One post tagged with "embedding"

Embeddings power many AI applications we interact with — search engines, RAG systems — but how do we know if they’re actually any good? Existing benchmarks tend to focus on a narrow set of tasks, often evaluating models in isolation without considering real-world, multilingual challenges. This can make it tough to figure out which models are truly effective, and where they might fall short. That's why we need a more comprehensive way to evaluate embeddings - one that takes into account the messy, multilingual nature of real-world language use. MMTEB is designed to fill this gap, providing a broad and diverse set of evaluation tasks that can help us better understand what works, and what doesn't, in the world of embeddings.