sijunhe/nezha-cn-base

Model

12

stars

4

commits

2

linked in READMEs

Jun 24, 2022

updated

endpoints_compatible
fill-mask
nezha
pytorch
transformers

README

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-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)

Contributors

sijunhe

4 commits

sijunhe/nezha-cn-base

Model

12

stars

4

commits

2

linked in READMEs

Jun 24, 2022

updated

endpoints_compatible
fill-mask
nezha
pytorch
transformers

README

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-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)

Contributors

sijunhe

4 commits