# ConvBERT

## Overview

The ConvBERT model was proposed in [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng

Yan.

The abstract from the paper is the following:

*Pre-trained language models like BERT and its variants have recently achieved impressive performance in various

natural language understanding tasks. However, BERT heavily relies on the global self-attention block and thus suffers

large memory footprint and computation cost. Although all its attention heads query on the whole input sequence for

generating the attention map from a global perspective, we observe some heads only need to learn local dependencies,

which means the existence of computation redundancy. We therefore propose a novel span-based dynamic convolution to

replace these self-attention heads to directly model local dependencies. The novel convolution heads, together with the

rest self-attention heads, form a new mixed attention block that is more efficient at both global and local context

learning. We equip BERT with this mixed attention design and build a ConvBERT model. Experiments have shown that

ConvBERT significantly outperforms BERT and its variants in various downstream tasks, with lower training cost and

fewer model parameters. Remarkably, ConvBERTbase model achieves 86.4 GLUE score, 0.7 higher than ELECTRAbase, while

using less than 1/4 training cost. Code and pre-trained models will be released.*

This model was contributed by [abhishek](https://huggingface.co/abhishek). The original implementation can be found

here: https://github.com/yitu-opensource/ConvBert

## Usage tips

ConvBERT training tips are similar to those of BERT. For usage tips refer to [BERT documentation](bert).

## Resources

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

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

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

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

- [Multiple choice task guide](../tasks/multiple_choice)

## ConvBertConfig

[[autodoc]] ConvBertConfig

## ConvBertTokenizer

[[autodoc]] ConvBertTokenizer

    - build_inputs_with_special_tokens

    - get_special_tokens_mask

    - create_token_type_ids_from_sequences

    - save_vocabulary

## ConvBertTokenizerFast

[[autodoc]] ConvBertTokenizerFast

## ConvBertModel

[[autodoc]] ConvBertModel

    - forward

## ConvBertForMaskedLM

[[autodoc]] ConvBertForMaskedLM

    - forward

## ConvBertForSequenceClassification

[[autodoc]] ConvBertForSequenceClassification

    - forward

## ConvBertForMultipleChoice

[[autodoc]] ConvBertForMultipleChoice

    - forward

## ConvBertForTokenClassification

[[autodoc]] ConvBertForTokenClassification

    - forward

## ConvBertForQuestionAnswering

[[autodoc]] ConvBertForQuestionAnswering

    - forward

## TFConvBertModel

[[autodoc]] TFConvBertModel

    - call

## TFConvBertForMaskedLM

[[autodoc]] TFConvBertForMaskedLM

    - call

## TFConvBertForSequenceClassification

[[autodoc]] TFConvBertForSequenceClassification

    - call

## TFConvBertForMultipleChoice

[[autodoc]] TFConvBertForMultipleChoice

    - call

## TFConvBertForTokenClassification

[[autodoc]] TFConvBertForTokenClassification

    - call

## TFConvBertForQuestionAnswering

[[autodoc]] TFConvBertForQuestionAnswering

    - call