Erlangshen-Roberta-330M-NLI

Github: Fengshenbang-LM
Docs: Fengshenbang-Docs

简介 Brief Introduction

中文的RoBERTa-wwm-ext-large在数个推理任务微调后的版本

This is the fine-tuned version of the Chinese RoBERTa-wwm-ext-large model on several NLI 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	330M	中文-自然语言推断 Chinese-NLI

模型信息 Model Information

基于chinese-roberta-wwm-ext-large，我们在收集的4个中文领域的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 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.

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

模型下载地址 Download Address

Huggingface地址：Erlangshen-Roberta-330M-NLI

加载模型 Loading Models

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))

数据样本示例 Data Examples

{
  "texta": "身上裹一件工厂发的棉大衣,手插在袖筒里",
  "textb": "身上至少一件衣服", 
  "label": 2, 
  "id": 0
 }

标签映射：模型输出0表示两个句子矛盾，1表示没有关系，2表示蕴含关系

"id2label": {
    "0": "CONTRADICTION",
    "1": "NEUTRAL",
    "2": "ENTAILMENT"
  },

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}},
}