parquet9.3 KB
Text Embeddings — 16-dim (Parquet)
The same 16-dimensional embeddings as Apache Parquet — id and text columns plus one column per dimension. The columnar twin, for testing analytics engines and Parquet-based vector pipelines.
Preview — schema + first 8 rowsparquet
| id | text | emb_00 | emb_01 | emb_02 |
|---|---|---|---|---|
| doc-000 | The battery lasts all day and the sc… | -0.07038400322198868 | -0.09160999953746796 | 0.24376200139522552 |
| doc-001 | Arrived two weeks late and the box w… | -0.029543999582529068 | 0.25906801223754883 | 0.16588300466537476 |
| doc-002 | It works as described. Nothing surpr… | -0.24579299986362457 | -0.40040600299835205 | -0.05987099930644035 |
| doc-003 | Best purchase I've made this year — … | 0.2533159852027893 | 0.08270400017499924 | 0.0461140014231205 |
| doc-004 | Stopped charging after a month. Very… | 0.12307199835777283 | 0.08135800063610077 | 0.038029998540878296 |
| doc-005 | Setup took a while but support was h… | -0.07104899734258652 | -0.10132499784231186 | 0.09806299954652786 |
| doc-006 | Incredibly comfortable and the build… | -0.06588000059127808 | 0.14317099750041962 | 0.3456229865550995 |
| doc-007 | The app crashes every time I open th… | 0.020137999206781387 | -0.0990620031952858 | 0.07678800076246262 |
Decoded Parquet — first 8 rows; the full file has 16 emb_NN columns.
Specifications
- Rows
- 24
- Columns
- 18
- Dimensions
- 16
- Format
- Apache Parquet
- Seed
- 1729
Code examples
import pandas as pd # pip install pyarrow
df = pd.read_parquet("embeddings.parquet")
print(df.head())
print(df.dtypes)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.
- avroAvro — Row Binary + SchemaThe same records as Apache Avro — a compact row-based binary format that embeds its own schema, widely used in Kafka pipelines. For testing Avro decoders and schema evolution.
Generated by generation/ai_datasets.py. Free for any use, no attribution required — license.