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README.md
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# ImplicitIR
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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 [ImplicitIR Benchmark](https://github.com/VXRealLimited/ImplicitIR-Benchmark) eval harness; the reasoning-annotated training splits (with raw field data and derivation traces, used for SFT/GRPO) live in the [ImplicitIR](https://github.com/VXRealLimited/ImplicitIR) training repo.
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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. See [LICENSE](LICENSE) for details.
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## Dataset structure
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`test.jsonl` is a doc-level JSONL file: one line per document image, with a list of `samples` (question/answer pairs) for that document.
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```json
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{
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"doc_id": "91814768_91814769",
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"image": "images/real/91814768_91814769.png",
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"category": "Administrative",
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"samples": [
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{
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"id": 1,
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"field": "total_to_date_minus_previously_reported",
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"question_type": "derivation",
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"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.",
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"field_present": true,
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"answer": "48085.00"
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}
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]
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}
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```
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**Fields per sample:**
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| Field | Description |
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|---|---|
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| `id` | Sample index within the document. |
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| `field` | Short identifier for the field/value the question targets. |
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| `question_type` | `derivation` (compute/combine values) or `spatial` (reason about layout/position/references within the document). |
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| `prompt` | The question posed about the document. |
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| `field_present` | Whether the queried field actually exists in the document (always `true` in this split). |
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| `answer` | The final answer. |
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Document-level fields: `doc_id`, `image` (path relative to this directory), `category` (`Administrative`, `Commercial`, `Advertisement`, `Accommodation`, or `Synthetic`).
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## Splits
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| Split | Documents | Samples |
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|---|---:|---:|
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| `test.jsonl` | 3,427 | 4,151 |
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Images live under `images/` (3,427 total): `images/real/` (427 real-world documents, JPG) and `images/synthetic/` (3,000 procedurally generated documents, PNG).
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## Links
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Github:
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- [ImplicitIR](https://github.com/VXRealLimited/ImplicitIR) — training code (SFT + GRPO) that consumes this dataset
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- [ImplicitIR-Benchmark](https://github.com/VXRealLimited/ImplicitIR-Benchmark) — serves and scores checkpoints trained on this dataset
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