Erlangshen-Roberta-110M-Similarity

Github: Fengshenbang-LM
Docs: Fengshenbang-Docs

简介 Brief Introduction

中文的RoBERTa-wwm-ext-base在数个相似度任务微调后的版本

This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-base model on several similarity 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 Erlangshen	Roberta	110M	相似度 Similarity

模型信息 Model Information

基于chinese-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 on chinese-roberta-wwm-ext-base, we fine-tuned a similarity version on 20 Chinese paraphrase datasets, with totaling 2,773,880 samples.

下游效果 Performance

Model	BQ	BUSTM	AFQMC
Erlangshen-Roberta-110M-Similarity	85.41	95.18	81.72
Erlangshen-Roberta-330M-Similarity	86.21	99.29	93.89
Erlangshen-MegatronBert-1.3B-Similarity	86.31	-	-

使用 Usage

模型下载地址 Download Address

Huggingface地址：Erlangshen-Roberta-110M-Similarity

加载模型 Loading Models

from transformers import BertForSequenceClassification
from transformers import BertTokenizer
import torch

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

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))

数据样本示例 Data Examples

{
  "texta": "可以換其他银行卡吗？", 
  "textb": "分期的如何用别的银行卡还钱", 
  "label": 1, 
  "id": 0
  }

标签映射：模型输出0表示不相似，输出1表示相似

"id2label":{
    "0":"not similarity",
    "1":"similarity"
     }

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}},
}