else:
print("%s: time:%.2fs, speed:%.2fst/s; acc: %.4f" %
(name, time_cost, speed, acc))
return pred_results, pred_scores
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Tuning with NCRF++')
# parser.add_argument('--status', choices=['train', 'decode'], help='update algorithm', default='train')
parser.add_argument('--config', help='Configuration File')
args = parser.parse_args()
data = Data()
use_gpu = torch.cuda.is_available() and data.HP_gpu
data.HP_device = torch.device("cuda" if use_gpu else "cpu")
data.read_config(args.config)
status = data.status.lower()
print("Seed num:", seed_num)
if status == 'train':
print("MODEL: train")
data_initialization(data)
data.generate_instance('train')
data.generate_instance('dev')
data.generate_instance('test')
data.build_pretrain_emb()
train(data)
elif status == 'decode':
print("MODEL: decode")
data.load(data.dset_dir)
parser.add_argument('--seg', default="True")
parser.add_argument('--raw')
parser.add_argument('--loadmodel')
parser.add_argument('--output')
args = parser.parse_args()
data = Data()
data.train_dir = args.train
data.dev_dir = args.dev
data.test_dir = args.test
data.model_dir = args.savemodel
data.dset_dir = args.savedset
print("aaa", data.dset_dir)
status = args.status.lower()
save_model_dir = args.savemodel
data.HP_device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
print("Seed num:", seed_num)
data.number_normalized = True
data.word_emb_dir = "../data/glove.6B.100d.txt"
if status == 'train':
print("MODEL: train")
data_initialization(data)
data.use_char = True
data.HP_batch_size = 10
data.HP_lr = 0.015
data.char_seq_feature = "CNN"
data.generate_instance('train')
data.generate_instance('dev')
data.generate_instance('test')
data.build_pretrain_emb()
