4.2 KiB
MPNet
Overview
The MPNet model was proposed in MPNet: Masked and Permuted Pre-training for Language Understanding 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.
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
- Token classification task guide
- Question answering task guide
- Masked language modeling task guide
- Multiple choice task guide
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