set which is a trained set and a lot of unlabeled data set because what it does it takes a little bit of the label set and a bit of unlabeled set and forms in a mix of labeled and unlabeled and the reason being that it tries and labels all the unlabeled um examples that it has in this mix so that the end training sample has got more no i i know i know i know like the idea behind but i i i'm not sure what i was just asking what was the state of the art before like before me this mix of algorithms for semi-supervised it was a simple