to thirty percent of the labels that you need so this way sub supervised learning is actually letting you to be very very data efficient for supervised learning and it also means like you need to hire very few data annotators now like instead of collecting 10,000 labels here you're collecting something like 2,000 naples or fewer right so your data annotation is much faster because you already have a great set of features and your and-and-and the most interesting thing is even on even when you have all the labels that is when you have 100% of