Update README.md

README.md

@@ -4,7 +4,7 @@ language:
 license: apache-2.0
 
 tags:
-- bert
+- roberta
 - NLU
 - Sentiment
 - Chinese
@@ -15,11 +15,41 @@ widget:
 - text: "今天心情不好"
 
 ---
-# Erlangshen-Roberta-110M-Semtiment, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
-We collect 8  sentiment datasets in the Chinese domain for finetune, with a total of 227347 samples. Our model is mainly based on [roberta](https://huggingface.co/hfl/chinese-roberta-wwm-ext)
+# Erlangshen-Roberta-110M-Sentiment
 
-## Usage
-```python
+- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
+- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
+
+## 简介 Brief Introduction
+
+中文的RoBERTa-wwm-ext-base在数个情感分析任务微调后的版本
+
+This is the fine-tuned version of the Chinese BERT model on several sentiment analysis 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 | Roberta |      110M      |     Sentiment     |
+
+## 模型信息 Model Information
+
+基于[chinese-roberta-wwm-ext-base](https://huggingface.co/hfl/chinese-roberta-wwm-ext-base)，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Sentiment版本。
+
+Based on [chinese-roberta-wwm-ext-base](https://huggingface.co/hfl/chinese-roberta-wwm-ext-base), 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   | ASAP-SENT    |  ASAP-ASPECT  | ChnSentiCorp    |
+| :--------:    | :-----:  | :----:  | :-----:   | 
+| Erlangshen-Roberta-110M-Sentiment | 97.77     |   97.31    | 96.61     |
+| Erlangshen-Roberta-330M-Sentiment | 97.9      |   97.51    | 96.66      |  
+| Erlangshen-MegatronBert-1.3B-Sentiment | 98.1     |   97.8    | 97      | 
+
+
+## 使用 Usage
+
+``` python
 from transformers import BertForSequenceClassification
 from transformers import BertTokenizer
 import torch
@@ -31,17 +61,29 @@ text='今天心情不好'
 
 output=model(torch.tensor([tokenizer.encode(text)]))
 print(torch.nn.functional.softmax(output.logits,dim=-1))
-
 ```
-## Scores on downstream chinese tasks
-|    Model   | ASAP-SENT    |  ASAP-ASPECT  | ChnSentiCorp    |
-| :--------:    | :-----:  | :----:  | :-----:   | 
-| Erlangshen-Roberta-110M-Sentiment | 97.77     |   97.31    | 96.61     |
-| Erlangshen-Roberta-330M-Sentiment | 97.9      |   97.51    | 96.66      |  
-| Erlangshen-MegatronBert-1.3B-Sentiment | 98.1     |   97.8    | 97      |
-## Citation
-If you find the resource is useful, please cite the following website in your paper.
+
+## 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    = {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](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},