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
from transformers import BertTokenizer, NezhaModel
tokenizer = BertTokenizer.from_pretrained("sijunhe/nezha-base-wwm")
model = NezhaModel.from_pretrained("sijunhe/nezha-base-wwm")
text = "我爱北京天安门"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
- Downloads last month
- 10
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.