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143 lines
4.6 KiB
Plaintext
<!--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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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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# MobileBERT
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## Overview
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The MobileBERT model was proposed in [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny
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Zhou. It's a bidirectional transformer based on the BERT model, which is compressed and accelerated using several
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approaches.
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The abstract from the paper is the following:
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*Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds
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of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot
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be deployed to resource-limited mobile devices. In this paper, we propose MobileBERT for compressing and accelerating
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the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied to
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various downstream NLP tasks via simple fine-tuning. Basically, MobileBERT is a thin version of BERT_LARGE, while
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equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.
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To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated BERT_LARGE
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model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical studies show that MobileBERT is
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4.3x smaller and 5.5x faster than BERT_BASE while achieving competitive results on well-known benchmarks. On the
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natural language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7 (0.6 lower than BERT_BASE), and 62 ms
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latency on a Pixel 4 phone. On the SQuAD v1.1/v2.0 question answering task, MobileBERT achieves a dev F1 score of
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90.0/79.2 (1.5/2.1 higher than BERT_BASE).*
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Tips:
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- MobileBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
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than the left.
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- MobileBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
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efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
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with a causal language modeling (CLM) objective are better in that regard.
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This model was contributed by [vshampor](https://huggingface.co/vshampor). The original code can be found [here](https://github.com/google-research/mobilebert).
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## MobileBertConfig
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[[autodoc]] MobileBertConfig
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## MobileBertTokenizer
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[[autodoc]] MobileBertTokenizer
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## MobileBertTokenizerFast
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[[autodoc]] MobileBertTokenizerFast
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## MobileBert specific outputs
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[[autodoc]] models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput
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[[autodoc]] models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput
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## MobileBertModel
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[[autodoc]] MobileBertModel
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- forward
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## MobileBertForPreTraining
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[[autodoc]] MobileBertForPreTraining
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- forward
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## MobileBertForMaskedLM
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[[autodoc]] MobileBertForMaskedLM
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- forward
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## MobileBertForNextSentencePrediction
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[[autodoc]] MobileBertForNextSentencePrediction
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- forward
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## MobileBertForSequenceClassification
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[[autodoc]] MobileBertForSequenceClassification
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- forward
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## MobileBertForMultipleChoice
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[[autodoc]] MobileBertForMultipleChoice
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- forward
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## MobileBertForTokenClassification
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[[autodoc]] MobileBertForTokenClassification
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- forward
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## MobileBertForQuestionAnswering
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[[autodoc]] MobileBertForQuestionAnswering
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- forward
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## TFMobileBertModel
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[[autodoc]] TFMobileBertModel
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- call
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## TFMobileBertForPreTraining
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[[autodoc]] TFMobileBertForPreTraining
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- call
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## TFMobileBertForMaskedLM
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[[autodoc]] TFMobileBertForMaskedLM
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- call
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## TFMobileBertForNextSentencePrediction
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[[autodoc]] TFMobileBertForNextSentencePrediction
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- call
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## TFMobileBertForSequenceClassification
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[[autodoc]] TFMobileBertForSequenceClassification
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- call
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## TFMobileBertForMultipleChoice
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[[autodoc]] TFMobileBertForMultipleChoice
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- call
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## TFMobileBertForTokenClassification
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[[autodoc]] TFMobileBertForTokenClassification
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- call
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## TFMobileBertForQuestionAnswering
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[[autodoc]] TFMobileBertForQuestionAnswering
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- call
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