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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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the License. You may obtain a copy of the License at |
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http://www.apache.org/licenses/LICENSE-2.0 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on |
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the |
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specific language governing permissions and limitations under the License. |
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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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### Examples |
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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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