For a multi-label classification, you could use nn.BCEWithLogitsLoss with your target tensor directly (after casting it to a FloatTensor):
output = torch.tensor([[0.3416, 0.6336, 0.3775, 0.2556, 0.6288]])
target = torch.tensor([[1,0,1,0,1]]).float()

criterion = nn.BCEWithLogitsLoss()
loss = criterion(output, target)