Display and submit LLM benchmarks
Text-To-Speech (TTS) Evaluation using objective metrics.
Evaluate model predictions with TruLens
Compare LLM performance across benchmarks
Submit deepfake detection models for evaluation
Convert PaddleOCR models to ONNX format
Request model evaluation on COCO val 2017 dataset
Explore and benchmark visual document retrieval models
Display LLM benchmark leaderboard and info
Convert Hugging Face model repo to Safetensors
View and submit machine learning model evaluations
Compare code model performance on benchmarks
Evaluate open LLMs in the languages of LATAM and Spain.
The š Multilingual MMLU Benchmark Leaderboard is a comprehensive platform designed for evaluating and comparing the performance of large language models (LLMs) across multiple languages. It provides a standardized framework to benchmark, submit, and track the performance of different models on a variety of tasks and datasets. This leaderboard serves as a central hub for researchers, developers, and practitioners to assess and improve multilingual language models in a transparent and competitive environment.
ā¢ Multilingual Support: The leaderboard evaluates models across dozens of languages, ensuring a comprehensive understanding of their global capabilities. ā¢ Comprehensive Benchmarking: It offers a wide range of tasks and datasets to assess models on translation, summarization, question-answering, and more. ā¢ Real-Time Tracking: Users can track model performance in real-time, enabling quick comparisons and updates. ā¢ Open Submission: Researchers and developers can submit their models for evaluation, fostering collaboration and innovation. ā¢ ** Detailed Results**: The leaderboard provides in-depth analysis and visualizations to help users understand model strengths and weaknesses. ā¢ Community Engagement: It encourages discussions and knowledge sharing among participants to advance the field of multilingual NLP.
1. What is the purpose of the š Multilingual MMLU Benchmark Leaderboard?
The leaderboard aims to provide a standardized platform for evaluating and comparing multilingual language models, promoting transparency and innovation in NLP research.
2. Can I submit my own model for evaluation?
Yes, the leaderboard allows researchers and developers to submit their models for evaluation, provided they adhere to the submission guidelines and requirements.
3. How often are the results updated?
The results are updated in real-time as new models are submitted and evaluated, ensuring the leaderboard reflects the latest advancements in multilingual NLP.