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+*This model was published in HF papers on 2020-04-20 and contributed to Hugging Face Transformers on 2020-12-09.*
+
+# MPNet
+
+
+## Overview
+
+The MPNet model was proposed in [MPNet: Masked and Permuted Pre-training for Language Understanding](https://huggingface.co/papers/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
+
+MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of
+masked language modeling and permuted language modeling for natural language understanding.
+
+The abstract from the paper is the following:
+
+*BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models.
+Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for
+pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and
+thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel
+pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the
+dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position
+information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in
+XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of
+down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large
+margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g.,
+BERT, XLNet, RoBERTa) under the same model setting.*
+
+The original code can be found [here](https://github.com/microsoft/MPNet).
+
+## Usage tips
+
+MPNet doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just
+separate your segments with the separation token `tokenizer.sep_token` (or `[sep]`).
+
+## Resources
+
+- [Text classification task guide](../tasks/sequence_classification)
+- [Token classification task guide](../tasks/token_classification)
+- [Question answering task guide](../tasks/question_answering)
+- [Masked language modeling task guide](../tasks/masked_language_modeling)
+- [Multiple choice task guide](../tasks/multiple_choice)
+
+## MPNetConfig
+
+[[autodoc]] MPNetConfig
+
+## MPNetTokenizer
+
+[[autodoc]] MPNetTokenizer
+    - get_special_tokens_mask
+    - save_vocabulary
+
+## MPNetTokenizerFast
+
+[[autodoc]] MPNetTokenizerFast
+
+## MPNetModel
+
+[[autodoc]] MPNetModel
+    - forward
+
+## MPNetForMaskedLM
+
+[[autodoc]] MPNetForMaskedLM
+    - forward
+
+## MPNetForSequenceClassification
+
+[[autodoc]] MPNetForSequenceClassification
+    - forward
+
+## MPNetForMultipleChoice
+
+[[autodoc]] MPNetForMultipleChoice
+    - forward
+
+## MPNetForTokenClassification
+
+[[autodoc]] MPNetForTokenClassification
+    - forward
+
+## MPNetForQuestionAnswering
+
+[[autodoc]] MPNetForQuestionAnswering
+    - forward