readme: ---
  language: 
    - zh
  license: apache-2.0
  widget:
  - text: "生活的真谛是[MASK]。"
  ---
  
  
  # Mengzi-BERT base model (Chinese)
  
  Pretrained model on 300G Chinese corpus. Masked language modeling(MLM), part-of-speech(POS) tagging and sentence order prediction(SOP) are used as training task.
  
  [Mengzi: A lightweight yet Powerful Chinese Pre-trained Language Model](https://arxiv.org/abs/2110.06696)
  
  ## Usage
  
  ```python
  from transformers import BertTokenizer, BertModel
  tokenizer = BertTokenizer.from_pretrained("Langboat/mengzi-bert-base")
  model = BertModel.from_pretrained("Langboat/mengzi-bert-base")
  ```
  
  ## Scores on nine chinese tasks (without any data augmentation)
  | Model | AFQMC | TNEWS | IFLYTEK | CMNLI | WSC | CSL | CMRC2018 | C3 | CHID |
  |-|-|-|-|-|-|-|-|-|-|
  |RoBERTa-wwm-ext| 74.30 | 57.51 | 60.80 | 80.70 | 67.20 | 80.67 | 77.59 | 67.06 | 83.78 |
  |Mengzi-BERT-base| 74.58 | 57.97 | 60.68 | 82.12 | 87.50 | 85.40 | 78.54 | 71.70 | 84.16 |
  
  RoBERTa-wwm-ext scores are from CLUE baseline
  
  
  ## Citation
  If you find the technical report or resource is useful, please cite the following technical report in your paper.
  ```
  @misc{zhang2021mengzi,
        title={Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese}, 
        author={Zhuosheng Zhang and Hanqing Zhang and Keming Chen and Yuhang Guo and Jingyun Hua and Yulong Wang and Ming Zhou},
        year={2021},
        eprint={2110.06696},
        archivePrefix={arXiv},
        primaryClass={cs.CL}
  }
  ```