{
 "id": "ruslan/bioleaflets-biomedical-ner",
 "owner": "ruslan",
 "name": "bioleaflets-biomedical-ner",
 "downloads": 72,
 "likes": 5,
 "last_modified": "2023-02-08T20:34:51.000Z",
 "stated_licence": "apache-2.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "text-generation",
  "text2text-generation"
 ],
 "citation": "@inproceedings{yermakov-etal-2021-biomedical,\n    title = \"Biomedical Data-to-Text Generation via Fine-Tuning Transformers\",\n    author = \"Yermakov, Ruslan  and\n      Drago, Nicholas  and\n      Ziletti, Angelo\",\n    booktitle = \"Proceedings of the 14th International Conference on Natural Language Generation\",\n    month = aug,\n    year = \"2021\",\n    address = \"Aberdeen, Scotland, UK\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.inlg-1.40\",\n    pages = \"364--370\",\n    abstract = \"Data-to-text (D2T) generation in the biomedical domain is a promising - yet mostly unexplored - field of research. Here, we apply neural models for   D2T generation to a real-world dataset consisting of package leaflets of European medicines. We show that fine-tuned transformers are able to generate realistic, multi-sentence text from data in the biomedical domain, yet have important limitations. We also release a new dataset (BioLeaflets) for benchmarking D2T generation models in the biomedical domain.\",\n    }",
 "signal": "permissive",
 "reason": "The declared licence (apache-2.0) is classified as permissive.",
 "revision": "bd980e88405e0c3cbf3880f6a670dcd047384661",
 "stated_evidence": "https://huggingface.co/datasets/ruslan/bioleaflets-biomedical-ner/blob/bd980e88405e0c3cbf3880f6a670dcd047384661/README.md",
 "file_evidence": "https://huggingface.co/datasets/ruslan/bioleaflets-biomedical-ner/tree/bd980e88405e0c3cbf3880f6a670dcd047384661",
 "card_nc": false
}