| # nspam — Nostr reply-spam classifier (v2.2) |
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| On-device spam scoring for Nostr reply notes. See `model_card.md` for details. |
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| ## Files |
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|
| | file | purpose | |
| |---|---| |
| | `model.txt / model.json` | trained model | |
| | `calibration.npz` | isotonic calibration table | |
| | `config.json` | feature layout + hashing convention | |
| | `parity_fixtures.jsonl` | reference inputs + expected scores | |
| | `hash_fixtures.jsonl` | token-level hash checks | |
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|
| ## Integrating |
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| A port needs: |
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| 1. **Feature extraction** — reproduce preprocessing + hashing + structural |
| features. See `config.json` for the exact layout. |
| 2. **Model inference** — load `model.txt` via LightGBM4j (Kotlin/Java), |
| LightGBM C API, or ONNX conversion. |
| 3. **Calibration** — piecewise-linear interpolate the raw score through |
| `calibration.npz` knots. |
|
|
| Validate your port against `parity_fixtures.jsonl`. |
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|
