Analyze and visualize Hugging Face model download stats
NSFW Text Generator for Detecting NSFW Text
More advanced and challenging multi-task evaluation
Display and analyze PyTorch Image Models leaderboard
Multilingual metrics for the LMSys Arena Leaderboard
Generate benchmark plots for text generation models
Monitor application health
Submit evaluations for speaker tagging and view leaderboard
Display CLIP benchmark results for inference performance
Label data for machine learning models
Display competition information and manage submissions
Check system health
Analyze autism data and generate detailed reports
Transformer Stats is a data visualization tool designed to help users analyze and visualize download statistics of Hugging Face models. It provides insights into the popularity and usage trends of transformer-based models, empowering developers and researchers to make informed decisions. By leveraging this tool, users can gain a clearer understanding of model adoption and performance metrics.
• Real-time Statistics: Access up-to-date download counts and trends for Hugging Face models.
• Interactive Visualizations: Explore data through interactive charts and graphs for better comprehension.
• Model Comparison: Compare the performance and popularity of different transformer models.
• Customizable Filters: Narrow down data by specific models, timeframes, or categories.
• Download Trends: Track how model downloads change over time to identify patterns.
• User-Friendly Interface: Easy-to-use dashboard for seamless navigation and analysis.
What models does Transformer Stats support?
Transformer Stats supports a wide range of Hugging Face models, including popular transformer-based architectures like BERT, RoBERTa, and GPT models.
Is the data provided in real-time?
Yes, Transformer Stats provides real-time data, ensuring users have access to the most up-to-date download statistics.
How often is the data updated?
The data is updated continuously to reflect the latest download trends. For exact update frequencies, refer to the platform's documentation.