{
 "id": "microsoft/mediflow",
 "owner": "microsoft",
 "name": "mediflow",
 "downloads": 386,
 "likes": 54,
 "last_modified": "2025-12-15T19:41:56.000Z",
 "stated_licence": "cdla-permissive-2.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "text-generation"
 ],
 "citation": "@inproceedings{corbeil-etal-2025-modular,\n        title = \"A Modular Approach for Clinical {SLM}s Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment\",\n        author = \"Corbeil, Jean-Philippe  and\n          Dada, Amin  and\n          Attendu, Jean-Michel  and\n          Ben Abacha, Asma  and\n          Sordoni, Alessandro  and\n          Caccia, Lucas  and\n          Beaulieu, Francois  and\n          Lin, Thomas  and\n          Kleesiek, Jens  and\n          Vozila, Paul\",\n        editor = \"Che, Wanxiang  and\n          Nabende, Joyce  and\n          Shutova, Ekaterina  and\n          Pilehvar, Mohammad Taher\",\n        booktitle = \"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n        month = jul,\n        year = \"2025\",\n        address = \"Vienna, Austria\",\n        publisher = \"Association for Computational Linguistics\",\n        url = \"https://aclanthology.org/2025.acl-long.950/\",\n        doi = \"10.18653/v1/2025.acl-long.950\",\n        pages = \"19352--19374\",\n        ISBN = \"979-8-89176-251-0\",\n        abstract = \"High computation costs and latency of large language models such as GPT-4 have limited their deployment in clinical settings. Small language models (SLMs) offer a cost-effective alternative, but their limited capacity requires biomedical domain adaptation, which remains challenging. An additional bottleneck is the unavailability and high sensitivity of clinical dat",
 "signal": "unclear",
 "reason": "The checks could not identify a recognised licence ID in the dataset's metadata.",
 "revision": "18d7c1d519e5885473959e94d7d791c013611353",
 "stated_evidence": "https://huggingface.co/datasets/microsoft/mediflow/blob/18d7c1d519e5885473959e94d7d791c013611353/README.md",
 "file_evidence": "https://huggingface.co/datasets/microsoft/mediflow/tree/18d7c1d519e5885473959e94d7d791c013611353",
 "card_nc": false
}