Sherlock-Comms/wikipedia-en-2026-07-01-qwen3-embed-4b

English Wikipedia Passage Embeddings — Qwen3-Embedding-4B (2026-07-01) Dense vectors for every passage of English Wikipedia, ready for retrieval- augmented generation. Publishing these saves ~63 GPU-hours of embedding. Pairs with the passage text at wikipedia-en-2026-07-01-passages and prebuilt FAISS indexes at wikipedia-en-2026-07-01-faiss. What is here 17,473,199 passage vectors, 1024-dim, float16, L2-normalized. 34 GB across 57 .npy shards (vecs_gpu*.npy), one… See the full description on the dataset page: https://huggingface.co/datasets/Sherlock-Comms/wikipedia-en-2026-07-01-qwen3-embed-4b

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