# ConceptNet — 4-layer enterprise voice intent classifier, 98.6% accuracy, 9 languages, token-free, open source

> Source: <https://discuss.huggingface.co/t/conceptnet-4-layer-enterprise-voice-intent-classifier-98-6-accuracy-9-languages-token-free-open-source/179274#post_4>
> Published: 2026-08-27 14:59:22+00:00

For now, here’s what I found from some light testing in Colab:

I tried to reproduce the public numbers first, then deliberately looked for easy failure modes rather than assuming the headline accuracy was telling the whole story.

The short version is: **the basic result held up better than I expected**.

On the public 80/20-style split I reconstructed, I got:

That is broadly consistent with the results described in the [repository](https://github.com/wushu75/ConceptNet) and [model card](https://huggingface.co/conceptnetUk/intent-classifier).

I also tried a stronger grouped holdout where related lexical families were kept together rather than randomly split. The neural model was still essentially unchanged:

So I **didn’t** find evidence for the simple explanation that the reported neural accuracy is mostly coming from generic near-duplicate/template leakage.

The more interesting signal was narrower: **L3 / Predictive seems to have much less diversity in how its core temporal relation is expressed than L2 or L4**.

If I were extending the evaluation, my default route would therefore be quite small:

That would probably tell more than simply adding another large random test split.

1. What reproduced, including the negative resultOverall, the part I found most encouraging was actually the failed attempt to break the neural result with a generic lexical-family split. **The obvious “99% only because the random split leaked the templates” explanation did not survive that check.**

The next useful question therefore seems narrower: whether the four execution semantics remain stable when the same relation is expressed differently, and when multiple relations appear in one utterance.

If this were my evaluation budget, I would spend the next small increment on:

Those are all relatively cheap, and each one answers a different deployment question without requiring a redesign of the core model.