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IDEA-CCNL/Erlangshen-Roberta-330M-Sentiment

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face
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Erlangshen-Roberta-330M-Sentiment

简介 Brief Introduction

中文的RoBERTa-wwm-ext-large在数个情感分析任务微调后的版本

This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-large model on several sentiment analysis datasets.

模型分类 Model Taxonomy

需求 Demand任务 Task系列 Series模型 Model参数 Parameter额外 Extra
通用 General自然语言理解 NLU二郎神 ErlangshenRoberta330M中文-情感分析 Chinese-Sentiment

模型信息 Model Information

基于chinese-roberta-wwm-ext-large，我们在收集的8个中文领域的情感分析数据集，总计227347个样本上微调了一个Semtiment版本。

Based on chinese-roberta-wwm-ext-large, we fine-tuned a sentiment analysis version on 8 Chinese sentiment analysis datasets, with totaling 227,347 samples.

下游效果 Performance

模型 ModelASAP-SENTASAP-ASPECTChnSentiCorp
Erlangshen-Roberta-110M-Sentiment97.7797.3196.61
Erlangshen-Roberta-330M-Sentiment97.997.5196.66
Erlangshen-MegatronBert-1.3B-Sentiment98.197.897

使用 Usage

python
from transformers import BertForSequenceClassification
from transformers import BertTokenizer
import torch

tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-330M-Sentiment')
model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-330M-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:

text
@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}
}

也可以引用我们的网站:

You can also cite our website:

text
@misc{Fengshenbang-LM,
  title={Fengshenbang-LM},
  author={IDEA-CCNL},
  year={2021},
  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}