M
- machine learning (ML)
- Building systems that learn patterns from examples instead of following hand-written rules. Mailwoman's neural classifier is trained on millions of labeled addresses rather than programmed with parsing rules.
- macro F1
- The unweighted average of per-class F1 scores — treats every class equally. Mailwoman's primary label-level eval metric.
- max sequence length
- The maximum number of tokens the model can process at once. Inputs longer than the cap are truncated, dropping tail context.
- Mermaid
- A Markdown-friendly diagram syntax used in these docs for flowcharts.
- microhood
- A Who's On First placetype for a very fine-grained neighbourhood subdivision, sitting below neighbourhood in the hierarchy.
- Modal
- A cloud GPU platform (modal.com) where Mailwoman trains its neural models on NVIDIA A100 GPUs. Training runs are launched via scripts/modal/train_remote.py and typically complete in ~1 hour.
- model card
- A JSON metadata file (model-card.json) shipped with each weights bundle. It declares the model version, lineage, label set, required inference channels (anchor, gazetteer), calibration data, and training provenance.
- model weights
- The learned parameters of the neural classifier, shipped as ONNX files in the @mailwoman/neural-weights-* packages. Weights are locale-specific bundles that include the model, tokenizer, and a model-card.json metadata file.
- morphology FST
- A finite-state transducer encoding street-typing affixes (Avenue, rue, Calle) from libpostal dictionaries, for morphological street recognition.
- MUA (Medically Underserved Area)
- A US federal designation for inadequate primary-care access, scored using the Index of Medical Underservice. Another downstream consumer of accurate geocoding.
- multi-order training
- Synthesizing the same address in multiple word orders — native postcode-first vs international house-number-first — so the model learns to read both layouts rather than overfitting one direction.