def test_good_entity(self):
self.assertIs(calltip.get_entity('int'), int)
def test_bad_entity(self):
self.assertIsNone(calltip.get_entity('1/0'))
def test_good_entity(self):
self.assertIs(calltip.get_entity('int'), int)
dropout_keep=args.dropout, optimizer=args.optimizer, lr=args.lr, clip_grad=args.clip, tag2label=tag2label,
vocab=word2id, shuffle=args.shuffle, model_path=ckpt_file, summary_path=summary_path, CRF=args.CRF,
update_embedding=args.update_embedding)
model.build_graph()
print('test data: {}'.format(test_size))
model.test(test_data)
elif args.mode == 'demo':
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
model = BiLSTM_CRF(batch_size=args.batch_size, epoch_num=args.epoch, hidden_dim=args.hidden_dim, embeddings=embeddings,
dropout_keep=args.dropout, optimizer=args.optimizer, lr=args.lr, clip_grad=args.clip, tag2label=tag2label,
vocab=word2id, shuffle=args.shuffle, model_path=ckpt_file, summary_path=summary_path, CRF=args.CRF,
update_embedding=args.update_embeddings)
model.build_graph()
saver = tf.train.Saver()
with tf.Session as sess:
saver.restore(sess, ckpt_file)
while True:
print('Please input your sentence:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
PER, LOC, ORG = get_entity(tag, demo_sent)
print('PER: {}\nLOC: {}\nORG: {}'.format(PER, LOC, ORG))
def test_bad_entity(self):
self.assertIsNone(calltip.get_entity('1/0'))
