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--- |
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language: |
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- zh |
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license: apache-2.0 |
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tags: |
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- classification |
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inference: false |
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--- |
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# IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese |
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- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) |
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- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) |
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## 简介 Brief Introduction |
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110M参数的句子表征Topic Classification BERT (TCBert)。 |
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The TCBert with 110M parameters is pre-trained for sentence representation for Chinese topic classification tasks. |
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## 模型分类 Model Taxonomy |
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| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | |
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| :----: | :----: | :----: | :----: | :----: | :----: | |
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| 通用 General | 句子表征 | 二郎神 Erlangshen | TCBert (sentence representation) | 110M | Chinese | |
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## 模型信息 Model Information |
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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。 |
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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. |
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### 下游效果 Performance |
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Stay tuned. |
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## 使用 Usage |
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```python |
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from transformers import BertForMaskedLM, BertTokenizer |
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import torch |
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tokenizer=BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese") |
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model=BertForMaskedLM.from_pretrained("IDEA-CCNL/Erlangshen-TCBert-110M-Sentence-Embedding-Chinese") |
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``` |
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Stay tuned for more details on usage for sentence representation. |
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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/): |
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You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): |
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```text |
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@misc{Fengshenbang-LM, |
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title={Fengshenbang-LM}, |
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author={IDEA-CCNL}, |
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year={2021}, |
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howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, |
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} |
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``` |
