Migrate doc files to Markdown. (#24376)
* Rename index.mdx to index.md * With saved modifs * Address review comment * Treat all files * .mdx -> .md * Remove special char * Update utils/tests_fetcher.py Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr> --------- Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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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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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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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, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la
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Clergerie, Djamé Seddah, and Benoît 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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Tips:
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- This implementation is the same as RoBERTa. Refer to the [documentation of RoBERTa](roberta) for usage examples
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as well as the information relative to the inputs and outputs.
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This model was contributed by [camembert](https://huggingface.co/camembert). The original code can be found [here](https://camembert-model.fr/).
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## Documentation 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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## 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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## 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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