How to get F1 score per label using Sklearn's cross_validation (multi-label classification)

I am trying to do multi-label classification using sklearn's cross_val_score function (http://scikit-learn.org/stable/modules/cross_validation.html).

scores = cross_validation.cross_val_score(clf, X_train, y_train,
        cv = 10, scoring = make_scorer(f1_score, average = None))

I want the F1-score for each label returned. This sort of works for the first fold, but gives an error right after:

ValueError: scoring must return a number, got [ 0.55555556  0.81038961  0.82474227  0.67153285  0.76494024  0.89087657 0.93502377  0.11764706  0.81611208] (<type 'numpy.ndarray'>)

I assume this error is raised because cross_val_score expects a number to be returned. Is there any other way I can use cross_val_score to get the F1-score per label?