MinHash: benchmark memory, speed and accuracy with varying r and b about open-nllb

[CreativeSelf0]

fine-tuning the parameters rows per band and number of bands in MinHash depends heavily on the data and the specific use case, such as fuzzy deduplication of text pairs in your scenario. These parameters control the trade-off between accuracy and performance (speed and memory usage):

1. Accuracy: Increasing r and b generally increases the accuracy of the MinHash similarity estimates, as more hash functions are utilized, capturing more aspects of the data.
2. Speed: However, higher values of r and b can slow down the computation, as more hash functions need to be evaluated.
3. Memory Usage: Likewise, more memory is required to store the additional hash values.

The ideal settings for r and b could vary depending on:

• The size and nature of your dataset.
• The level of similarity/dissimilarity among text pairs in your dataset.
• The computational resources available to you.
• Your tolerance for error versus your need for speed and lower memory usage.

An empirical approach, where you run experiments with different values of r and b on a representative subset of your data, can be very informative. By analyzing how the performance metrics (speed, memory usage, and accuracy) change with different settings, you can better understand the trade-offs and find an optimal configuration for your specific use case.