Update README.md

README.md

@@ -12,16 +12,47 @@ inference: true
 widget:
 - text: "生活的真谛是[MASK]。"
 ---
-# Erlangshen-Deberta-97M-Chinese，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
-The 97 million parameter deberta-V2 base model, using 180G Chinese data, 24 A100(40G) training for 7 days，which is a encoder-only transformer structure. Consumed totally 1B samples.
 
+# Erlangshen-Deberta-97M-Chinese
 
-## Task Description
+- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
+- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
 
-Erlangshen-Deberta-97M-Chinese is pre-trained by bert like mask task from Deberta [paper](https://readpaper.com/paper/3033187248)
+## 简介 Brief Introduction
 
+善于处理NLU任务，采用全词掩码的，中文版的0.97亿参数DeBERTa-v2。
+
+Good at solving NLU tasks, adopting Whole Word Masking, Chinese DeBERTa-v2 with 97M parameters.
+
+## 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 | DeBERTa-v2 |      97M      |     Chinese     |
+
+
+## 模型信息 Model Information
+
+参考论文：[Deberta](https://readpaper.com/paper/3033187248)
+
+为了得到一个中文版的DeBERTa-v2（97M），我们用悟道语料库(180G版本)进行预训练。具体地，我们在预训练阶段中使用了[封神框架](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen)大概花费了24张A100约7天。
+
+To get a Chinese DeBERTa-v2 (97M), we use WuDao Corpora (180 GB version) for pre-training. Specifically, we use the [fengshen framework](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen) in the pre-training phase which cost about 7 days with 24 A100 GPUs.
+
+### 下游任务 Performance
+
+我们展示了下列下游任务的结果（dev集）：
+
+We present the results (dev set) on the following tasks:
+
+| Model                              | OCNLI | CMNLI  |
+| ---------------------------------- | ----- | ------ |
+| RoBERTa-base                       | 0.743 | 0.7973 |
+| Erlangshen-Deberta-97M-Chinese     | 0.752 | 0.807  |
+
+
+## 使用 Usage
 
-## Usage
 ```python
 from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline
 import torch
@@ -33,22 +64,31 @@ fillmask_pipe = FillMaskPipeline(model, tokenizer, device=7)
 print(fillmask_pipe(text, top_k=10))
 ```
 
-## Finetune
+## 引用 Citation
 
-We present the dev results on some tasks.
+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)：
 
-| Model                              | OCNLI | CMNLI  |
-| ---------------------------------- | ----- | ------ |
-| RoBERTa-base                       | 0.743 | 0.7973 |
-| **Erlangshen-Deberta-97M-Chinese** | 0.752 | 0.807  |
+If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
 
-## Citation
-If you find the resource is useful, please cite the following website in your paper.
+```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},
-  year={2022},
+  year={2021},
   howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
 }
-```
+```