Generate and view leaderboard for LLM evaluations
Text-To-Speech (TTS) Evaluation using objective metrics.
Persian Text Embedding Benchmark
Rank machines based on LLaMA 7B v2 benchmark results
Find recent high-liked Hugging Face models
Convert Hugging Face models to OpenVINO format
Display model benchmark results
Visualize model performance on function calling tasks
Create demo spaces for models on Hugging Face
Display leaderboard for earthquake intent classification models
Download a TriplaneGaussian model checkpoint
Explore and submit models using the LLM Leaderboard
Compare LLM performance across benchmarks
Arabic MMMLU Leaderborad is a model benchmarking tool designed to evaluate and compare the performance of different large language models (LLMs) on Arabic language tasks. It provides a comprehensive leaderboard where researchers and developers can assess model capabilities across a variety of NLP tasks specific to Arabic. The platform allows for transparent and standardized evaluation, enabling the community to track progress in Arabic NLP.
What is the purpose of the Arabic MMMLU Leaderborad?
The purpose is to provide a standardized platform for evaluating and comparing LLMs on Arabic language tasks, fostering transparency and collaboration in NLP research.
How can I get started with the leaderboard?
Start by preparing your model, selecting tasks, and following the step-by-step instructions provided on the platform.
Can I customize the evaluation metrics?
Yes, the platform allows users to define and track specific evaluation metrics tailored to their needs.