How do you compare the quality of different embedding models? What is MTEB?

To compare the quality of different embedding models, datasets that reflect various aspects of text understanding and semantics are usually used. Key evaluation methods include:

• Clustering and visualization: how well embeddings group similar objects.
• Semantic search tasks: how effectively the model finds relevant documents for a query.
• Classification and ranking: accuracy in downstream tasks.
• Similarity metrics: for example, cosine similarity between embeddings.

MTEB (Massive Text Embedding Benchmark) is an open benchmark for evaluating embedding models across a variety of tasks (classification, clustering, search, semantic comparison, etc.). It combines different datasets and metrics, allowing comprehensive comparison of models based on their versatility and text representation quality.

Using MTEB helps to understand how well embeddings are suited for real-world applications, not just for a single specific task.