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118 lines
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118 lines
3.8 KiB
Plaintext
<!--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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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# DeBERTa
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## Overview
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The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google's
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BERT model released in 2018 and Facebook's RoBERTa model released in 2019.
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It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in
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RoBERTa.
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The abstract from the paper is the following:
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*Recent progress in pre-trained neural language models has significantly improved the performance of many natural
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language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with
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disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the
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disentangled attention mechanism, where each word is represented using two vectors that encode its content and
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position, respectively, and the attention weights among words are computed using disentangled matrices on their
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contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to
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predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency
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of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of
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the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
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(90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and
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pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.*
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This model was contributed by [DeBERTa](https://huggingface.co/DeBERTa). This model TF 2.0 implementation was
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contributed by [kamalkraj](https://huggingface.co/kamalkraj) . The original code can be found [here](https://github.com/microsoft/DeBERTa).
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## DebertaConfig
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[[autodoc]] DebertaConfig
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## DebertaTokenizer
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[[autodoc]] DebertaTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## DebertaTokenizerFast
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[[autodoc]] DebertaTokenizerFast
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- build_inputs_with_special_tokens
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- create_token_type_ids_from_sequences
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## DebertaModel
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[[autodoc]] DebertaModel
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- forward
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## DebertaPreTrainedModel
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[[autodoc]] DebertaPreTrainedModel
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## DebertaForMaskedLM
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[[autodoc]] DebertaForMaskedLM
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- forward
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## DebertaForSequenceClassification
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[[autodoc]] DebertaForSequenceClassification
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- forward
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## DebertaForTokenClassification
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[[autodoc]] DebertaForTokenClassification
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- forward
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## DebertaForQuestionAnswering
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[[autodoc]] DebertaForQuestionAnswering
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- forward
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## TFDebertaModel
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[[autodoc]] TFDebertaModel
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- call
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## TFDebertaPreTrainedModel
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[[autodoc]] TFDebertaPreTrainedModel
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- call
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## TFDebertaForMaskedLM
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[[autodoc]] TFDebertaForMaskedLM
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- call
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## TFDebertaForSequenceClassification
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[[autodoc]] TFDebertaForSequenceClassification
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- call
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## TFDebertaForTokenClassification
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[[autodoc]] TFDebertaForTokenClassification
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- call
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## TFDebertaForQuestionAnswering
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[[autodoc]] TFDebertaForQuestionAnswering
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- call
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