**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-large-wwm")
model = NezhaModel.from_pretrained("sijunhe/nezha-large-wwm")
text = "我爱北京天安门"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
{"license": "afl-3.0"}
transformers pytorch nezha fill-mask arxiv:1909.00204 license:afl-3.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #nezha #fill-mask #arxiv-1909.00204 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 

Please use 'Bert' related tokenizer classes and 'Nezha' related model classes

NEZHA: Neural Contextualized Representation for Chinese Language Understanding
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

## Example Usage


## Example Usage
TAGS
#transformers #pytorch #nezha #fill-mask #arxiv-1909.00204 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## Example Usage
passage: TAGS
#transformers #pytorch #nezha #fill-mask #arxiv-1909.00204 #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
## Example Usage