Explore and filter model evaluation results
Search and save datasets generated with a LLM in real time
Explore how datasets shape classifier biases
Generate images based on data
Migrate datasets from GitHub or Kaggle to Hugging Face Hub
Analyze and visualize your dataset using AI
Compare classifier performance on datasets
More advanced and challenging multi-task evaluation
Evaluate model predictions and update leaderboard
Select and analyze data subsets
This project is a GUI for the gpustack/gguf-parser-go
Analyze weekly and daily trader performance in Olas Predict
Transfer GitHub repositories to Hugging Face Spaces
GTBench is a data visualization tool designed to help users explore and filter model evaluation results. It provides an interactive interface to analyze and compare performance metrics of different models, enabling deeper insights into their effectiveness.
• Interactive Visualization: Explore model performance through dynamic and customizable visualizations. • Advanced Filtering: Apply filters to narrow down results based on specific criteria such as model type, dataset, or performance metrics. • Real-Time Updates: Get instant feedback as you adjust filters or visualization settings. • Multi-Model Support: Compare results from multiple models in a single interface. • Customizable Dashboards: Tailor the layout to focus on the metrics that matter most. • Export Capabilities: Save and share visualizations or raw data for further analysis.
What does GTBench stand for?
GTBench stands for Graph Tool Benchmark, a utility for analyzing and visualizing model evaluation data.
Can I use GTBench for models other than graphs?
Yes, GTBench supports a variety of model types, including but not limited to graph-based models.
How do I export visualization results from GTBench?
To export results, use the "Export" button in the toolbar, which allows you to save visualizations as images or raw data as CSV files.