Shekswess/medical_gemma_instruct_dataset_short

---
language:
- en
size_categories:
- 1K<n<10K
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: 4250252
num_examples: 2000
download_size: 1922523
dataset_size: 4250252
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- medical
---

Dataset made for instruction supervised finetuning of Gemma LLMs, by combining of medical datasets and getting 2k entries from them:

- 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.

原始信息汇总

数据集概述

基本信息

• 语言: 英语
• 大小范围: 1K<n<10K
• 任务类别: 问答

数据集特征

• 输出 (output): 字符串类型
• 输入 (input): 字符串类型
• 指令 (instruction): 字符串类型
• 提示 (prompt): 字符串类型

数据集分割

• 训练集 (train):
  • 示例数量: 2000
  • 字节数: 4250252

数据集大小

• 下载大小: 1922523字节
• 数据集大小: 4250252字节

配置

• 配置名称: default
• 数据文件:
  • 分割: 训练
  • 路径: data/train-*

标签

• 医疗