7.9 KiB
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
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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