
# Erlangshen-Roberta-330M-Similarity

- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)

## 简介 Brief Introduction

中文的RoBERTa-wwm-ext-large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-large 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 |      330M      |    中文-相似度 Similarity     |

## 模型信息 Model Information

基于[chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)，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-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large), 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

``` python
from transformers import BertForSequenceClassification
from transformers import BertTokenizer
import torch

tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-330M-Similarity')
model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-330M-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[论文](https://arxiv.org/abs/2209.02970)：

If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):

```text
@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](https://github.com/IDEA-CCNL/Fengshenbang-LM/):

You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):

```text
@misc{Fengshenbang-LM,
  title={Fengshenbang-LM},
  author={IDEA-CCNL},
  year={2021},
  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
{"language": ["zh"], "license": "apache-2.0", "tags": ["roberta", "NLU", "Similarity", "Chinese"], "inference": true, "widget": [{"text": "\u4eca\u5929\u5fc3\u60c5\u4e0d\u597d[SEP]\u4eca\u5929\u5f88\u5f00\u5fc3"}]}
transformers pytorch safetensors bert text-classification roberta NLU Similarity Chinese zh arxiv:2209.02970 license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #roberta #NLU #Similarity #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

Erlangshen-Roberta-330M-Similarity
==================================


* Main Page:Fengshenbang
* Github: Fengshenbang-LM


简介 Brief Introduction
---------------------


中文的RoBERTa-wwm-ext-large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-large model on several similarity datasets.


模型分类 Model Taxonomy
-------------------



模型信息 Model Information
----------------------


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


### 下游效果 Performance



使用 Usage
--------


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:


也可以引用我们的网站:


You can also cite our website:



### 下游效果 Performance



使用 Usage
--------


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:


也可以引用我们的网站:


You can also cite our website:
TAGS
#transformers #pytorch #safetensors #bert #text-classification #roberta #NLU #Similarity #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### 下游效果 Performance



使用 Usage
--------


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:


也可以引用我们的网站:


You can also cite our website:
passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #roberta #NLU #Similarity #Chinese #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
### 下游效果 Performance



使用 Usage
--------


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:


也可以引用我们的网站:


You can also cite our website: