Calibrating a similarity threshold

0.80 is a fact about one embedding model measured on one collection of pairs, and it moves the moment either changes, so a global rule about sameness travels badly. This chapter calibrates a cut-off against labelled pairs, reads precision and recall at each operating point, shows why raising the bar to 0.95 discards a third of the real duplicates while keeping the reversed-role clause that prompted the change, and puts the score where it belongs: in front of a second stage that can see what cosine cannot.

Vector arithmetic — read the current version at https://daviddominguez.dev/learn/en/vector-arithmetic/calibrating-a-similarity-threshold

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