BelgoClassifier — Belgian bank-transaction categorizer (Core ML)
⚠️ Archive channel. This repository is a public archive of the Stay Steady app's on-device transaction classifier. The app itself distributes models via its own backend — files here are for reference and research, not consumed by any released app.
Current model: v4.6.1 (2026-07-20)
BelgoClassifier_v4.6.1.mlmodelc.zip — classifier head over Apple
NLEmbedding sentence embeddings (512-dim, English). 34 spending categories.
Trained on 21,565 samples: synthetic Belgian transaction text plus manually
audited real bank transactions (personal/P2P counterparties excluded, merchant
names canonicalized — branch towns, store numbers and card fragments stripped).
Training accuracy 99.38%.
Runtime contract: embed input text with
NLEmbedding.sentenceEmbedding(for: .english) (512-dim), UPPERCASE,
diacritics folded, merchant canonicalized. Input tensor embeddings
[1, 512] Float32; output probabilities over 34 classes.
⚠️ v4.5.0 is defective — do not use
BelgoClassifier_v4.5.0.mlmodelc.zip has been removed from this
repository. It was trained by a different pipeline against
all-MiniLM-L6-v2 embeddings (384-dim) and therefore cannot score
NLEmbedding vectors at all; it also measured only 86.7% validation accuracy
and carried a merchant+location labeling bias. If you have a cached copy,
discard it.
Version history
| Version | Embeddings | Notes |
|---|---|---|
| 4.6.1 | NLEmbedding 512 | Audited real data, canonicalized merchants — current |
| MiniLM 384 | Defective, removed (wrong pipeline, 86.7%) | |
| 2.2.0 | NLEmbedding 512 | First NLEmbedding retrain |
| 2.1.0 / 1.0.0 | — | Early iterations, archive only |
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