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README.md

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-# The Wikipedia Corpus in MedRAG
+# The PubMed Corpus in MedRAG
 
-This HF dataset contains the chunked snippets from the Wikipedia corpus used in [MedRAG](https://arxiv.org/abs/2402.13178). It can be used for medical Retrieval-Augmented Generation (RAG).
+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).
 
 ## News
-- (02/26/2024) The "id" column has been reformatted. A new "wiki_id" column is added.
+- (02/26/2024) The "id" column has been reformatted. A new "PMID" column is added.
 
 ## Dataset Details
 
 ### Dataset Descriptions
 
-As a large-scale open-source encyclopedia, Wikipedia is frequently used as a corpus in information retrieval tasks.
-We select Wikipedia as one of the corpora to see if the general domain database can be used to improve the ability of medical QA. 
-We downloaded the processed Wikipedia data from [HuggingFace](https://huggingface.co/datasets/wikipedia) and chunked the text using [LangChain](https://www.langchain.com/) as snippets with no more than 1000 characters. 
-This HF dataset contains our ready-to-use chunked snippets for the Wikipedia corpus, including 29,913,202 snippets with an average of 162 tokens.
+[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 Wikipedia, which includes the following features:
+Each row is a snippet of PubMed, which includes the following features:
 
 - id: a unique identifier of the snippet
-- title: the title of the Wikipedia article from which the snippet is collected
-- content: the content 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
@@ -44,7 +43,7 @@ Each row is a snippet of Wikipedia, which includes the following features:
 <!-- This section describes suitable use cases for the dataset. -->
 
 ```shell
-git clone https://huggingface.co/datasets/MedRAG/wikipedia
+git clone https://huggingface.co/datasets/MedRAG/pubmed
 ```
 
 ### Use in MedRAG
@@ -60,7 +59,7 @@ git clone https://huggingface.co/datasets/MedRAG/wikipedia
     "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="Wikipedia")
+>> 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
 ```
 

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