5.2 KiB
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