keras-bert-tpu 0.1.7
BERT implemented in Keras of Tensorflow package on TPU
License: MIT

Keras BERT TPU
==============


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This is a fork of `CyberZHG/keras_bert <https://github.com/CyberZHG/keras-bert>`_ which supports Keras BERT on TPU.

Implementation of the `BERT <https://arxiv.org/pdf/1810.04805.pdf>`_. Official pre-trained models could be loaded for feature extraction and prediction.

Colab Demo
----------

`HighCWu/keras-bert-tpu <https://colab.research.google.com/github/HighCWu/keras-bert-tpu/blob/master/demo/load_model/load_and_predict.ipynb>`_

Install
-------

.. code-block:: bash

   pip install keras-bert-tpu

Usage
-----

Load Official Pre-trained Models
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In `feature extraction demo <./demo/load_model/load_and_extract.py>`_\ , you should be able to get the same extraction result as the official model. And in `prediction demo <./demo/load_model/load_and_predict.py>`_\ , the missing word in the sentence could be predicted.

Train & Use
^^^^^^^^^^^

.. code-block:: python

   from keras_bert import get_base_dict, get_model, gen_batch_inputs


   # A toy input example
   sentence_pairs = [
       [['all', 'work', 'and', 'no', 'play'], ['makes', 'jack', 'a', 'dull', 'boy']],
       [['from', 'the', 'day', 'forth'], ['my', 'arm', 'changed']],
       [['and', 'a', 'voice', 'echoed'], ['power', 'give', 'me', 'more', 'power']],
   ]


   # Build token dictionary
   token_dict = get_base_dict()  # A dict that contains some special tokens
   for pairs in sentence_pairs:
       for token in pairs[0] + pairs[1]:
           if token not in token_dict:
               token_dict[token] = len(token_dict)
   token_list = list(token_dict.keys())  # Used for selecting a random word


   # Build & train the model
   model = get_model(
       token_num=len(token_dict),
       head_num=5,
       transformer_num=12,
       embed_dim=25,
       feed_forward_dim=100,
       seq_len=20,
       pos_num=20,
       dropout_rate=0.05,
   )
   model.summary()

   def _generator():
       while True:
           yield gen_batch_inputs(
               sentence_pairs,
               token_dict,
               token_list,
               seq_len=20,
               mask_rate=0.3,
               swap_sentence_rate=1.0,
           )

   model.fit_generator(
       generator=_generator(),
       steps_per_epoch=1000,
       epochs=100,
       validation_data=_generator(),
       validation_steps=100,
       callbacks=[
           keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)
       ],
   )


   # Use the trained model
   inputs, output_layer = get_model(  # `output_layer` is the last feature extraction layer (the last transformer)
       token_num=len(token_dict),
       head_num=5,
       transformer_num=12,
       embed_dim=25,
       feed_forward_dim=100,
       seq_len=20,
       pos_num=20,
       dropout_rate=0.05,
       training=False,  # The input layers and output layer will be returned if `training` is `False`
   )

Custom Feature Extraction
^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: python

   def _custom_layers(x, trainable=True):
       return keras.layers.LSTM(
           units=768,
           trainable=trainable,
           name='LSTM',
       )(x)

   model = get_model(
       token_num=200,
       embed_dim=768,
       custom_layers=_custom_layers,
   )