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mailwoman-en-us
sister-software/mailwoman-en-us
mailwoman-en-us is a token classification model from sister-software. Use it when you need labels on individual words, such as names. It is set up for onnxruntime. The card lists the license as agpl-3.0.
Open-source neural address parser that runs entirely in the browser via ONNX Runtime Web.
Access
Public
Updated May 28, 2026
Repo size
59.9 MB
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At a glance
- Task
- Token Classification
- Library
- onnxruntime
- License
- agpl-3.0
- Access
- Public
- Created
- May 28, 2026
- Updated
- May 28, 2026
- SHA
- 5e3fc3e7
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Base models
Model card
From the Hugging Face model README
Mailwoman — Neural Address Parser (en-US, v0.5.4)
Open-source neural address parser that runs entirely in the browser via ONNX Runtime Web.
- Source: https://github.com/sister-software/mailwoman
- Demo: https://mailwoman.sister.software/demo
- Docs: https://mailwoman.sister.software/docs
- License: AGPL-3.0
Usage
import * as ort from "onnxruntime-web/webgpu"
// Load the model
const session = await ort.InferenceSession.create("https://huggingface.co/sister-software/mailwoman-en-us/resolve/main/model.onnx")
// Tokenize with the bundled SentencePiece tokenizer (see @mailwoman/neural for the wrapper)
// Run inference → 21 BIO labels per token
For a high-level API see the mailwoman npm package.
Model details
| Field | Value |
|---|---|
| Architecture | Transformer encoder (h384, 6L, 6H) |
| Parameters | 38M (29M encoder + 9M embedding) |
| Vocabulary | 48,000 (SentencePiece unigram, byte-fallback) |
| Max sequence length | 128 |
| Output labels | 21 (1 O + 10 BIO tags × 2) |
| Quantization | int8 dynamic (28 MB) |
| FP32 size | 117 MB |
| ONNX opset | 17 |
Labels (Stage 2)
The model emits BIO-encoded labels for 10 address components:
country,region,locality,dependent_locality,postcode,subregion,cedexvenue,street,house_number
Stage 3 (street decomposition + unit + po_box + intersection) is planned for v0.6.0.
Training
| Field | Value |
|---|---|
| Corpus version | 0.4.0 |
| Tokenizer version | 0.6.0-a0 (multi-script, 0% CJK byte-fallback) |
| Steps | 100,000 |
| Hardware | NVIDIA A100-SXM4-40GB |
| Recipe | v0.5.1 (constant LR, no smoothing, wof-admin: 2.0) |
Training data sources:
- OpenAddresses (CC-BY / public domain subsets) — global street-level addresses
- Who's On First (CC-BY-4.0 + ODbL-1.0) — admin hierarchy for US, FR, JP, CN, KR, DE, GB
- TIGER/Line (public domain) — US street segments
- BAN (ODbL-1.0) — French street-level addresses
Evaluation
| Test | Result |
|---|---|
| Demo presets (6 canonical addresses) | 6/6 correct |
| Golden eval exact match (4,535 entries) | 17.0% |
Note: exact-match metrics are not comparable across tokenizer versions. The v0.5.3 baseline (25.3%) used a different tokenizer and most "failures" are schema mismatch (golden set expects Stage 3 tags the model doesn't emit).
Limitations
- English only: trained on US addresses primarily. French and other locales planned.
- Non-Latin scripts: tokenizer handles CJK/Korean/Thai (0% byte-fallback) but training data is mostly Latin. CJK address parsing not yet validated.
- Stage 2 schema: doesn't decompose streets into prefix/suffix or emit unit/po_box/intersection. Stage 3 v0.6.0 will.
Citation
@software{mailwoman2026,
title = {Mailwoman: Neural Address Parser},
author = {Ellis, Teffen and contributors},
year = {2026},
url = {https://github.com/sister-software/mailwoman},
license = {AGPL-3.0}
}
- Task
- Token Classification
- Library
- onnxruntime
- License
- agpl-3.0
- Languages
- en
- Created
- May 28, 2026
- Updated
- May 28, 2026