readme: This is finetune version of [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://arxiv.org/abs/2104.08821)
  , train unsupervised on 570K stroke sentences from : stroke books, quora medical, quora's stroke and human annotates.
  
  ### Extract sentence representation
  ```
  from transformers import AutoTokenizer, AutoModel  
  tokenizer = AutoTokenizer.from_pretrained("demdecuong/stroke_simcse")
  model = AutoModel.from_pretrained("demdecuong/stroke_simcse")
  
  text = "What are disease related to red stroke's causes?"
  inputs = tokenizer(text, return_tensors='pt')
  outputs = model(**inputs)[1]
  ```
  ### Build up embedding for database
  
  ```
  database = [
      'What is the daily checklist for stroke returning home',
      'What are some tips for stroke adapt new life',
      'What  should I consider when using nursing-home care'
  ]
  
  embedding = torch.zeros((len(database),768))
  
  for i in range(len(database)):
    inputs = tokenizer(database[i], return_tensors="pt")
    outputs = model(**inputs)[1]
    embedding[i] = outputs
  
  print(embedding.shape)
  ```
  
  ### Result
  On our Poc testset , which contains pairs of matching question related to stroke from human-generated. 
  
  | Model  | Top-1 Accuracy |
  | ------------- | ------------- |
  | SimCSE (supervised)  | 75.83  |
  | SimCSE (ours)  | 76.66  |