NEZHA: Neural Contextualized Representation for Chinese Language Understanding
Paper • 1909.00204 • Published
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)
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