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Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

docs/source/model_doc/xlm-roberta.mdx

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+<!--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
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+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.
+-->
+
+# XLM-RoBERTa
+
+## Overview
+
+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
+Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's
+RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl
+data.
+
+The abstract from the paper is the following:
+
+*This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a
+wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred
+languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly
+outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on
+XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on
+low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We
+also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the
+trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource
+languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing
+per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We
+will make XLM-R code, data, and models publicly available.*
+
+Tips:
+
+- XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does
+  not require `lang` tensors to understand which language is used, and should be able to determine the correct
+  language from the input ids.
+- This implementation is the same as RoBERTa. Refer to the [documentation of RoBERTa](roberta) for usage examples
+  as well as the information relative to the inputs and outputs.
+
+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).
+
+
+## XLMRobertaConfig
+
+[[autodoc]] XLMRobertaConfig
+
+## XLMRobertaTokenizer
+
+[[autodoc]] XLMRobertaTokenizer
+    - build_inputs_with_special_tokens
+    - get_special_tokens_mask
+    - create_token_type_ids_from_sequences
+    - save_vocabulary
+
+## XLMRobertaTokenizerFast
+
+[[autodoc]] XLMRobertaTokenizerFast
+
+## XLMRobertaModel
+
+[[autodoc]] XLMRobertaModel
+    - forward
+
+## XLMRobertaForCausalLM
+
+[[autodoc]] XLMRobertaForCausalLM
+    - forward
+
+## XLMRobertaForMaskedLM
+
+[[autodoc]] XLMRobertaForMaskedLM
+    - forward
+
+## XLMRobertaForSequenceClassification
+
+[[autodoc]] XLMRobertaForSequenceClassification
+    - forward
+
+## XLMRobertaForMultipleChoice
+
+[[autodoc]] XLMRobertaForMultipleChoice
+    - forward
+
+## XLMRobertaForTokenClassification
+
+[[autodoc]] XLMRobertaForTokenClassification
+    - forward
+
+## XLMRobertaForQuestionAnswering
+
+[[autodoc]] XLMRobertaForQuestionAnswering
+    - forward
+
+## TFXLMRobertaModel
+
+[[autodoc]] TFXLMRobertaModel
+    - call
+
+## TFXLMRobertaForMaskedLM
+
+[[autodoc]] TFXLMRobertaForMaskedLM
+    - call
+
+## TFXLMRobertaForSequenceClassification
+
+[[autodoc]] TFXLMRobertaForSequenceClassification
+    - call
+
+## TFXLMRobertaForMultipleChoice
+
+[[autodoc]] TFXLMRobertaForMultipleChoice
+    - call
+
+## TFXLMRobertaForTokenClassification
+
+[[autodoc]] TFXLMRobertaForTokenClassification
+    - call
+
+## TFXLMRobertaForQuestionAnswering
+
+[[autodoc]] TFXLMRobertaForQuestionAnswering
+    - call