USearchWiki Multi model embedding dataset built on HuggingFace FineWiki, designed for approximate nearest neighbor (ANN) search benchmarking with USearch and other vector search engines. The same Wikipedia corpus — chunked, cleaned, and enriched with graph metadata — is embedded by multiple models spanning dense BERT like encoders, GPT style decoder based LLMs, and late interaction ColBERT style architectures. Each model's embeddings ship with precomputed ground truth k nearest neighbors, enabling reproducible recall and throughput benchmarks without re running expensive exact search. Why USearchWiki? Existing ANN benchmarks suffer from three gaps: 1. Stale descriptors. The most popular benchmarks (SIFT 1B, Deep 1B, GloVe) use features from 2014 2021 — image descriptors and word vectors, not modern text embeddings. 2. Single model datasets. Each benchmark is produced by one model. You cannot compare how the same retrieval engine handles different vector distributions without re embedding. 3. No decoder embeddings. State of the art embedding models (GTE Qwen, Llama Embed Nemotron, Qwen3 Embedding) are decoder based LLMs, yet no ANN benchmark uses their outputs. USearchWiki fixes all…
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