Generate and view leaderboard for LLM evaluations
Browse and evaluate ML tasks in MLIP Arena
Calculate VRAM requirements for LLM models
Display leaderboard of language model evaluations
GIFT-Eval: A Benchmark for General Time Series Forecasting
Display genomic embedding leaderboard
Run benchmarks on prediction models
Track, rank and evaluate open LLMs and chatbots
Convert Hugging Face models to OpenVINO format
Evaluate model predictions with TruLens
Visualize model performance on function calling tasks
Text-To-Speech (TTS) Evaluation using objective metrics.
Pergel: A Unified Benchmark for Evaluating Turkish LLMs
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.