Datasets:
Tasks:
Document Question Answering
Size:
n<1K
Tags:
benchmark
document-ai
information-extraction
structured-extraction
key-information-extraction
ocr
License:
File size: 2,703 Bytes
ca66b51 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | # Scoring
DocuBench scores schema-guided extraction output against hand-verified JSON labels. The scorer is intentionally small and inspectable; the canonical implementation is [`scorer.py`](../scorer.py).
## Inputs
Each scoring call takes:
- `result`: the system output JSON object, usually from the top-level `data` key in `results/<system>/<doc_id>.json`
- `schema`: the JSON Schema in `schemas/<doc_id>.json`
- `label`: the hand-verified target JSON in `labels/<doc_id>.json`
The schema determines which fields are scored.
## Normalization
Before comparison:
- numeric values are cast to floats and rounded to six decimal places
- strings are lowercased
- whitespace is removed from strings
- punctuation and non-word separators are removed from strings
- empty strings, empty arrays, and empty objects are stripped
- `null` is preserved because it can represent an intentional labeled value
This normalization is meant to avoid penalizing harmless formatting differences. It is not semantic matching.
## Non-Array Fields
Non-array leaves are scored as binary exact matches after normalization.
- matching leaf: `1`
- mismatching leaf: `0`
- both sides empty: skipped
- one side empty: mismatch
The non-array score is `correct / total` over scored leaf fields.
## Array Fields
Arrays are scored order-independently.
1. Each labeled item is compared with each predicted item.
2. Item similarity is computed as binary leaf-field accuracy over the union of leaves present in either item.
3. The scorer greedily assigns the highest-scoring label/result item pairs without reusing either side.
4. Unmatched label or result items count against the score through the denominator.
Array scores are weighted by the average non-empty leaf count in labeled array items. This keeps the final score approximately leaf-weighted rather than array-count-weighted.
## Aggregate Metric
For one document, the final score is a weighted average across array and non-array components.
For a system, the headline benchmark score is the macro average of per-document final scores. Each document contributes equally to the headline aggregate.
## Known Tradeoffs
- Array matching is greedy, not globally optimal.
- String matching is normalized exact matching, not semantic equivalence.
- Extra fields outside the schema are ignored for non-array objects.
- Extra fields inside array items can affect item-level similarity because array item comparison uses the union of leaves.
- Field-specific tolerances are not yet expressed in schema metadata.
These tradeoffs are documented so scores are interpretable and reproducible. Changes to scoring behavior should be treated as benchmark-version changes.
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