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https://github.com/FreedomIntelligence/Huatuo-26M
The Largest-scale Chinese Medical QA Dataset： with 26,000,000 question answer pairs.
https://github.com/FreedomIntelligence/Huatuo-26M
Last synced: 27 days ago
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The Largest-scale Chinese Medical QA Dataset： with 26,000,000 question answer pairs.
- Host: GitHub
- URL: https://github.com/FreedomIntelligence/Huatuo-26M
- Owner: FreedomIntelligence
- Created: 2023-05-02T14:59:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-14T04:54:23.000Z (9 months ago)
- Last Synced: 2024-08-03T09:06:55.916Z (4 months ago)
- Size: 671 KB
- Stars: 188
- Watchers: 8
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Medical-Healthcare-Dataset-For-LLM - 下载链接
- StarryDivineSky - FreedomIntelligence/Huatuo-26M
README
# Huatuo-26M
📃 Paper • 🤗 Huatuo-Lite • 🤗 huatuo_encyclopedia_qa • 🤗 knowledge_graph_qa • 🤗 huatuo_consultation_qa
中文 | English
## 👩🏻⚕Introduction
- Huatuo-26M is currently the largest Chinese medical question-and-answer dataset. This dataset contains over 26 million high-quality medical Q&A pairs, covering various aspects such as diseases, symptoms, treatment methods, and drug information.
- Huatuo-Lite is a refined and optimized dataset based on Huatuo-26M, having undergone multiple purifications and rewrites. It features more data dimensions and higher data quality.
## 📚Data Content
The Huatuo-26M dataset is collected and integrated from multiple sources, including:
- Online Medical Encyclopedia [huatuo_encyclopedia_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa)
- Online Medical Knowledge Bases [huatuo_knowledge_graph_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_knowledge_graph_qa)
- Online Medical Consultation Records（answer in the form of URLs） [huatuo_consultation_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_consultation_qa)
- Streamlined version [Huatuo-Lite](https://huggingface.co/datasets/FreedomIntelligence/Huatuo26M-Lite)
Each question-answer pair in the dataset contains the following fields：
- questions：Problem Description
- answers：Doctor/Expert Answers
- Huatuo-Lite dataset also includes **Hospital Department** and **Related Diseases** fields
The following is the huatuo test set we used in the paper, which consists of random sampling of data from multiple sources.
- Testdatasets：[huatuo26M-testdatasets](https://huggingface.co/datasets/FreedomIntelligence/huatuo26M-testdatasets)
## 🤖Data Usage
The Huatuo-26M dataset can be used for a variety of AI research and applications in the medical field, such as:
- Natural Language Processing: Including but not limited to Q&A systems, text classification, sentiment analysis, etc.
- Machine Learning model training: Such as disease prediction, personalized treatment recommendation, etc.
- AI applications in the medical field: Such as intelligent diagnosis systems, medical consultation chatbots, etc.
## 🚀Quick Start
To start using the Huatuo-26M dataset, you can follow the steps below:
```python
import datasets
# part 1
knowledge_graph_dataset = datasets.load_dataset('FreedomIntelligence/huatuo_knowledge_graph_qa')
# part 2
encyclopedia_dataset = datasets.load_dataset('FreedomIntelligence/huatuo_encyclopedia_qa')
# part 3 (only url)
consultation_dataset = datasets.load_dataset('FreedomIntelligence/huatuo_consultation_qa')
# testdatasets (6k)
huatuo_testdatasets = datasets.load_dataset('FreedomIntelligence/huatuo26M-testdatasets')
```
## 👩🏻🔬Experiment Record
### Benchmark
- Retrieval Evaluation:
Click to expand
- Answer Generation Evaluation:
Click to expand
### Application
- Zero-shot transfer to other QA datasets:
Click to expand
- As external knowledge for RAG:
Click to expand
- As pre-training data for language model (LM):
Click to expand
- As fine-tuning data for Medical LLM:
Click to expand
## 🚁License
The Huatuo-26M dataset is licensed under Apache 2.0. Please make sure you have read and agreed to the license terms before using it.
## 📱Contact Us
If you have any questions or need help, please feel free to ask us via email （[[email protected]](mailto:[email protected])）or in the Issues section.
------
## 😁Citation
```
@misc{li2023huatuo26m,
title={Huatuo-26M, a Large-scale Chinese Medical QA Dataset},
author={Jianquan Li and Xidong Wang and Xiangbo Wu and Zhiyi Zhang and Xiaolong Xu and Jie Fu and Prayag Tiwari and Xiang Wan and Benyou Wang},
year={2023},
eprint={2305.01526},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```