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README.md

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+---
+license: cc-by-sa-4.0
+language:
+  - en
+task_categories:
+  - text-generation
+tags:
+  - jq
+  - json
+  - benchmark
+  - code-generation
+  - evaluation
+pretty_name: jq-bench (human slice)
+size_categories:
+  - n<1K
+---
+
+# jq-bench — human slice
+
+**The first benchmark for natural-language-to-`jq` translation.** 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.
+
+It is the canonical evaluation set of
+[**jq-coder-0.6B**](https://huggingface.co/DominuZ/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](https://huggingface.co/DominuZ/jq-coder-0.6B) (v13, Q8_0) | 9/30 | 10/30 |
+
+Yes, the human slice is hard — that is the point. Reference scores for frontier models
+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:
+
+```bibtex
+@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}
+}
+```