文本情感分类 Sentiment Analysis
分析处理带有情感色彩的主观性文本,判断文本观点的正负倾向性。
Analyze and handle subjective texts with emotional overtones, and determine the positive and negative tendencies of textual views.
推荐模型 Recommended Models
Erlangshen-Roberta-110M-Sentiment:RoBERTa-wwm-ext-base在数个情感分析任务微调后的版本。
Erlangshen-Roberta-330M-Sentiment:RoBERTa-wwm-ext-large在数个情感分析任务微调后的版本。
Erlangshen-MegatronBert-1.3B-Sentiment:2021年登顶FewCLUE和ZeroCLUE的中文BERT,在数个情感分析任务微调后的版本。
下游效果 Performance
| 模型 Model | ASAP-SENT | ASAP-ASPECT | ChnSentiCorp |
| :--------: | :-----: | :----: | :-----: |
| Erlangshen-Roberta-110M-Sentiment | 97.77 | 97.31 | 96.61 |
| Erlangshen-Roberta-330M-Sentiment | 97.9 | 97.51 | 96.66 |
| Erlangshen-MegatronBert-1.3B-Sentiment | 98.1 | 97.8 | 97 |
使用 Usage
from transformers import AutoModelForSequenceClassification
from transformers import BertTokenizer
import torch
tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment')
model=AutoModelForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment')
text='今天心情不好'
output=model(torch.tensor([tokenizer.encode(text)]))
print(torch.nn.functional.softmax(output.logits,dim=-1))
引用 Citation
如果您在您的工作中使用了我们的模型,可以引用我们的论文:
If you are using the resource for your work, please cite the our paper:
@article{fengshenbang,
author = {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 and Ruyi Gan and Jiaxing Zhang},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
也可以引用我们的网站:
You can also cite our website:
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}