datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
med_mcqaFrom "MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering"
(Pal et al.), MedMCQA is a "multiple-choice question answering (MCQA) dataset designed to address
real-world medical entrance exam questions." The dataset "...has more than 194k high-quality AIIMS & NEET PG
entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average
token length of 12.77 and high topical diversity."
The following is an example from… See the full description on the dataset page: https://huggingface.co/datasets/lighteval/med_mcqa.MedMCQA-Mixtral-CoT
Dataset Card for medmcqa-cot
Synthetically enhanced responses to the medmcqa dataset using mixtral.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the MedMCQA 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… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/MedMCQA-Mixtral-CoT.medmcqa-cot-llama31
medqa-cot-llama31
Synthetically enhanced responses to the MedMCQA dataset. Used to train Aloe-Beta model.
Dataset Details
Dataset Description
To increase the quality of answers from the training splits of the MedMCQA 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… See the full description on the dataset page: https://huggingface.co/datasets/HPAI-BSC/medmcqa-cot-llama31.medmcqa
MedMCQA (AIIMS & NEET PG Medical Entrance MCQs)
Dataset Summary
This dataset is a re-upload of the MedMCQA dataset introduced by Pal et al. in MedMCQA: A Large-Scale Multi-Subject Multi-Choice Dataset for Medical Domain Question Answering (ACL 2022).
MedMCQA is a large-scale multiple-choice question answering dataset sourced from Indian medical entrance examinations (AIIMS PG and NEET PG). It covers 20 medical subjects and contains over 194,000 questions with four answer… See the full description on the dataset page: https://huggingface.co/datasets/awinml/medmcqa.med_mcqaFrom "MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering"
(Pal et al.), MedMCQA is a "multiple-choice question answering (MCQA) dataset designed to address
real-world medical entrance exam questions." The dataset "...has more than 194k high-quality AIIMS & NEET PG
entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average
token length of 12.77 and high topical diversity."
The following is an example from… See the full description on the dataset page: https://huggingface.co/datasets/ap878/med_mcqa.medmcqa-alignmentGlobMed_MedMCQA
🌍 GlobMed: MedMCQA
GlobMed_MedMCQA covers 20 languages, including 13 high-resource languages (Arabic, Chinese, English, French, German, Hindi, Indonesian, Japanese, Korean, Portuguese, Russian, Spanish, and Thai) and 7 low-resource languages (Bengali, Malay, Swahili, Urdu, Wolof, Yoruba, and Zulu).
Code
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en
fr
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Language
Arabic
Bengali
Chinese
English
French
German
Hindi
Indonesian
Japanese
Korean
Malay
Portuguese
Russian… See the full description on the dataset page: https://huggingface.co/datasets/ruiyang-medinfo/GlobMed_MedMCQA.MedMCQA
MedMCQA-CoT: 医学多肢選択問題with Chain-of-Thought推論
データセット概要
MedMCQA-CoTは、MedMCQAデータセットの拡張版で、各医学多肢選択問題に高品質なChain-of-Thought（CoT）推論を追加したデータセットです。医学的な推論プロセスを説明できるAIシステムの開発を支援することを目的としています。
主な特徴
2,020件の医学MCQ問題 - 元のMedMCQAデータセットから抽出
Chain-of-Thought推論 - DeepSeek-R1モデルで生成
95.5%の回答精度 - 生成されたCoTが正解に導く割合
0.952の平均品質スコア - 医学用語密度と推論品質に基づく評価
包括的なメタデータ - 品質スコア、医学専門分野、生成統計を含む
データセット詳細
各レコードの構成:
question: MedMCQAからの元の医学問題
answer: 正解の選択肢（A, B, C, D）
cot:… See the full description on the dataset page: https://huggingface.co/datasets/weblab-llm-competition-2025-bridge/MedMCQA.gemma-health-synthetic-telugu-medmcqa-sft
Gemma Health Telugu SFT
Splits:
train: 17481 rows
test: 6150 rows
Each row contains:
messages: TRL/Unsloth conversational SFT format.
text: plain serialized chat text fallback.
source, variant, prompt, response: traceability fields.
from datasets import load_dataset
dataset = load_dataset("RohithMidigudla/gemma-health-synthetic-telugu-medmcqa-sft", split="train", streaming=True)
test_dataset = load_dataset("RohithMidigudla/gemma-health-synthetic-telugu-medmcqa-sft"… See the full description on the dataset page: https://huggingface.co/datasets/RohithMidigudla/gemma-health-synthetic-telugu-medmcqa-sft.MedMCQA-Benchmark
💻 Dataset Usage
Run the following command to load the testing set (4,183 examples):
from datasets import load_dataset
dataset = load_dataset("shuyuej/MedMCQA-Benchmark", split="test")
print(dataset)
phi-3-medmcqa-openbiollmmedmcqadpo_medmcqa