Datasets:
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language:
- en
- zh
license: other
task_categories:
- image-to-text
tags:
- document-parsing
- ocr
- multi-page
- rlvr
- prime-environments
size_categories:
- n<1K
---
# MPDocBench-Parse for RLVR (images → gold Markdown)
A Hugging Face repackage of **MPDocBench-Parse** (*Benchmarking Practical
Multi-page Document Parsing*, arXiv:2605.22100; USTC + Tongyi Lab, Alibaba),
built for RL training/evaluation with Prime Intellect Environments (`verifiers`).
- **Input**: `images` — the ordered page images of one document.
- **Output**: `output` — the benchmark ground truth reconstructed into
the single Markdown string the official evaluator expects a parser to
produce: reading order across pages, `#` heading levels from the
parent/son heading tree, HTML `<table>` tables, LaTeX display formulas
(GT delimiters preserved: mostly `$$...$$`, some `\[...\]`), figures as `` with 0–1000 boxes, and
text/tables truncated across pages merged. Page furniture (running
headers/footers, page numbers) is excluded, matching the evaluator.
Reconstruction code: `scripts/build_mpdocbench_dataset.py` in the source
repo; the equivalence target is `Tongyi-Zhiwen/Qwen-Doc`'s
`dataset/end2end_dataset.py` GT handling. Example gold files:
`gold_examples/`.
## Split
A single `test` split with all 420 documents. MPDocBench-Parse is a
benchmark and has no official train split. Page images for the 11
`pptSlides` documents come from the gated `NTT-hil-insight/SlideVQA`
dataset, per upstream's `tools/download_data_from_slidevqa.py`.
## Columns
Exactly two: `images` (list of page images, in order) and `output`
(the gold Markdown string).
## Use with Prime Environments
```python
from datasets import load_dataset
ds = load_dataset("albertklorer/mpdocbench")
print(ds["test"][0]["output"][:500])
```
The companion environment `environments/mpdocbench_parse/` maps rows to a
multimodal `verifiers` prompt (one `image_url` part per page) with
`answer=output` and a composite reward approximating the benchmark's
8 tasks (text NED, table, formula, figure F1, heading hierarchy).
## Validation of the gold reconstruction
Feeding `output` back as a "prediction" to the official upstream
evaluator (15 documents, one per document type; CDM skipped — no LaTeX
rendering stack): FigureF1 1.000, table TEDS 1.000, merged-table TEDS
1.000, merged-text edit distance 0.000, text edit distance (whole) 0.003,
reading-order edit distance 0.021, HeadTEDS 0.975. The non-zero residuals
are evaluator matching artifacts (e.g. multi-line GT titles must be joined
onto one Markdown heading line), not missing content.
## License / attribution
Upstream code is Apache-2.0. Upstream dataset terms: overall CC BY 4.0, with
unauthorised portions under CC BY-NC-SA 4.0 (academic use only) and some
sources link-only/not redistributed (paper App. D). Check those terms before
redistributing this dataset or using it commercially. This card is not legal
advice. Cite the MPDocBench-Parse paper when using this data.
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