you could adjust so for some tasks for each unlabeled sample you might want to make a couple of augmentations and use both and as for the model they try a few different initialization schemes so all are working on the transformer architecture applied in Beart so they have just a random initialization then they have bird base bird large and Bert large fine-tuned on on the unlabeled data the in domain on little data so the same unlabeled data that they're using and and for each setting they compare the performance for each of these