Datasets:
Submission Format
DocuBench result sets live under:
results/<system_name>/<doc_id>.json
<system_name> should be lowercase, filesystem-safe, and stable across reports.
Required Per-Document File
Each document result should be a JSON object with a top-level data key:
{
"data": {
"invoice_number": "INV-001",
"line_items": []
}
}
The data object is scored against labels/<doc_id>.json using schemas/<doc_id>.json.
Recommended Metadata
Include run metadata when available:
{
"data": {},
"cost": 0.0,
"time_sec": 0.0,
"meta": {
"system": "example-system",
"model": "model-or-api-version",
"run_date": "2026-06-26",
"configuration": "short human-readable config"
}
}
If a system fails to complete, refuses, or returns output that cannot be parsed into the
requested schema, still write a result file with an empty data object:
{
"status": "failed",
"error": {
"type": "schema_mismatch",
"message": "model output did not conform to the schema"
},
"data": {},
"meta": {
"model": "model-or-api-version"
}
}
This makes the failure visible and causes all non-empty labeled fields for that document to be counted as errors.
Validation And Scoring
Run:
docubench validate
docubench score --engine <system_name>
To regenerate reports for all result sets:
docubench report
Prompts And Run Configuration
Commit the prompt and run configuration that produced a submission under
prompts/, following the existing baselines. The committed LLM baselines
load their instruction from prompts/extraction_prompt.txt
at runtime, and each result file stamps its model id, provider, and schema_mode in
meta — together these make a result set reproducible and auditable.
Review Expectations
Public submissions should state:
- system name and version
- model or provider version, if applicable
- extraction settings that affect output
- date of run
- whether the run was produced by released code, proprietary code, or manual workflow
- any documents skipped or failed
- cost and latency if measured