learning to see far earlier to a model that's big enough they passed 26 million parameters the error rates are getting significantly lower so this shows that this technique scales with the number of parameters use next is how you can actually match the complete supervised baselines with just using order of magnitude fewer table data points so the complete supervised baseline uses 50,000 labels and this one uses 4,000 labels so that's 10x R or at like more than 10x fewer and and and if you look at the numbers you basically