MTEB is a framework designed to evaluate the performance of text embedding models across a wide range of tasks and datasets. It offers a standardized way to benchmark embeddings models, facilitating comparison and reproducibility. MTEB primarily focuses on large-scale multilingual evaluations, covering multiple languages and use cases. Wide Task Coverage: Includes benchmarks for various tasks like: Text classification Clustering Semantic search Pair classification (e.g., entailment) Reranking Summarization Supports a diverse set of applications where embeddings are critical.
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