readme: ---
  language: zh
  tags:
  - cross-encoder
  datasets:
  - dialogue
  ---
  
  # Data
  train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
  
  ## Model
  model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is cross-encoder, pretrained model is hfl/chinese-roberta-wwm-ext.
  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.
  
  ### Usage
  ```python
  >>> from sentence_transformers.cross_encoder import CrossEncoder
  >>> model = CrossEncoder(model_save_path, device="cuda", max_length=64)
  >>> sentences = ["今天天气不错", "今天心情不错"]
  >>> score = model.predict([sentences])
  >>> print(score[0])
  ```
  
  #### Code
  train code from https://github.com/TTurn/cross-encoder