Ranking Eval Queries (JSONL)
Short query list for ranking benchmark harness smoke tests.
{"query": "sample q0", "doc_id": "d0"}
{"query": "sample q1", "doc_id": "d1"}
{"query": "sample q2", "doc_id": "d2"}
{"query": "sample q3", "doc_id": "d3"}
{"query": "sample q4", "doc_id": "d4"}
{"query": "sample q5", "doc_id": "d5"}
Specifications
- Records
- 6
What is a .jsonl file?
JSONL (JSON Lines) is a text format where each line is a complete, independent JSON value, allowing records to be streamed and appended without parsing the whole file. It is not itself a JSON array and each line must stand alone. It is common in logging, machine learning datasets, and data pipelines.
How to use this file
Use an example JSONL to test line-by-line streaming parsers, append-and-resume ingestion, and batch pipelines that process one record per line.
How to use this file for testing
“Ranking Eval Queries (JSONL)” is a deterministic Novus Examples fixture for Model evaluation, JSON parsing. Benchmark results, confusion matrices, ROC curves, and classification reports in CSV and JSON — for testing eval dashboards, metric parsers, and leaderboard importers.
Documented properties for this file: 6 records. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
AI/ML fixtures are fully synthetic with documented schemas — no real people or data. Test data loaders, tokenizers, annotation converters, embedding/vector stores, or eval-metric parsers against the known structure and fixed seeds.
Code examples
import json
with open("ranking-queries.jsonl") as f:
rows = [json.loads(line) for line in f]
print(len(rows), rows[0])Related files
- jsonClassification Report (JSON)A per-class classification report in the scikit-learn structure — precision, recall, F1, and support for each class plus accuracy and macro/weighted averages. A fixture for testing metric parsers and report renderers.
- jsonConfusion Matrix — 3 Classes (JSON)JSON twin of the 3-class confusion matrix.
- jsonConfusion Matrix — 3-class (JSON)The same 3-class confusion matrix as JSON — a labels array plus a nested counts matrix. The structured twin of the CSV, for testing evaluation tooling.
- jsonDetection Eval Metrics (JSON)Synthetic mAP evaluation summary for object-detection benchmark harness tests.
- jsonEval Metric — Accuracy MiniMinimal SAMPLE eval metric JSON (accuracy) for dashboard parsers.
- jsonEval Metric — Bleu MiniMinimal SAMPLE eval metric JSON (bleu) for dashboard parsers.
Generated by generation/ai_wave_f.py. Free for any use, no attribution required — license.