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* Add init barthez * Add barthez model, tokenizer and docs BARThez is a pre-trained french seq2seq model that uses BART objective. * Apply suggestions from code review docs typos Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Add license * Change URLs scheme * Remove barthez model keep tokenizer * Fix style * Fix quality * Update tokenizer * Add fast tokenizer * Add fast tokenizer test Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
42 lines
2.3 KiB
ReStructuredText
42 lines
2.3 KiB
ReStructuredText
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`
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<https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct,
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2020.
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The abstract of the paper:
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*Inductive transfer learning, enabled by self-supervised learning, have taken the entire Natural Language Processing
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(NLP) field by storm, with models such as BERT and BART setting new state of the art on countless natural language
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understanding tasks. While there are some notable exceptions, most of the available models and research have been
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conducted for the English language. In this work, we introduce BARThez, the first BART model for the French language
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(to the best of our knowledge). BARThez was pretrained on a very large monolingual French corpus from past research
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that we adapted to suit BART's perturbation schemes. Unlike already existing BERT-based French language models such as
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CamemBERT and FlauBERT, BARThez is particularly well-suited for generative tasks, since not only its encoder but also
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its decoder is pretrained. In addition to discriminative tasks from the FLUE benchmark, we evaluate BARThez on a novel
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summarization dataset, OrangeSum, that we release with this paper. We also continue the pretraining of an already
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pretrained multilingual BART on BARThez's corpus, and we show that the resulting model, which we call mBARTHez,
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provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT.*
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The Authors' code can be found `here <https://github.com/moussaKam/BARThez>`__.
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Examples
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_______________________________________________________________________________________________________________________
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- BARThez can be fine-tuned on sequence-to-sequence tasks in a similar way as BART, check: `examples/seq2seq/
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<https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.
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BarthezTokenizer
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.. autoclass:: transformers.BarthezTokenizer
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:members:
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