We injected a postcode's country signal straight into the model, and German addresses jumped thirty-five points. Then three retrains taught us the same anchor can't help an address written in the other order, because it never learned to find the city. It learned which way to look.
We taught a resolver to drop a postcode into the city polygon that contains it. Japan has no city polygons, so we resolved it another way and hit 94%. Then Korea let us nail the coordinate every time and refused to give up the name — and the gazetteer is the reason.
We thought a postcode was a strong enough signal to delete a configuration flag. Then we measured it. A five-digit code pins its own country less than half the time — and that number talked us out of writing the code we were about to write.
A neural address parser that scores 20% on our test suite and beats Pelias on real addresses. Both numbers are true — here's why we trust the second one.
We set out to fix our address parser's overconfidence. We discovered overconfidence was never the problem — and that you can't calibrate your way out of a coverage gap.
We pulled Who's On First data for 7 countries, trained a new tokenizer on 2.19M multi-script place names, and eliminated CJK byte-fallback entirely.
Japanese addresses run backwards: prefecture, then city, then block, then number. They have no street names. Wepulled 6,373 of them from Who's On First.
v0.6.0 expands the schema from 10 to 16 component tags. PO box recognition: 0% → 51.9% in one training run.
The v0.5.3 model now runs with a 9 MB finite-state transducer in the browser — 94K US admin places as an emission prior for the neural classifier.
A one-line import change fixed a WebGPU inference bug that took hours to diagnose. The root cause: onnxruntime-web ships two WebGPU backends, and the default one is broken.