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BERT implemented in Keras

Project description

Keras BERT

Travis Coverage

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

Install

pip install keras-bert

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.

Tokenizer

The Tokenizer class is used for splitting texts and generating indices:

from keras_bert import Tokenizer

token_dict = {
    '[CLS]': 0,
    '[SEP]': 1,
    'un': 2,
    '##aff': 3,
    '##able': 4,
    '[UNK]': 5,
}
tokenizer = Tokenizer(token_dict)
print(tokenizer.tokenize('unaffable'))  # The result should be `['[CLS]', 'un', '##aff', '##able', '[SEP]']`
indices, segments = tokenizer.encode('unaffable')
print(indices)  # Should be `[0, 2, 3, 4, 1]`
print(segments)  # Should be `[0, 0, 0, 0, 0]`

print(tokenizer.tokenize(first='unaffable', second='钢'))
# The result should be `['[CLS]', 'un', '##aff', '##able', '[SEP]', '钢', '[SEP]']`
indices, segments = tokenizer.encode(first='unaffable', second='钢', max_len=10)
print(indices)  # Should be `[0, 2, 3, 4, 1, 5, 1, 0, 0, 0]`
print(segments)  # Should be `[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]`

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,
        return_sequences=True,
        name='LSTM',
    )(x)

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

Project details


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