Evaluate diversity in data sets to improve fairness
Visualize amino acid changes in protein sequences interactively
Generate a detailed dataset report
Explore and submit NER models
Mapping Nieman Lab's 2025 Journalism Predictions
Browse and filter LLM benchmark results
Browse and submit evaluation results for AI benchmarks
Evaluate LLMs using Kazakh MC tasks
Analyze and visualize car data
Uncensored General Intelligence Leaderboard
https://huggingface.co/spaces/VIDraft/mouse-webgen
Transfer GitHub repositories to Hugging Face Spaces
Generate financial charts from stock data
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.