license: other
license_name: nutrient-eval-restricted
pretty_name: Document Classification Benchmark (open-vocab, zero-shot)
task_categories:
- zero-shot-image-classification
- image-classification
language:
- en
tags:
- document-ai
- open-vocabulary
- zero-shot-image-classification
- document-image-classification
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
- name: candidate_labels
list: string
- name: source
dtype: string
- name: license_tag
dtype: string
- name: doc_id
dtype: string
- name: track
dtype: string
splits:
- name: test
num_bytes: 453775818
num_examples: 1274
download_size: 453674933
dataset_size: 453775818
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
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.