nutrientdocs/document-classification-benchmark

Document Classification Benchmark (open-vocab, zero-shot)

Given a document image and an arbitrary set of text labels, which one is right? A held-out, zero-shot, open-vocabulary evaluation for document-type classification — labels are supplied at inference, not baked into a head. Test split only; not for training. Every image is drawn from a permissively-licensed, redistributable source.

Powers the document-classification-leaderboard and evaluates document-classification-v2 (commercial) and the open-weight document-classification-v1; try it live in the demo.

Tracks (macro-F1)

• DocLayNet — document page categories (financial reports, scientific articles, manuals, patents, …).
• Forms — form vs non-form and fine form sub-types.
• OOD — document types absent from training (invoices, handwriting, charts, tables) — the open-vocab stress test.
• OOV — an evaluation protocol (not extra rows): score the same images against never-seen synonym paraphrases of each label ("invoice" → "bill") to measure concept-matching vs memorized wording.

Contents & sources

897 test images (track column selects the slice). All sources are redistributable:

Source	Track	License
DocLayNet (ds4sd/DocLayNet)	doclaynet	CDLA-Permissive-1.0
synthetic IRS forms (public-domain templates + faked fields)	forms	public-domain
mixed permissive HF sources (invoice / handwriting / chart / table)	ood	per upstream source

Tobacco3482 is deliberately excluded. Its research-only license is not redistributable, so it is not part of this public benchmark (it is used only in internal evaluation).

from datasets import load_dataset
ds = load_dataset("nutrientdocs/document-classification-benchmark", split="test")

Schema

field	meaning
image	the document image (RGB)
label	gold document-type label
candidate_labels	the open-vocab candidate set for the row's track
source	originating dataset
license_tag	redistribution tier of the source
doc_id	provenance id
track	doclaynet · forms · ood

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.