Teach, test, evaluate language models with MTEB Arena
Submit deepfake detection models for evaluation
Compare code model performance on benchmarks
Open Persian LLM Leaderboard
Convert a Stable Diffusion XL checkpoint to Diffusers and open a PR
Convert Hugging Face model repo to Safetensors
Request model evaluation on COCO val 2017 dataset
Submit models for evaluation and view leaderboard
View RL Benchmark Reports
Display genomic embedding leaderboard
Create and manage ML pipelines with ZenML Dashboard
Visualize model performance on function calling tasks
Convert PyTorch models to waifu2x-ios format
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.