18 posts tagged with "Neural classifier"

We set out to fix a small wart in our address parser and came away with a number that told us to put the screwdriver down.

Here is the wart. When our postcode extractor sees a five-digit run and wants to know whether it's a real postcode or just a house number that happens to look like one, it peeks at the words sitting next to it and checks them against every country's street vocabulary we know — American, German, French, all at once. That "all at once" is fine at three countries. At twenty it gets loud, and a German street suffix starts shadowing an English word by sheer coincidence. So we went looking for the clean way to tell the extractor which country's words to bother with.

That question has a much bigger sibling, and chasing the sibling is where the story is.

Our neural address parser passes 20.7% of our test suite. The rule-based parser it's meant to replace passes 93.7%. By that scoreboard, we should delete the neural model and go home.

We shipped the neural model instead. Here's why both numbers are true — and why the one that matters says the opposite.

We spent a night trying to make our neural address parser less cocky. We ended it having learned something more useful. The model wasn't cocky — it was uninformed. It had never been shown whole categories of address.

This is the story of chasing the wrong number, and the diagnostics that pointed at the right one.

The v0.5.0-a1 tokenizer was trained exclusively on US and French addresses. When it encountered Chinese, Japanese, Korean, Thai, or Arabic text, it fell back to encoding individual bytes — 50-75% of tokens for CJK scripts. Every byte-fallback token is a lost opportunity for the model to learn meaningful subword patterns.

Today we fixed that.

In Tokyo, the address of Tokyo Tower is 〒105-0011 東京都港区芝公園4-2-8.

In English: "4-2-8 Shibakōen, Minato City, Tokyo 105-0011".

The Japanese form runs right-to-left compared to the English form. The prefecture (都道府県) comes first, then the city or ward (市区町村), then a district (丁目) and a block-number-style locator. There's no street name — just a grid.

This is why every rule-based address parser written for Western addresses breaks on Japan.

For its first six versions, Mailwoman emitted ten BIO tags. The model could pick street out of a row but not street_prefix, street_suffix, unit, or po_box. Real addresses are messier than that. The golden eval set has known examples — 6220 SE Salmon St, Portland, OR 97215 (Stage 2 collapses prefix+name+suffix), 123 Main St Apt 4B, Springfield, IL 62701 (loses the apartment), PO Box 123, Burlington, VT 05401 (treats it as a malformed street).

v0.6.0 adds six tags: street_prefix, street_suffix, unit, po_box, intersection_a, intersection_b. The model is the same h384/6L/6H transformer. The recipe is the same v0.5.1 settings. The tokenizer is the same v0.6.0-a0 multi-script bundle. The only structural change is the output head: 21 BIO labels → 33.

The /demo page now loads a 9 MB FST (finite-state transducer) gazetteer alongside the 29 MB ONNX model. 94,000 US admin places with Wikipedia importance scores feed directly into the neural classifier's Viterbi decoder as emission priors — the same pipeline that runs server-side now runs entirely in the browser.

We shipped a browser-based address parser that runs a 29 MB ONNX model entirely client-side. The Playwright tests showed perfect results. Chrome desktop looked great. Then someone opened it on an iPhone.