# DeBERTa

## Overview

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

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](https://huggingface.co/DeBERTa). This model TF 2.0 implementation was

contributed by [kamalkraj](https://huggingface.co/kamalkraj) . The original code can be found [here](https://github.com/microsoft/DeBERTa).

## 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](https://huggingface.co/blog/accelerate-deepspeed) with DeBERTa.

- A blog post on [Supercharged Customer Service with Machine Learning](https://huggingface.co/blog/supercharge-customer-service-with-machine-learning) with DeBERTa.

- [`DebertaForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb).

- [`TFDebertaForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb).

- [Text classification task guide](../tasks/sequence_classification)

- [`DebertaForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/token-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb).

- [`TFDebertaForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/token-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb).

- [Token classification](https://huggingface.co/course/chapter7/2?fw=pt) chapter of the 🤗 Hugging Face Course.

- [Byte-Pair Encoding tokenization](https://huggingface.co/course/chapter6/5?fw=pt) chapter of the 🤗 Hugging Face Course.

- [Token classification task guide](../tasks/token_classification)

- [`DebertaForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#robertabertdistilbert-and-masked-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb).

- [`TFDebertaForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_mlmpy) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).

- [Masked language modeling](https://huggingface.co/course/chapter7/3?fw=pt) chapter of the 🤗 Hugging Face Course.

- [Masked language modeling task guide](../tasks/masked_language_modeling)

- [`DebertaForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb).

- [`TFDebertaForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb).

- [Question answering](https://huggingface.co/course/chapter7/7?fw=pt) chapter of the 🤗 Hugging Face Course.

- [Question answering task guide](../tasks/question_answering)

## 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