config = Config()
config.optim = 'Adam'
config.lr = 0.015
config.hidden_dim = 200
config.bid_flag = True
config.number_normalized = False
data_initialization(config, train_file, test_file)
config.gpu = gpu
config.word_features = name
print('Word features: ', config.word_features)
config.generate_instance(train_file, 'train')
# config.generate_instance(dev_file, 'dev')
config.generate_instance(test_file, 'test')
if emb_file:
print('load word emb file...norm: ', config.norm_word_emb)
config.build_word_pretain_emb(emb_file)
# if char_emb_file != 'none':
# print('load char emb file...norm: ', config.norm_char_emb)
# config.build_char_pretrain_emb(char_emb_file)
name = 'intelligence_train_all_bio'
train(config, name, dset_dir, save_model_dir, seg)
elif status == 'test':
data = load_data_setting(dset_dir)
# data.generate_instance(dev_file, 'dev')
# load_model_decode(model_dir, data, 'dev', gpu, seg)
data.generate_instance(test_file, 'test')
load_model_decode(model_dir, data, 'test', gpu, seg)
elif status == 'decode':
data = load_data_setting(dset_dir)
data.generate_instance(raw_file, 'raw')
decode_results, gold_results = load_model_decode(
