Generate and view leaderboard for LLM evaluations
SolidityBench Leaderboard
Browse and filter ML model leaderboard data
Teach, test, evaluate language models with MTEB Arena
Display leaderboard of language model evaluations
Determine GPU requirements for large language models
Convert PyTorch models to waifu2x-ios format
Rank machines based on LLaMA 7B v2 benchmark results
Display LLM benchmark leaderboard and info
Display genomic embedding leaderboard
Calculate memory usage for LLM models
Browse and submit model evaluations in LLM benchmarks
Browse and submit LLM evaluations
The Arabic MMMLU Leaderborad is a platform designed to evaluate and compare the performance of large language models (LLMs) specifically for the Arabic language. It provides a comprehensive leaderboard that ranks models based on their performance across various tasks and metrics, offering insights into their capabilities and limitations.
What is the purpose of the Arabic MMMLU Leaderborad?
The platform aims to provide a standardized way to evaluate and compare Arabic language models, helping researchers and developers identify top-performing models for specific tasks.
How are models ranked on the leaderboard?
Models are ranked based on their performance across a variety of tasks and datasets. Rankings are updated regularly as new evaluations are conducted.
Can I submit my own model for evaluation?
Yes, the platform allows submissions from researchers and developers. Check the submission guidelines for requirements and instructions.