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Labelmodel takes up a huge amount of memory when training, and it also wouldn't release the memory
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During the training, labelmodel takes up a lot of memory causing an out-of-memory- error, and reducing the batch size doesn't help. Apart from that, labelmodel seems to still take up...
The split of labeled data to train and val should be managed so that validation set contains samples for every class (ideally the same number of samples per class).