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CamemBERT: a Tasty French Language Model

Abstract : Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models—in all languages except English—very limited. Aiming to address this issue for French, we release CamemBERT, a French version of the Bi-directional Encoders for Transformers (BERT). We measure the performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural language inference. CamemBERT improves the state of the art for most of the tasks considered. We release the pretrained model for CamemBERT hoping to foster research and downstream applications for French NLP.
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Preprints, Working Papers, ...
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Contributor : Benoît Sagot Connect in order to contact the contributor
Submitted on : Monday, January 20, 2020 - 3:01:47 PM
Last modification on : Friday, January 21, 2022 - 3:23:13 AM

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  • HAL Id : hal-02445946, version 1
  • ARXIV : 1911.03894



Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, et al.. CamemBERT: a Tasty French Language Model. 2019. ⟨hal-02445946⟩



Les métriques sont temporairement indisponibles