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* Map model_type and doc pages names * Add script * Fix typo * Quality * Manual check for Auto Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
127 lines
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127 lines
4.3 KiB
Plaintext
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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# XLM-RoBERTa
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## Overview
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The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume
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Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's
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RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl
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data.
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The abstract from the paper is the following:
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*This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a
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wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred
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languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly
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outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on
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XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on
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low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We
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also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the
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trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource
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languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing
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per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We
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will make XLM-R code, data, and models publicly available.*
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Tips:
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- XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
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not require `lang` tensors to understand which language is used, and should be able to determine the correct
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language from the input ids.
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- This implementation is the same as RoBERTa. Refer to the [documentation of RoBERTa](roberta) for usage examples
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as well as the information relative to the inputs and outputs.
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This model was contributed by [stefan-it](https://huggingface.co/stefan-it). The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/examples/xlmr).
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## XLMRobertaConfig
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[[autodoc]] XLMRobertaConfig
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## XLMRobertaTokenizer
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[[autodoc]] XLMRobertaTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## XLMRobertaTokenizerFast
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[[autodoc]] XLMRobertaTokenizerFast
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## XLMRobertaModel
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[[autodoc]] XLMRobertaModel
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- forward
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## XLMRobertaForCausalLM
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[[autodoc]] XLMRobertaForCausalLM
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- forward
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## XLMRobertaForMaskedLM
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[[autodoc]] XLMRobertaForMaskedLM
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- forward
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## XLMRobertaForSequenceClassification
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[[autodoc]] XLMRobertaForSequenceClassification
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- forward
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## XLMRobertaForMultipleChoice
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[[autodoc]] XLMRobertaForMultipleChoice
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- forward
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## XLMRobertaForTokenClassification
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[[autodoc]] XLMRobertaForTokenClassification
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- forward
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## XLMRobertaForQuestionAnswering
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[[autodoc]] XLMRobertaForQuestionAnswering
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- forward
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## TFXLMRobertaModel
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[[autodoc]] TFXLMRobertaModel
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- call
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## TFXLMRobertaForMaskedLM
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[[autodoc]] TFXLMRobertaForMaskedLM
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- call
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## TFXLMRobertaForSequenceClassification
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[[autodoc]] TFXLMRobertaForSequenceClassification
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- call
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## TFXLMRobertaForMultipleChoice
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[[autodoc]] TFXLMRobertaForMultipleChoice
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- call
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## TFXLMRobertaForTokenClassification
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[[autodoc]] TFXLMRobertaForTokenClassification
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- call
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## TFXLMRobertaForQuestionAnswering
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[[autodoc]] TFXLMRobertaForQuestionAnswering
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- call
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