HPAI-BSC/medmcqa-cot-llama31

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The declared licence (llama3.1) is classified as having use restrictions.

Declared licence: llama3.1, from the tagEvidence: README.md at 281585f
Licence files: No licence file at the top level of the repositoryEvidence: file list at 281585f
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Citation text from the card

@misc{gururajan2024aloe,
      title={Aloe: A Family of Fine-tuned Open Healthcare LLMs}, 
      author={Ashwin Kumar Gururajan and Enrique Lopez-Cuena and Jordi Bayarri-Planas and Adrian Tormos and Daniel Hinjos and Pablo Bernabeu-Perez and Anna Arias-Duart and Pablo Agustin Martin-Torres and Lucia Urcelay-Ganzabal and Marta Gonzalez-Mallo and Sergio Alvarez-Napagao and Eduard Ayguadé-Parra and Ulises Cortés Dario Garcia-Gasulla},
      year={2024},
      eprint={2405.01886},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@InProceedings{pmlr-v174-pal22a,
  title = 	 {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering},
  author =       {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan},
  booktitle = 	 {Proceedings of the Conference on Health, Inference, and Learning},
  pages = 	 {248--260},
  year = 	 {2022},
  editor = 	 {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan},
  volume = 	 {174},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {07--08 Apr},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v174/pal22a/pal22a.pdf},
  url = 	 {https://proceedings.mlr.press/v174/pal22a.html},
  abstract = 	 {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 MCQ

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dataset_licence({ "id": "HPAI-BSC/medmcqa-cot-llama31" })
signal: "restrictive"
stated_licence: "llama3.1"
sources: 0