Once the training is finished, we select the model with the best micro-F 1 score on the validation set as our final model and evaluate its performance on the test set.
in order to model label correlations, label powerset (LP) (Tsoumakas and Katakis, 2006) transforms a multi-label problem to a multi-class problem with a classifier trained on all unique label combinations.