mirror of https://github.com/huggingface/transformers.git synced 2025-07-04 05:10:06 +06:00

* toctree

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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* fix

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---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* "to be not" -> "not to be" (#32636)

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Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>

2025-03-03 10:33:46 -08:00

4.6 KiB

Raw Blame History

CamemBERT

Overview

The CamemBERT model was proposed in CamemBERT: a Tasty French Language Model by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook's RoBERTa model released in 2019. It is a model trained on 138GB of French text.

The abstract from the paper is the following:

Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models --in all languages except English-- very limited. Aiming to address this issue for French, we release CamemBERT, a French version of the Bi-directional Encoders for Transformers (BERT). We measure the performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural language inference. CamemBERT improves the state of the art for most of the tasks considered. We release the pretrained model for CamemBERT hoping to foster research and downstream applications for French NLP.

This model was contributed by the ALMAnaCH team (Inria). The original code can be found here.

This implementation is the same as RoBERTa. Refer to the documentation of RoBERTa for usage examples as well as the information relative to the inputs and outputs.

Resources

CamembertConfig

autodoc CamembertConfig

CamembertTokenizer

autodoc CamembertTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary

CamembertTokenizerFast

autodoc CamembertTokenizerFast

CamembertModel

autodoc CamembertModel

CamembertForCausalLM

autodoc CamembertForCausalLM

CamembertForMaskedLM

autodoc CamembertForMaskedLM

CamembertForSequenceClassification

autodoc CamembertForSequenceClassification

CamembertForMultipleChoice

autodoc CamembertForMultipleChoice

CamembertForTokenClassification

autodoc CamembertForTokenClassification

CamembertForQuestionAnswering

autodoc CamembertForQuestionAnswering

TFCamembertModel

autodoc TFCamembertModel

TFCamembertForCausalLM

autodoc TFCamembertForCausalLM

TFCamembertForMaskedLM

autodoc TFCamembertForMaskedLM

TFCamembertForSequenceClassification

autodoc TFCamembertForSequenceClassification

TFCamembertForMultipleChoice

autodoc TFCamembertForMultipleChoice

TFCamembertForTokenClassification

autodoc TFCamembertForTokenClassification

TFCamembertForQuestionAnswering

autodoc TFCamembertForQuestionAnswering

4.6 KiB Raw Blame History