report = classification_report(actual_val.values.astype( int), np.array(merged_preds_val_list)) print(report) prediction_dict_test, merged_preds_test, embs_test = evaluate_on_set( test_generator, predictor, emb_gen=args.freeze_bert, c_val=opt_c) if args.output_train_stats: prediction_dict_train, merged_preds_train, embs_train = evaluate_on_set( training_generator, predictor, emb_gen=args.freeze_bert, c_val=opt_c) else: merged_preds_train, embs_train = {}, {} # save predictor json.dump(predictor_params, open(os.path.join( args.output_dir, 'predictor_params.json'), 'w')) torch.save(predictor.state_dict(), os.path.join( args.output_dir, 'predictor.pt')) # save model if not args.freeze_bert: model_to_save = model.module if hasattr( model, 'module') else model # Only save the model it-self output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME) output_config_file = os.path.join(args.output_dir, CONFIG_NAME) torch.save(model_to_save.state_dict(), output_model_file) model_to_save.config.to_json_file(output_config_file) tokenizer.save_vocabulary(args.output_dir) # save args json.dump(vars(args), open(os.path.join( args.output_dir, 'argparse_args.json'), 'w'))
report = classification_report(actual_val.values.astype(int), np.array(merged_preds_val_list)) print(report) prediction_dict_test, merged_preds_test, embs_test = evaluate_on_set( test_generator, predictor, emb_gen=args.freeze_bert, c_val=opt_c) if args.output_train_stats: prediction_dict_train, merged_preds_train, embs_train = evaluate_on_set( training_generator, predictor, emb_gen=args.freeze_bert, c_val=opt_c) else: merged_preds_train, embs_train = {}, {} # save predictor json.dump(predictor_params, open(os.path.join(args.output_dir, 'predictor_params.json'), 'w')) torch.save(predictor.state_dict(), os.path.join(args.output_dir, 'predictor.pt')) # save model if not args.freeze_bert: model_to_save = model.module if hasattr( model, 'module') else model # Only save the model it-self output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME) output_config_file = os.path.join(args.output_dir, CONFIG_NAME) torch.save(model_to_save.state_dict(), output_model_file) model_to_save.config.to_json_file(output_config_file) tokenizer.save_vocabulary(args.output_dir) # save args json.dump(vars(args), open(os.path.join(args.output_dir, 'argparse_args.json'), 'w'))