If you are building an AI-powered system for semantic search, recommendation engines, or information retrieval, you’re likely familiar with embeddin

A Guide to Open-Source Embedding Models

submited by

Style Pass

2024-10-30 01:30:04

If you are building an AI-powered system for semantic search, recommendation engines, or information retrieval, you’re likely familiar with embedding models. These models are useful for transforming text, images, and other data types into vectors that capture semantic meaning. Embedding models help systems understand and retrieve relevant content based on similarity in meaning.

NV-Embed-v2 is the latest release of the generalist embedding models developed by NVIDIA. It delivers state-of-the-art performance across a wide variety of tasks, ranking No. 1 on the MTEB leaderboard. It achieves an impressive score of 72.31 across 56 different tasks, spanning retrieval, classification, clustering, STS, and more. It’s worth mentioning that the previous version NV-Embed-v1 also earned the top spot on the same leaderboard.

BGE (BAAI General Embedding) models are a family of text embedding models developed by the Beijing Academy of Artificial Intelligence (BAAI). One of the most popular versions in the series is BGE-M3. It stands out due to its versatility in multi-functionality, multi-linguality, and multi-granularity capabilities, also known as M3.