Erlagshe-RoBERTa-110M-Similarity

• Github: Fegshebag-LM
• Docs: Fegshebag-Docs

简介 Brief Itroductio

中文的RoBERTa-wwm-ext-base在数个相似度任务微调后的版本

This is the fie-tued versio of the Chiese RoBERTa-wwm-ext-base model o several similarity datasets.

模型分类 Model Taxoomy

模型信息 Model Iformatio

基于chiese-roberta-wwm-ext-base，我们在收集的20个中文领域的改写数据集，总计2773880个样本上微调了一个Similarity版本。

Based o chiese-roberta-wwm-ext-base, we fie-tued a similarity versio o 20 Chiese paraphrase datasets, with totalig 2,773,880 samples.

下游效果 Performace

使用 Usage

from modelscope.pipelies import pipelie
from modelscope.utils.costat import Tasks

p = pipelie(Tasks.text_classificatio, 'Fegshebag/Erlagshe-RoBERTa-110M-Similarity')
p(iput='今天心情不好[SEP]今天很开心')

引用 Citatio

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 usig the resource for your work, please cite the our paper:

@article{fegshebag,
  author    = {Jujie Wag ad Yuxiag Zhag ad Li Zhag ad Pig Yag ad Xiyu Gao ad Ziwei Wu ad Xiaoqu Dog ad Juqig He ad Jiaheg Zhuo ad Qi Yag ad Yogfeg Huag ad Xiayu Li ad Yagha Wu ad Juyu Lu ad Xiyu Zhu ad Weifeg Che ad Tig Ha ad Kuhao Pa ad Rui Wag ad Hao Wag ad Xiaoju Wu ad Zhogshe Zeg ad Chogpei Che ad Ruyi Ga ad Jiaxig Zhag},
  title     = {Fegshebag 1.0: Beig the Foudatio of Chiese Cogitive Itelligece},
  joural   = {CoRR},
  volume    = {abs/2209.02970},
  year      = {2022}
}

也可以引用我们的网站:

You ca also cite our website:

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

Erlangshen-RoBERTa-110M-Similarity Github: Fengshenbang-LM Docs: Fengshenbang-Docs 简介 Brief Introd