90.0/79.2 (1.5/2.1 higher than BERT_BASE).
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.
This model was contributed by vshampor. The original code can be found here.
MobileBertConfig
[[autodoc]] MobileBertConfig
MobileBertTokenizer
[[autodoc]] MobileBertTokenizer
MobileBertTokenizerFast
[[autodoc]] MobileBertTokenizerFast
MobileBert specific outputs
[[autodoc]] models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput
