Keras BERT
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
- Tokenizer
- Train & Use
- Use Warmup
- Download Pretrained Checkpoints
- Extract Features
External Links
- Kashgari is a Production-ready NLP Transfer learning framework for text-labeling and text-classification
- Keras ALBERT
Load Official Pre-trained Models
In feature extraction demo, you should be able to get the same extraction results as the official model chinese_L-12_H-768_A-12. And in prediction demo, the missing word in the sentence could be predicted.
Run on TPU
The extraction demo shows how to convert to a model that runs on TPU.
The classification demo shows how to apply the model to simple classification tasks.
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, 0, 0, 0]`
Train & Use
from tensorflow import keras
from keras_bert import get_base_dict, get_model, compile_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,
)
compile_model(model)
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(
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`
trainable=False, # Whether the model is trainable. The default value is the same with `training`
output_layer_num=4, # The number of layers whose outputs will be concatenated as a single output.
# Only available when `training` is `False`.
)
Use Warmup
AdamWarmup optimizer is provided for warmup and decay. The learning rate will reach lr in warmpup_steps steps, and decay to min_lr in decay_steps steps. There is a helper function calc_train_steps for calculating the two steps:
import numpy as np
from keras_bert import AdamWarmup, calc_train_steps
train_x = np.random.standard_normal((1024, 100))
total_steps, warmup_steps = calc_train_steps(
num_example=train_x.shape[0],
batch_size=32,
epochs=10,
warmup_proportion=0.1,
)
optimizer = AdamWarmup(total_steps, warmup_steps, lr=1e-3, min_lr=1e-5)
Download Pretrained Checkpoints
Several download urls has been added. You can get the downloaded and uncompressed path of a checkpoint by:
from keras_bert import get_pretrained, PretrainedList, get_checkpoint_paths
model_path = get_pretrained(PretrainedList.multi_cased_base)
paths = get_checkpoint_paths(model_path)
print(paths.config, paths.checkpoint, paths.vocab)
Extract Features
You can use helper function extract_embeddings if the features of tokens or sentences (without further tuning) are what you need. To extract the features of all tokens:
from keras_bert import extract_embeddings
model_path = 'xxx/yyy/uncased_L-12_H-768_A-12'
texts = ['all work and no play', 'makes jack a dull boy~']
embeddings = extract_embeddings(model_path, texts)
The returned result is a list with the same length as texts. Each item in the list is a numpy array truncated by the length of the input. The shapes of outputs in this example are (7, 768) and (8, 768).
When the inputs are paired-sentences, and you need the outputs of NSP and max-pooling of the last 4 layers:
from keras_bert import extract_embeddings, POOL_NSP, POOL_MAX
model_path = 'xxx/yyy/uncased_L-12_H-768_A-12'
texts = [
('all work and no play', 'makes jack a dull boy'),
('makes jack a dull boy', 'all work and no play'),
]
embeddings = extract_embeddings(model_path, texts, output_layer_num=4, poolings=[POOL_NSP, POOL_MAX])
There are no token features in the results. The outputs of NSP and max-pooling will be concatenated with the final shape (768 x 4 x 2,).
The second argument in the helper function is a generator. To extract features from file:
import codecs
from keras_bert import extract_embeddings
model_path = 'xxx/yyy/uncased_L-12_H-768_A-12'
with codecs.open('xxx.txt', 'r', 'utf8') as reader:
texts = map(lambda x: x.strip(), reader)
embeddings = extract_embeddings(model_path, texts)
Use tensorflow.python.keras
Add TF_KERAS=1 to environment variables to use tensorflow.python.keras.
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