Top embedding models on the MTEB leaderboard
Blog post from Modal
The MTEB leaderboard is a comprehensive benchmark that evaluates the performance of embedding models across various tasks, providing a standardized way to compare different models. While high ranking on the leaderboard doesn't guarantee the best fit for a specific use case, considering factors such as task-specific performance, computational requirements, and domain relevance can help make an informed decision. Top models currently on the MTEB leaderboard include generalist embedding models like NV-Embed-v2, Nomic-Embed-Text-v1.5, and bge-en-icl, which have been fine-tuned for specific tasks or domains such as medicine, finance, law, code, math, Japanese, Korean, Chinese, French, Arabic, among others. Domain-specific embedding models can offer superior performance for specialized applications, making it essential to explore these models alongside top performers on the leaderboard to find the best fit for a particular use case.