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()