Docs v2026.7
Technical docs
How our scoring models work: measured results, clear limits.
Download the official Model Card and Technical Report for the models behind every DotCheck score. Held-out test results, explained in plain language.
Measured results
Published held-out results for Docs v2026.7. Everyday scoring uses one image model, one video-frame model, one audio model, and seven language-specific text models on a shared feature stack.
Primary models
- Vermeer (image)
inhouse@12 - real average 0.000 · AI average 0.938 · balanced accuracy 0.9950
- Valla (text English)
inhouse-text@9 - human average 0.045 · AI average 0.909 · balanced accuracy 0.980
- Muybridge (video)
inhouse-video@2 - bag p90 real average 0.004 · AI average 0.940 · balanced accuracy 0.985
- Helmholtz (audio)
inhouse-audio@2 - real average 0.004 · AI average 0.984 · balanced accuracy 0.986
Text by language
Shared TMR + Fakespot features; one text model per language. Same absolute floors as English (human ≤ 0.12 · AI ≥ 0.85 · bal. acc. ≥ 0.90). Unsupported languages are not scored.
| Language | Model | Human ↓ | AI ↑ | Bal. acc. |
|---|---|---|---|---|
| English | Valla inhouse-text@9 |
0.045 | 0.909 | 0.980 |
| Spanish | Valla inhouse-text-es_v2 |
0.088 | 0.953 | 0.972 |
| Portuguese | Valla inhouse-text-pt_BR_v1 |
0.103 | 0.945 | 0.928 |
| French | Valla inhouse-text-fr_v2 |
0.082 | 0.976 | 0.975 |
| Italian | Valla inhouse-text-it_v1 |
0.069 | 0.986 | 0.972 |
| German | Valla inhouse-text-de_v1 |
0.064 | 0.968 | 0.958 |
| Dutch | Valla inhouse-text-nl_v1 |
0.101 | 0.978 | 0.933 |
Portuguese (pt) uses the Brazilian-trained text model. Full methods and limits are in the PDFs above.
Need programmatic scores? See the Pro API docs (keys from Dashboard).
Model cards and open weights on Hugging Face (Apache-2.0). Everyday scoring still runs through DotCheck: huggingface.co/DotCheck · vermeer-image-v12 · valla-text-v9 · muybridge-video-v2 · helmholtz-audio-v2 · DotCheck Engines collection.