license: cc-by-sa-4.0
language:
- en
task_categories:
- text-generation
annotations_creators:
- expert-generated
source_datasets:
- original
multilinguality:
- monolingual
pretty_name: jq-bench
size_categories:
- n<1K
tags:
- jq
- json
- benchmark
- evaluation
- execution-benchmark
- code-generation
- text-to-jq
- natural-language-to-code
- stackoverflow
configs:
- config_name: human
data_files:
- split: test
path: humana.jsonl
jq-bench — execution-verified benchmark for natural language → jq
Part of the jq-coder project — all artifacts ·
jq-coder-0.6B model ·
jqc CLI
A benchmark for natural-language-to-jq translation built from real questions asked by real people — not from a synthetic grammar. 30 hand-curated tasks derived from real StackOverflow questions, with gold filters and gold outputs verified by executing real jq — no LLM-as-judge anywhere: scoring is run the filter, diff the canonical output.
It is the canonical evaluation set of jq-coder-0.6B, but it is model-agnostic: any system that turns a natural-language request (+ a JSON sample) into a jq filter can be scored on it.
Why a human slice
Synthetic benchmarks generated by the same grammar that generated the training data
measure in-distribution memorization, not competence. This slice is independent of
any generation grammar by construction: every item comes from a real question asked
by a real person, and questions that seeded the jq-coder training grammar (the
top-voted tier) are explicitly excluded. The items favor constructs and compositions
that synthetic grammars tend to miss: del, with_entries, compound to_entries,
index, first/last, descending sort_by, regex test, if-has-else, update
assignment |=, array subtraction, join, keys, recursive merge *, += on
nested paths, group_by into a dynamic-key object, map_values, and hyphenated-key
projection (.stuff["info-spec"]).
Format
One JSON object per line in humana.jsonl:
| Field | Meaning |
|---|---|
pedido_nl |
The natural-language request (the question author's intent, tool mentions removed) |
programa |
Gold jq filter (accepted answer, adapted to a pure filter — no -r/-s/shell flags) |
familia |
humana/Q<question_id> — unique per item |
idioma |
Request language (en) |
documentos |
≥2 input JSON documents: the original from the question plus a variant with the same skeleton |
saidas |
Gold outputs, one per document, canonical jq -cS |
origem |
Provenance: StackOverflow question_id, url, titulo, autor, votos, licenca |
Every gold output was computed by running the gold filter with real jq (1.8) and is re-validated automatically by the project's test suite — curation you can re-execute, not curation you have to trust.
Scoring
Run the candidate filter against every document of the item and diff the canonical
output (jq -cS) against gold. Two published metrics:
- strict — byte-identical to gold on all documents;
- task-solved — additionally accepts equivalent output shapes (stream vs. array wrapper and similar convention differences).
An item only counts if all its documents pass — a filter that hardcodes values from the sample fails the variant document.
Baseline
| Model | strict | task-solved |
|---|---|---|
| jq-coder-0.6B (v14, Q8_0) | 10/30 | 11/30 |
| jq-coder-0.6B (v13, Q8_0) | 9/30 | 10/30 |
Yes, the human slice is hard — that is the point: these are compositions real people actually needed, not grammar samples. Reference scores for frontier models and for the base model (zero-shot) are on the roadmap.
License and attribution
StackOverflow content is CC BY-SA 4.0; this dataset is redistributed under the
same license. Each item carries per-item attribution in origem: the question URL,
the author's display name, and the vote count at collection time. The gold programs
are adapted from the accepted answers of the linked questions.
If you use jq-bench, please cite:
@misc{jqbench2026,
title = {jq-bench: an execution-verified benchmark for natural-language-to-jq translation},
author = {Edelmar Schneider},
year = {2026},
url = {https://huggingface.co/datasets/DominuZ/jq-bench}
}