transformers/docs/source/en/model_doc/deberta.md

DeBERTa

Overview

The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google's BERT model released in 2018 and Facebook's RoBERTa model released in 2019.

It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in RoBERTa.

The abstract from the paper is the following:

Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (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 pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.

This model was contributed by DeBERTa. This model TF 2.0 implementation was contributed by kamalkraj . The original code can be found here.

Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DeBERTa. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

• A blog post on how to Accelerate Large Model Training using DeepSpeed with DeBERTa.
• A blog post on Supercharged Customer Service with Machine Learning with DeBERTa.
• [DebertaForSequenceClassification] is supported by this example script and notebook.
• [TFDebertaForSequenceClassification] is supported by this example script and notebook.
• Text classification task guide

• [DebertaForTokenClassification] is supported by this example script and notebook.
• [TFDebertaForTokenClassification] is supported by this example script and notebook.
• Token classification chapter of the 🤗 Hugging Face Course.
• Byte-Pair Encoding tokenization chapter of the 🤗 Hugging Face Course.
• Token classification task guide

• [DebertaForMaskedLM] is supported by this example script and notebook.
• [TFDebertaForMaskedLM] is supported by this example script and notebook.
• Masked language modeling chapter of the 🤗 Hugging Face Course.
• Masked language modeling task guide

• [DebertaForQuestionAnswering] is supported by this example script and notebook.
• [TFDebertaForQuestionAnswering] is supported by this example script and notebook.
• Question answering chapter of the 🤗 Hugging Face Course.
• Question answering task guide

DebertaConfig

autodoc DebertaConfig

DebertaTokenizer

autodoc DebertaTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary

DebertaTokenizerFast

autodoc DebertaTokenizerFast - build_inputs_with_special_tokens - create_token_type_ids_from_sequences

DebertaModel

autodoc DebertaModel - forward

DebertaPreTrainedModel

autodoc DebertaPreTrainedModel

DebertaForMaskedLM

autodoc DebertaForMaskedLM - forward

DebertaForSequenceClassification

autodoc DebertaForSequenceClassification - forward

DebertaForTokenClassification

autodoc DebertaForTokenClassification - forward

DebertaForQuestionAnswering

autodoc DebertaForQuestionAnswering - forward

TFDebertaModel

autodoc TFDebertaModel - call

TFDebertaPreTrainedModel

autodoc TFDebertaPreTrainedModel - call

TFDebertaForMaskedLM

autodoc TFDebertaForMaskedLM - call

TFDebertaForSequenceClassification

autodoc TFDebertaForSequenceClassification - call

TFDebertaForTokenClassification

autodoc TFDebertaForTokenClassification - call

TFDebertaForQuestionAnswering

autodoc TFDebertaForQuestionAnswering - call