Left-pad strings with TypeSafe AI's Jev. For reasons. - f/jev-leftpad
87 comments
important to note that the "confidence" score is... maybe not what people think it is - kind of useless, and just a convenience step from the probabilities.
from the docs: "confidence is a statistic computed from the probability distribution the answer already gives you." [0] I actually encourage people to visit the docs because it has a specific page on this with a little applet to really make this clear.
What is the use case for a classifier that works 90% of the time...? I feel like if I'm classifying something, I probably care enough that 90% ain't gonna cut it...
I guess the answer is just agential stuff that effectively gets double checked by the LLM in the driver seat, anyway? That tracks, though it means that jev is mostly just for the people making harnesses. Which is all of us but still!
"""
def fizz_buzz_encode(i):
if i % 15 == 0: return np.array([0, 0, 0, 1])
elif i % 5 == 0: return np.array([0, 0, 1, 0])
elif i % 3 == 0: return np.array([0, 1, 0, 0])
else: return np.array([1, 0, 0, 0])
interviewer: OK, that's probably enough.me: That's enough setup, you're exactly right. [<--- !!] [...]
"""
Damn, Claude was there all along
So that's where in the training set current models get that phrase! /j
It works great, but maybe your implementation could save me some money. I’ll test it and report back.
The tests mock Jev.
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