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

Testing contract

Expected to pass

Scenario: Exercise Text Embeddings: 16-dim (JSON) in its embeddings workflow. 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.
Expected result: array length is 24; first-record keys are id, text, embedding. Declared feature checks: dimensions=16; normalized=L2.

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 Testaroo 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.