# MobileBERT (MobileBERT: A Compact Task-Agnostic BERT for Resource-Limited Devices)

[MobileBERT](https://arxiv.org/abs/2004.02984)
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. This repository contains TensorFlow 2.x
implementation for MobileBERT.

## Network Implementations

Following
[MobileBERT TF1 implementation](https://github.com/google-research/google-research/tree/master/mobilebert),
we re-implemented MobileBERT encoder and layers using `tf.keras` APIs in NLP
modeling library:

  * [mobile_bert_encoder.py](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/networks/mobile_bert_encoder.py)
  contains `MobileBERTEncoder` implementation.
  * [mobile_bert_layers.py](https://github.com/tensorflow/models/blob/master/official/nlp/modeling/layers/mobile_bert_layers.py)
  contains `MobileBertEmbedding`, `MobileBertTransformer` and `MobileBertMaskedLM`
  implementation.

## Pre-trained Models

We converted the originial TF 1.x pretrained English MobileBERT checkpoint to
TF 2.x checkpoint, which is compatible with the above implementations.
In addition, we also provide new multiple-lingual MobileBERT checkpoint
trained using multi-lingual Wiki data. Furthermore, we export the checkpoints to
TF-HUB SavedModel. Please find the details in the following table:

Model                          | Configuration                            | Number of Parameters | Training Data | Checkpoint & Vocabulary                                                                                                                                    | TF-Hub SavedModel                                                                                                                      | Metrics
------------------------------ | :--------------------------------------: | :------------------- | :-----------: | :-----------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------: | :-----:
MobileBERT uncased English     | uncased_L-24_H-128_B-512_A-4_F-4_OPT     | 25.3 Million         | Wiki + Books  | [Download](https://storage.cloud.google.com/tf_model_garden/nlp/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT.tar.gz)     | [TF-Hub](https://tfhub.dev/tensorflow/mobilebert_en_uncased_L-24_H-128_B-512_A-4_F-4_OPT/1)     | Squad v1.1 F1 90.0, GLUE 77.7
MobileBERT cased Multi-lingual | multi_cased_L-24_H-128_B-512_A-4_F-4_OPT | 36 Million           | Wiki          | [Download](https://storage.cloud.google.com/tf_model_garden/nlp/mobilebert/multi_cased_L-24_H-128_B-512_A-4_F-4_OPT.tar.gz) | [TF-Hub](https://tfhub.dev/tensorflow/mobilebert_multi_cased_L-24_H-128_B-512_A-4_F-4_OPT/1) | XNLI (zero-short):64.7

### Restoring from Checkpoints

To load the pre-trained MobileBERT checkpoint in your code, please follow the
example below:

```python
import tensorflow as tf
from official.nlp.projects.mobilebert import model_utils

bert_config_file = ...
model_checkpoint_path = ...

bert_config = model_utils.BertConfig.from_json_file(bert_config_file)

# `pretrainer` is an instance of `nlp.modeling.models.BertPretrainerV2`.
pretrainer = model_utils.create_mobilebert_pretrainer(bert_config)
checkpoint = tf.train.Checkpoint(**pretrainer.checkpoint_items)
checkpoint.restore(model_checkpoint_path).assert_existing_objects_matched()

# `mobilebert_encoder` is an instance of
# `nlp.modeling.networks.MobileBERTEncoder`.
mobilebert_encoder = pretrainer.encoder_network
```

### Use TF-Hub models

For the usage of MobileBert TF-Hub model, please see the TF-Hub site
([English model](https://tfhub.dev/tensorflow/mobilebert_en_uncased_L-24_H-128_B-512_A-4_F-4_OPT/1)
or
[Multilingual model](https://tfhub.dev/tensorflow/mobilebert_multi_cased_L-24_H-128_B-512_A-4_F-4_OPT/1)).