What Do MTEB Scores Mean, and How Should You Choose an Embedding Model?
MTEB, the Massive Text Embedding Benchmark [1], is the standard way to compare text embedding models — the models that turn a piece of text into a list of numbers, so that search and RAG systems can find related text. It collects many datasets into one score per model, and its public leaderboard on Hugging Face is where most teams go to pick a model. That leaderboard also has a per-language view, which is what this post is about. If you build search for Malayalam or Hindi, that column is probably the only evidence you have ever seen about how a model will behave in your language. ...