def test_siamese_bilstm_model(self):
# word, char
spm_model = SiameseBiLSTM(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,
max_len=10).build_model()
# char, bert
spm_model = SiameseBiLSTM(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=10).build_model()
# test save and load
json_file = os.path.join(self.checkpoint_dir,
'siamese_bilstm_spm.json')
weights_file = os.path.join(self.checkpoint_dir,
'siamese_bilstm_spm.hdf5')
save_keras_model(spm_model, json_file, weights_file)
assert os.path.exists(json_file)
assert os.path.exists(weights_file)
load_keras_model(json_file,
weights_file,
custom_objects=get_custom_objects())
os.remove(json_file)
os.remove(weights_file)
assert not os.path.exists(json_file)
assert not os.path.exists(weights_file)
def test_bert_model(self):
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=10).build_model()
# test save and load
json_file = os.path.join(self.checkpoint_dir, 'bert_spm.json')
weights_file = os.path.join(self.checkpoint_dir, 'bert_spm.hdf5')
save_keras_model(spm_model, json_file, weights_file)
assert os.path.exists(json_file)
assert os.path.exists(weights_file)
load_keras_model(json_file,
weights_file,
custom_objects=get_custom_objects())
os.remove(json_file)
os.remove(weights_file)
assert not os.path.exists(json_file)
assert not os.path.exists(weights_file)
def test_save(self):
save_keras_model(self.model, self.test_json_file,
self.test_weights_file)
def test_bilstm_cnn_model(self):
# char, no CRF, no word input
ner_model = BiLSTMCNNNER(num_class=self.num_class,
char_embeddings=self.char_embeddings,
char_vocab_size=self.char_vocab_size,
char_embed_dim=self.char_embed_dim,
char_embed_trainable=False,
use_word=False,
use_crf=False).build_model()
# char, CRF, no word, no bert input
ner_model = BiLSTMCNNNER(num_class=self.num_class,
char_embeddings=self.char_embeddings,
char_vocab_size=self.char_vocab_size,
char_embed_dim=self.char_embed_dim,
char_embed_trainable=False,
use_word=False,
use_crf=True).build_model()
# char, CRF, word, no bert input
ner_model = BiLSTMCNNNER(num_class=self.num_class,
char_embeddings=self.char_embeddings,
char_vocab_size=self.char_vocab_size,
char_embed_dim=self.char_embed_dim,
char_embed_trainable=False,
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_crf=True).build_model()
# char, CRF, word, bert
ner_model = BiLSTMCNNNER(num_class=self.num_class,
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,
bert_trainable=True,
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,
max_len=16,
use_crf=True).build_model()
# test save and load
json_file = os.path.join(self.checkpoint_dir, 'bilstm_cnn_ner.json')
weights_file = os.path.join(self.checkpoint_dir, 'bilstm_cnn_ner.hdf5')
save_keras_model(ner_model, json_file, weights_file)
assert os.path.exists(json_file)
assert os.path.exists(weights_file)
load_keras_model(json_file,
weights_file,
custom_objects=get_custom_objects())
os.remove(json_file)
os.remove(weights_file)
assert not os.path.exists(json_file)
assert not os.path.exists(weights_file)
