license: agpl-3.0 language:

• fr library_name: onnx pipeline_tag: token-classification tags:
• token-classification
• named-entity-recognition
• address-parsing
• postal-address
• geocoding
• onnx
• sequence-labeling metrics:
• f1 model-index:
• name: mailwoman-neural-weights-fr-fr results:
  • task: type: token-classification name: Address parsing (per-component F1) dataset: type: openaddresses name: Mailwoman FR golden set metrics:
    • type: f1 value: 0.99 name: postcode F1 (anchor-on)
    • type: f1 value: 0.946 name: house_number F1 (anchor-on, canonical order)

mailwoman — neural address-parser weights (fr-fr)

The trained-model bundle (@mailwoman/neural-weights-fr-fr) for Mailwoman, a postal-address parser. This package is data only — model.onnx, tokenizer.model, and metadata. It has no JavaScript; it is loaded at inference time by @mailwoman/neural.

This locale is served by the shared multi-locale model. The fr-fr bundle ships the same encoder as the en-us release at an earlier checkpoint (multi-locale, step 20000) — one model serves both locales. For the latest training provenance and recipe see the en-us card.

What this is

Mailwoman is a calibrated, retrieval-augmented sequence labeler over a microlanguage — coupled to a gazetteer that resolves its output to coordinates. This bundle is the sequence-labeler half: a small transformer encoder doing BIO token classification over the address schema. It is not an LLM and nothing about it is generative; for a closed label set over short strings, boring NER is a feature.

The design splits the problem in two: the model learns the grammar, the gazetteer knows the atlas. The model never memorizes place names — postcode and gazetteer knowledge arrive at inference as soft input features (anchors) retrieved from provenance-tracked databases that grow without retraining. Knowledge informs; it never overrides.

• Base model: none. Trained from scratch (no pretrained checkpoint).
• Tokenizer: SentencePiece unigram, byte_fallback=true.
• Format: ONNX int8 dynamic, opset 17, max sequence length 128.

Intended use

Parsing free-text French postal addresses into structured components (country, region, locality, dependent_locality, postcode, subregion, cedex, venue, street, house_number) for geocoding — resolving a parsed address to coordinates via a gazetteer/resolver. The model is the parsing front-end of that pipeline, not a standalone geocoder.

Ship-config requirement (read before using)

The Mailwoman model expects the soft anchor + gazetteer channels fed at inference. Running it with those channels off is out-of-distribution and silently collapses the admin tags (country/region/locality/postcode) — an anchor-off metric on an anchor-trained model is systematically misleading. Construct the scorer through @mailwoman/neural's createScorer (the canonical ProductionScorer), which reads the bundle's requires/channel contract and fails closed if a declared channel isn't fed. Do not hand-wire the raw ONNX session with the anchor input zero-filled. Every honest eval here is anchor-on.

Evaluation

We grade the assembled coordinate (the resolved place), not raw label-F1 in isolation. For FR, the model resolves addresses well when given the correct default country (the gazetteer holds ~114k FR places); on a real FR sample the neural front-end out-parses the rules baseline it replaces on locality-match.

The two model-index entries above (postcode ≈ 0.99 F1, house_number ≈ 0.946 F1) are the multi-locale model's strong FR tags, anchor-on, canonical order. Two honest caveats on the FR numbers:

• FR region is a known open gap — F1 is low and tracked as an outstanding issue; do not expect region-level accuracy on FR comparable to en-us.
• Reversed-order (postcode-first) FR house numbers are harder. On a diversified golden that mixes both orders, house_number drops well below the canonical-order figure — the published frontier for reordered house numbers is ~90–91% (neural parsers collapse much further on reorder). The en-us release's later checkpoint adds a reversed-order shard that recovers this; this fr-fr bundle predates that lever.

Because the older anchor-off / pre-diversified-golden figures in this bundle's model-card.json eval block were measured before the anchor-on grading discipline, treat them as historical; the per-tag parity tables under docs/articles/evals/ carry the config-stated numbers.

Calibration

Mailwoman confidences are isotonic-calibrated (PAVA) against held-out data and applied opt-in via @mailwoman/core's createCalibrator; default parse output is byte-stable when calibration is omitted.

Training

From-scratch (no pretrained base) on an NVIDIA A100 (Modal cloud), CE loss only (the dual CRF loss diverged and was retired; CRF is inference-only Viterbi). The gazetteer anchor channel is fed during training, which is why it is required at inference. This bundle is the shared multi-locale model (step 20000); the en-us card carries the full corpus + recipe provenance.

Limitations

• Expects its channels. See Ship-config requirement — anchor-off is OOD.
• FR region is an open gap (low F1, tracked).
• Reversed-order FR house numbers are harder than canonical order (see Evaluation).
• All-caps / shouting input degrades the admin tags (mixed-case training); @mailwoman/neural's normalizeCase opt recovers detected all-caps ASCII.
• Non-Latin scripts (CJK, Cyrillic) fall through to byte-fallback tokens; quality there is unmeasured.

License & links

• License: AGPL-3.0-only.
• Docs & concepts: https://mailwoman.sister.software
• Loader / scorer API: @mailwoman/neural (createScorer).
• The functional contract for this bundle is model-card.json; this README.md is the HuggingFace-facing card.