document-classification-v2 · commercial

Classify any document image against labels you choose at runtime. document-classification-v2 is an open-vocabulary, zero-shot document classifier: supply one label + a short description per class at inference, and it scores a document page against any such set — invoices, letters, memos, forms, scientific articles, resumes, and whatever label you type next. No fixed class list, no per-class training.

It is the document specialist in a field of generalists. General open-vocab models were trained on web photos; this one is built for document pages — and it shows on the leaderboard. Weights are commercial (not downloadable here); this page is a spec + scorecard.

• 🎯 Try it: document-classification-demo
• 🏆 Leaderboard: document-classification-leaderboard
• 📊 Benchmark: document-classification-benchmark

Results

Macro-F1, zero-shot, on the held-out benchmark — higher is better. The flagship vs the previously-shipped in-house model and a cloud frontier VLM reference.

Benchmark (macro-F1)	document-classification-v2	best generalist	best cloud VLM
DocLayNet (page types)	0.88	0.63	0.83
Forms	1.00	0.11	1.00
Tobacco (doc types)	0.69	0.43	0.85
OOD (unseen doc types)	0.97	—	—
OOV (synonym wording)	0.80	—	—

Latency: ~8 docs/s on an A40 (p50 106 ms / p95 194 ms). The open-weight v1 runs ~3–7 docs/s on the same GPU.

Every model — ours and cloud — is scored by the same open macro-F1 scorer; full per-model ranking on the leaderboard.

• Matches/leads the cloud on visual document-type tracks (DocLayNet, Forms) at zero per-request API cost — and runs on a single GPU.
• Trails on Tobacco (0.69 vs 0.85): that track rewards reading fine header text (memo vs letter vs email) — a large VLM reads it; an embedding model can't.
• OOD robustness: on document types absent from training (invoices, handwriting, charts, tables), 0.97 macro-F1. Open-vocab (OOV): under never-seen synonym label wording, 0.80 — matches the concept, not your exact string. (Cloud VLMs train on ~all data, so these held-out axes aren't reported for them.)

Intended use & limits

• Use it for: zero-shot classification of document page images in a free-label setting — the caller supplies the candidate labels (and optional descriptions). Multi-page documents supported; optional page OCR sharpens fine-grained form / tax-code distinctions.
• Limits: optimized for document imagery; English label strings are the primary target. Scores are per-label match probabilities (independent per label), not a softmax across the set.

License & data

The model weights are offered under a commercial Nutrient license — deployed on-prem, so your documents never leave your infrastructure. The training set is not redistributed. Evaluation runs on the held-out document-classification-benchmark.

📩 Get access

document-classification-v2 is commercial and its weights are not downloadable here. To run it on-prem — open-vocabulary, calibrated, private — contact Nutrient: nutrient.io/contact-sales.

About the author

This project is maintained and funded by Nutrient - The deterministic document infrastructure enterprises run their highest-stakes workflows on: replayable output, clear exceptions, and full audit trails on the messy, regulated documents where AI alone breaks.