README.md

metadata

task_categories:
  - text-retrieval
  - image-to-text
  - sentence-similarity
language:
  - en
tags:
  - embeddings
  - vector-database
  - benchmark

GAS Indexing Artifacts

Dataset Description

This dataset contains pre-computed deterministic centroids and associated geometric metadata generated using our GAS (Geometry-Aware Selection) algorithm. These artifacts are designed to benchmark Approximate Nearest Neighbor (ANN) search performance in privacy-preserving or dynamic vector database environments.

Purpose

To serve as a standardized benchmark resource for evaluating the efficiency and recall of vector databases implementing the GAS architecture. It is specifically designed for integration with VectorDBBench.

Dataset Summary

• Source Data:
  • Wikipedia (Public Dataset)
  • LAION0400M (Public Dataset)
• Embedding Model:
  • google/embeddinggemma-300m
  • sentence-transformers/clip-ViT-B-32

Dataset Structure

For each embedding model, the directory contains two key file:

Data	Description
centroids.npy	centroids as followed IVF

Data Fields

Centroids: centroids.npy

• Purpose: Finding the nearest clusters for IVF (Inverted File Index)
• Type: NumPy array (np.ndarray)
• Shape: [32768, 768] or [1024, 512]
• Description: 768-dimensional vectors representing 32,768 cluster centroids, or 512-dimensional vectors representing 1,024 cluster centroids.
• Normalization: L2-normalized (unit norm)
• Format: float32

Dataset Creation

Source Data

Source dataset is a large public dataset:

• Wikipedia: mixedbread-ai/wikipedia-data-en-2023-11
• LAION: LAION-400M.

Preprocessing

1. Create Centroids by GAS approach:

   Description TBD

2. Chunking (for text): For texts exceeding 2048 tokens:

   • Split into chunks with ~100 token overlap
   • Embedded each chunk separately
   • Averaged chunk embeddings for final representation

3. Normalization: All embeddings are L2-normalized

Embedding Generation

• Text:

  • Model: google/embeddinggemma-300m
  • Dimension: 768
  • Max Token Length: 2048
  • Normalization: L2-normalized

• Multi-Modal:

  • Model: sentence-transformers/clip-ViT-B-32
  • Dimension: 512
  • Normalization: L2-normalized

Usage

import wget

def download_centroids(embedding_model: str, dataset_dir: str) -> None:
    """Download pre-computed centroids for IVF_GAS."""
    dataset_link = f"https://huggingface.co/datasets/cryptolab-playground/gas-centroids/resolve/main/{embedding_model}"
    wget.download(f"{dataset_link}/centroids.npy", out="centroids.npy")

License

Apache 2.0

Citation

If you use this dataset, please cite:

@dataset{gas-centroids,
  author = {CryptoLab, Inc.},
  title = {GAS Centroids},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/cryptolab-playground/gas-centroids}
}

Source Dataset Citation

@dataset{wikipedia_data_en_2023_11,
  author = {mixedbread-ai},
  title = {Wikipedia Data EN 2023 11},
  year = {2023},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/mixedbread-ai/wikipedia-data-en-2023-11}
}

@dataset{laion400m,
  author = {Schuhmann, Christoph and others},
  title = {LAION-AI},
  year = {2021},
  publisher = {LAION},
  url = {https://laion.ai/blog/laion-400-open-dataset}
}

Embedding Model Citation

@misc{embeddinggemma,
  title={Embedding Gemma},
  author={Google},
  year={2024},
  url={https://huggingface.co/google/embeddinggemma-300m}
}

@misc{clipvitb32,
  title={CLIP ViT-B/32},
  author={Open AI},
  year={2021},
  url={https://huggingface.co/sentence-transformers/clip-ViT-B-32}
}

Acknowledgments

• Original dataset:
  • mixedbread-ai/wikipedia-data-en-2023-11
  • LAION-400M
• Embedding model:
  • google/embeddinggemma-300m
  • sentence-transformers/clip-ViT-B-32
• Benchmark framework: VectorDBBench