ConceptNet Intent Classifier
Fine-tuned distilbert-base-multilingual-cased on the ConceptNet 4-layer enterprise voice intent taxonomy.
Accuracy
- Standard test set: 100% (epochs 4 and 5)
- Adversarial holdout: 99.3% (independently verified — Hugging Face community)
- Fast-path classifier: 83% · <5ms latency
- Dataset: 757 examples across 9 languages
Independent Evaluation
Independently tested by the Hugging Face community (john6666):
- Confirmed 99.315% on reconstructed public test split
- Grouped lexical-family holdout: 99.78%
- Conclusion: "The obvious train/test leakage explanation did not survive that check"
- All 4 identified improvements implemented within 24 hours
Cascade Performance
| Threshold | Fast coverage | Fast accuracy | Final accuracy |
|---|---|---|---|
| 0.50 | 69.2% | 95.0% | 95.9% |
| 0.55 | 60.3% | 98.9% | 99.3% |
| 0.65 | 43.2% | 100% | 100% |
Layer Precedence
Mixed semantics: L4 > L3 > L2 > L1
The 4 Layers
- L1 Basic — "Do X" — immediate execution
- L2 Context-Aware — "Do X when Y" — conditional
- L3 Predictive — "Do X before/ahead of/prior to Y" — proactive
- L4 Autonomous — "Do X always" — persistent agent
Languages
English · French · Spanish · German · Italian · Portuguese · Chinese · Arabic · Russian
Usage
from transformers import pipeline
classifier = pipeline("text-classification", model="conceptnetUk/intent-classifier")
classifier("Send the report when the contract is signed")
Links
- Sandbox: https://conceptnet.co.uk/sandbox/
- GitHub: https://github.com/wushu75/ConceptNet
- Website: https://conceptnet.co.uk
© 2026 ConceptNet Ltd · Patents pending