datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MedQA-USMLE-4-optionsOriginal dataset introduced by Jin et al. in What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Citation information:
@article{jin2020disease,
title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={arXiv preprint arXiv:2009.13081},
year={2020}
}
MedQA-USMLE-4-options-hfOriginal dataset introduced by Jin et al. in What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Citation information:
@article{jin2020disease,
title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={arXiv preprint arXiv:2009.13081},
year={2020}
}
medical_meadow_medqa
Dataset Card for MedQA
Dataset Summary
This is the data and baseline source code for the paper: Jin, Di, et al. "What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams."
From https://github.com/jind11/MedQA:
The data that contains both the QAs and textbooks can be downloaded from this google drive folder. A bit of details of data are explained as below:
For QAs, we have three sources: US, Mainland of China, and… See the full description on the dataset page: https://huggingface.co/datasets/medalpaca/medical_meadow_medqa.MedQA-Diag
🔭 Overview
R2MED: First Reasoning-Driven Medical Retrieval Benchmark
R2MED is a high-quality, high-resolution synthetic information retrieval (IR) dataset designed for medical scenarios. It contains 876 queries with three retrieval tasks, five medical scenarios, and twelve body systems.
Dataset
#Q
#D
Avg. Pos
Q-Len
D-Len
Biology
103
57359
3.6
115.2
83.6
Bioinformatics77
47473
2.9
273.8
150.5
Medical Sciences
88
34810
2.8
107.1
122.7
MedXpertQA-Exam
97… See the full description on the dataset page: https://huggingface.co/datasets/R2MED/MedQA-Diag.medqaMedQA-Mixtral-CoT
Dataset Card for medqa-cot
Synthetically enhanced responses to the medqa dataset using mixtral.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the MedQA dataset, we leverage Mixtral-8x7B to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a
hand-crafted list of few-shot examples. For a multichoice answer, we ask the model to rephrase and explain the question… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/MedQA-Mixtral-CoT.medqa
MedQA (USMLE 4-option, US subset + English textbook corpus)
Dataset Summary
This dataset is a re-upload of the English USMLE 4-option question subset and the English textbook corpus from the original jind11/MedQA release introduced by Jin et al. in What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams.
The original MedQA release contains question sets in English, Simplified Chinese, and Traditional Chinese, and also… See the full description on the dataset page: https://huggingface.co/datasets/awinml/medqa.medqa
Dataset Card for MedQA
Homepage: https://github.com/jind11/MedQA
This is an unofficial curation of the MedQA dataset, uploaded here with minimal (i.e., no content-modifying) processing.
Paper: What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams (MDPI)
Languages: English (en), Taiwanese (tw), and Chinese (zh).
Dataset Subsets
This dataset contains multiple configs:
QA with four possible answers (as reported… See the full description on the dataset page: https://huggingface.co/datasets/mathewhe/medqa.MedQAOriginal dataset introduced by Jin et al. in What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
En split
Just edited columns. Contents are same.
Ko split
Train
The train dataset is translated by "solar-1-mini-translate-enko".
Test
The test dataset is translated by DeepL Pro.
reference-free COMET score: 0.7989 (Unbabel/wmt23-cometkiwi-da-xxl)
Citation information:
@article{jin2020disease… See the full description on the dataset page: https://huggingface.co/datasets/ChuGyouk/MedQA.medqa-cot-llama31
medqa-cot-llama31
Synthetically enhanced responses to the MedQa dataset. Used to train Aloe-Beta model.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the MedQA dataset, we leverage Llama-3.1-70B-Instruct to generate Chain of Thought(CoT) answers. We create a custom prompt for the dataset, along with a hand-crafted… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/medqa-cot-llama31.MedQA-MCMLE-Benchmark
💻 Dataset Usage
Run the following command to load the testing set (3,426 examples):
from datasets import load_dataset
dataset = load_dataset("shuyuej/MedQA-MCMLE-Benchmark", split="train")
print(dataset)
MedQA-MM
MedQA-MM Identifier Release
Paper repository ·
Hugging Face dataset
MedQA-MM is a 1,000-item shortcut-mitigated medical multimodal multiple-choice benchmark constructed from MedThinkVQA, MedXpertQA-MM, and the Health and Medicine portion of MMMU. This public release is intentionally identifier-only.
