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docs/source/model_doc/xlm.mdx

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+<!--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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+
+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.
+-->
+
+# XLM
+
+## Overview
+
+The XLM model was proposed in [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by
+Guillaume Lample, Alexis Conneau. It's a transformer pretrained using one of the following objectives:
+
+- a causal language modeling (CLM) objective (next token prediction),
+- a masked language modeling (MLM) objective (BERT-like), or
+- a Translation Language Modeling (TLM) object (extension of BERT's MLM to multiple language inputs)
+
+The abstract from the paper is the following:
+
+*Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding.
+In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We
+propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual
+data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain
+state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our
+approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we
+obtain 34.3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised
+machine translation, we obtain a new state of the art of 38.5 BLEU on WMT'16 Romanian-English, outperforming the
+previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.*
+
+Tips:
+
+- XLM has many different checkpoints, which were trained using different objectives: CLM, MLM or TLM. Make sure to
+  select the correct objective for your task (e.g. MLM checkpoints are not suitable for generation).
+- XLM has multilingual checkpoints which leverage a specific `lang` parameter. Check out the [multi-lingual](../multilingual) page for more information.
+
+This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/facebookresearch/XLM/).
+
+
+## XLMConfig
+
+[[autodoc]] XLMConfig
+
+## XLMTokenizer
+
+[[autodoc]] XLMTokenizer
+    - build_inputs_with_special_tokens
+    - get_special_tokens_mask
+    - create_token_type_ids_from_sequences
+    - save_vocabulary
+
+## XLM specific outputs
+
+[[autodoc]] models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput
+
+## XLMModel
+
+[[autodoc]] XLMModel
+    - forward
+
+## XLMWithLMHeadModel
+
+[[autodoc]] XLMWithLMHeadModel
+    - forward
+
+## XLMForSequenceClassification
+
+[[autodoc]] XLMForSequenceClassification
+    - forward
+
+## XLMForMultipleChoice
+
+[[autodoc]] XLMForMultipleChoice
+    - forward
+
+## XLMForTokenClassification
+
+[[autodoc]] XLMForTokenClassification
+    - forward
+
+## XLMForQuestionAnsweringSimple
+
+[[autodoc]] XLMForQuestionAnsweringSimple
+    - forward
+
+## XLMForQuestionAnswering
+
+[[autodoc]] XLMForQuestionAnswering
+    - forward
+
+## TFXLMModel
+
+[[autodoc]] TFXLMModel
+    - call
+
+## TFXLMWithLMHeadModel
+
+[[autodoc]] TFXLMWithLMHeadModel
+    - call
+
+## TFXLMForSequenceClassification
+
+[[autodoc]] TFXLMForSequenceClassification
+    - call
+
+## TFXLMForMultipleChoice
+
+[[autodoc]] TFXLMForMultipleChoice
+    - call
+
+## TFXLMForTokenClassification
+
+[[autodoc]] TFXLMForTokenClassification
+    - call
+
+## TFXLMForQuestionAnsweringSimple
+
+[[autodoc]] TFXLMForQuestionAnsweringSimple
+    - call