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15 docs tagged with "Evaluation"

Measuring the parser — metrics, golden sets, parity scorecards, calibration, and eval discipline.

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Confidence calibration

Every span the parser emits carries a number when the model says it's 94% sure, is it right 94% of the time?

Eval discipline — reading the numbers carefully

Mailwoman's eval methodology learned its most important lessons the hard way — from shipping two model versions that regressed on headline F1 but told a different story when the failures were examined properly. This article documents the discipline: what to measure, what not to trust, and how to read a model release report.

Failure report — v264 · v265

Cross-model parser failure comparison over golden:dev — which address classes each candidate trades, and what stays beyond reach.

How close is close enough?

A geocoder turns an address into a point, and the obvious thing to ask is whether the point is right. That question has no answer, because "right" assumes a single tolerance and there isn't one. A point that is right enough to tell you which county regulates this facility can be uselessly wrong for routing a truck to its loading dock. The question that does have an answer is the one the operator keeps coming back to, and the one this page is about: close enough for what?