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

ConvBERT

Overview

The ConvBERT model was proposed in ConvBERT: Improving BERT with Span-based Dynamic Convolution 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. 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.

Resources

• Text classification task guide
• Token classification task guide
• Question answering task guide
• Masked language modeling task guide
• Multiple choice task guide

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