Generate and view leaderboard for LLM evaluations
View NSQL Scores for Models
Compare code model performance on benchmarks
Display and submit LLM benchmarks
Evaluate reward models for math reasoning
Benchmark LLMs in accuracy and translation across languages
Find recent high-liked Hugging Face models
Measure execution times of BERT models using WebGPU and WASM
Text-To-Speech (TTS) Evaluation using objective metrics.
Browse and submit evaluations for CaselawQA benchmarks
Calculate survival probability based on passenger details
View and compare language model evaluations
Browse and filter ML model leaderboard data
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.