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Novus Examples
json75 B

Classification Label Map (JSON)

Label id/name map companion for Wave F classification JSONL fixtures.

ML training dataJSON parsing
Preview, first 8 linesjson
{
  "labels": [
    "positive",
    "negative",
    "neutral"
  ]
}

Specifications

Labels
3

Testing contract

Expected to pass
Scenario
Exercise Classification Label Map (JSON) in its nlp workflow. Label id/name map companion for Wave F classification JSONL fixtures.
Expected result
top-level keys are labels; array lengths: labels=3. Declared feature checks: labels=3.

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

“Classification Label Map (JSON)” is a deterministic Novus Examples fixture for ML training data, JSON parsing. Labelled, synthetic datasets in the shapes ML pipelines expect (JSONL for text tasks, image annotations, embeddings, and sample weights) for testing data loaders, tokenizers, and training tooling.

Documented properties for this file: JSON · 75 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.

These are labelled, training-shaped fixtures with a documented schema. Test your data loader, tokenizer, or format converter against it; every label and value is synthetic.

Code examples

import json

with open("classification-labels.json") as f:
    data = json.load(f)
print(type(data), len(data))

Related files

Generated by generation/ai_wave_f.py. Free for any use, no attribution required, license.