readme: ---
  license: afl-3.0
  ---
  **Please use 'Bert' related tokenizer classes and 'Nezha' related model classes**
  
  [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204)
  Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
  
  The original checkpoints can be found [here](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/NEZHA-PyTorch)
  
  ## Example Usage
  
  ```
  from transformers import BertTokenizer, NezhaModel
  tokenizer = BertTokenizer.from_pretrained('sijunhe/nezha-cn-base')
  model = NezhaModel.from_pretrained("sijunhe/nezha-cn-base")
  text = "我爱北京天安门"
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  ```