so first we don't label the data we do supervised self supervision then we fine-tune on a hundred percent of the data and then we do this distillation again you would of course be even better and I think they have this somewhere in a table but this is already pretty pretty impressive and another claim they make right here is about the model sizes so and this figure is description this now relates to the title they say bigger models yield larger gains when fine-tuning with fewer labeled examples so there there