61 lines
3.0 KiB
Markdown
61 lines
3.0 KiB
Markdown
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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,
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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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This model was contributed by [moussakam](https://huggingface.co/moussakam). The Authors' code can be found [here](https://github.com/moussaKam/BARThez).
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<Tip>
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BARThez implementation is the same as BART, except for tokenization. Refer to [BART documentation](bart) for information on
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configuration classes and their parameters. BARThez-specific tokenizers are documented below.
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</Tip>
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## Resources
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- BARThez can be fine-tuned on sequence-to-sequence tasks in a similar way as BART, check:
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[examples/pytorch/summarization/](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization/README.md).
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## BarthezTokenizer
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[[autodoc]] BarthezTokenizer
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## BarthezTokenizerFast
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[[autodoc]] BarthezTokenizerFast
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