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