Embedding vectors for the mmbench personalized retrieval benchmark. Vectors only
(no FAISS indexes) — rebuild Flat/IVF/HNSW locally (CPU, fast).
Model: intfloat/e5-base-v2 · dim 768 · L2-normalized · metric inner product (cosine)
Prefixes: documents/dialogue turns embedded as passage: ...; queries as query: ...
dtype: float16
docs/docvecs.npy — (1,990,000, 768) shared document corpus; row i ↔ docs/docs_ids.json[i]… See the full description on the dataset page:
https://huggingface.co/datasets/tkdrnjs0621/mmbench-vectors.
