Inductive transfer learning , enabled by self-supervised learning, have taken the entire Natural Language Processing (NLP ) field by storm, with models such as BERT and BART setting new state of the art on countless natural language understanding tasks. While there are some notable exceptions , most of the available models and research have been conducted for the Englishlanguage . In this work, we introduce BARThez, the first BART model for the Frenchlanguage (to the best of our knowledge). BARThez was pretrained on a very large monolingual French corpus from past research that we adapted to suit BART's perturbation schemes. Unlike already existing BERT-based French language models such as CamemBERT and FlauBERT , BARThez is particularly well-suited for generative tasks , since not only its encoder but also its decoder is pretrained. In addition to discriminative tasks from the FLUE benchmark , we evaluate BARThez on a novel summarization dataset, OrangeSum, that we release with this paper. We also continue the pretraining of an already pretrained multilingual BART on BARThez' s corpus, and we show that the resulting model, which we call mBARTHez, provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT .
Inductive transfer learning has taken the entire NLP field by storm, with models such as BERT and BART setting new state of the art on countless NLU tasks. However , most of the available models and research have been conducted for English . In this work, we introduce BARThez, the first large-scale pretrained seq2seq model for French. Being based on BART , BARThez is particularly well-suited for generative tasks . We evaluate BARThez on five discriminative tasks from the FLUE benchmark and two generative tasks from a novel summarization dataset, OrangeSum, that we created for this research. We show BARThez to be very competitive with state-of-the-art BERT-based French language models such as CamemBERT and FlauBERT. We also continue the pretraining of a multilingual BART on BARThez' corpus, and show our resulting model, mBARThez, to significantly boost BARThez' generative performance. Code, data and models are publicly available .
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