Skip to main content

BERT implemented in Keras of Tensorflow package on TPU

Project description

Travis Coverage

This is a fork of CyberZHG/keras_bert which supports Keras BERT on TPU.

Implementation of the BERT. Official pre-trained models could be loaded for feature extraction and prediction.

Install

pip install keras-bert-tpu

Usage

Load Official Pre-trained Models

In feature extraction demo, you should be able to get the same extraction result as the official model. And in prediction demo, the missing word in the sentence could be predicted.

Train & Use

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

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,
)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for keras-bert-tpu, version 0.1.5
Filename, size File type Python version Upload date Hashes
Filename, size keras-bert-tpu-0.1.5.tar.gz (17.5 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page