import bert_classifier from utils import config from utils import data_repairer if config.need_repair: data_repairer.repair_train_data() cm = bert_classifier.ClassificationModel(gpu=True, seed=0) if config.load_frompretrain != "None": cm.load_model(config.model_state_path, config.model_config_path) else: cm.new_model() # cm.save_model(config.save_path + '/model',config.save_path + '/config') cm.train(config.epochs, config.batch_size, config.lr, config.plot_path, config.save_path + '/model', config.save_path + '/config') cm.create_test_predictions("./pred.csv")
import bert_classifier from utils import config config.USE_GPU = False cm = bert_classifier.ClassificationModel(gpu=False, seed=0) if config.load_frompretrain == True: cm.load_model(config.model_state_path, config.model_config_path) else: cm.new_model() # cm.save_model(config.save_path + '/model',config.save_path + '/config') cm.train(config.epochs, config.batch_size, config.lr, config.plot_path, config.save_path + '/model', config.save_path + 'config') cm.create_test_predictions("./pred.csv") if __name__ == '__main__': print('running') config.USE_GPU = False