model string | embedding_dim int64 | pooling string | metric string | index_factory string | nlist int64 | pq_m int64 | default_nprobe int64 | n_vectors int64 | index_file string | ids_file string | ids_dtype string | build_seconds float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
facebook/esm2_t33_650M_UR50D | 1,280 | mean over residue embeddings | cosine (inner product on L2-normalized vectors) | OPQ64_256,IVF65536,PQ64 | 65,536 | 64 | 32 | 49,800,000 | esm2_uniref50.index | ids.npy | S24 | 13,363.6 |
ESM2 UniRef50 FAISS Index
FAISS index over ESM2 (esm2_t33_650M_UR50D) mean-pooled embeddings of
GO-annotated UniRef50 proteins. Used by the
genomenet/functional-distance
Space for nearest-neighbor search.
Files
| File | Description |
|---|---|
esm2_uniref50.index |
FAISS index (OPQ + IVF + PQ, cosine / inner product on L2-normalized vectors) |
ids.npy |
UniRef50 cluster IDs aligned with FAISS positions (dtype='S24') |
metadata.json |
Build parameters (dim, factory, nprobe, n_vectors, ...) |
Usage
import faiss, numpy as np
from huggingface_hub import snapshot_download
local = snapshot_download(repo_id="genomenet/esm2-uniref50-faiss", repo_type="dataset")
index = faiss.read_index(f"{local}/esm2_uniref50.index")
ids = np.load(f"{local}/ids.npy")
# embed query with ESM2 (mean-pooled, L2-normalize), then:
# scores, idxs = index.search(query_emb, k=10)
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