Teach, test, evaluate language models with MTEB Arena
View and compare language model evaluations
Measure execution times of BERT models using WebGPU and WASM
Explore and submit models using the LLM Leaderboard
Evaluate adversarial robustness using generative models
Display and submit LLM benchmarks
Browse and submit LLM evaluations
Browse and submit evaluations for CaselawQA benchmarks
Evaluate code generation with diverse feedback types
Evaluate model predictions with TruLens
Merge machine learning models using a YAML configuration file
Evaluate open LLMs in the languages of LATAM and Spain.
Upload ML model to Hugging Face Hub
MTEB Arena is a comprehensive platform designed for model benchmarking, specifically tailored for teaching, testing, and evaluating language models. It provides an intuitive environment where users can compare, analyze, and optimize the performance of language models across various tasks and datasets. Whether you're a researcher or a developer, MTEB Arena streamlines the process of understanding and improving model capabilities.
• Support for Multiple Models: Easily integrate and benchmark different language models.
• Extensive Benchmark Suites: Access a wide range of pre-defined tasks and datasets for evaluation.
• Customizable Workflows: Tailor evaluations to specific use cases or requirements.
• Cross-Model Comparisons: Compare performance metrics of multiple models side by side.
• Reproducibility Tools: Ensure consistent and reliable results with robust evaluation pipelines.
• Advanced Visualization: Gain insights through detailed graphs, charts, and analysis tools.
What models are supported by MTEB Arena?
MTEB Arena supports a wide range of popular language models, including but not limited to transformers and other state-of-the-art architectures.
Can I use custom datasets with MTEB Arena?
Yes, MTEB Arena allows users to upload and use custom datasets for evaluation, providing flexibility for specific use cases.
How do I ensure reproducibility in my evaluations?
MTEB Arena provides tools for setting fixed seeds, saving configurations, and replicating experiments to ensure reproducible results.