pegasus-large-chinese-cluecorpussmall

Chinese Pegasus

Model description

This model is pre-trained by UER-py, which is introduced in this paper. Besides, the models could also be pre-trained by TencentPretrain introduced in this paper, which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework.

You can download the set of Chinese PEGASUS models either from the UER-py Modelzoo page, or via HuggingFace from the links below:

How to use

You can use this model directly with a pipeline for text2text generation (take the case of PEGASUS-Base):

>>> from transformers import BertTokenizer, PegasusForConditionalGeneration, Text2TextGenerationPipeline
>>> tokenizer = BertTokenizer.from_pretrained("uer/pegasus-base-chinese-cluecorpussmall")
>>> model = PegasusForConditionalGeneration.from_pretrained("uer/pegasus-base-chinese-cluecorpussmall")
>>> text2text_generator = Text2TextGenerationPipeline(model, tokenizer)  
>>> text2text_generator("内容丰富、版式设计考究、图片华丽、印制精美。[MASK]纸箱内还放了充气袋用于保护。", max_length=50, do_sample=False)
    [{'generated_text': '书 的 质 量 很 好 。'}]

Training data

CLUECorpusSmall is used as training data.

Training procedure

The model is pre-trained by UER-py on Tencent Cloud. We pre-train 1,000,000 steps with a sequence length of 512. Taking the case of PEGASUS-Base

python3 preprocess.py --corpus_path corpora/cluecorpussmall_bert.txt \
                      --vocab_path models/google_zh_vocab.txt \
                      --dataset_path cluecorpussmall_pegasus_seq512_dataset.pt \
                      --processes_num 32 --seq_length 512 \
                      --data_processor gsg --sentence_selection_strategy random

python3 pretrain.py --dataset_path cluecorpussmall_pegasus_seq512_dataset.pt \
                    --vocab_path models/google_zh_vocab.txt \
                    --config_path models/pegasus/base_config.json \
                    --output_model_path models/cluecorpussmall_pegasus_base_seq512_model.bin \
                    --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
                    --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
                    --learning_rate 1e-4 --batch_size 8

Finally, we convert the pre-trained model into Huggingface's format:

python3 scripts/convert_pegasus_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_pegasus_base_seq512_model.bin-1000000 \                                                             
                                                           --output_model_path pytorch_model.bin \                                           
                                                           --layers_num 12

BibTeX entry and citation info

@inproceedings{zhang2020pegasus,
  title={Pegasus: Pre-training with extracted gap-sentences for abstractive summarization},
  author={Zhang, Jingqing and Zhao, Yao and Saleh, Mohammad and Liu, Peter},
  booktitle={International Conference on Machine Learning},
  pages={11328--11339},
  year={2020},
  organization={PMLR}
}

@article{zhao2019uer,
  title={UER: An Open-Source Toolkit for Pre-training Models},
  author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
  journal={EMNLP-IJCNLP 2019},
  pages={241},
  year={2019}
}

@article{zhao2023tencentpretrain,
  title={TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities},
  author={Zhao, Zhe and Li, Yudong and Hou, Cheng and Zhao, Jing and others},
  journal={ACL 2023},
  pages={217},
  year={2023}