You could use the hamming loss 59 or “hamming score”:

target = torch.FloatTensor([[0, 1, 0],
                           [1, 1, 1],
                           [0 ,0 ,0]])

pred = torch.FloatTensor([[0, 1, 1],
                          [1, 1, 1],
                          [0 ,1 ,0]])

hamming_score = 1 - (target != pred).sum() / float(target.nelement())

Scikit provides other metrics like Jaccard similarity coefficient 21.
Would this work for you?