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.
| 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 |
Specifications
- Rows
- 24
- Columns
- 18
- Dimensions
- 16
- Format
- Apache Parquet
- Seed
- 1729
What is a .parquet file?
Apache Parquet (.parquet) is a binary, columnar storage format for analytical data. It stores each column separately with per-column compression and encoding, embeds a schema and statistics, and is the de-facto standard for data lakes and engines like Spark, DuckDB, and pandas/pyarrow.
How to use this file
Use an example .parquet file to test columnar readers (pyarrow, DuckDB, Spark), schema and predicate-pushdown handling, and Parquet-to-CSV/JSON converters.
How to use this file for testing
“Text Embeddings — 16-dim (Parquet)” is a deterministic Novus Examples fixture for ML training data, Embeddings, Data engineering, 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 rows · 18 columns. 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 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.
- 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.