Deduplicating 401,000 Equipment Auction Records with LLM Calibration

The blog post discusses the development and application of a machine learning-based approach to deduplicating records in a dataset of used bulldozers. The key points are:

1. Dataset Context: The data comes from Kaggle's Bulldozer Blue Book for Machine Learning, which contains information about used bulldozers including model numbers, years, sale prices, and states.

2. GoldenMatch Library: This is an open-source Python library designed to help with record linkage and deduplication tasks. It uses a combination of string matching techniques and machine learning models (like GPT-4o-mini) to identify duplicate records accurately.

3. Optimal Configuration:

   • The post describes the creation of an optimal configuration for GoldenMatch that leverages advanced techniques such as ANN hybrid blocking and iterative LLM calibration.
   • ANN Hybrid Blocking: This technique helps in handling oversized blocks efficiently by using approximate nearest neighbors (ANN) to break them down into smaller, manageable sub-blocks. It uses Vertex AI's Matching Engine or a similar service.
   • Iterative Calibration: Instead of scoring every borderline candidate pair with the LLM, this approach samples pairs across the score range and refines the threshold iteratively until it converges.

Read the full article at DEV Community

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