It does not contain source questions, answer choices, gold answers, images, clinical text, or repaired payloads. It provides stable source locators, pinned source revisions, and a… See the full description on the dataset page: https://huggingface.co/datasets/bio-nlp-umass/MedQA-MM.medqaMedQA-USMLE-4-options
Mirrored by Aurigene AI
Discovery stage: Evidence and literature
US Medical Licensing Exam style questions in four-option multiple choice form.
Rows: 11,451 (phrases_no_exclude_test.jsonl 1,273, phrases_no_exclude_train.jsonl 10,178)
Pairs with Aurigene-AI/BioMistral-7B from our model catalogue.
Upstream: GBaker/MedQA-USMLE-4-options - all credit to the original authors and to the researchers who produced the underlying data; the dataset card and licence below are theirs.… See the full description on the dataset page: https://huggingface.co/datasets/Aurigene-AI/MedQA-USMLE-4-options.MedQA-USMLE-Benchmark
💻 Dataset Usage
Run the following command to load the testing set (1,273 examples):
from datasets import load_dataset
dataset = load_dataset("shuyuej/MedQA-USMLE-Benchmark", split="test")
print(dataset)
MedQA_DutchTranslation of the English version of MedQA,
to Dutch using the GPT 4.1 mini LLM by OpenAI.
Attribution
If you use this dataset please use the following to credit the creators of MedQA:
@article{jin2021disease,
title={What disease does this patient have? a large-scale open domain question answering dataset from medical exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={Applied Sciences}… See the full description on the dataset page: https://huggingface.co/datasets/UMCU/MedQA_Dutch.medqa
MedQA (USMLE 4-option, US subset + English textbook corpus)
Dataset Summary
This dataset is a re-upload of the English USMLE 4-option question subset and the English textbook corpus from the original jind11/MedQA release introduced by Jin et al. in What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams.
The original MedQA release contains question sets in English, Simplified Chinese, and Traditional Chinese… See the full description on the dataset page: https://huggingface.co/datasets/premmahadik05/medqa.MedQA-Evol-Korean
MedQA-Evol
Original Data: TsinghuaC3I/UltraMedical.
Translated into Korean by "solar-1-mini-translate-enko".
medqa-distill-sft
MedQA-Distill-SFT
医学多选题 → 中文推理 SFT 数据（由 MedQA 题库经 LLM API 蒸馏生成）
从 MedQA（美国 USMLE / 中国执业医师考试）多选题蒸馏而来：每题包含题目、选项、逐步临床推理（中文） 和答案。用于微调中文医学大模型（SFT / LoRA）。
数据统计
字段
数值
总条数
39,618
训练集
35,656
验证集
3,962
来源 Provider
DeepSeek / TokenRhythm / Agnes-CN / NVIDIA
推理语言
中文（英文题也生成中文推理）
数据格式（Alpaca）
{
"instruction": "题目：A 23-year-old pregnant woman...\n选项：\nA. ...\nB. ...\nC. ...\nD. ...\nE. ...",
"input": "",
"output":… See the full description on the dataset page: https://huggingface.co/datasets/rewrewrv343/medqa-distill-sft.MedQA-CS-ExamBenchmarking LLMs Clinical Skills for Patient-Centered Diagnostics and Documentation
Project github: https://github.com/bio-nlp/MedQA-CS
MedQA-CS-Student dataset: https://huggingface.co/datasets/bio-nlp-umass/MedQA-CS-Student
MeD_QAMedQA-USMLE-4-options-hfmedqa_cotmedqa1MedQA_SymptomDisease_small_DutchA Dutch translation of this huggingface dataset using GPT4.1 mini, with the courtesy of Prognosis.
medqa_5optionsBased on MedQA (4opt), added new choice "I am not sure".
MedQA-USMLE-4-optionsOriginal dataset introduced by Jin et al. in What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Citation information:
@article{jin2020disease,
title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={arXiv preprint arXiv:2009.13081},
year={2020}
}
MedQA-CS-StudentmedqaMedQA-USMLE-4-optionsOriginal dataset introduced by Jin et al. in What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Citation information:
@article{jin2020disease,
title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={arXiv preprint arXiv:2009.13081},
year={2020}
}