FlauBERT
Source: transformers

# FlauBERT<div class="flex flex-wrap space-x-1"><a href="https://huggingface.co/models?filter=flaubert"><img alt="Models" src="https://img.shields.io/badge/All_model_pages-flaubert-blueviolet"></a><a href="https://huggingface.co/spaces/docs-demos/flaubert_small_cased"><img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"></a></div>## OverviewThe FlauBERT model was proposed in the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le et al. It's a transformer model pretrained using a masked languagemodeling (MLM) objective (like BERT).The abstract from the paper is the following:*Language models have become a key step to achieve state-of-the art results in many different Natural LanguageProcessing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient wayto pre-train continuous word representations that can be fine-tuned for a downstream task, along with theircontextualization at the sentence level. This has been widely demonstrated for English using contextualizedrepresentations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al.,2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large andheterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre forScientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (textclassification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of thetime they outperform other pretraining approaches. Different versions of FlauBERT as well as a unified evaluationprotocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the researchcommunity for further reproducible experiments in French NLP.*This model was contributed by [formiel](https://huggingface.co/formiel). The original code can be found [here](https://github.com/getalp/Flaubert).Tips:- Like RoBERTa, without the sentence ordering prediction (so just trained on the MLM objective).## Resources- [Text classification task guide](../tasks/sequence_classification)- [Token classification task guide](../tasks/token_classification)- [Question answering task guide](../tasks/question_answering)- [Masked language modeling task guide](../tasks/masked_language_modeling)- [Multiple choice task guide](../tasks/multiple_choice)## FlaubertConfig[[autodoc]] FlaubertConfig## FlaubertTokenizer[[autodoc]] FlaubertTokenizer<frameworkcontent><pt>## FlaubertModel[[autodoc]] FlaubertModel    - forward## FlaubertWithLMHeadModel[[autodoc]] FlaubertWithLMHeadModel    - forward## FlaubertForSequenceClassification[[autodoc]] FlaubertForSequenceClassification    - forward## FlaubertForMultipleChoice[[autodoc]] FlaubertForMultipleChoice    - forward## FlaubertForTokenClassification[[autodoc]] FlaubertForTokenClassification    - forward## FlaubertForQuestionAnsweringSimple[[autodoc]] FlaubertForQuestionAnsweringSimple    - forward## FlaubertForQuestionAnswering[[autodoc]] FlaubertForQuestionAnswering    - forward</pt><tf>## TFFlaubertModel[[autodoc]] TFFlaubertModel    - call## TFFlaubertWithLMHeadModel[[autodoc]] TFFlaubertWithLMHeadModel    - call## TFFlaubertForSequenceClassification[[autodoc]] TFFlaubertForSequenceClassification    - call## TFFlaubertForMultipleChoice[[autodoc]] TFFlaubertForMultipleChoice    - call## TFFlaubertForTokenClassification[[autodoc]] TFFlaubertForTokenClassification    - call## TFFlaubertForQuestionAnsweringSimple[[autodoc]] TFFlaubertForQuestionAnsweringSimple    - call</tf></frameworkcontent>