README.md

metadata

language:
  - en
size_categories:
  - 10K<n<100K
task_categories:
  - question-answering
dataset_info:
  features:
    - name: output
      dtype: string
    - name: input
      dtype: string
    - name: instruction
      dtype: string
    - name: prompt
      dtype: string
  splits:
    - name: train
      num_bytes: 68827329
      num_examples: 26357
  download_size: 29156486
  dataset_size: 68827329
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
tags:
  - medical

Dataset made for instruction supervised finetuning of Mistral LLMs, by combining of medical datasets:

• Medical meadow wikidoc (https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/blob/main/README.md)
• Medquad (https://www.kaggle.com/datasets/jpmiller/layoutlm)

Medical meadow wikidoc

The Medical Meadow Wikidoc dataset comprises question-answer pairs sourced from WikiDoc, an online platform where medical professionals collaboratively contribute and share contemporary medical knowledge. WikiDoc features two primary sections: the "Living Textbook" and "Patient Information". The "Living Textbook" encompasses chapters across various medical specialties, from which we extracted content. Utilizing GTP-3.5-Turbo, the paragraph headings are transformed into questions and utilized the respective paragraphs as answers. Notably, the structure of "Patient Information" is distinct; each section's subheading already serves as a question, eliminating the necessity for rephrasing.

Medquad

MedQuAD is a comprehensive collection consisting of 47,457 medical question-answer pairs compiled from 12 authoritative sources within the National Institutes of Health (NIH), including domains like cancer.gov, niddk.nih.gov, GARD, and MedlinePlus Health Topics. These question-answer pairs span 37 distinct question types, covering a wide spectrum of medical subjects, including diseases, drugs, and medical procedures. The dataset features additional annotations provided in XML files, facilitating various Information Retrieval (IR) and Natural Language Processing (NLP) tasks. These annotations encompass crucial information such as question type, question focus, synonyms, Unique Identifier (CUI) from the Unified Medical Language System (UMLS), and Semantic Type. Moreover, the dataset includes categorization of question focuses into three main categories: Disease, Drug, or Other, with the exception of collections from MedlinePlus, which exclusively focus on diseases.