Showing models are contaminated by trusted benchmark data
Predict NCM codes from product descriptions
Compare AI models by voting on responses
Convert files to Markdown format
Detect if text was generated by GPT-2
Test SEO effectiveness of your content
Submit model predictions and view leaderboard results
List the capabilities of various AI models
Extract relationships and entities from text
Analyze text using tuned lens and visualize predictions
Deduplicate HuggingFace datasets in seconds
Find the best matching text for a query
Detect harms and risks with Granite Guardian 3.1 8B
Benchmark Data Contamination is a tool designed to analyze and identify potential contamination of machine learning models by trusted benchmark datasets. It helps users compare text similarities between models and original examples to uncover unintended memorization or replication of benchmark data. This tool is especially useful for evaluating model integrity and ensuring data privacy.
What is benchmark data contamination?
Benchmark data contamination occurs when models unintentionally memorize or replicate data from trusted benchmark datasets, potentially violating data privacy or skewing performance metrics.
How are contamination results interpreted?
Results are interpreted through similarity scores, where higher scores indicate greater contamination. Scores are benchmarked against industry standards to determine significance.
How can contamination be mitigated?
Mitigation strategies include data anonymization, dataset diversification, and regularization techniques to reduce model reliance on specific benchmark examples.