def test_train_bert_model(self):
preprocessor = SPMPreprocessor(
(self.train_data[0] + self.valid_data[0],
self.train_data[1] + self.valid_data[1]),
self.train_labels + self.valid_labels,
use_word=False,
use_char=False,
use_bert=True,
use_bert_model=True,
bert_vocab_file=self.bert_vocab_file,
max_len=10)
spm_model = BertSPM(num_class=self.num_class,
bert_config_file=self.bert_config_file,
bert_checkpoint_file=self.bert_model_file,
bert_trainable=True,
max_len=preprocessor.max_len).build_model()
spm_trainer = SPMTrainer(spm_model, preprocessor)
spm_trainer.train(self.train_data,
self.train_labels,
self.valid_data,
self.valid_labels,
batch_size=6,
epochs=2)
assert not os.path.exists(self.json_file)
assert not os.path.exists(self.weights_file)
def load(self,
preprocessor_file: str,
json_file: str,
weights_file: str,
custom_objects: Optional[Dict[str, Any]] = None) -> None:
"""load spm application
Args:
preprocessor_file: path to load preprocessor
json_file: path to load model architecture
weights_file: path to load model weights
custom_objects: Optional dictionary mapping names (strings) to custom classes or
functions to be considered during deserialization. Must provided when
using custom layer.
"""
self.preprocessor = SPMPreprocessor.load(preprocessor_file)
logging.info('Load preprocessor from {}'.format(preprocessor_file))
custom_objects = custom_objects or {}
custom_objects.update(get_custom_objects())
with open(json_file, 'r') as reader:
self.model = tf.keras.models.model_from_json(
reader.read(), custom_objects=custom_objects)
logging.info('Load model architecture from {}'.format(json_file))
self.model.load_weights(weights_file)
logging.info('Load model weight from {}'.format(weights_file))
self.trainer = SPMTrainer(self.model, self.preprocessor)
self.predictor = SPMPredictor(self.model, self.preprocessor)
def test_spm_generator(self):
test_file = os.path.join(os.path.dirname(__file__), '../../../data/spm/webank/example.txt')
x_train, y_train = load_spm_data_and_labels(test_file)
preprocessor = SPMPreprocessor(x_train, y_train)
generator = SPMGenerator(preprocessor, x_train, batch_size=64)
assert len(generator) == math.ceil(len(x_train[0]) / 64)
for i, (features, y) in enumerate(generator):
if i < len(generator) - 1:
assert features[0].shape[0] == features[1].shape[0] == 64
assert y is None
else:
assert features[0].shape[0] == features[1].shape[0] == \
len(x_train[0]) - 64 * (len(generator) - 1)
assert y is None
def setup_class(self):
x_train, y_train = load_spm_data_and_labels(self.test_file)
self.preprocessor = SPMPreprocessor(
x_train,
y_train,
use_word=True,
use_char=True,
use_bert=False,
bert_vocab_file=self.bert_vocab_file,
char_embed_type='word2vec',
word_embed_type='word2vec',
max_len=10)
self.num_class = self.preprocessor.num_class
self.char_embeddings = self.preprocessor.char_embeddings
self.char_vocab_size = self.preprocessor.char_vocab_size
self.char_embed_dim = self.preprocessor.char_embed_dim
self.word_embeddings = self.preprocessor.word_embeddings
self.word_vocab_size = self.preprocessor.word_vocab_size
self.word_embed_dim = self.preprocessor.word_embed_dim
self.checkpoint_dir = os.path.dirname(__file__)
def test_train_no_word(self):
preprocessor = SPMPreprocessor(
(self.train_data[0] + self.valid_data[0],
self.train_data[1] + self.valid_data[1]),
self.train_labels + self.valid_labels,
use_word=False,
use_char=True,
use_bert=True,
bert_vocab_file=self.bert_vocab_file,
char_embed_type='word2vec',
max_len=10)
self.num_class = preprocessor.num_class
self.char_embeddings = preprocessor.char_embeddings
self.char_vocab_size = preprocessor.char_vocab_size
self.char_embed_dim = preprocessor.char_embed_dim
spm_model = SiameseCNN(num_class=self.num_class,
use_word=False,
use_char=True,
char_embeddings=self.char_embeddings,
char_vocab_size=self.char_vocab_size,
char_embed_dim=self.char_embed_dim,
char_embed_trainable=False,
use_bert=True,
bert_config_file=self.bert_config_file,
bert_checkpoint_file=self.bert_model_file,
max_len=preprocessor.max_len).build_model()
spm_trainer = SPMTrainer(spm_model, preprocessor)
spm_trainer.train(self.train_data,
self.train_labels,
self.valid_data,
self.valid_labels,
batch_size=6,
epochs=2)
assert not os.path.exists(self.json_file)
assert not os.path.exists(self.weights_file)
def setup_class(self):
self.train_data, self.train_labels, self.valid_data, self.valid_labels = \
load_spm_data_and_labels(self.test_file, split_mode=1)
self.preprocessor = SPMPreprocessor(
(self.train_data[0] + self.valid_data[0],
self.train_data[1] + self.valid_data[1]),
self.train_labels + self.valid_labels,
use_word=True,
use_char=True,
bert_vocab_file=self.bert_vocab_file,
word_embed_type='word2vec',
char_embed_type='word2vec',
max_len=10)
self.num_class = self.preprocessor.num_class
self.char_embeddings = self.preprocessor.char_embeddings
self.char_vocab_size = self.preprocessor.char_vocab_size
self.char_embed_dim = self.preprocessor.char_embed_dim
self.word_embeddings = self.preprocessor.word_embeddings
self.word_vocab_size = self.preprocessor.word_vocab_size
self.word_embed_dim = self.preprocessor.word_embed_dim
self.checkpoint_dir = os.path.dirname(__file__)
self.spm_model = SiameseCNN(
num_class=self.num_class,
use_word=True,
word_embeddings=self.word_embeddings,
word_vocab_size=self.word_vocab_size,
word_embed_dim=self.word_embed_dim,
word_embed_trainable=False,
use_char=True,
char_embeddings=self.char_embeddings,
char_vocab_size=self.char_vocab_size,
char_embed_dim=self.char_embed_dim,
char_embed_trainable=False,
use_bert=False,
bert_config_file=self.bert_config_file,
bert_checkpoint_file=self.bert_model_file,
bert_trainable=True,
max_len=self.preprocessor.max_len,
max_word_len=self.preprocessor.max_word_len).build_model()
self.swa_model = SiameseCNN(
num_class=self.num_class,
use_word=True,
word_embeddings=self.word_embeddings,
word_vocab_size=self.word_vocab_size,
word_embed_dim=self.word_embed_dim,
word_embed_trainable=False,
use_char=True,
char_embeddings=self.char_embeddings,
char_vocab_size=self.char_vocab_size,
char_embed_dim=self.char_embed_dim,
char_embed_trainable=False,
use_bert=False,
bert_config_file=self.bert_config_file,
bert_checkpoint_file=self.bert_model_file,
bert_trainable=True,
max_len=self.preprocessor.max_len,
max_word_len=self.preprocessor.max_word_len).build_model()
self.spm_trainer = SPMTrainer(self.spm_model, self.preprocessor)
self.json_file = os.path.join(self.checkpoint_dir,
'siamese_cnn_spm.json')
self.weights_file = os.path.join(self.checkpoint_dir,
'siamese_cnn_spm.hdf5')
def fit(self,
train_data: Tuple[List[str], List[str]],
train_labels: List[str],
valid_data: Optional[Tuple[List[str], List[str]]] = None,
valid_labels: Optional[List[str]] = None,
spm_model_type: str = 'siamese_cnn',
use_word: bool = True,
external_word_dict: List[str] = None,
word_embed_type: Optional[str] = 'word2vec',
word_embed_dim: int = 300,
word_embed_trainable: bool = True,
use_char: bool = False,
char_embed_type: Optional[str] = 'word2vec',
char_embed_dim: int = 300,
char_embed_trainable: bool = True,
use_bert: bool = False,
bert_vocab_file: Optional[str] = None,
bert_config_file: Optional = None,
bert_checkpoint_file: Optional = None,
bert_trainable: bool = False,
max_len: Optional[int] = None,
max_word_len: Optional[int] = None,
optimizer: Union[str, tf.keras.optimizers.Optimizer] = 'adam',
batch_size: int = 32,
epochs: int = 50,
callback_list: Optional[List[str]] = None,
checkpoint_dir: Optional = None,
model_name: Optional = None,
load_swa_model: bool = False,
**kwargs) -> None:
"""Train spm model using provided data
Args:
train_data: list of untokenized text pairs for training,
like ``[['我是中国人', ...], ['我爱中国', ...]]``
train_labels: labels string of train_data
valid_data: list of untokenized text pairs for evaluation
valid_labels: labels string of valid data
spm_model_type: str, which spm model to use
use_word: boolean, whether to use word embedding as input
external_word_dict: external word dictionary
word_embed_type: str, can be a pre-trained embedding filename or pre-trained embedding
methods (word2vec, glove, fastext)
word_embed_dim: int, dimensionality of word embedding
word_embed_trainable: boolean, whether to update word embedding during training
use_char: boolean, whether to use char as input
char_embed_type: str, similar as 'word_embed_type'
char_embed_dim: int, similar as 'word_embed_dim'
char_embed_trainable: boolean, similar as 'word_embed_trainable'
use_bert: boolean, whether to use bert embedding as input
bert_vocab_file: str, path to bert's vocabulary file
bert_config_file: str, path to bert's configuration file
bert_checkpoint_file: str, path to bert's checkpoint file
bert_trainable: boolean, whether to update bert during training
use_bert_model: boolean, whether to use traditional bert model which combines two sentences
as one input
max_len: int, max sequence length. If None, we dynamically use the max length of one batch
as max_len. However, max_len must be provided when using bert as input.
max_word_len: int, max word length. If None, we dynamically use the max word length of one
batch as max_word_len.
optimizer: str or instance of `keras.optimizers.Optimizer`, indicating the optimizer to
use during training
batch_size: num of samples per gradient update
epochs: num of epochs to train the model
callback_list: list of str, each item indicates the callback to apply during training
Currently, we support using 'modelcheckpoint' for `ModelCheckpoint`
callback, 'earlystopping` for 'Earlystopping` callback, 'swa' for
'SWA' callback. We will automatically add `SPMMetric` callback when
valid_data and valid_labels are both provided.
checkpoint_dir: str, directory to save spm model, must be provided when using
`ModelCheckpoint` or `SWA` callback.
model_name: str, prefix of spm model's weights file must be provided when using
`ModelCheckpoint` or `SWA` callback.
For example, if checkpoint_dir is 'ckpt' and model_name is 'model', the
weights of spm model saved by `ModelCheckpoint` callback will be
'ckpt/model.hdf5' and by `SWA` callback will be 'ckpt/model_swa.hdf5'
load_swa_model: boolean, whether to load swa model, only apply when using SWA Callback.
**kwargs: other argument for building spm model, such as "rnn_units", "fc_dim" etc.
"""
use_bert_model = True if spm_model_type == 'bert' else False
# data preprocessing
self.preprocessor = SPMPreprocessor(
train_data=train_data,
train_labels=train_labels,
use_word=use_word,
use_char=use_char,
use_bert=use_bert,
use_bert_model=use_bert_model,
external_word_dict=external_word_dict,
bert_vocab_file=bert_vocab_file,
char_embed_type=char_embed_type,
char_embed_dim=char_embed_dim,
word_embed_type=word_embed_type,
word_embed_dim=word_embed_dim,
max_len=max_len,
max_word_len=max_word_len)
# build model
self.model = self.get_spm_model(
spm_model_type=spm_model_type,
num_class=self.preprocessor.num_class,
use_word=use_word,
word_embeddings=self.preprocessor.word_embeddings,
word_vocab_size=self.preprocessor.word_vocab_size,
word_embed_dim=self.preprocessor.word_embed_dim,
word_embed_trainable=word_embed_trainable,
use_char=use_char,
char_embeddings=self.preprocessor.char_embeddings,
char_vocab_size=self.preprocessor.char_vocab_size,
char_embed_dim=self.preprocessor.char_embed_dim,
char_embed_trainable=char_embed_trainable,
use_bert=use_bert,
bert_config_file=bert_config_file,
bert_checkpoint_file=bert_checkpoint_file,
bert_trainable=bert_trainable,
max_len=self.preprocessor.max_len,
max_word_len=self.preprocessor.max_word_len,
optimizer=optimizer,
**kwargs)
# build swa model
if 'swa' in callback_list:
swa_model = self.get_spm_model(
spm_model_type=spm_model_type,
num_class=self.preprocessor.num_class,
use_word=use_word,
word_embeddings=self.preprocessor.word_embeddings,
word_vocab_size=self.preprocessor.word_vocab_size,
word_embed_dim=self.preprocessor.word_embed_dim,
word_embed_trainable=word_embed_trainable,
use_char=use_char,
char_embeddings=self.preprocessor.char_embeddings,
char_vocab_size=self.preprocessor.char_vocab_size,
char_embed_dim=self.preprocessor.char_embed_dim,
char_embed_trainable=char_embed_trainable,
use_bert=use_bert,
bert_config_file=bert_config_file,
bert_checkpoint_file=bert_checkpoint_file,
bert_trainable=bert_trainable,
max_len=self.preprocessor.max_len,
max_word_len=self.preprocessor.max_word_len,
optimizer=optimizer,
**kwargs)
else:
swa_model = None
# train model
self.trainer = SPMTrainer(self.model, self.preprocessor)
self.trainer.train_generator(train_data, train_labels, valid_data,
valid_labels, batch_size, epochs,
callback_list, checkpoint_dir, model_name,
swa_model, load_swa_model)
# predict model
self.predictor = SPMPredictor(self.model, self.preprocessor)
if valid_data is not None and valid_labels is not None:
logging.info('Evaluating on validation data...')
self.score(valid_data, valid_labels)
class SPM(object):
"""SPM application"""
def __init__(self, use_pretrained: bool = True) -> None:
self.preprocessor = None
self.model = None
self.trainer = None
self.predictor = None
if use_pretrained:
self.load_pretrained_model()
def fit(self,
train_data: Tuple[List[str], List[str]],
train_labels: List[str],
valid_data: Optional[Tuple[List[str], List[str]]] = None,
valid_labels: Optional[List[str]] = None,
spm_model_type: str = 'siamese_cnn',
use_word: bool = True,
external_word_dict: List[str] = None,
word_embed_type: Optional[str] = 'word2vec',
word_embed_dim: int = 300,
word_embed_trainable: bool = True,
use_char: bool = False,
char_embed_type: Optional[str] = 'word2vec',
char_embed_dim: int = 300,
char_embed_trainable: bool = True,
use_bert: bool = False,
bert_vocab_file: Optional[str] = None,
bert_config_file: Optional = None,
bert_checkpoint_file: Optional = None,
bert_trainable: bool = False,
max_len: Optional[int] = None,
max_word_len: Optional[int] = None,
optimizer: Union[str, tf.keras.optimizers.Optimizer] = 'adam',
batch_size: int = 32,
epochs: int = 50,
callback_list: Optional[List[str]] = None,
checkpoint_dir: Optional = None,
model_name: Optional = None,
load_swa_model: bool = False,
**kwargs) -> None:
"""Train spm model using provided data
Args:
train_data: list of untokenized text pairs for training,
like ``[['我是中国人', ...], ['我爱中国', ...]]``
train_labels: labels string of train_data
valid_data: list of untokenized text pairs for evaluation
valid_labels: labels string of valid data
spm_model_type: str, which spm model to use
use_word: boolean, whether to use word embedding as input
external_word_dict: external word dictionary
word_embed_type: str, can be a pre-trained embedding filename or pre-trained embedding
methods (word2vec, glove, fastext)
word_embed_dim: int, dimensionality of word embedding
word_embed_trainable: boolean, whether to update word embedding during training
use_char: boolean, whether to use char as input
char_embed_type: str, similar as 'word_embed_type'
char_embed_dim: int, similar as 'word_embed_dim'
char_embed_trainable: boolean, similar as 'word_embed_trainable'
use_bert: boolean, whether to use bert embedding as input
bert_vocab_file: str, path to bert's vocabulary file
bert_config_file: str, path to bert's configuration file
bert_checkpoint_file: str, path to bert's checkpoint file
bert_trainable: boolean, whether to update bert during training
use_bert_model: boolean, whether to use traditional bert model which combines two sentences
as one input
max_len: int, max sequence length. If None, we dynamically use the max length of one batch
as max_len. However, max_len must be provided when using bert as input.
max_word_len: int, max word length. If None, we dynamically use the max word length of one
batch as max_word_len.
optimizer: str or instance of `keras.optimizers.Optimizer`, indicating the optimizer to
use during training
batch_size: num of samples per gradient update
epochs: num of epochs to train the model
callback_list: list of str, each item indicates the callback to apply during training
Currently, we support using 'modelcheckpoint' for `ModelCheckpoint`
callback, 'earlystopping` for 'Earlystopping` callback, 'swa' for
'SWA' callback. We will automatically add `SPMMetric` callback when
valid_data and valid_labels are both provided.
checkpoint_dir: str, directory to save spm model, must be provided when using
`ModelCheckpoint` or `SWA` callback.
model_name: str, prefix of spm model's weights file must be provided when using
`ModelCheckpoint` or `SWA` callback.
For example, if checkpoint_dir is 'ckpt' and model_name is 'model', the
weights of spm model saved by `ModelCheckpoint` callback will be
'ckpt/model.hdf5' and by `SWA` callback will be 'ckpt/model_swa.hdf5'
load_swa_model: boolean, whether to load swa model, only apply when using SWA Callback.
**kwargs: other argument for building spm model, such as "rnn_units", "fc_dim" etc.
"""
use_bert_model = True if spm_model_type == 'bert' else False
# data preprocessing
self.preprocessor = SPMPreprocessor(
train_data=train_data,
train_labels=train_labels,
use_word=use_word,
use_char=use_char,
use_bert=use_bert,
use_bert_model=use_bert_model,
external_word_dict=external_word_dict,
bert_vocab_file=bert_vocab_file,
char_embed_type=char_embed_type,
char_embed_dim=char_embed_dim,
word_embed_type=word_embed_type,
word_embed_dim=word_embed_dim,
max_len=max_len,
max_word_len=max_word_len)
# build model
self.model = self.get_spm_model(
spm_model_type=spm_model_type,
num_class=self.preprocessor.num_class,
use_word=use_word,
word_embeddings=self.preprocessor.word_embeddings,
word_vocab_size=self.preprocessor.word_vocab_size,
word_embed_dim=self.preprocessor.word_embed_dim,
word_embed_trainable=word_embed_trainable,
use_char=use_char,
char_embeddings=self.preprocessor.char_embeddings,
char_vocab_size=self.preprocessor.char_vocab_size,
char_embed_dim=self.preprocessor.char_embed_dim,
char_embed_trainable=char_embed_trainable,
use_bert=use_bert,
bert_config_file=bert_config_file,
bert_checkpoint_file=bert_checkpoint_file,
bert_trainable=bert_trainable,
max_len=self.preprocessor.max_len,
max_word_len=self.preprocessor.max_word_len,
optimizer=optimizer,
**kwargs)
# build swa model
if 'swa' in callback_list:
swa_model = self.get_spm_model(
spm_model_type=spm_model_type,
num_class=self.preprocessor.num_class,
use_word=use_word,
word_embeddings=self.preprocessor.word_embeddings,
word_vocab_size=self.preprocessor.word_vocab_size,
word_embed_dim=self.preprocessor.word_embed_dim,
word_embed_trainable=word_embed_trainable,
use_char=use_char,
char_embeddings=self.preprocessor.char_embeddings,
char_vocab_size=self.preprocessor.char_vocab_size,
char_embed_dim=self.preprocessor.char_embed_dim,
char_embed_trainable=char_embed_trainable,
use_bert=use_bert,
bert_config_file=bert_config_file,
bert_checkpoint_file=bert_checkpoint_file,
bert_trainable=bert_trainable,
max_len=self.preprocessor.max_len,
max_word_len=self.preprocessor.max_word_len,
optimizer=optimizer,
**kwargs)
else:
swa_model = None
# train model
self.trainer = SPMTrainer(self.model, self.preprocessor)
self.trainer.train_generator(train_data, train_labels, valid_data,
valid_labels, batch_size, epochs,
callback_list, checkpoint_dir, model_name,
swa_model, load_swa_model)
# predict model
self.predictor = SPMPredictor(self.model, self.preprocessor)
if valid_data is not None and valid_labels is not None:
logging.info('Evaluating on validation data...')
self.score(valid_data, valid_labels)
def score(self, valid_data: Tuple[List[str], List[str]],
valid_labels: List[str]) -> float:
"""Return the f1 score of the model over validation data
Args:
valid_data: list of untokenized text pairs
valid_labels: list of label strings
Returns:
"""
if self.trainer:
return self.trainer.evaluate(valid_data, valid_labels)
else:
logging.fatal(
'Trainer is None! Call fit() or load() to get trainer.')
def predict(self, test_text: Tuple[str, str]) -> str:
"""Return prediction of the model for test data
Args:
test_text: a pair of untokenized text
Returns:
"""
if self.predictor:
return self.predictor.matching(test_text)
else:
logging.fatal(
'Predictor is None! Call fit() or load() to get predictor.')
def predict_batch(self, test_texts: Tuple[List[str],
List[str]]) -> List[str]:
"""Return predictions of the model for test data
Args:
test_texts: list of untokenized text pairs
Returns:
"""
if self.predictor:
return self.predictor.matching_batch(test_texts)
else:
logging.fatal(
'Predictor is None! Call fit() or load() to get predictor.')
def analyze(self, text: Tuple[str, str]) -> Tuple[str, np.ndarray]:
"""Analyze text and return matching result with probability.
Args:
text: a pair of untokenized text
Returns:
"""
if self.predictor:
return self.predictor.matching_with_prob(text)
else:
logging.fatal(
'Predictor is None! Call fit() or load() to get predictor.')
def analyze_batch(
self, texts: Tuple[List[str],
List[str]]) -> List[Tuple[str, np.ndarray]]:
"""Analyze text and return matching result with probability.
Args:
texts: list of untokenized text pairs
Returns:
"""
if self.predictor:
return self.predictor.matching_with_prob_batch(texts)
else:
logging.fatal(
'Predictor is None! Call fit() or load() to get predictor.')
def save(self,
preprocessor_file: str,
json_file: str,
weights_file: Optional[str] = None) -> None:
"""save spm application
Args:
preprocessor_file: path to save preprocessor
json_file: path to save model architecture
weights_file: path to save model weights, can be None. When we use `ModelCheckpoint`
or `SWA` callback, model's weights will be saved to disk after training.
In that case, we don't need to save it again. We usually set weights_file
to be None.
"""
self.preprocessor.save(preprocessor_file)
logging.info('Save preprocessor to {}'.format(preprocessor_file))
model_json = self.model.to_json()
with open(json_file, 'w') as writer:
writer.write(model_json)
logging.info('Save model architecture to {}'.format(json_file))
if weights_file:
self.model.save_weights(weights_file)
logging.info('Save model weights to {}'.format(weights_file))
def load(self,
preprocessor_file: str,
json_file: str,
weights_file: str,
custom_objects: Optional[Dict[str, Any]] = None) -> None:
"""load spm application
Args:
preprocessor_file: path to load preprocessor
json_file: path to load model architecture
weights_file: path to load model weights
custom_objects: Optional dictionary mapping names (strings) to custom classes or
functions to be considered during deserialization. Must provided when
using custom layer.
"""
self.preprocessor = SPMPreprocessor.load(preprocessor_file)
logging.info('Load preprocessor from {}'.format(preprocessor_file))
custom_objects = custom_objects or {}
custom_objects.update(get_custom_objects())
with open(json_file, 'r') as reader:
self.model = tf.keras.models.model_from_json(
reader.read(), custom_objects=custom_objects)
logging.info('Load model architecture from {}'.format(json_file))
self.model.load_weights(weights_file)
logging.info('Load model weight from {}'.format(weights_file))
self.trainer = SPMTrainer(self.model, self.preprocessor)
self.predictor = SPMPredictor(self.model, self.preprocessor)
@staticmethod
def get_spm_model(spm_model_type: str, num_class: int, use_word: bool,
word_embeddings: Optional[np.ndarray],
word_vocab_size: int, word_embed_dim: int,
word_embed_trainable: bool, use_char: bool,
char_embeddings: Optional[np.ndarray],
char_vocab_size: int, char_embed_dim: int,
char_embed_trainable: bool, use_bert: bool,
bert_config_file: Optional[str],
bert_checkpoint_file: Optional[str],
bert_trainable: bool, max_len: Optional[int],
max_word_len: Optional[int],
optimizer: Union[str, tf.keras.optimizers.Optimizer],
**kwargs) -> tf.keras.models.Model:
"""build spm models by model_type
Args:
spm_model_type: str, which spm model to use
num_class: int: the number of classification class
use_word: boolean, whether to use word embedding as input
word_embeddings: np.ndarray, word embeddings
word_vocab_size: int, the number of words in vocabulary
word_embed_dim: int, dimensionality of word embedding
word_embed_trainable: boolean, whether to update word embedding during training
use_char: boolean, whether to use char as input
char_embeddings: ndarray, char_embeddings
char_vocab_size: int, the number of chars in vocabulary
char_embed_dim: int, dimensionality of char embedding
char_embed_trainable: boolean, similar as 'word_embed_trainable'
use_bert: boolean, whether to use bert embedding as input
bert_config_file: str, path to bert's configuration file
bert_checkpoint_file: str, path to bert's checkpoint file
bert_trainable: boolean, whether to update bert during training
max_len: int, max sequence length. If None, we dynamically use the max length of one batch
as max_len. However, max_len must be provided when using bert as input.
max_word_len: int, max word length. If None, we dynamically use the max word length of one
batch as max_word_len.
optimizer: str or instance of `keras.optimizers.Optimizer`, indicating the optimizer to
use during training
**kwargs: other argument for building spm model, such as "rnn_units", "fc_dim" etc.
"""
spm_model_all = {
'siamese_cnn': SiameseCNN,
'siamese_bilstm': SiameseBiLSTM,
'siamese_bigru': SiameseBiGRU,
'esim': ESIM,
'bimpm': BiMPM,
'bert': BertSPM
}
if spm_model_type in spm_model_all:
spm_model = spm_model_all[spm_model_type](
num_class=num_class,
use_word=use_word,
word_embeddings=word_embeddings,
word_vocab_size=word_vocab_size,
word_embed_dim=word_embed_dim,
word_embed_trainable=word_embed_trainable,
use_char=use_char,
char_embeddings=char_embeddings,
char_vocab_size=char_vocab_size,
char_embed_dim=char_embed_dim,
char_embed_trainable=char_embed_trainable,
use_bert=use_bert,
bert_config_file=bert_config_file,
bert_checkpoint_file=bert_checkpoint_file,
bert_trainable=bert_trainable,
max_len=max_len,
max_word_len=max_word_len,
optimizer=optimizer,
**kwargs)
else:
raise ValueError(
'`spm_model_type` not understood: {}'.format(spm_model_type))
return spm_model.build_model()
def load_pretrained_model(self) -> None:
cache_subdir = 'pretrained_models'
preprocessor_file = tf.keras.utils.get_file(
fname='webank_spm_siamese_cnn_word_preprocessor.pkl',
origin=MODEL_STORAGE_PREFIX +
'webank_spm_siamese_cnn_word_preprocessor.pkl',
cache_subdir=cache_subdir,
cache_dir=CACHE_DIR)
json_file = tf.keras.utils.get_file(
fname='webank_spm_siamese_cnn_word.json',
origin=MODEL_STORAGE_PREFIX + 'webank_spm_siamese_cnn_word.json',
cache_subdir=cache_subdir,
cache_dir=CACHE_DIR)
weights_file = tf.keras.utils.get_file(
fname='webank_spm_siamese_cnn_word.hdf5',
origin=MODEL_STORAGE_PREFIX + 'webank_spm_siamese_cnn_word.hdf5',
cache_subdir=cache_subdir,
cache_dir=CACHE_DIR)
self.load(preprocessor_file, json_file, weights_file)