readme: ---
  language: zh
  tags:
  - sbert
  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 bi-encoder
  
  ### Usage
  ```python
  >>> from sentence_transformers import SentenceTransformer, util
  >>> model = SentenceTransformer("tuhailong/bi_encoder_roberta-wwm-ext", device="cuda:1") 
  >>> model.max_seq_length=32
  >>> sentences = ["今天天气不错", "今天心情不错"]
  >>> embeddings1 = model.encode([sentences[0]], convert_to_tensor=True)
  >>> embeddings2 = model.encode([sentences[1]], convert_to_tensor=True)
  >>> scores = util.cos_sim(embeddings1, embeddings2).cpu().numpy()
  >>> print(scores)
  ```
  
  #### Code
  train code from https://github.com/TTurn/bi-encoder
  
  ##### PS
  Because add the pooling layer and dense layer after model，has folders in model files. So here will
  be additional files "1_Pooling-config.json", "2_Dense-config.json" and "2_Dense-pytorch_model.bin".
  after download these files, rename them as "1_Pooling/config.json", "2_Dense/config.json" and "2_Dense/pytorch_model.bin".