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README.md

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 license: afl-3.0
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+**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-large')
+model = NezhaModel.from_pretrained("sijunhe/nezha-cn-large")
+text = "我爱北京天安门"
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```