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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.
A collection of datasets curated by Intel Labs specifically for evaluating and benchmarking vector search algorithms and databases.
ANN-Benchmarks is a benchmarking platform specifically for evaluating the performance of approximate nearest neighbor (ANN) search algorithms, which are foundational to vector database evaluation and comparison.
VectorDBBench is a benchmarking tool developed by ZillizTech for evaluating the performance of various vector databases, aiding users in selecting suitable vector database solutions for their needs.
A 2024 paper introducing CANDY, a benchmark for continuous ANN search with a focus on dynamic data ingestion, crucial for next-generation vector databases.
A massive text embedding benchmark for evaluating the quality of text embedding models, crucial for vector database applications.
BEIR (Benchmarking IR) is a heterogeneous benchmark suite designed for evaluating information retrieval and vector search systems across a wide range of tasks and datasets. It provides a standardized framework for comparing the performance of NLP-based retrieval models and vector databases.
benchmark, evaluation, vector-search, datasets