Class weight degrades Multi Label Classification Performance
Adding class weight but not changing the way you measure performance will usually degrade overall performance as it is designed to allow increased loss on lower-weighted classes.

I would recommend also weighting your accuracy measures. This is a bit tricky with accuracy/precision etc. so maybe calculated the weighted logloss and compare it to the unweighted logloss of the unweighted model.

Basically it comes down to the question: are you happy with a model that performs worse overall but better on your heavily weighted classes?