Evaluate diversity in data sets to improve fairness
Calculate VRAM requirements for running large language models
Check system health
Analyze Shark Tank India episodes
Mapping Nieman Lab's 2025 Journalism Predictions
Analyze and visualize car data
Generate detailed data profile reports
Explore and filter model evaluation results
Display a Bokeh plot
Uncensored General Intelligence Leaderboard
Multilingual metrics for the LMSys Arena Leaderboard
Visualize dataset distributions with facets
Browse LLM benchmark results in various categories
Measuring-diversity is a tool designed to evaluate diversity in data sets with the goal of improving fairness and reducing bias. It provides insights into how well-represented different groups are within a dataset, helping users identify disparities and take corrective actions.
• Comprehensive analysis: Assess diversity across multiple dimensions such as gender, race, age, and more.
• Bias detection: Identify underrepresented or overrepresented groups in your data.
• Visualization tools: Generate charts and graphs to clearly illustrate diversity metrics.
• Customizable thresholds: Set benchmarks for fairness and receive alerts when thresholds are not met.
• Integration: Easily incorporate into existing data workflows and pipelines.
pip install measuring-diversity).What types of data can measuring-diversity analyze?
Measuring-diversity can analyze any structured dataset, including CSV files, databases, and DataFrames. It is particularly effective for datasets with demographic information.
How does measuring-diversity detect bias?
The tool compares the representation of different groups in your dataset to predefined fairness thresholds. If a group falls below the threshold, it is flagged as underrepresented.
Can I customize the fairness thresholds?
Yes, measuring-diversity allows users to set custom thresholds based on their specific needs or industry standards. This ensures tailored fairness evaluations.