README.md

metadata

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

Erlangshen-MegatronBert-1.3B-NLI

• Github: Fengshenbang-LM
• Docs: Fengshenbang-Docs

简介 Brief Introduction

2021年登顶FewCLUE和ZeroCLUE的中文BERT，在数个推理任务微调后的版本

This is the fine-tuned version of the Chinese BERT model on several NLI 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	NLI

模型信息 Model Information

基于Erlangshen-MegatronBert-1.3B，我们在收集的4个用于finetune的中文领域的NLI（自然语言推理）数据集，总计1014787个样本上微调了一个NLI版本。

Based on Erlangshen-MegatronBert-1.3B, we fine-tuned an NLI version on 4 Natural Language Inference (NLI) datasets that are commonly used for fine-tuning in Chinese field, with totaling 1,014,787 samples.

下游效果 Performance

模型 Model	cmnli	ocnli	snli
Erlangshen-Roberta-110M-NLI	80.83	78.56	88.01
Erlangshen-Roberta-330M-NLI	82.25	79.82	88
Erlangshen-MegatronBert-1.3B-NLI	84.52	84.17	88.67

使用 Usage

from transformers import AutoModelForSequenceClassification
from transformers import BertTokenizer
import torch
tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-MegatronBert-1.3B-NLI')
model=AutoModelForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-MegatronBert-1.3B-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))

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}},
}