have words like the be literally 7% of all your data so you if you now usually train or direct algorithm without subsampling or resampling based on the frequency distribution seven percent of your computes going to modeling the word though and then you know some important word in New York City or something or phrase is just basically lost in the noise so we use a real weighting function I believe it's the inverse fifth root so it just works and that just heavily truncates the frequency distribution so they're basically doing the same thing