Teach, test, evaluate language models with MTEB Arena
Load AI models and prepare your space
Download a TriplaneGaussian model checkpoint
Compare code model performance on benchmarks
View NSQL Scores for Models
Convert Hugging Face models to OpenVINO format
Convert PaddleOCR models to ONNX format
Retrain models for new data at edge devices
Measure over-refusal in LLMs using OR-Bench
Rank machines based on LLaMA 7B v2 benchmark results
Upload a machine learning model to Hugging Face Hub
Persian Text Embedding Benchmark
Convert Stable Diffusion checkpoint to Diffusers and open a PR
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.