{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"MTEB evaluates text and multimodal embeddings against a suite of standardized tasks and benchmarks, providing scores and rankings for embedding models and retrieval systems.","skillfed_tags":["embedding-evaluation","benchmark-suite","information-retrieval"],"use_cases":["Benchmark a new sentence transformer model against standard tasks to compare its quality to published results.","Run MTEB tasks via CLI to evaluate multiple embedding models and generate results for submission to the leaderboard.","Select specific task subsets (e.g., only classification or retrieval) to evaluate embeddings on domain-relevant problems.","Load and analyze existing model results from the benchmark to understand performance patterns across tasks.","Integrate MTEB evaluation into a model training pipeline to measure embedding quality during development."],"what_it_does":"MTEB is a benchmarking framework for evaluating text and multimodal embeddings against a collection of standardized tasks. It provides both a Python API and a command-line interface to run models through tasks like text classification, semantic similarity, and information retrieval, collecting results that feed into a public leaderboard. The package wraps embedding models (from sentence_transformers, transformers, or custom implementations) and executes them against predefined task datasets, scoring performance using domain-specific metrics.\n\nThe framework is designed for researchers and practitioners who need to compare embedding quality across models or track performance improvements. It handles model loading, task selection, evaluation execution, and result aggregation. Dependencies include datasets for task data, numpy and scipy for numerical computation, torch and transformers for deep learning, and scikit-learn for metrics\u2014a typical modern ML stack. The package is actively maintained and in production use.","worth_installing":"Yes. MTEB is actively maintained, has no known vulnerabilities, uses a permissive license, and offers low-friction installation. It is the standard tool for evaluating text embeddings and retrieval systems in the community. Install it if you need to benchmark embeddings, compare models, or contribute results to the public leaderboard."},"id":"mteb","links":{"html":"https://skillfed.io/packages/mteb","md":"https://skillfed.io/packages/mteb.md","pypi":"https://pypi.org/project/mteb/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"mteb","python_support":"supports_current","summary":"Massive Text Embedding Benchmark"},"popularity":{"monthly_downloads":3180141,"position":2710,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.19.1"}