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B1tta/Drug-ACE

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Permissive or share-alike

The declared licence (apache-2.0) is classified as permissive.

Declared licence: apache-2.0, from the tagEvidence: README.md at 77125c9
Licence files: No licence file at the top level of the repositoryEvidence: file list at 77125c9
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

text-classification

Citation text from the card

If you find our paper, code, or dataset helpful, please consider citing our work:

```bibtex
@inproceedings{luo-etal-2026-applicability,
    title = "Applicability Condition Extraction for Therapeutic Drug-Disease Relations",
    author = "Luo, Guanting  and
      Nishida, Noriki  and
      Matsumoto, Yuji  and
      Arase, Yuki",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-acl.154/",
    pages = "3135--3148",
    ISBN = "979-8-89176-395-1",
    abstract = "Identifying conditions that a certain drug takes therapeutic effect on a target disease is crucial for clinical decision-making support. However, most existing biomedical information extraction methods have focused on identifying only relations between drugs and diseases, while largely overlooking the context-specific conditions where such relations can apply. To address this problem, we introduce the task of applicability condition extraction for therapeutic drug{--}disease relations from biomedical research literature. We create the first dataset that has manually annotated triples of drugs, diseases, and applicability conditions on biomedical paper abstracts wit

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dataset_licence({ "id": "B1tta/Drug-ACE" })
signal: "permissive"
stated_licence: "apache-2.0"
sources: 0