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conceptnetUk/intent-classifier

sourceHugging Facemitupdated 25d agoView on Hugging Face

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Model Card

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

python

from transformers import pipeline
classifier = pipeline("text-classification", model="conceptnetUk/intent-classifier")
classifier("Send the report when the contract is signed")