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Loaded by @mailwoman/neural ","maintainers":[{"name":"teffenellis","email":"teffen@nirri.us"}],"readme":"---\nlicense: AGPL-3.0\nlanguage:\n  - fr\nlibrary_name: onnx\npipeline_tag: token-classification\ntags:\n  - token-classification\n  - named-entity-recognition\n  - address-parsing\n  - postal-address\n  - geocoding\n  - onnx\n  - sequence-labeling\nmetrics:\n  - f1\nmodel-index:\n  - name: mailwoman-neural-weights-fr-fr\n    results:\n      - task:\n          type: token-classification\n          name: Address parsing (per-component F1)\n        dataset:\n          type: openaddresses\n          name: Mailwoman FR golden set\n        metrics:\n          - type: f1\n            value: 0.99\n            name: postcode F1 (anchor-on)\n          - type: f1\n            value: 0.946\n            name: house_number F1 (anchor-on, canonical order)\n---\n\n# mailwoman — neural address-parser weights (fr-fr)\n\nThe trained-model bundle (`@mailwoman/neural-weights-fr-fr`) for\n[Mailwoman](https://mailwoman.ai), a postal-address parser. This\npackage is **data only** — `model.onnx`, `tokenizer.model`, and metadata. It has\nno JavaScript; it is loaded at inference time by `@mailwoman/neural`.\n\n> **This locale is served by the shared multi-locale model.** The fr-fr bundle\n> ships the same encoder as the en-us release at an earlier checkpoint\n> (multi-locale, step 20000) — one model serves both locales. For the latest\n> training provenance and recipe see the en-us card.\n\n## What this is\n\nMailwoman is a **calibrated, retrieval-augmented sequence labeler over a\nmicrolanguage** — coupled to a gazetteer that resolves its output to\ncoordinates. This bundle is the sequence-labeler half: a small transformer\nencoder doing BIO token classification over the address schema. It is not an LLM\nand none of it is generative; for a closed label set over short strings,\nboring NER is a feature.\n\nThe design splits the problem in two: **the model learns the grammar, the\ngazetteer knows the atlas.** The model never memorizes place names — postcode\nand gazetteer knowledge arrive at inference as _soft input features_ (anchors)\nretrieved from provenance-tracked databases that grow without retraining.\nKnowledge informs; it never overrides.\n\n- **Base model:** none. Trained **from scratch** (no pretrained checkpoint).\n- **Tokenizer:** SentencePiece unigram, `byte_fallback=true`.\n- **Format:** ONNX int8 dynamic, opset 17, max sequence length 128.\n\n## Intended use\n\nThe model parses free-text French postal addresses into structured components\n(country, region, locality, dependent_locality, postcode, subregion, cedex,\nvenue, street, house_number) for **geocoding** — resolving a parsed address to\ncoordinates via a gazetteer/resolver. The model is the parsing front-end of that\npipeline rather than a standalone geocoder.\n\n## Ship-config requirement (read before using)\n\nThe Mailwoman model expects the soft anchor + gazetteer channels fed at\ninference. A run with those channels off is out-of-distribution and silently\ncollapses the admin tags (country/region/locality/postcode) — an anchor-off\nmetric on an anchor-trained model is systematically misleading. Construct the\nscorer through `@mailwoman/neural`'s `createScorer` (the canonical\n`ProductionScorer`), which reads the bundle's `requires`/channel interface and\n**fails closed** if a declared channel isn't fed. Do not hand-wire the raw ONNX\nsession with the anchor input zero-filled. Every eval here is\n**anchor-on**.\n\n## Evaluation\n\nWe grade the **assembled coordinate** (the resolved place) rather than raw label-F1 in\nisolation. For FR, the model resolves addresses well when given the correct\ndefault country (the gazetteer holds ~114k FR places); on a real FR sample the\nneural front-end out-parses the rules baseline it replaces on locality-match.\n\nThe two `model-index` entries above (postcode ≈ 0.99 F1, house_number ≈ 0.946 F1)\nare the multi-locale model's strong FR tags, **anchor-on, canonical order**. Two\nknown gaps on the FR numbers:\n\n- **FR `region` is a known open gap** — F1 is low and tracked as an outstanding\n  issue; do not expect region-level accuracy on FR comparable to en-us.\n- **Reversed-order (postcode-first) FR house numbers are harder.** On a\n  diversified golden that mixes both orders, house_number drops well below the\n  canonical-order figure — the published frontier for reordered house numbers is\n  ~90–91% (neural parsers collapse much further on reorder). The en-us release's\n  later checkpoint adds a reversed-order training subset that recovers this; this fr-fr\n  bundle predates that change.\n\nBecause the older anchor-off / pre-diversified-golden figures in this bundle's\n`model-card.json` `eval` block were measured before the anchor-on grading\ndiscipline, treat them as historical; the per-tag parity tables under\n[`docs/articles/evals/`](https://mailwoman.ai) carry the\nconfig-stated numbers.\n\n## Calibration\n\nMailwoman confidences are isotonic-calibrated (PAVA) against held-out data and\napplied **opt-in** via `@mailwoman/core`'s `createCalibrator`; default parse\noutput is byte-stable when calibration is omitted.\n\n## Train\n\nFrom-scratch (no pretrained base) on an NVIDIA A100 (Modal cloud), CE loss only\n(the dual CRF loss diverged and was retired; CRF is inference-only Viterbi). The\ngazetteer anchor channel is fed during training, which is why it is required at\ninference. This bundle is the shared multi-locale model (step 20000); the en-us\ncard carries the full corpus + recipe provenance.\n\n## Limitations\n\n- **Expects its channels.** See _Ship-config requirement_ — anchor-off is OOD.\n- **FR `region` is an open gap** (low F1, tracked).\n- **Reversed-order FR house numbers** are harder than canonical order (see\n  _Evaluation_).\n- **All-caps / shouting input degrades** the admin tags (mixed-case training);\n  `@mailwoman/neural`'s `normalizeCase` opt recovers detected all-caps ASCII.\n- **Non-Latin scripts** (CJK, Cyrillic) fall through to byte-fallback tokens;\n  quality there is unmeasured.\n\n## License & links\n\n- **License:** [AGPL-3.0-only](https://www.gnu.org/licenses/AGPL-3.0.html).\n- **Docs & concepts:** https://mailwoman.ai\n- **Loader / scorer API:** `@mailwoman/neural` (`createScorer`).\n- The functional interface for this bundle is `model-card.json`; this `README.md`\n  is the HuggingFace-facing card.\n","readmeFilename":"README.md"}