Erlangshen Roberta 330M NLI

🚀 Erlangshen-Roberta-330M-NLI

This is a fine-tuned version of the Chinese RoBERTa model for Natural Language Inference (NLI) tasks, offering high performance in multiple NLI datasets.

• Main Page: Fengshenbang
• Github: Fengshenbang-LM

🚀 Quick Start

This model is a fine-tuned version of the Chinese RoBERTa-wwm-ext-large model on several NLI datasets. It is designed to handle natural language inference tasks effectively.

✨ Features

• Based on the powerful chinese-roberta-wwm-ext-large architecture.
• Fine-tuned on 4 Chinese Natural Language Inference (NLI) datasets, with a total of 1,014,787 samples.
• Demonstrates excellent performance in multiple NLI benchmarks.

📦 Installation

No specific installation steps are provided in the original document.

📚 Documentation

Model Taxonomy

Model Information

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.

Performance

💻 Usage Examples

Basic 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))

📄 License

This project is licensed under the Apache-2.0 license.

📚 Citation

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}
}

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}},
}