+- content: the abstract of the PubMed article from which the snippet is collected
+- 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
+git clone https://huggingface.co/datasets/MedRAG/pubmed
+```
+
+### 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="PubMed")
+>> 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}
+}
+```