json13.5 KB

Text 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.

ML training data Embeddings JSON parsing Conversion testing

Preview — first 50 linesjson

[
  {
    "id": "doc-000",
    "text": "The battery lasts all day and the screen is gorgeous.",
    "embedding": [
      -0.07038400322198868,
      -0.09160999953746796,
      0.24376200139522552,
      0.21044300496578217,
      -0.3516930043697357,
      0.26279300451278687,
      -0.374983012676239,
      0.04282499849796295,
      -0.3350670039653778,
      -0.1756259948015213,
      -0.2920919954776764,
      -0.09252999722957611,
      -0.03663000091910362,
      -0.43194499611854553,
      0.06749600172042847,
      -0.34392601251602173
    ]
  },
  {
    "id": "doc-001",
    "text": "Arrived two weeks late and the box was crushed.",
    "embedding": [
      -0.029543999582529068,
      0.25906801223754883,
      0.16588300466537476,
      0.43884000182151794,
      -0.1419380009174347,
      0.018154999241232872,
      0.36142000555992126,
      -0.4795039892196655,
      0.11238999664783478,
      -0.3011859953403473,
      0.1829179972410202,
      -0.3171060085296631,
      -0.010068999603390694,
      0.03817199915647507,
      0.17642299830913544,
      0.24666999280452728
    ]
  },
  {
    "id": "doc-002",
    "text": "It works as described. Nothing surprising either way.",
    "embedding": [
      -0.24579299986362457,

… 531 lines total — download for the full file.

Specifications

Records: 24
Dimensions: 16
Normalized: L2
Schema: id, text, embedding[]
Seed: 1729

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

“Text Embeddings — 16-dim (JSON)” is a deterministic Novus Examples fixture for ML training data, Embeddings, JSON parsing, Conversion testing. 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: seed 1729 · 24 records · schema: id, text, embedding[]. 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("embeddings.json") as f:
    data = json.load(f)
print(type(data), len(data))

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

Specifications

What is a .json file?

How to use this file

How to use this file for testing

Code examples

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