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--- |
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language: zh |
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tags: |
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- cross-encoder |
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datasets: |
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- dialogue |
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--- |
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# Data |
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train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs. |
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## Model |
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model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext. |
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This model structure is as same as [tuhailong/cross_encoder_roberta-wwm-ext_v0](https://huggingface.co/tuhailong/cross_encoder_roberta-wwm-ext_v0)，the difference is changing the order of input sentences and put them in train dataset, the performance is better in my dataset. |
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### Usage |
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```python |
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>>> from sentence_transformers.cross_encoder import CrossEncoder |
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>>> model = CrossEncoder(model_save_path, device="cuda", max_length=64) |
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>>> sentences = ["今天天气不错", "今天心情不错"] |
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>>> score = model.predict([sentences]) |
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>>> print(score[0]) |
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``` |
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#### Code |
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train code from https://github.com/TTurn/cross-encoder |
