Text Embeddings — 24×16 matrix (NumPy .npy)
The embeddings as a raw NumPy array — a 24×16 float32 matrix in .npy format, loadable with numpy.load. The binary twin of the JSON and Parquet files, for testing tensor and matrix loaders.
| dim_00 | dim_01 | dim_02 | dim_03 | dim_04 |
|---|---|---|---|---|
| -0.07038400322198868 | -0.09160999953746796 | 0.24376200139522552 | 0.21044300496578217 | -0.3516930043697357 |
| -0.029543999582529068 | 0.25906801223754883 | 0.16588300466537476 | 0.43884000182151794 | -0.1419380009174347 |
| -0.24579299986362457 | -0.40040600299835205 | -0.05987099930644035 | -0.19663900136947632 | 0.0441880002617836 |
| 0.2533159852027893 | 0.08270400017499924 | 0.0461140014231205 | -0.3194110095500946 | -0.06899599730968475 |
| 0.12307199835777283 | 0.08135800063610077 | 0.038029998540878296 | 0.2000340074300766 | -0.04078400135040283 |
| -0.07104899734258652 | -0.10132499784231186 | 0.09806299954652786 | -0.45681801438331604 | -0.16166099905967712 |
| -0.06588000059127808 | 0.14317099750041962 | 0.3456229865550995 | 0.20294399559497833 | -0.27621299028396606 |
| 0.020137999206781387 | -0.0990620031952858 | 0.07678800076246262 | -0.48722898960113525 | 0.2195259928703308 |
Specifications
- Shape
- 24x16
- Dtype
- float32
- Format
- NumPy .npy v1.0
- Seed
- 1729
What is a .npy file?
NPY is NumPy's native binary format for a single array. A short header records the dtype, shape, and memory order, followed by the raw array bytes, so an array round-trips exactly without any text parsing. It is the standard way to persist embeddings, tensors, and numeric matrices in the Python data stack.
How to use this file
Use an example .npy to test array loaders (numpy.load), tensor and embedding pipelines, and converters between .npy, JSON, and columnar formats like Parquet.
How to use this file for testing
“Text Embeddings — 24×16 matrix (NumPy .npy)” is a deterministic Novus Examples fixture for ML training data, Embeddings, 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 · NumPy .npy v1.0. 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.
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.
- 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.
- xmlDetection Annotations — Warehouse Pascal VOC (XML)Pascal VOC XML annotations for the warehouse detection scene.
- 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.
- pngObject-detection Scene (PNG, 640×480)A simple rendered street scene with a person, a car, and a tree at known pixel coordinates — the image the COCO, YOLO, and Pascal-VOC annotation twins describe. A fixture for testing object-detection loaders and annotation converters.
- safetensorsTiny Model Weights (safetensors)A genuinely-valid safetensors file with two small float32 tensors (36 parameters total) — an 8×4 weight and a length-4 bias. The values are meaningless sample data, not a trained model; a fixture for testing safetensors loaders and weight inspectors.
Generated by generation/ai_datasets.py. Free for any use, no attribution required — license.