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In recent years, pre-trained language models (PLMs) achieve the bestperformance on a wide range of natural language processing (NLP) tasks. Whilethe first models were trained on general domain data, specialized ones haveemerged to more effectively treat specific domains. In this paper, we proposean 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 fromthe web and private data from healthcare establishments. We also evaluatedifferent learning strategies on a set of biomedical tasks. In particular, weshow that we can take advantage of already existing biomedical PLMs in aforeign language by further pre-train it on our targeted data. Finally, werelease the first specialized PLMs for the biomedical field in French, calledDrBERT, as well as the largest corpus of medical data under free license onwhich these models are trained.

DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains