<!--Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer. -->

This model was released on 2019-12-11 and added to Hugging Face Transformers on 2020-11-16.

FlauBERT

<div class="flex flex-wrap space-x-1"> </div>

Overview

The FlauBERT model was proposed in the paper FlauBERT: Unsupervised Language Model Pre-training for French by Hang Le et al. It's a transformer model pretrained using a masked language modeling (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 Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their contextualization at the sentence level. This has been widely demonstrated for English using contextualized representations (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 and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the time they outperform other pretraining approaches. Different versions of FlauBERT as well as a unified evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research community for further reproducible experiments in French NLP.

This model was contributed by formiel. The original code can be found here.

Tips:

• Like RoBERTa, without the sentence ordering prediction (so just trained on the MLM objective).

Resources

• Text classification task guide
• Token classification task guide
• Question answering task guide
• Masked language modeling task guide
• Multiple choice task guide

FlaubertConfig

[[autodoc]] FlaubertConfig

FlaubertTokenizer

[[autodoc]] FlaubertTokenizer

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