@inproceedings{labrak-etal-2023-drbert,
    title = "{D}r{BERT}: A Robust Pre-trained Model in {F}rench for Biomedical and Clinical domains",
    author = "Labrak, Yanis  and
      Bazoge, Adrien  and
      Dufour, Richard  and
      Rouvier, Mickael  and
      Morin, Emmanuel  and
      Daille, B{\'e}atrice  and
      Gourraud, Pierre-Antoine",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.896",
    doi = "10.18653/v1/2023.acl-long.896",
    pages = "16207--16221",
    abstract = "In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains. In this paper, we propose an original study of PLMs in the medical domain on French language. We compare, for the first time, the performance of PLMs trained on both public data from the web and private data from healthcare establishments. We also evaluate different learning strategies on a set of biomedical tasks. In particular, we show that we can take advantage of already existing biomedical PLMs in a foreign language by further pre-train it on our targeted data. Finally, we release the first specialized PLMs for the biomedical field in French, called DrBERT, as well as the largest corpus of medical data under free license on which these models are trained.",
}

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains. In this paper, we propose an original study of PLMs in the medical domain on French language. We compare, for the first time, the performance of PLMs trained on both public data from the web and private data from healthcare establishments. We also evaluate different learning strategies on a set of biomedical tasks. In particular, we show that we can take advantage of already existing biomedical PLMs in a foreign language by further pre-train it on our targeted data. Finally, we release the first specialized PLMs for the biomedical field in French, called DrBERT, as well as the largest corpus of medical data under free license on which these models are trained.
Labrak, Yanis Bazoge, Adrien Dufour, Richard Rouvier, Mickael Morin, Emmanuel Daille, B{\'e}atrice Gourraud, Pierre-Antoine
DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains