Jev vs RapidFuzz vs Sentence Transformers for Duplicate Records

It compares three ways to match duplicate product records: RapidFuzz fuzzy string matching, Sentence Transformers embeddings, and TypeSafe Jev, including a section that combines Sentence Transformers with Jev and a closing section on which approach to use.

The Abt-Buy benchmark, which has two product lists of 1,081 Abt products and 1,092 Buy.com products, plus a file of the 1,097 known matches used to count each tool's correct and wrong merges.

Merging every product in the first list with its closest match gave 740 correct merges and 341 wrong ones out of 1,081, so nearly one in three merges was wrong. The article shows a dishwasher example where the right match scored 75.9 while a wrong match scored 87.0.

The article installs rapidfuzz, sentence-transformers, typesafe-sdk, pandas, and python-dotenv with pip. The versions used are rapidfuzz v3.14.6, sentence-transformers v5.6.0, typesafe-sdk v0.7.5, and pandas v2.3.3.

The article shows only the call that matters for each tool; the data download and the complete script are in the GitHub companion folder linked from the post.

The article notes an LLM could decide what makes two products the same, for example with PydanticAI forcing a yes or no answer, but running one on every product would be slow and expensive. Jev is presented as a cheaper option that picks from fixed answers.

Fuzzy matching catches typos and word order, and embeddings catch different words with the same meaning, such as NYC and New York City. Neither handled two records describing the same product in different words, like a Haier HTR13 listed as a silver tube TV and as a 13in CRT TV with an ATSC tuner.