DocMIDE
Document images paired with question/answer pairs that require implicit reasoning over the document — deriving, cross-referencing, or computing a value — rather than plain OCR transcription. This is the held-out test split used by the DocMIDE Benchmark eval harness; the reasoning-annotated training splits (with raw field data and derivation traces, used for SFT/GRPO) live in the DocMIDE training repo.
Most document images are real-world documents sourced from the Unikie benchmark (MIT licensed); the Synthetic category is procedurally generated and not derived from Unikie. This dataset (questions, answers, and synthetic images) is released under CC-BY-4.0; the images/real/ subset remains additionally subject to Unikie's MIT license and attribution requirement.
Dataset structure
test.jsonl is a doc-level JSONL file: one line per document image, with a list of samples (question/answer pairs) for that document.
{
"doc_id": "91814768_91814769",
"image": "images/real/91814768_91814769.png",
"category": "Administrative",
"samples": [
{
"id": 1,
"field": "total_to_date_minus_previously_reported",
"question_type": "derivation",
"prompt": "This form lists three separate 'Total Expenditures or Disbursements' figures near the bottom: one for This Report, one Previously Reported, and one to Date. By how much does the 'to Date' total exceed the 'Previously Reported' total? Answer with the number only — no currency symbols, commas, or units.",
"field_present": true,
"answer": "48085.00"
}
]
}
Fields per sample:
| Field | Description |
|---|---|
id |
Sample index within the document. |
field |
Short identifier for the field/value the question targets. |
question_type |
derivation (compute/combine values) or spatial (reason about layout/position/references within the document). |
prompt |
The question posed about the document. |
field_present |
Whether the queried field actually exists in the document (always true in this split). |
answer |
The final answer. |
Document-level fields: doc_id, image (path relative to this directory), category (Administrative, Commercial, Advertisement, Accommodation, or Synthetic).
Splits
| Split | Documents | Samples |
|---|---|---|
test.jsonl |
3,427 | 4,151 |
Images live under images/ (3,427 total): images/real/ (427 real-world documents, JPG) and images/synthetic/ (3,000 procedurally generated documents, PNG).
Links
Github:
- DocMIDE — training code (SFT + GRPO) that consumes this dataset
- DocMIDE-Benchmark — serves and scores checkpoints trained on this dataset