Browse and submit language model benchmarks
Explain GPU usage for model training
Upload ML model to Hugging Face Hub
Demo of the new, massively multilingual leaderboard
Compare model weights and visualize differences
Request model evaluation on COCO val 2017 dataset
Submit deepfake detection models for evaluation
Browse and evaluate language models
Calculate VRAM requirements for LLM models
View and submit LLM benchmark evaluations
Compare code model performance on benchmarks
Push a ML model to Hugging Face Hub
Explore and benchmark visual document retrieval models
The HHEM Leaderboard is a platform designed for model benchmarking, allowing users to browse and submit language model benchmarks. It serves as a centralized hub for comparing the performance of various language models across different tasks and datasets. The leaderboard provides a transparent and standardized way to track advancements in language model capabilities.
What does HHEM stand for?
HHEM stands for Human-Human Empirical Metrics, focusing on evaluating language models based on human-like performance benchmarks.
Can I submit my own language model benchmarks?
Yes, HHEM Leaderboard allows users to submit benchmarks for their own language models, provided they follow the submission guidelines and criteria.
How often are the benchmarks updated?
The benchmarks are updated regularly as new models are submitted or as existing models are re-evaluated with updated metrics.