Nico.vibe
@n
Working on a ML model which will output an 'address score' similar to how credit scores work (~low is 300-500, mid 500-650, high 650+) some interesting data: me: 529.52 @alexpaden: 567.79 @cassie: 685.06 @jc: 698.19 @dwr.eth: 663.29 @dcposch.eth: 752.81
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Nico.vibe
@n
Just if it's not clear: this pre-trained model will be open-sourced. I am training with FC addresses to start, and trying to get an idea on the scoring scale - are these too low? too high?
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