Metric for Multi Label Classification

I have a multi label problem. i have coded the model, used BCEloss(with logits), can get the prediction from the model. But i am confused on what metric to use. I used accuracy score, but its obvious accuracy score will give good score

label = [1,0,0,0,0,0,0,0,1,0,0,0]
pred = [0,0,0,0,0,0,0,0,0,0,0,0]

This means even a 0-model(returning 0 always) gets more accuracy.
But i searched the whole internet(may be not whole), people don’t usually use metric or use accuracy score as metric.

Shouldn’t we use a metric which considers the positive labels and predictions like precision, recall…?

You can use F1 score, i think F1 micro should work,I have also used kappa score in the past.Not sure if it will be useful in your usecase.