Update README.md

README.md

@@ -1,3 +1,61 @@
 ---
+language: 
+  - zh
+
 license: apache-2.0
+
+tags:
+  - classification
+
+inference: false
+
 ---
+
+# IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese
+
+- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
+- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
+
+## 简介 Brief Introduction
+
+110M参数的Topic Classification BERT (TCBert)。
+
+The TCBert with 110M parameters is pre-trained for, not limited to, Chinese topic classification tasks.
+
+## 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  | 句子表征 | 二郎神 Erlangshen | TCBert      |      110M     |   Chinese     |
+
+## 模型信息 Model Information
+
+
+为了提高模型在话题分类上句子表征效果，我们收集了大量话题分类数据进行基于prompts的对比学习预训练。
+
+To improve the model performance on the topic classification task, we collected numerous topic classification datasets for contrastive pre-training based on general prompts.
+### 下游效果 Performance
+
+Stay tuned.
+
+## 使用 Usage
+
+```python
+from transformers import BertForMaskedLM, BertTokenizer
+import torch
+tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese")
+model=BertForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-TCBert-110M-Classification-Chinese')
+```
+Stay tuned for more details.
+如果您在您的工作中使用了我们的模型，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}},
+}
+```