Explore how datasets shape classifier biases
Create detailed data reports
Submit evaluations for speaker tagging and view leaderboard
Calculate VRAM requirements for running large language models
Compare classifier performance on datasets
Migrate datasets from GitHub or Kaggle to Hugging Face Hub
Explore and filter model evaluation results
Evaluate model predictions and update leaderboard
Generate a data profile report
Execute commands and visualize data
Generate detailed data reports
Display and analyze PyTorch Image Models leaderboard
Embed and use ZeroEval for evaluation tasks
dataset-worldviews is a powerful tool designed to explore and analyze how different datasets influence the biases of classifiers. It provides insights into how various data compositions can shape the behavior and decision-making of machine learning models. By examining these relationships, users can better understand and address potential biases in their datasets.
What types of biases can dataset-worldviews detect?
dataset-worldviews can identify various types of biases, including selection bias, confirmation bias, and imbalanced class distributions.
How do I handle imbalanced data using dataset-worldviews?
The tool offers several strategies, including resampling techniques, cost-sensitive learning, and data augmentation.
What file formats does dataset-worldviews support?
dataset-worldviews supports common formats such as CSV, Excel, and Pandas DataFrames.