datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OmniDocBenchForked from opendatalab/OmniDocBench.
Sampler
We have added a simple Python tool for filtering and performing stratified sampling on OmniDocBench data.
Features
Filter JSON entries based on custom criteria
Perform stratified sampling based on multiple categories
Handle nested JSON fields
Installation
Local Development Install (Recommended)
git clone https://huggingface.co/Quivr/OmniDocBench.git
cd OmniDocBench
pip install -r requirements.txt #… See the full description on the dataset page: https://huggingface.co/datasets/Quivr/OmniDocBench.omnidocbench-render-compare
OmniDocBench Render-and-Compare
This dataset contains the rendered HTML reconstructions and comparison images produced
by a render-and-compare pipeline — a reference-free visual similarity evaluation
framework for OCR systems.
Overview
The pipeline processes each page of OmniDocBench through
a Qwen3.5-122B-A10B OCR model, renders the structured output back to a PNG via HTML
(reconstructed.png), and compares it against the original page scan (masked_original.png)
using… See the full description on the dataset page: https://huggingface.co/datasets/gt-free-ocr-metrics/omnidocbench-render-compare.omnidocbench-qwen-ocr-logprobs
OmniDocBench Qwen OCR Log-Probabilities
This dataset provides token-level and bounding-box-level OCR log-probabilities produced by
running Qwen3.5-122B-A10B (via vLLM) on the original page scans of the
OmniDocBench benchmark.
It is a reference-free auxiliary signal — no ground-truth text is used.
Dataset Structure
ocr_logprobs/
<page_id>/
ocr_logprobs.json # full per-token logprobs + top-5 alternatives
ocr_html.html # raw HTML output from the OCR model… See the full description on the dataset page: https://huggingface.co/datasets/gt-free-ocr-metrics/omnidocbench-qwen-ocr-logprobs.omnidocbench-render-compare-sample
OmniDocBench Render-and-Compare — Sample
This is a 60-page stratified sample of
gt-free-ocr-metrics/omnidocbench-render-compare
(the full dataset is ~10 GB).
It is provided to help reviewers explore the data without downloading the full dataset,
as recommended by the NeurIPS 2025 Datasets & Benchmarks Track guidelines.
Sampling Methodology
Pages were selected by stratified random sampling from the full dataset:
Each page in ocr_all (1 355 pages) was assigned to one… See the full description on the dataset page: https://huggingface.co/datasets/gt-free-ocr-metrics/omnidocbench-render-compare-sample.