README.md

---
license: other
license_name: mixed-per-source-see-below
license_link: https://huggingface.co/datasets/bhaskar1225/verifydocbench#licensing-by-source
task_categories:
- token-classification
- document-question-answering
language:
- en
- de
- es
- fr
- zh
tags:
- document-extraction
- calibration
- selective-prediction
- conformal-prediction
- grounding
- key-information-extraction
- trustworthy-ml
pretty_name: VerifyDocBench
size_categories:
- 10K<n<100K
---

# VerifyDocBench

Genuine per-field trust records for document-extraction calibration, risk-controlled
selective prediction, and grounding research. Companion resource to:

- **VerifyDocBench** (benchmark paper) — first released benchmark scoring per-field
  calibration, risk-controlled selective prediction, and grounding-conditioned trust
  for document extraction.
- **"Valid Per-Field Selective Risk Control for Document Extraction"** (method paper) —
  the validity ladder / conformal risk-control procedures this data was captured to
  evaluate.

Code, loaders, scoring harness, and both papers' reproducibility artifacts:
<https://github.com/bhaskargurram-ai/verifydoc> (Apache-2.0).

## What's in this repository

Nine genuine per-field capture files, **36,265 records total**, from four extractor
families over four corpora:

| file | extractor | fields | docs | source corpus |
|---|---|---|---|---|
| `cord_claude-sonnet-5.json` | claude-sonnet-5 (text-layer) | 13,859 | 800 | CORD (receipts) |
| `funsd_claude-sonnet-5.json` | claude-sonnet-5 (text-layer) | 1,999 | 175 | FUNSD (forms) |
| `xfund_de_claude-sonnet-5.json` | claude-sonnet-5 (text-layer) | 523 | 42 | XFUND German |
| `xfund_es_claude-sonnet-5.json` | claude-sonnet-5 (text-layer) | 632 | 43 | XFUND Spanish |
| `xfund_fr_claude-sonnet-5.json` | claude-sonnet-5 (text-layer) | 772 | 44 | XFUND French |
| `xfund_zh_claude-sonnet-5.json` | claude-sonnet-5 (text-layer) | 461 | 27 | XFUND Chinese |
| `cord_claude-haiku-4-5.json` | claude-haiku-4-5 (text-layer) | 5,341 | 400 | CORD (frozen-config confirmation) |
| `cord_qwen2.5-14b.json` | Qwen2.5-14B (vLLM, open-weights, text-layer) | 6,168 | 398 | CORD |
| `cord_gpt-4o.json` | **gpt-4o (genuinely vision-based)** | 6,510 | 399 | CORD |

**Only `cord_gpt-4o.json` is genuinely vision-based** — the model was sent the
rendered page image (base64-encoded) alongside the OCR text layer as assistive
context. Every other file is **text-layer prompted**: the model reads only the
document's OCR text, never pixels, despite "VLM" being the natural shorthand for a
closed frontier API model — we correct that framing here rather than let it stand
for three of the four extractor families.

Each record is one field prediction:

```json
{
  "doc_id": "cord-train-00000",
  "path": "menu.nm",
  "value": "Nasi Campur Bali",
  "verbalized": 0.95,
  "consistency": 1.0,
  "grounded": true,
  "support": 0.83,
  "correct": 1,
  "entailment": 0.91
}
```

`verbalized` = the model's self-reported confidence; `consistency` = k-sample
self-consistency agreement; `grounded`/`support` = whether/how strongly the value was
located in the document's text layer (ambiguity-penalized); `entailment` = NLI
cross-encoder score for "does the grounded span entail this value"; `correct` = scored
against gold (see the paper for exact-match/numeric/semantic scoring rules per field
type).

**Not included, and not claimed as released:** DocILE and SROIE — no genuine capture
exists for either in this project (DocILE download is pending a valid access token;
SROIE has loader code but was never run for a real capture). Source document *images*
are not re-hosted for any corpus (see licensing below) — only our own derived per-field
predictions and scoring.

## Licensing by source

**Our own contributions** — the per-field correctness labels, grounding/support scores,
schemas, and this derived record format — are released under **Apache-2.0**, matching
the parent repository's license.

This does **not** relicense the underlying source document collections, each of which
keeps its own license:

| source | license | notes |
|---|---|---|
| CORD | **CC BY 4.0** | Permissive; our Apache-2.0 annotations are consistent with it. |
| FUNSD | **Non-commercial, research/educational use only** (custom terms) | Not a CC license. Use of `funsd_claude-sonnet-5.json` must stay within these terms. |
| XFUND (de/es/fr/zh) | **CC BY-NC-SA 4.0** | Non-commercial, share-alike; uniform across languages. |

We redistribute only our own added annotations (values, confidence/grounding/
correctness signals) and reference the original dataset downloads for source
documents/images — restrictively licensed source images are never re-hosted here.
If you redistribute derivatives of the FUNSD or XFUND slices, you must comply with
their non-commercial terms.

## Reproducibility

Captured via the harness at `scripts/apivlm_perfield_rich.py` (seed-pinned, k=3
self-consistency sampling, doc-level splits, regression-gated). Every number in both
companion papers traces to these files or to the scoring harness's deterministic
transforms of them — see `paper/HANDOFF.md` and `paper/generated/real-runs/` in the
GitHub repository for the full campaign log.

## Human-gold audit

A subset of these records was independently re-judged by three blind human annotators
(Fleiss' κ=0.83 on 600 CORD/FUNSD items, κ=0.939 on a further 400 XFUND items) to bound
automatic-label noise. See the method paper's confirmation section and
`benchmark/card.md` in the GitHub repository for the full audit.

## Citation

```bibtex
@misc{verifydocbench2026,
  title  = {VerifyDocBench: Measuring Per-Field Calibration, Selective Risk, and Grounding for Document Extraction},
  author = {Gurram, Bhaskar},
  year   = {2026},
  url    = {https://github.com/bhaskargurram-ai/verifydoc}
}
```

## Maintenance

Maintained by the repository owner via GitHub issues:
<https://github.com/bhaskargurram-ai/verifydoc/issues>. No formal versioning/erratum
policy beyond the issue tracker as of this release.