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---
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参数的句子表征Topic Classification BERT (TCBert)。
The TCBert with 110M parameters is pre-trained for sentence representation for 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 (sentence representation) | 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 sentence representation for 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-Sentence-Embedding-Chinese")
model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese")
```
Stay tuned for more details on usage for sentence representation.
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}},
}
``` |