training_zh_songnet_demo.py

# -*- coding: utf-8 -*-
"""
@author:XuMing(xuming624@qq.com)
@description:
"""
import os
import argparse
from loguru import logger
import sys

sys.path.append('../..')
from textgen.language_modeling import SongNetModel


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--train_file', default='../data/zh_songci.txt', type=str, help='Training data file')
    parser.add_argument('--test_file', default='../data/zh_songci.txt', type=str, help='Test data file')
    parser.add_argument('--model_type', default='songnet', type=str, help='Transformers model type')
    parser.add_argument('--model_name', default='shibing624/songnet-base-chinese', type=str,
                        help='SongNet model or path')  # SongNet pretrained model
    parser.add_argument('--do_train', action='store_true', help='Whether to run training.')
    parser.add_argument('--do_predict', action='store_true', help='Whether to run predict.')
    parser.add_argument('--output_dir', default='./outputs/songci_zh_songnet_finetuned/', type=str,
                        help='Model output directory')
    parser.add_argument('--max_length', default=128, type=int, help='Max input text length')
    parser.add_argument('--num_epochs', default=3, type=int, help='Number of training epochs')
    parser.add_argument('--batch_size', default=16, type=int, help='Batch size')
    args = parser.parse_args()
    print(args)

    if args.do_train:
        logger.info('Training...')
        model_args = {
            "reprocess_input_data": True,
            "overwrite_output_dir": True,
            "max_length": args.max_length,
            "train_batch_size": args.batch_size,
            "num_train_epochs": args.num_epochs,
            "save_eval_checkpoints": False,
            "save_model_every_epoch": False,
            "save_optimizer_and_scheduler": True,
            "evaluate_generated_text": True,
            "evaluate_during_training": True,
            "evaluate_during_training_verbose": True,
            "use_multiprocessing": False,
            "save_best_model": True,
            "output_dir": args.output_dir,
            "best_model_dir": os.path.join(args.output_dir, "best_model"),
            "use_early_stopping": True,
        }
        model = SongNetModel(
            model_type=args.model_type,
            model_name=args.model_name,
            args=model_args
        )
        logger.info(f"{model.tokenizer}, {model.args}")
        model.train_model(args.train_file, eval_file=args.test_file)
        print(model.eval_model(args.test_file))

    if args.do_predict:
        # Use fine-tuned model
        model = SongNetModel(model_type=args.model_type, model_name=args.output_dir)
        sentences = [
            "严蕊<s1>如梦令<s2>道是梨花不是。</s>道是杏花不是。</s>白白与红红，别是东风情味。</s>曾记。</s>曾记。</s>人在武陵微醉。",
            "张抡<s1>春光好<s2>烟澹澹，雨。</s>水溶溶。</s>帖水落花飞不起，小桥东。</s>翩翩怨蝶愁蜂。</s>绕芳丛。</s>恋馀红。</s>不恨无情桥下水，恨东风。"
        ]
        print("inputs:", sentences)
        print("outputs:", model.generate(sentences))
        sentences = [
            "秦湛<s1>卜算子<s2>_____，____到。_______，____俏。_____，____报。_______，____笑。",
            "秦湛<s1>卜算子<s2>_雨___，____到。______冰，____俏。____春，__春_报。__山花___，____笑。"
        ]
        print("inputs:", sentences)
        print("outputs:", model.fill_mask(sentences))


if __name__ == '__main__':
    main()