document-classification-v2 — commercial open-vocab document classifier

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README.md

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+---
+license: other
+license_name: nutrient-commercial
+pipeline_tag: zero-shot-image-classification
+language:
+  - en
+tags:
+  - zero-shot-image-classification
+  - image-classification
+  - document-ai
+  - document-image-classification
+  - open-vocabulary
+datasets:
+  - nutrientdocs/document-classification-benchmark
+metrics:
+  - accuracy
+---
+
+# 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](https://huggingface.co/spaces/nutrientdocs/document-classification-leaderboard). **Weights are
+commercial** (not downloadable here); this page is a spec + scorecard.
+
+- 🎯 **Try it:** [document-classification-demo](https://huggingface.co/spaces/nutrientdocs/document-classification-demo)
+- 🏆 **Leaderboard:** [document-classification-leaderboard](https://huggingface.co/spaces/nutrientdocs/document-classification-leaderboard)
+- 📊 **Benchmark:** [document-classification-benchmark](https://huggingface.co/datasets/nutrientdocs/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.
+
+<!-- RESULTS-TABLE:START — filled from leaderboard/leaderboard.json by the scoring run -->
+| 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`](https://huggingface.co/nutrientdocs/document-classification-v1) runs ~3–7 docs/s on the same GPU.
+<!-- RESULTS-TABLE:END -->
+
+Every model — ours and cloud — is scored by the same open macro-F1 scorer; full per-model ranking on the
+[leaderboard](https://huggingface.co/spaces/nutrientdocs/document-classification-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](https://huggingface.co/datasets/nutrientdocs/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](https://www.nutrient.io/contact-sales/).**
+
+## About the author
+
+<a href="https://nutrient.io/">
+  <img src="https://avatars2.githubusercontent.com/u/1527679?v=3&s=200" height="80" />
+</a>
+
+This project is maintained and funded by [Nutrient](https://nutrient.io/) - 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.