dedupe VS record-linkage-resources

I think this problem is known as data deduplication, in particular, entity deduplication. I googled a bit and it seems approaches vary from manual deduplication to some sort of active learning (if I am not mistaken). I am also curios if pre-trained transformer-based cross encoders can provide any good results (they are trained on sentences I think, but may be worth a try). Another problem here is how to measure progress (compare different approaches)?

I've had a good experience in the past with the dedupe package for these type activities. Unsure if it works for out-of-core type situations though, as my data set fit easily into memory.

Data deduplication is a super common problem, so it's useful experience to work on it. It's generally useful for companies, but I don't think it could be sold as a product unless is solving a very complicated, domain-specific de-duping problem. Otherwise, there are generic, open source de-duping tools such as: dedupe. It sounds like your model is similar to that.

Tools used: Python to scrape guest lists, dedupeio to better identify guests, Google Sheets to store and analyze the data, and Datawrapper to make the charts.

Try https://github.com/dedupeio/dedupe

Entity resolution/record linkage/deduplication is an oddly specialized domain of knowledge given that it's such a common problem. I put together a page of resources a while back if anyone is interested: https://github.com/ropeladder/record-linkage-resources