{
 "id": "redasers/difraud",
 "owner": "redasers",
 "name": "difraud",
 "downloads": 406,
 "likes": 2,
 "last_modified": "2024-07-25T22:39:38.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": "mit",
 "licence_files": [
  "LICENSE.txt"
 ],
 "sources": [],
 "gated": false,
 "tasks": [
  "text-classification",
  "zero-shot-classification"
 ],
 "citation": "If you use any of these resources, please cite our dataset DIFrauD paper:\n\n    @inproceedings{boumber-etal-2024-domain,\n    title = \"Domain-Agnostic Adapter Architecture for Deception Detection: Extensive Evaluations with the {DIF}rau{D} Benchmark\",\n    author = \"Boumber, Dainis A.  and Qachfar, Fatima Zahra  and Verma, Rakesh\",\n    editor = \"Calzolari, Nicoletta  and Kan, Min-Yen  and Hoste, Veronique  and Lenci, Alessandro  and Sakti, Sakriani  and Xue, Nianwen\",\n    booktitle = \"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)\",\n    month = may,\n    year = \"2024\",\n    address = \"Torino, Italia\",\n    publisher = \"ELRA and ICCL\",\n    url = \"https://aclanthology.org/2024.lrec-main.468\",\n    pages = \"5260--5274\"}",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "f8fa04aff083aa22c6878d005b4655e34a4cbd85",
 "stated_evidence": "https://huggingface.co/datasets/redasers/difraud/blob/f8fa04aff083aa22c6878d005b4655e34a4cbd85/README.md",
 "file_evidence": "https://huggingface.co/datasets/redasers/difraud/blob/f8fa04aff083aa22c6878d005b4655e34a4cbd85/LICENSE.txt",
 "card_nc": false
}