Cohere Embed
A text embedding model introduced by Cohere that maps text into dense vectors for semantic retrieval. It supports multiple languages and emphasizes representation efficiency and retrieval quality.
Timeline (last 90 days)
After customer @ada_cx deployed Cohere Embed on Model Vault, the median latency of the retrieval pipeline dropped to 77 milliseconds.
@ada_cx achieved a 2.7x improvement in throughput per dollar on offline embedding tasks.
@ada_cx's vector storage footprint shrank to one quarter of the original, with no measurable degradation in retrieval quality observed.
The case reports a top-10 recall rate of 90.7%.
Current mainstream embedding models perform significantly worse at handling numbers and precise data than at handling concepts and semantics.
All Facts (5)
After customer @ada_cx deployed Cohere Embed on Model Vault, the median latency of the retrieval pipeline dropped to 77 milliseconds.
50%Unverified@ada_cx achieved a 2.7x improvement in throughput per dollar on offline embedding tasks.
50%Unverified@ada_cx's vector storage footprint shrank to one quarter of the original, with no measurable degradation in retrieval quality observed.
50%UnverifiedThe case reports a top-10 recall rate of 90.7%.
50%UnverifiedCurrent mainstream embedding models perform significantly worse at handling numbers and precise data than at handling concepts and semantics.
50%