Embedding Similarity Matrix (JSON)
Tiny 2×2 cosine similarity matrix for embedding eval unit tests.
{
"matrix": [
[
1.0,
0.2
],
[
0.2,
1.0
]
]
}
Specifications
- Size
- 2
Testing contract
Expected to pass- Scenario
- Exercise Embedding Similarity Matrix (JSON) in its embeddings workflow. Tiny 2×2 cosine similarity matrix for embedding eval unit tests.
- Expected result
- top-level keys are matrix; array lengths: matrix=2. Declared feature checks: size=2.
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
“Embedding Similarity Matrix (JSON)” is a deterministic Novus Examples fixture for Model evaluation. 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 · 101 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("similarity-matrix.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- jsonlCosine Similarity Pairs (JSONL)Precomputed cosine similarity pairs for embedding eval scripts.
- jsonBM25 Retrieval Scores (JSON)BM25 score snapshot for comparing neural rerankers against a lexical baseline.
- jsonCaption Eval References (JSON)Multiple reference captions per image for BLEU/CIDEr caption eval.
- 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.
- csvConfusion Matrix — 3 Classes (CSV)3×3 confusion matrix CSV for classification metric calculators.
- jsonConfusion Matrix — 3 Classes (JSON)JSON twin of the 3-class confusion matrix.
Generated by generation/ai_wave_f.py. Free for any use, no attribution required, license.