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vector-index-bench
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vibe

Tasks:
Sentence Similarity
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metadata
license: cc-by-4.0
task_categories:
  - sentence-similarity

This repository contains the datasets that are meant to be used with VIBE (Vector Index Benchmark for Embeddings):

https://github.com/vector-index-bench/vibe

The datasets can be downloaded manually from this repository, but the benchmark framework also downloads them automatically.

Datasets

Name Type n d Distance
agnews-mxbai-1024-euclidean Text 769,382 1024 euclidean
arxiv-nomic-768-normalized Text 1,344,643 768 any
gooaq-distilroberta-768-normalized Text 1,475,024 768 any
imagenet-clip-512-normalized Image 1,281,167 512 any
landmark-nomic-768-normalized Image 760,757 768 any
yahoo-minilm-384-normalized Text 677,305 384 any
celeba-resnet-2048-cosine Image 201,599 2048 cosine
ccnews-nomic-768-normalized Text 495,328 768 any
codesearchnet-jina-768-cosine Code 1,374,067 768 cosine
glove-200-cosine Word 1,192,514 200 cosine
landmark-dino-768-cosine Image 760,757 768 cosine
simplewiki-openai-3072-normalized Text 260,372 3072 any
coco-nomic-768-normalized Text-to-Image 282,360 768 any
imagenet-align-640-normalized Text-to-Image 1,281,167 640 any
laion-clip-512-normalized Text-to-Image 1,000,448 512 any
yandex-200-cosine Text-to-Image 1,000,000 200 cosine
yi-128-ip Attention 187,843 128 IP
llama-128-ip Attention 256,921 128 IP

Credit

The glove-200-cosine dataset uses embeddings from Glove (released under PDDL 1.0): https://nlp.stanford.edu/projects/glove/

The laion-clip-512-normalized dataset uses a subset of embeddings from LAION-400M (released under CC-BY 4.0): https://laion.ai/blog/laion-400-open-dataset/

The yandex-200-cosine dataset uses a subset of embeddings from Yandex Text2Image (released under CC-BY 4.0): https://big-ann-benchmarks.com/neurips23.html

Dataset structure

Each dataset is distributed as an HDF5 file.

The HDF5 files contain the following attributes:

  • dimension: The dimensionality of the data.
  • distance: The distance metric to use.
  • point_type: The precision of the vectors, one of "float", "uint8", or "binary".

The HDF5 files contain the following HDF5 datasets:

  • train: numpy array of size (n_corpus, dim) containing the embeddings used to build the vector index
  • test: numpy array of size (n_test, dim) containing the test query embeddings
  • neighbors: numpy array of size (n_test, 100) containing the IDs of the true 100 k-nn of each test query
  • distances: numpy array of size (n_test, 100) containing the distances of the true 100 k-nn of each test query
  • avg_distances: numpy array of size n_test containing the average distance from each test query to the corpus points

Additionally, the HDF5 files of OOD datasets contain the following HDF5 datasets:

  • learn: numpy array of size (n_learn, dim) containing a larger sample from the query distribution
  • learn_neighbors: numpy array of size (n_learn, 100) containing the true 100 k-nn (from the corpus) for each point in learn