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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
+the License. You may obtain a copy of the License at
+
+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.
+-->
+
+# XLNet
+
+## Overview
+
+The XLNet model was proposed in [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov,
+Quoc V. Le. XLnet is an extension of the Transformer-XL model pre-trained using an autoregressive method to learn
+bidirectional contexts by maximizing the expected likelihood over all permutations of the input sequence factorization
+order.
+
+The abstract from the paper is the following:
+
+*With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves
+better performance than pretraining approaches based on autoregressive language modeling. However, relying on
+corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a
+pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive
+pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all
+permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive
+formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into
+pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large
+margin, including question answering, natural language inference, sentiment analysis, and document ranking.*
+
+Tips:
+
+- The specific attention pattern can be controlled at training and test time using the `perm_mask` input.
+- Due to the difficulty of training a fully auto-regressive model over various factorization order, XLNet is pretrained
+  using only a sub-set of the output tokens as target which are selected with the `target_mapping` input.
+- To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the `perm_mask` and
+  `target_mapping` inputs to control the attention span and outputs (see examples in
+  *examples/pytorch/text-generation/run_generation.py*)
+- XLNet is one of the few models that has no sequence length limit.
+
+This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/zihangdai/xlnet/).
+
+
+## XLNetConfig
+
+[[autodoc]] XLNetConfig
+
+## XLNetTokenizer
+
+[[autodoc]] XLNetTokenizer
+    - build_inputs_with_special_tokens
+    - get_special_tokens_mask
+    - create_token_type_ids_from_sequences
+    - save_vocabulary
+
+## XLNetTokenizerFast
+
+[[autodoc]] XLNetTokenizerFast
+
+## XLNet specific outputs
+
+[[autodoc]] models.xlnet.modeling_xlnet.XLNetModelOutput
+
+[[autodoc]] models.xlnet.modeling_xlnet.XLNetLMHeadModelOutput
+
+[[autodoc]] models.xlnet.modeling_xlnet.XLNetForSequenceClassificationOutput
+
+[[autodoc]] models.xlnet.modeling_xlnet.XLNetForMultipleChoiceOutput
+
+[[autodoc]] models.xlnet.modeling_xlnet.XLNetForTokenClassificationOutput
+
+[[autodoc]] models.xlnet.modeling_xlnet.XLNetForQuestionAnsweringSimpleOutput
+
+[[autodoc]] models.xlnet.modeling_xlnet.XLNetForQuestionAnsweringOutput
+
+[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetModelOutput
+
+[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetLMHeadModelOutput
+
+[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetForSequenceClassificationOutput
+
+[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetForMultipleChoiceOutput
+
+[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetForTokenClassificationOutput
+
+[[autodoc]] models.xlnet.modeling_tf_xlnet.TFXLNetForQuestionAnsweringSimpleOutput
+
+## XLNetModel
+
+[[autodoc]] XLNetModel
+    - forward
+
+## XLNetLMHeadModel
+
+[[autodoc]] XLNetLMHeadModel
+    - forward
+
+## XLNetForSequenceClassification
+
+[[autodoc]] XLNetForSequenceClassification
+    - forward
+
+## XLNetForMultipleChoice
+
+[[autodoc]] XLNetForMultipleChoice
+    - forward
+
+## XLNetForTokenClassification
+
+[[autodoc]] XLNetForTokenClassification
+    - forward
+
+## XLNetForQuestionAnsweringSimple
+
+[[autodoc]] XLNetForQuestionAnsweringSimple
+    - forward
+
+## XLNetForQuestionAnswering
+
+[[autodoc]] XLNetForQuestionAnswering
+    - forward
+
+## TFXLNetModel
+
+[[autodoc]] TFXLNetModel
+    - call
+
+## TFXLNetLMHeadModel
+
+[[autodoc]] TFXLNetLMHeadModel
+    - call
+
+## TFXLNetForSequenceClassification
+
+[[autodoc]] TFXLNetForSequenceClassification
+    - call
+
+## TFLNetForMultipleChoice
+
+[[autodoc]] TFXLNetForMultipleChoice
+    - call
+
+## TFXLNetForTokenClassification
+
+[[autodoc]] TFXLNetForTokenClassification
+    - call
+
+## TFXLNetForQuestionAnsweringSimple
+
+[[autodoc]] TFXLNetForQuestionAnsweringSimple
+    - call