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(