
MobileBERT
Overview
The MobileBERT model was proposed in MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny
Zhou. It's a bidirectional transformer based on the BERT model, which is compressed and accelerated using several
approaches.
The abstract from the paper is the following:
Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds
of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot
be deployed to resource-limited mobile devices. In this paper, we propose MobileBERT for compressing and accelerating
the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied to
various downstream NLP tasks via simple fine-tuning. Basically, MobileBERT is a thin version of BERT_LARGE, while
equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.
To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated BERT_LARGE
model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical studies show that MobileBERT is
4.3x smaller and 5.5x faster than BERT_BASE while achieving competitive results on well-known benchmarks. On the
natural language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7 (0.6 lower than BERT_BASE), and 62 ms
latency on a Pixel 4 phone. On the SQuAD v1.1/v2.0 question answering task, MobileBERT achieves a dev F1 score of
90.0/79.2 (1.5/2.1 higher than BERT_BASE).
This model was contributed by vshampor. The original code can be found here.
Usage tips

MobileBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
  than the left.
MobileBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
  efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
  with a causal language modeling (CLM) objective are better in that regard.

Resources

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

MobileBertConfig
[[autodoc]] MobileBertConfig
MobileBertTokenizer
[[autodoc]] MobileBertTokenizer
MobileBertTokenizerFast
[[autodoc]] MobileBertTokenizerFast
MobileBert specific outputs
[[autodoc]] models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput
[[autodoc]] models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput

MobileBertModel
[[autodoc]] MobileBertModel
    - forward
MobileBertForPreTraining
[[autodoc]] MobileBertForPreTraining
    - forward
MobileBertForMaskedLM
[[autodoc]] MobileBertForMaskedLM
    - forward
MobileBertForNextSentencePrediction
[[autodoc]] MobileBertForNextSentencePrediction
    - forward
MobileBertForSequenceClassification
[[autodoc]] MobileBertForSequenceClassification
    - forward
MobileBertForMultipleChoice
[[autodoc]] MobileBertForMultipleChoice
    - forward
MobileBertForTokenClassification
[[autodoc]] MobileBertForTokenClassification
    - forward
MobileBertForQuestionAnswering
[[autodoc]] MobileBertForQuestionAnswering
    - forward

TFMobileBertModel
[[autodoc]] TFMobileBertModel
    - call
TFMobileBertForPreTraining
[[autodoc]] TFMobileBertForPreTraining
    - call
TFMobileBertForMaskedLM
[[autodoc]] TFMobileBertForMaskedLM
    - call
TFMobileBertForNextSentencePrediction
[[autodoc]] TFMobileBertForNextSentencePrediction
    - call
TFMobileBertForSequenceClassification
[[autodoc]] TFMobileBertForSequenceClassification
    - call
TFMobileBertForMultipleChoice
[[autodoc]] TFMobileBertForMultipleChoice
    - call
TFMobileBertForTokenClassification
[[autodoc]] TFMobileBertForTokenClassification
    - call
TFMobileBertForQuestionAnswering
[[autodoc]] TFMobileBertForQuestionAnswering
    - call

