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    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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BARThez
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Overview
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The BARThez model was proposed in `BARThez: a Skilled Pretrained French Sequence-to-Sequence Model`
<https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct,
2020.

The abstract of the paper:


*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 English language. In this work, we introduce BARThez, the first BART model for the French language
(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.*

The Authors' code can be found `here <https://github.com/moussaKam/BARThez>`__.


Examples
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- BARThez can be fine-tuned on sequence-to-sequence tasks in a similar way as BART, check:
  :prefix_link:`examples/seq2seq/ <examples/seq2seq/README.md>`.


BarthezTokenizer
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.. autoclass:: transformers.BarthezTokenizer
    :members:


BarthezTokenizerFast
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.. autoclass:: transformers.BarthezTokenizerFast
    :members:
