def load_model(path):
"""
Load an existing SequenceTagger model (with weights) from HDF5 file.
:param path: String. The path to the pre-defined model.
:return: NER.
"""
labor = chunker.SequenceTagger(use_cudnn=False)
model = TextKerasModel._load_model(labor, path)
model.__class__ = SequenceTagger
return model
def __init__(self,
num_pos_labels,
num_chunk_labels,
word_vocab_size,
char_vocab_size=None,
word_length=12,
feature_size=100,
dropout=0.2,
classifier='softmax',
optimizer=None):
classifier = classifier.lower()
assert classifier in ['softmax', 'crf'
], "classifier should be either softmax or crf"
super(SequenceTagger,
self).__init__(chunker.SequenceTagger(use_cudnn=False),
vocabulary_size=word_vocab_size,
num_pos_labels=num_pos_labels,
num_chunk_labels=num_chunk_labels,
char_vocab_size=char_vocab_size,
max_word_len=word_length,
feature_size=feature_size,
dropout=dropout,
classifier=classifier,
optimizer=optimizer)
def load_model(path):
labor = chunker.SequenceTagger(use_cudnn=False)
model = TextKerasModel._load_model(labor, path)
model.__class__ = SequenceTagger
return model