Generate and view leaderboard for LLM evaluations
Determine GPU requirements for large language models
Visualize model performance on function calling tasks
Leaderboard of information retrieval models in French
Measure over-refusal in LLMs using OR-Bench
Calculate memory usage for LLM models
Find recent high-liked Hugging Face models
Display benchmark results
Evaluate reward models for math reasoning
Persian Text Embedding Benchmark
Browse and evaluate ML tasks in MLIP Arena
Rank machines based on LLaMA 7B v2 benchmark results
Create and upload a Hugging Face model card
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.