Zero byte-fallback: a multi-script tokenizer from WOF-earth
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.
The data
Who's On First ships one GitHub repo per country, each containing GeoJSON files for every administrative place. Every place has localized name variants — "New York" has a name:zho of "纽约", a name:jpn of "ニューヨーク", a name:kor of "뉴욕", and dozens more.
We cloned 7 priority countries (US, FR, JP, CN, KR, DE, GB) — 1.74 million GeoJSON files — and built them into a unified SQLite database using our WAL + Freeze pipeline:
| Country | GeoJSON files | Time |
|---|---|---|
| CN | 680K | - |
| US | 449K | - |
| FR | 231K | - |
| DE | 189K | - |
| GB | 73K | - |
| JP | 63K | - |
| KR | 54K | - |
| Total | 1.74M | 3 min |
The result: 1.29 million places with 10.2 million name variants in 20+ languages. 768K Chinese names, 184K Japanese, 264K French, 261K German, 285K Arabic.
The tokenizer
We extracted a balanced multi-script training set (2.19M lines) from the global WOF names table, shuffled across script groups:
- 500K Latin (English, French, German, Spanish, ...)
- 500K Chinese
- 468K Cyrillic (Russian, Ukrainian, ...)
- 285K Arabic
- 183K Japanese
- 94K Korean
- 160K other (Thai, Hindi, Hebrew, Greek, ...)
SentencePiece trained in 28 seconds. Same 48K vocab size as before, same user-defined symbols (US state abbreviations, postcode formats). The difference: the vocab now allocates subword pieces for CJK characters, Hangul syllables, Thai consonant clusters, and Arabic word fragments — instead of wasting slots on Latin-only subwords that the old training data biased toward.
The result
| Script | v0.5.0-a1 (old) | v0.6.0-a0 (new) |
|---|---|---|
| Chinese | 50-75% byte-fallback | 0% |
| Japanese | 58-60% | 0% |
| Korean | 41% | 0% |
| Thai | 30% | 0% |
| Arabic | 0% | 0% |
| Latin | 0% | 0% |
| Aggregate | 36.6% | 0.0% |
Issue #120 targeted less than 5% byte-fallback. We hit zero.
The tokenizer also produces fewer pieces per input. "北京市朝阳区建国路79号" (Beijing address) went from 19 pieces (63% byte-fallback) to 11 pieces (0% byte-fallback). That means more of the 128-token sequence budget is available for actual content instead of being consumed by byte encoding.
What's training
v0.5.4 is now running on a Modal A100 with the new tokenizer. It uses the v0.5.1 proven recipe (the one that achieved 0.638 F1) but with the multi-script tokenizer. If the model learns CJK address patterns as well as it learns Latin ones, this is the foundation for JP/CN/KR locale support.
The pipeline
The global WOF build pipeline follows the WAL + Freeze design brief:
- Enumerate: glob
**/data/**/*.geojsonacross all country repos - Ingest: WAL mode, parallel file reads (asyncParallelIterator), single-thread writer, batched transactions
- Freeze: WAL checkpoint, journal_mode=DELETE, create indexes, ANALYZE, VACUUM INTO
The frozen artifact is a clean 1.09 GB SQLite with no sidecars, verified read-only, integrity-checked. It's available for download from the Hugging Face bucket.