{
 "id": "openlifescienceai/medmcqa",
 "owner": "openlifescienceai",
 "name": "medmcqa",
 "downloads": 79841,
 "likes": 248,
 "last_modified": "2024-01-04T14:32:02.000Z",
 "stated_licence": "apache-2.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "question-answering",
  "multiple-choice"
 ],
 "citation": "@InProceedings{pmlr-v174-pal22a,\n  title = \t {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering},\n  author =       {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan},\n  booktitle = \t {Proceedings of the Conference on Health, Inference, and Learning},\n  pages = \t {248--260},\n  year = \t {2022},\n  editor = \t {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan},\n  volume = \t {174},\n  series = \t {Proceedings of Machine Learning Research},\n  month = \t {07--08 Apr},\n  publisher =    {PMLR},\n  pdf = \t {https://proceedings.mlr.press/v174/pal22a/pal22a.pdf},\n  url = \t {https://proceedings.mlr.press/v174/pal22a.html},\n  abstract = \t {This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. 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. Each sample contains a question, correct answer(s), and other options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects & topics. A detailed explanation of the solution, along with the above information, is provided in this study.}\n}",
 "signal": "permissive",
 "reason": "The declared licence (apache-2.0) is classified as permissive."
}