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.
[
{
"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,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.
Code examples
import json
with open("embeddings.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- jsonDetection Annotations — COCO (JSON)Object-detection annotations for the scene in the COCO JSON format — images, categories, and per-object bounding boxes as [x, y, width, height]. Grouped with YOLO and Pascal-VOC twins for testing annotation-format conversion.
- 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.
- jsonConfusion Matrix — 3-class (JSON)The same 3-class confusion matrix as JSON — a labels array plus a nested counts matrix. The structured twin of the CSV, for testing evaluation tooling.
- xmlDetection Annotations — Pascal VOC (XML)The same detection boxes in the Pascal VOC XML format — a per-image annotation with size, and one object element per box with pixel corner coordinates. The XML twin of the COCO and YOLO annotations.
- txtDetection Annotations — YOLO (TXT)The same detection boxes in the YOLO text format — one object per line as class id and box centre, width, and height normalised to 0–1. The format twin of the COCO and VOC annotations.
Generated by generation/ai_datasets.py. Free for any use, no attribution required — license.