ColBERT v2

Stanford · 2022 · Retrieval · USA

A to Z

A · App: No
B · Built By: Stanford
C · CEO & Politics: N/A (academic research) — No public partisan activity documented
D · Date: 2022
E · Expertise: Intermediate+
F · Free Tier: Yes
G · Generates: Text only
H · Deployment: Cloud + Local (open weights)
I · Inputs: Text
J · Jurisdiction: USA
K · Key Staffers: Keshav Santhanam (Co-lead author), Omar Khattab (Co-lead author), Matei Zaharia (Senior author)
L · Languages: English-primary
M · Model Type: Retrieval
N · Safety Issues: None reported
O · Open Source: Yes — open weights
P · Pricing: Free (open weights, MIT)
Q · AIDb Rating: [UNKNOWN]
R · What Makes It Special: Stanford's ColBERT (v2), a fast, accurate retrieval model for scalable BERT-based search over large text collections in tens of milliseconds: it encodes passages as matrices of token-level embeddings and matches queries through contextual late interaction (MaxSim), beating single-vector models while scaling to large corpora; ColBERTv2 (NAACL 2022) made late interaction lightweight.
S · Stock / Status: University
T · Training Cutoff: [UNKNOWN]
U · Users: [UNKNOWN]
V · Voice Chat: No
W · Weights: Downloadable
X · X Handle: @StanfordAILab
Y · Year Founded: [UNKNOWN]
Z · Bottom Line: Open-weights Retrieval from Stanford, released 2022.

Data completeness 85% · Last verified 2026-05-12 · Primary source ↗

“ColBERT v2.” AIDb — The AI Database. Verified 2026-05-12. https://theaidatabase.com/m/colbert-v2.html