Embedding ID Mapping (JSON)
ID list and model metadata for the Wave F embedding set.
{
"ids": [
"t00",
"t01",
"t02",
"t03",
"t04",
"t05",
"t06",
"t07",
"t08",
"t09",
"t10",
"t11"
],
"model": "sample-embed-v1",
"dimensions": 8
}
Specifications
- Ids
- 12
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 ID Mapping (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 · 217 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("wave-f-id-map.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- jsonText Embeddings — 16-dim (JSON)A set of 24 L2-normalised 16-dimensional text embeddings as JSON — each record pairs an id and its source text with a float vector. A fixture for testing vector stores, similarity search, and embedding loaders. Parquet and .npy twins included.
- jsonlChat Fine-tuning Dataset — Anthropic Format (JSONL)The same synthetic conversations in the Anthropic Messages JSONL shape — a top-level system prompt plus a messages array of user and assistant turns. The format twin of the OpenAI file, for testing chat-format conversion.
- jsonlChat Fine-tuning Dataset — OpenAI Format (JSONL)A chat fine-tuning dataset in the OpenAI JSONL format — one conversation per line as a messages array with system, user, and assistant turns. Synthetic Q&A content. Paired with an Anthropic-format twin for testing format converters.
- jsonlClassification Dataset — Hierarchical (JSONL)Hierarchical category labels for taxonomy-aware classifiers.
- jsonlClassification Dataset — Imbalanced (JSONL)Imbalanced label distribution — 2 rare vs 8 common rows.
- jsonlClassification Dataset — Intent (JSONL)Intent classification JSONL for dialog systems.
Generated by generation/ai_wave_f.py. Free for any use, no attribution required — license.