A Comprehensive Leaderboard of Powerful Embedding Models

Embedding models are algorithms designed to transform high-dimensional data, such as words or sentences or images or even audio, into low-dimensional vectors, known as embeddings.

Embeddings are valuable for knowledge extension of LLMs (Language Model Models) as they condense and encode information from vast textual datasets, enabling LLMs to generalize and understand language patterns better, even for out-of-domain or unseen data.

Here is an example of creating embeddings using general all-MiniLM-L6-v2 model for semantic search of your GMail emails: https://igor.technology/sbert-transformer-for-semantic-search-within-gmail/