Have you ever wondered how to tackle the massive index size of multi-vector models like ColBERT? The traditional approach is to cluster embeddings after training, but this has its limitations. That’s where CRISP comes in – a paper by Google Deepmind that proposes integrating clustering during training to force the model to learn inherently ‘clusterable’ representations.
I recently spent the weekend analyzing an open-source PyTorch implementation of CRISP and was excited to see how it compares to the traditional post-hoc approach. The repository provides a clean head-to-head experiment to test the claim, and the results are fascinating.
By integrating clustering during training, the CRISP-tuned model assigns a significantly higher similarity score to the correct document. This is a game-changer for anyone working with multi-vector models. The implications are huge – imagine being able to search and retrieve documents more efficiently than ever before.
If you’re interested in exploring CRISP further, I recommend checking out the GitHub repository, which provides a detailed breakdown of the results. It’s a great resource for anyone looking to dive deeper into the world of machine learning.
What do you think about CRISP and its potential to revolutionize the field of machine learning? Share your thoughts in the comments!