npy1.6 KB
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.
Preview — schema + first 8 rowsnpy
| 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 |
Decoded NumPy array — first 8 of 24 rows, first 5 of 16 dims (float32).
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.
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.
- 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.
- 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.
Generated by generation/ai_datasets.py. Free for any use, no attribution required — license.