readme: ---
  language: zh
  datasets: CLUECorpusSmall
  widget: 
  - text: "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。"
  
  
  ---
  
  # Chinese Pegasus
  
  ## Model description
  
  This model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658).
  
  You can download the set of Chinese PEGASUS models either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the links below:
  
  |                   |              Link              |
  | ----------------- | :----------------------------: |
  | **PEGASUS-Base** | [**L=12/H=768 (Base)**][base] |
  | **PEGASUS-Large**  | [**L=16/H=1024 (Large)**][large] |
  
  ## How to use
  
  You can use this model directly with a pipeline for text2text generation (take the case of PEGASUS-Base):
  
  ```python
  >>> 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](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data.
  
  ## Training procedure
  
  The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). 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.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 cluecorpussmall_pegasus_base_seq512_model.bin-250000 \                                                                
                                                             --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}
  }
  ```
  
  [base]:https://huggingface.co/uer/pegasus-base-chinese-cluecorpussmall
  [large]:https://huggingface.co/uer/pegasus-large-chinese-cluecorpussmall