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Clinical data in hospitals are increasingly accessible for research throughclinical data warehouses, however these documents are unstructured. It istherefore necessary to extract information from medical reports to conductclinical studies. Transfer learning with BERT-like models such as CamemBERT hasallowed major advances, especially for named entity recognition. However, thesemodels are trained for plain language and are less efficient on biomedicaldata. This is why we propose a new French public biomedical dataset on which wehave continued the pre-training of CamemBERT. Thus, we introduce a firstversion of CamemBERT-bio, a specialized public model for the French biomedicaldomain that shows 2.54 points of F1 score improvement on average on differentbiomedical named entity recognition tasks. Our findings demonstrate the successof continual pre-training from a French model and contrast with recentproposals on the same domain and language. One of our key contributionshighlights the importance of using a standard evaluation protocol that enablesa clear view of the current state-of-the-art for French biomedical models.

CamemBERT-bio: a Tasty French Language Model Better for your Health