@inproceedings {leFlauBERTModelesLangue2020,
	title = {FlauBERT : des mod{\`e}les de langue contextualis{\'e}s pr{\'e}-entra{\^\i}n{\'e}s pour le fran{\c c}ais (FlauBERT : Unsupervised Language Model Pre-training for French)},
	year = {2020},
	month = {jun},
	pages = {268{\textendash}278},
	publisher = {ATALA et AFCP},
	address = {Nancy, France},
	abstract = {Les mod{\`e}les de langue pr{\'e}-entra{\^\i}n{\'e}s sont d{\'e}sormais indispensables pour obtenir des r{\'e}sultats {\`a} l{\textquoteright}{\'e}tat-de-l{\textquoteright}art dans de nombreuses t{\^a}ches du TALN. Tirant avantage de l{\textquoteright}{\'e}norme quantit{\'e} de textes bruts disponibles, ils permettent d{\textquoteright}extraire des repr{\'e}sentations continues des mots, contextualis{\'e}es au niveau de la phrase. L{\textquoteright}efficacit{\'e} de ces repr{\'e}sentations pour r{\'e}soudre plusieurs t{\^a}ches de TALN a {\'e}t{\'e} d{\'e}montr{\'e}e r{\'e}cemment pour l{\textquoteright}anglais. Dans cet article, nous pr{\'e}sentons et partageons FlauBERT, un ensemble de mod{\`e}les appris sur un corpus fran{\c c}ais h{\'e}t{\'e}rog{\`e}ne et de taille importante. Des mod{\`e}les de complexit{\'e} diff{\'e}rente sont entra{\^\i}n{\'e}s {\`a} l{\textquoteright}aide du nouveau supercalculateur Jean Zay du CNRS. Nous {\'e}valuons nos mod{\`e}les de langue sur diverses t{\^a}ches en fran{\c c}ais (classification de textes, paraphrase, inf{\'e}rence en langage naturel, analyse syntaxique, d{\'e}sambigu{\"\i}sation automatique) et montrons qu{\textquoteright}ils surpassent souvent les autres approches sur le r{\'e}f{\'e}rentiel d{\textquoteright}{\'e}valuation FLUE {\'e}galement pr{\'e}sent{\'e} ici.},
	author = {Le, Hang and Vial, Lo{\"\i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Beno{\^\i}t Crabb{\'e} and Besacier, Laurent and Schwab, Didier}
}
@inproceedings {leFlauBERTUnsupervisedLanguage2020,
	title = {FlauBERT: Unsupervised Language Model Pre-training for French},
	year = {2020},
	month = {may},
	pages = {2479{\textendash}2490},
	publisher = {European Language Resources Association},
	address = {Marseille, France},
	abstract = {<p>Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their contextualization at the sentence level. This has been widely demonstrated for English using contextualized representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research community for further reproducible experiments in French NLP.</p>
},
	isbn = {979-10-95546-34-4},
	author = {Le, Hang and Vial, Lo{\"\i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Beno{\^\i}t Crabb{\'e} and Besacier, Laurent and Schwab, Didier}
}
