README.md

metadata

license: mit

This repository contains the development data files used in the SISAP2026 indexing challenge.

Datasets for previous editions:

• https://huggingface.co/datasets/sadit/SISAP2025

Datasets

• WIKIPEDIA (English articles):

  • repo: https://huggingface.co/datasets/wikimedia/wikipedia
  • BGE m3 model: https://huggingface.co/BAAI/bge-m3
  • File: benchmark-dev-wikipedia-bge-m3.h5
  • similarity: Cosine / dot product
  • Content of the h5 file:
    • dataset train: a 6.35 million vector database, i.e., a matrix of size $1024 \times 6350000$ (f16)
    • group itrain: collection of data related to in-distribution queries (articles removed from the English Wikipedia corpus):
      • itest/queries: a 10'000 vector database, i.e., a matrix of size $1024 \times 10000$ (f16)
      • itest/knns: the gold-standard identifiers for the 1000 nearest neighbors of itest/queries in train, i.e., a matrix $1000 \times 10000$ (i32).
      • itest/dists: the gold-standard distances (1-dot) for the 1000 nearest neighbors of itest/queries in train, i.e., a matrix $1000 \times 10000$ (f32).
    • group otrain: collection of data related to out-of-distribution queries (same model in random articles from the Spanish Wikipedia, i.e., cross-lingual retrieval):
      • otest/queries: a 10'000 vector database, i.e., a matrix of size $1024 \times 10000$ (f16)
      • otest/knns: the gold-standard identifiers for the 1000 nearest neighbors of itest/queries in train, i.e., a matrix $1000 \times 10000$ (i32).
      • otest/dists: the gold-standard distances (1-dot) for the 1000 nearest neighbors of itest/queries in train, i.e., a matrix $1000 \times 10000$ (f32).
    • group allknn:
      • allknn/knns: the gold-standard identifiers for the all-knn graph of train i.e., a matrix $32 \times 6350000$ (i32).
      • allknn/dists: the gold-standard distances (1-dot) for the all-knn graph of train i.e., a matrix $32 \times 6350000$ (f32).

• WIKIPEDIA Small (English articles):

  • This is small version of WIKIPEDIA database for testing and developing purposes, more precisely, the train dataset is a 200k vector database.
  • File: benchmark-dev-wikipedia-bge-m3-small.h5

Note: h5py/HDF5.jl packages read matrices in the expected platform order, so be careful since it could permute dimensions w.r.t what is here explained, however, the final order is what is expected anyway for fast implementations. ```