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'))