readme: ---
  language: Chinese
  datasets: CLUECorpusSmall
  widget: 
  - text: "作为电子[MASK]的平台，京东绝对是领先者。如今的刘强[MASK]已经是身价过[MASK]的老板。"
  
  
  ---
  
  # Chinese BART
  
  ## Model description
  
  This model is pre-trained by [UER-py](https://arxiv.org/abs/1909.05658).
  
  ## How to use
  
  You can use this model directly with a pipeline for text2text generation :
  
  ```python
  >>> from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline
  >>> tokenizer = BertTokenizer.from_pretrained("uer/bart-chinese-6-960-cluecorpussmall")
  >>> model = BartForConditionalGeneration.from_pretrained("uer/bart-chinese-6-960-cluecorpussmall")
  >>> text2text_generator = Text2TextGenerationPipeline(model, tokenizer)  
  >>> text2text_generator("中国的首都是[MASK]京", max_length=50, do_sample=False)
      [{'generated_text': '中 国 的 首 都 是 北 京'}]
  ```
  
  ## Training data
  
  [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) Common Crawl and some short messages are 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.
  
  
  we convert the pre-trained model into Huggingface's format:
  
  ```
  python3 scripts/convert_bart_from_uer_to_huggingface.py --input_model_path cluecorpussmall_bart_medium_seq512_model.bin-250000 \                                                                
                                                          --output_model_path pytorch_model.bin \                                                                                            
                                                          --layers_num 6
  ```
  
  
  ### BibTeX entry and citation info
  
  ```
  @article{lewis2019bart,
    title={Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension},
    author={Lewis, Mike and Liu, Yinhan and Goyal, Naman and Ghazvininejad, Marjan and Mohamed, Abdelrahman and Levy, Omer and Stoyanov, Ves and Zettlemoyer, Luke},
    journal={arXiv preprint arXiv:1910.13461},
    year={2019}
  }
  
  @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}
  }
  ```