readme: # Multilingual SimCSE
  
  #### A contrastive learning model using parallel language pair training
  
  ##### By using parallel sentence pairs in different languages, the text is mapped to the same vector space for pre-training similar to Simcse
  
  ##### Firstly, the [mDeBERTa](https://huggingface.co/microsoft/mdeberta-v3-base) model is used to load the pre-training parameters, and then the pre-training is carried out based on the [CCMatrix](https://github.com/facebookresearch/LASER/tree/main/tasks/CCMatrix) data set. 
  
  ##### Training data: 100 million parallel pairs
  
  ##### Taining equipment: 4 * 3090
  
  
  
  ## Pipline Code
  
  ```
  from transformers import AutoModel,AutoTokenizer
  
  model = AutoModel.from_pretrained('WENGSYX/Multilingual_SimCSE')
  tokenizer = AutoTokenizer.from_pretrained('WENGSYX/Multilingual_SimCSE')
  
  word1 = tokenizer('Hello,world.',return_tensors='pt')
  word2 = tokenizer('你好,世界',return_tensors='pt')
  out1 = model(**word1).last_hidden_state.mean(1)
  out2 = model(**word2).last_hidden_state.mean(1)
  print(F.cosine_similarity(out1,out2))
  ----------------------------------------------------
  tensor([0.8758], grad_fn=<DivBackward0>)
  ```
  
  
  
  ## Train Code
  
  
  
  ```
  from transformers import AutoModel,AutoTokenizer,AdamW
  
  model = AutoModel.from_pretrained('WENGSYX/Multilingual_SimCSE')
  tokenizer = AutoTokenizer.from_pretrained('WENGSYX/Multilingual_SimCSE')
  optimizer = AdamW(model.parameters(),lr=1e-5)
  
  def compute_loss(y_pred, t=0.05, device="cuda"):
      idxs = torch.arange(0, y_pred.shape[0], device=device)
      y_true = idxs + 1 - idxs % 2 * 2
      similarities = F.cosine_similarity(y_pred.unsqueeze(1), y_pred.unsqueeze(0), dim=2)
      similarities = similarities - torch.eye(y_pred.shape[0], device=device) * 1e12
      similarities = similarities / t
      loss = F.cross_entropy(similarities, y_true)
      return torch.mean(loss)
      
  wordlist = [['Hello,world','你好,世界'],['Pensa che il bianco rappresenti la purezza.','Он думает, что белые символизируют чистоту.']]
  
  input_ids, attention_mask, token_type_ids = [], [], []
  for x in wordlist:
      text1 = tokenizer(x[0], padding='max_length', truncation=True, max_length=512)
      input_ids.append(text1['input_ids'])
      attention_mask.append(text1['attention_mask'])
      text2 = tokenizer(x[1], padding='max_length', truncation=True, max_length=512)
      input_ids.append(text2['input_ids'])
      attention_mask.append(text2['attention_mask'])
  
  input_ids = torch.tensor(input_ids,device=device)
  attention_mask = torch.tensor(attention_mask,device=device)
  
  output = model(input_ids=input_ids,attention_mask=attention_mask)
  output = output.last_hidden_state.mean(1)
  loss = compute_loss(output)
  loss.backward()
  
  optimizer.step()
  optimizer.zero_grad()
  ```
  
  