# MPNet

## Overview

The MPNet model was proposed in [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/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

    - build_inputs_with_special_tokens

    - get_special_tokens_mask

    - create_token_type_ids_from_sequences

    - 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

## TFMPNetModel

[[autodoc]] TFMPNetModel

    - call

## TFMPNetForMaskedLM

[[autodoc]] TFMPNetForMaskedLM

    - call

## TFMPNetForSequenceClassification

[[autodoc]] TFMPNetForSequenceClassification

    - call

## TFMPNetForMultipleChoice

[[autodoc]] TFMPNetForMultipleChoice

    - call

## TFMPNetForTokenClassification

[[autodoc]] TFMPNetForTokenClassification

    - call

## TFMPNetForQuestionAnswering

[[autodoc]] TFMPNetForQuestionAnswering

    - call