169 lines
6.0 KiB
Markdown
169 lines
6.0 KiB
Markdown
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# DeBERTa-v2
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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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The following information is visible directly on the [original implementation
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repository](https://github.com/microsoft/DeBERTa). DeBERTa v2 is the second version of the DeBERTa model. It includes
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the 1.5B model used for the SuperGLUE single-model submission and achieving 89.9, versus human baseline 89.8. You can
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find more details about this submission in the authors'
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[blog](https://www.microsoft.com/en-us/research/blog/microsoft-deberta-surpasses-human-performance-on-the-superglue-benchmark/)
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New in v2:
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- **Vocabulary** In v2 the tokenizer is changed to use a new vocabulary of size 128K built from the training data.
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Instead of a GPT2-based tokenizer, the tokenizer is now
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[sentencepiece-based](https://github.com/google/sentencepiece) tokenizer.
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- **nGiE(nGram Induced Input Encoding)** The DeBERTa-v2 model uses an additional convolution layer aside with the first
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transformer layer to better learn the local dependency of input tokens.
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- **Sharing position projection matrix with content projection matrix in attention layer** Based on previous
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experiments, this can save parameters without affecting the performance.
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- **Apply bucket to encode relative positions** The DeBERTa-v2 model uses log bucket to encode relative positions
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similar to T5.
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- **900M model & 1.5B model** Two additional model sizes are available: 900M and 1.5B, which significantly improves the
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performance of downstream tasks.
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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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## Resources
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- [Text classification task guide](../tasks/sequence_classification)
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- [Token classification task guide](../tasks/token_classification)
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- [Question answering task guide](../tasks/question_answering)
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- [Masked language modeling task guide](../tasks/masked_language_modeling)
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- [Multiple choice task guide](../tasks/multiple_choice)
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## DebertaV2Config
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[[autodoc]] DebertaV2Config
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## DebertaV2Tokenizer
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[[autodoc]] DebertaV2Tokenizer
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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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## DebertaV2TokenizerFast
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[[autodoc]] DebertaV2TokenizerFast
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- build_inputs_with_special_tokens
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- create_token_type_ids_from_sequences
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<frameworkcontent>
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<pt>
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## DebertaV2Model
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[[autodoc]] DebertaV2Model
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- forward
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## DebertaV2PreTrainedModel
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[[autodoc]] DebertaV2PreTrainedModel
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- forward
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## DebertaV2ForMaskedLM
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[[autodoc]] DebertaV2ForMaskedLM
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- forward
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## DebertaV2ForSequenceClassification
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[[autodoc]] DebertaV2ForSequenceClassification
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- forward
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## DebertaV2ForTokenClassification
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[[autodoc]] DebertaV2ForTokenClassification
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- forward
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## DebertaV2ForQuestionAnswering
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[[autodoc]] DebertaV2ForQuestionAnswering
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- forward
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## DebertaV2ForMultipleChoice
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[[autodoc]] DebertaV2ForMultipleChoice
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- forward
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</pt>
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<tf>
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## TFDebertaV2Model
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[[autodoc]] TFDebertaV2Model
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- call
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## TFDebertaV2PreTrainedModel
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[[autodoc]] TFDebertaV2PreTrainedModel
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- call
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## TFDebertaV2ForMaskedLM
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[[autodoc]] TFDebertaV2ForMaskedLM
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- call
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## TFDebertaV2ForSequenceClassification
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[[autodoc]] TFDebertaV2ForSequenceClassification
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- call
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## TFDebertaV2ForTokenClassification
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[[autodoc]] TFDebertaV2ForTokenClassification
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- call
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## TFDebertaV2ForQuestionAnswering
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[[autodoc]] TFDebertaV2ForQuestionAnswering
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- call
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## TFDebertaV2ForMultipleChoice
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[[autodoc]] TFDebertaV2ForMultipleChoice
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- call
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</tf>
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</frameworkcontent>
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