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McGill-NLP/medal

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The checks could not identify a recognised licence ID in the dataset's metadata.

Declared licence: unknown, from the tagEvidence: README.md at 1b7eb6a
Licence files: No licence file at the top level of the repositoryEvidence: file list at 1b7eb6a
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Citation text from the card

@inproceedings{wen-etal-2020-medal,
    title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
    author = "Wen, Zhi  and
      Lu, Xing Han  and
      Reddy, Siva",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
    pages = "130--135",
    abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.",
}

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dataset_licence({ "id": "McGill-NLP/medal" })
signal: "unclear"
stated_licence: "unknown"
sources: 0