Eval Metric — Accuracy Mini
Minimal SAMPLE eval metric JSON (accuracy) for dashboard parsers.
{
"metric": "accuracy",
"value": 0.92,
"n": 100,
"sample": true
}
Specifications
- Wave
- I
- Metric
- accuracy
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“Eval Metric — Accuracy Mini” 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: JSON · 80 bytes. 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("accuracy-mini.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
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Generated by generation/pad_wave_i.py. Free for any use, no attribution required — license.