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.

翻译：近年来，预训练语言模型（PLMs）在众多自然语言处理（NLP）任务中取得了最佳性能。虽然最初模型基于通用领域数据训练，但针对特定领域的专业模型已陆续出现以提升处理效果。本文提出了法语医学领域PLMs的一项原创性研究。我们首次对比了基于网络公开数据与医疗机构私有数据训练的PLMs性能，并评估了多种学习策略在生物医学任务上的表现。特别地，我们证明可以通过在目标数据上进一步预训练，有效利用外语中已有的生物医学PLMs。最后，我们发布了首个法语生物医学领域专业PLMs——DrBERT，以及用于训练这些模型的最大自由许可医学语料库。