Demo of the new, massively multilingual leaderboard
View RL Benchmark Reports
Display model benchmark results
Convert Hugging Face models to OpenVINO format
Run benchmarks on prediction models
Browse and evaluate ML tasks in MLIP Arena
View and compare language model evaluations
Evaluate adversarial robustness using generative models
Explore and submit models using the LLM Leaderboard
Optimize and train foundation models using IBM's FMS
View and submit language model evaluations
Generate and view leaderboard for LLM evaluations
Benchmark AI models by comparison
Leaderboard 2 Demo is a cutting-edge tool designed for model benchmarking. It serves as a demo of the new, massively multilingual leaderboard, enabling users to select and customize benchmark tests for evaluating AI models across multiple languages. This platform simplifies the process of comparing model performance and identifying strengths and weaknesses in various linguistic contexts.
• Multilingual Support: Evaluate models across a wide range of languages. • Customizable Benchmarks: Tailor benchmark tests to specific requirements. • Interactive Interface: User-friendly design for easy navigation and analysis. • Visualizations: Detailed graphs and charts to present results clearly. • Cross-Model Comparison: Compare performance metrics of different models side-by-side.
What is the purpose of Leaderboard 2 Demo ?
The Leaderboard 2 Demo is designed to provide a robust platform for benchmarking and comparing AI models, particularly focusing on multilingual evaluation. It helps users identify the strengths and weaknesses of different models across various languages.
How do I get started with Leaderboard 2 Demo ?
To get started, access the platform, select the models you wish to evaluate, customize the benchmark settings, and run the tests. The interface is designed to be user-friendly, guiding you through each step seamlessly.
Which languages are supported by Leaderboard 2 Demo ?
Leaderboard 2 Demo supports a wide range of languages, making it a powerful tool for multilingual model evaluation. The exact list of supported languages can be found on the platform or in the documentation.