136 lines
4.2 KiB
Markdown
136 lines
4.2 KiB
Markdown
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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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# CamemBERT
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## Overview
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The CamemBERT model was proposed in [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by
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[Louis Martin](https://huggingface.co/louismartin), [Benjamin Muller](https://huggingface.co/benjamin-mlr), [Pedro Javier Ortiz Suárez](https://huggingface.co/pjox), Yoann Dupont, Laurent Romary, Éric Villemonte de la
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Clergerie, [Djamé Seddah](https://huggingface.co/Djame), and [Benoît Sagot](https://huggingface.co/sagot). It is based on Facebook's RoBERTa model released in 2019. It is a model
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trained on 138GB of French text.
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The abstract from the paper is the following:
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*Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available
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models have either been trained on English data or on the concatenation of data in multiple languages. This makes
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practical use of such models --in all languages except English-- very limited. Aiming to address this issue for French,
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we release CamemBERT, a French version of the Bi-directional Encoders for Transformers (BERT). We measure the
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performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging,
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dependency parsing, named-entity recognition, and natural language inference. CamemBERT improves the state of the art
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for most of the tasks considered. We release the pretrained model for CamemBERT hoping to foster research and
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downstream applications for French NLP.*
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This model was contributed by [the ALMAnaCH team (Inria)](https://huggingface.co/almanach). The original code can be found [here](https://camembert-model.fr/).
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<Tip>
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This implementation is the same as RoBERTa. Refer to the [documentation of RoBERTa](roberta) for usage examples as well
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as the information relative to the inputs and outputs.
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</Tip>
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## Resources
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- [Text classification task guide](../tasks/sequence_classification)
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- [Token classification task guide](../tasks/token_classification)
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- [Question answering task guide](../tasks/question_answering)
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- [Causal language modeling task guide](../tasks/language_modeling)
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- [Masked language modeling task guide](../tasks/masked_language_modeling)
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- [Multiple choice task guide](../tasks/multiple_choice)
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## CamembertConfig
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[[autodoc]] CamembertConfig
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## CamembertTokenizer
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[[autodoc]] CamembertTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## CamembertTokenizerFast
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[[autodoc]] CamembertTokenizerFast
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<frameworkcontent>
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<pt>
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## CamembertModel
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[[autodoc]] CamembertModel
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## CamembertForCausalLM
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[[autodoc]] CamembertForCausalLM
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## CamembertForMaskedLM
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[[autodoc]] CamembertForMaskedLM
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## CamembertForSequenceClassification
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[[autodoc]] CamembertForSequenceClassification
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## CamembertForMultipleChoice
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[[autodoc]] CamembertForMultipleChoice
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## CamembertForTokenClassification
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[[autodoc]] CamembertForTokenClassification
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## CamembertForQuestionAnswering
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[[autodoc]] CamembertForQuestionAnswering
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</pt>
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<tf>
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## TFCamembertModel
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[[autodoc]] TFCamembertModel
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## TFCamembertForCasualLM
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[[autodoc]] TFCamembertForCausalLM
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## TFCamembertForMaskedLM
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[[autodoc]] TFCamembertForMaskedLM
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## TFCamembertForSequenceClassification
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[[autodoc]] TFCamembertForSequenceClassification
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## TFCamembertForMultipleChoice
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[[autodoc]] TFCamembertForMultipleChoice
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## TFCamembertForTokenClassification
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[[autodoc]] TFCamembertForTokenClassification
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## TFCamembertForQuestionAnswering
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[[autodoc]] TFCamembertForQuestionAnswering
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</tf>
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</frameworkcontent>
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