Based on
chinese-roberta-wwm-ext-large
, we fine-tuned an NLI version on 4 Chinese Natural Language Inference (NLI) datasets, with totaling 1,014,787 samples.
下游效果 Performance
模型 Model
cmnli
ocnli
snli
Erlangshen-Roberta-110M-NLI
80.83
78.56
88.01
Erlangshen-Roberta-330M-NLI
82.25
79.82
88
Erlangshen-MegatronBert-1.3B-NLI
84.52
84.17
88.67
使用 Usage
from transformers import BertForSequenceClassification
from transformers import BertTokenizer
import torch
tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-330M-NLI')
model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-330M-NLI')
texta='今天的饭不好吃'
textb='今天心情不好'
output=model(torch.tensor([tokenizer.encode(texta,textb)]))
print(torch.nn.functional.softmax(output.logits,dim=-1))
If you are using the resource for your work, please cite the our
paper
:
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
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