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* add dialoGPT * update README.md * fix conflict * update readme * add code links to docs * Update README.md * Update dialo_gpt2.rst * Update pretrained_models.rst * Update docs/source/model_doc/dialo_gpt2.rst Co-Authored-By: Julien Chaumond <chaumond@gmail.com> * change filename of dialogpt Co-authored-by: Julien Chaumond <chaumond@gmail.com>
75 lines
2.6 KiB
ReStructuredText
75 lines
2.6 KiB
ReStructuredText
FlauBERT
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The FlauBERT model was proposed in the paper
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`FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le et al.
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It's a transformer pre-trained using a masked language modeling (MLM) objective (BERT-like).
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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
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way 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
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al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large
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and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre
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for 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
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of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified
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evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared
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to the research community for further reproducible experiments in French NLP.*
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The original code can be found `here <https://github.com/getalp/Flaubert>`_.
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FlaubertConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FlaubertConfig
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:members:
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FlaubertTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FlaubertTokenizer
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:members:
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FlaubertModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FlaubertModel
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:members:
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FlaubertWithLMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FlaubertWithLMHeadModel
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:members:
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FlaubertForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FlaubertForSequenceClassification
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:members:
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FlaubertForQuestionAnsweringSimple
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FlaubertForQuestionAnsweringSimple
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:members:
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FlaubertForQuestionAnswering
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FlaubertForQuestionAnswering
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:members:
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