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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()
```
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