IntelLabs's Vector Search Datasets

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6 result(s)

BEIR

BEIR (Benchmarking IR) is a benchmark suite for evaluating information retrieval and vector search systems across multiple tasks and datasets. Useful for comparing vector database performance.

MTEB Leaderboard

Featured

Massive Text Embedding Benchmark leaderboard covering 58 datasets across 112 languages and 8 embedding tasks. Industry-standard benchmark for comparing text embedding models.

ViDoRe Benchmark

Visual Document Retrieval benchmark designed to evaluate embedding models and retrieval systems on visually rich documents containing tables, charts, diagrams, and complex layouts. The standard benchmark for assessing multi-modal document understanding and retrieval performance.

MTEB (Massive Text Embedding Benchmark)

Comprehensive benchmark suite for evaluating embedding models across 58 datasets spanning 112 languages and eight task types including retrieval, clustering, and semantic similarity, the standard for comparing embedding quality.

MMTEB

Massive Multilingual Text Embedding Benchmark covering over 500 quality-controlled evaluation tasks across 250+ languages, representing the largest multilingual collection of embedding model evaluation tasks.

Deep1B Dataset

Billion-scale benchmark dataset containing 96-dimensional deep learning image embeddings. Provides real-world proxy for testing distributed systems and GPU-accelerated vector search at scale.