Massive Text Embedding Benchmark

non-profit

https://github.com/embeddings-benchmark

embeddings-benchmark

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Massive Text Embeddings Benchmark

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Muennighoff submitted a paper 3 days ago

Prefix Sliding for efficient test-time scaling

rasdani authored a paper 5 days ago

Prime Agent: A Self-Improving RLM Harness

rasdani authored a paper 5 days ago

INTELLECT-3: Technical Report

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Papers

MVEB: Massive Video Embedding Benchmark

MAEB: Massive Audio Embedding Benchmark

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MTEB is a Python framework for evaluating embeddings and retrieval systems for both text and image. MTEB covers more than 1000 languages and diverse tasks, from classics like classification and clustering to use-case specialized tasks such as legal, code, or healthcare retrieval.

You can get started using mteb.

Overview
📈 Leaderboard	The interactive leaderboard of the benchmark
Get Started.
🏃 Get Started	Overview of how to use mteb
🤖 Defining Models	How to use existing model and define custom ones
📋 Selecting tasks	How to select tasks, benchmarks, splits etc.
🏭 Running Evaluation	How to run the evaluations, including cache management, speeding up evaluations etc.
📊 Loading Results	How to load and work with existing model results
Overview.
📋 Tasks	Overview of available tasks
📐 Benchmarks	Overview of available benchmarks
🤖 Models	Overview of available Models
Contributing
🤖 Adding a model	How to submit a model to MTEB and to the leaderboard
👩‍💻 Adding a dataset	How to add a new task/dataset to MTEB
👩‍💻 Adding a benchmark	How to add a new benchmark to MTEB and to the leaderboard
🤝 Contributing	How to contribute to MTEB and set it up for development