metadata

language:
  - zh
license: apache-2.0
tags:
  - bert
  - NLU
  - Similarity
inference: true
widget:
  - text: 今天心情不好[SEP]今天很开心

Erlangshen-MegatronBert-1.3B-Similarity

Main Page:Fengshenbang
Github: Fengshenbang-LM

简介 Brief Introduction

2021年登顶FewCLUE和ZeroCLUE的中文BERT，在数个相似度任务上微调后的版本

This is the fine-tuned version of the Chinese BERT model on several similarity datasets, which topped FewCLUE and ZeroCLUE benchmark in 2021

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	MegatronBert	1.3B	相似度 Similarity

模型信息 Model Information

基于Erlangshen-MegatronBert-1.3B，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 Erlangshen-MegatronBert-1.3B, 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

from transformers import AutoModelForSequenceClassification
from transformers import BertTokenizer
import torch

tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-MegatronBert-1.3B-Similarity')
model=AutoModelForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-MegatronBert-1.3B-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))

引用 Citation

如果您在您的工作中使用了我们的模型，可以引用我们的对该模型的论文：

If you are using the resource for your work, please cite the our paper for this model:

@article{fengshenbang/erlangshen-megatronbert-sim,
  author    = {Junjie Wang and
               Yuxiang Zhang and
               Ping Yang and
               Ruyi Gan},
  title     = {Towards No.1 in {CLUE} Semantic Matching Challenge: Pre-trained Language
               Model Erlangshen with Propensity-Corrected Loss},
  journal   = {CoRR},
  volume    = {abs/2208.02959},
  year      = {2022}
}

如果您在您的工作中使用了我们的模型，也可以引用我们的总论文：

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

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}},
}