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  library_name: transformers
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  language:
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  - fr
 
 
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  ---
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  # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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  ## Model Details
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  ### Model Description
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  <!-- Provide a longer summary of what this model is. -->
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  ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
 
 
 
 
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  ### Recommendations
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  <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
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  library_name: transformers
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  language:
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  - fr
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+ datasets:
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+ - OpenLLM-France/Claire-Dialogue-French-0.1
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  ---
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  # Model Card for Model ID
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+ Pretrained model on French language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository.
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  ## Model Details
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+ CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
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+ It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
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+ For further information or requests, please go to Camembert Website
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+
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  ### Model Description
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  <!-- Provide a longer summary of what this model is. -->
 
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  ## Bias, Risks, and Limitations
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+ Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions:
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+ ### Authors
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+ CamemBERT was trained and evaluated by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
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+ If you this our work, please cite:
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+ @inproceedings{martin2020camembert,
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+ title={CamemBERT: a Tasty French Language Model},
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+ author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
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+ booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
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+ year={2020}
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+ }
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  ### Recommendations
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  <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->