Erlangshen Roberta 110M Sentiment

🚀 Erlangshen-Roberta-110M-Sentiment

This is a fine-tuned version of the Chinese RoBERTa-wwm-ext-base model on several sentiment analysis datasets, which can effectively handle sentiment analysis tasks in the Chinese language.

🚀 Quick Start

The Erlangshen-Roberta-110M-Sentiment model is a fine - tuned version based on the Chinese RoBERTa-wwm-ext-base, specifically designed for sentiment analysis tasks. You can use the following code to quickly start using this model:

from transformers import BertForSequenceClassification
from transformers import BertTokenizer
import torch

tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment')
model=BertForSequenceClassification.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))

✨ Features

• Fine - tuned on Multiple Datasets: Based on the chinese-roberta-wwm-ext-base, it is fine - tuned on 8 Chinese sentiment analysis datasets, with a total of 227,347 samples.
• Good Performance: Shows excellent performance on multiple sentiment analysis tasks, such as ASAP - SENT, ASAP - ASPECT, and ChnSentiCorp.

📦 Installation

There is no specific installation steps provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import BertForSequenceClassification
from transformers import BertTokenizer
import torch

tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment')
model=BertForSequenceClassification.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))

Advanced Usage

There is no advanced usage example provided in the original document, so this part is skipped.

📚 Documentation

Model Taxonomy

Model Information

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

Performance

🔧 Technical Details

There is no technical implementation details provided in the original document, so this section is skipped.

📄 License

The model uses the Apache 2.0 license.

📖 Citation

If you are using the resource for your work, please cite 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}
}

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}},
}