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-# 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()
-```
-