Modelpublic

sister-software/mailwoman-tokenizer-v0.6.0-a0

sourceHugging Faceagpl-3.0updated 4mo agoView on Hugging Face

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Model Card

Mailwoman SentencePiece Tokenizer v0.6.0-a0

Multi-script SentencePiece tokenizer trained for address parsing. Achieves 0% byte-fallback on CJK, Korean, Thai, and Arabic scripts (down from 36–75% on prior tokenizers).

—Source: https://github.com/sister-software/mailwoman
—License: AGPL-3.0

Training data

2.19 million place-name records from Who's On First across 7 countries:

Script group	Lines
Latin (en/fr/de/es/it/pt/nl/sv/pl)	500,000
Chinese (zho)	500,000
Cyrillic (rus/ukr/bul/srp)	468,111
Arabic (ara/fas/urd)	285,109
Japanese (jpn)	183,219
Korean (kor)	94,414
Other (tha/hin/ben/heb/ell)	160,407

Format

Field	Value
Algorithm	SentencePiece unigram
Vocabulary size	48,000
Character coverage	0.9999
Byte-fallback	enabled
Split digits	false
User-defined symbols	64 (US state abbreviations + postcode formats)
Model size	1.0 MB

Byte-fallback rates

Measured on 12 hard multi-script address examples:

Script	Old (v0.5.0-a1)	This tokenizer (v0.6.0-a0)
Chinese	50–75%	0%
Japanese	58–60%	0%
Korean	41%	0%
Thai	30%	0%
Arabic	0%	0%
Latin	0%	0%
Aggregate	36.6%	0.0%

Usage

python

import sentencepiece as spm
tokenizer = spm.SentencePieceProcessor()
tokenizer.load("tokenizer.model")

pieces = tokenizer.encode_as_pieces("東京都新宿区西新宿2-8-1")
# ['▁', '東京', '都', '新宿', '区', '西', '新宿', '2', '-', '8', '-', '1']

Files

File	Description
`tokenizer.model`	SentencePiece binary (load with `spm.SentencePieceProcessor.load`)
`tokenizer.vocab`	Plain-text vocabulary listing (one piece per line, with score)
`model_card.json`	Training provenance metadata (SHA256, training lines, etc.)