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SimCSE loss function pytorch implement
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# note this is a copy from https://paste.ubuntu.com/p/Nx5CcSmhHn/ for convenience | |
import torch | |
import torch.nn.functional as F | |
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
def SimCSE_loss(pred, tau=0.05): | |
ids = torch.arange(0, pred.shape[0], device=device) | |
y_true = ids + 1 - ids % 2 * 2 | |
similarities = F.cosine_similarity(pred.unsqueeze(1), pred.unsqueeze(0), dim=2) | |
# mask h_{i}^{0} with h_{i}^{0} | |
similarities = similarities - torch.eye(pred.shape[0], device=device) * 1e12 | |
similarities = similarities / tau | |
return torch.mean(F.cross_entropy(similarities, y_true)) | |
# sentence embedding [A, A, B, B] | |
pred = torch.tensor([[0.3, 0.2, 2.1, 3.1], | |
[0.3, 0.2, 2.1, 3.1], | |
[-1.79, -3, 2.11, 0.89], | |
[-1.79, -3, 2.11, 0.89]]) | |
SimCSE_loss(pred) |
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