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* Integrate DeBERTa v2(the 1.5B model surpassed human performance on SuperGLUE); Add DeBERTa v2 900M,1.5B models; * DeBERTa-v2 * Fix v2 model loading issue (#10129) * Doc members * Update src/transformers/models/deberta/modeling_deberta.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Address Sylvain's comments * Address Patrick's comments Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Style Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
99 lines
4.4 KiB
ReStructuredText
99 lines
4.4 KiB
ReStructuredText
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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DeBERTa
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The DeBERTa model was proposed in `DeBERTa: Decoding-enhanced BERT with Disentangled Attention
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<https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google's
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BERT model released in 2018 and Facebook's RoBERTa model released in 2019.
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It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in
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RoBERTa.
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The abstract from the paper is the following:
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*Recent progress in pre-trained neural language models has significantly improved the performance of many natural
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language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with
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disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the
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disentangled attention mechanism, where each word is represented using two vectors that encode its content and
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position, respectively, and the attention weights among words are computed using disentangled matrices on their
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contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to
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predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency
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of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of
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the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
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(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
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pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.*
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The original code can be found `here <https://github.com/microsoft/DeBERTa>`__.
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DebertaConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DebertaConfig
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:members:
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DebertaTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DebertaTokenizer
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:members: build_inputs_with_special_tokens, get_special_tokens_mask,
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create_token_type_ids_from_sequences, save_vocabulary
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DebertaModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DebertaModel
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:members: forward
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DebertaPreTrainedModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DebertaPreTrainedModel
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:members:
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DebertaForMaskedLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DebertaForMaskedLM
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:members: forward
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DebertaForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DebertaForSequenceClassification
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:members: forward
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DebertaForTokenClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DebertaForTokenClassification
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:members: forward
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DebertaForQuestionAnswering
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DebertaForQuestionAnswering
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:members: forward
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