Training pipeline will perform following transformation functions.
The categorical string as is--no change to case, punctuation,
spelling, tense, and so on.
Convert the category name to a dictionary lookup index and
generate an embedding for each index.
Categories that appear less than 5 times in the training dataset
are treated as the "unknown" category. The "unknown" category gets
its own special lookup index and resulting embedding.
Training pipeline will perform following transformation functions.
The categorical string as is--no change to case, punctuation,
spelling, tense, and so on.
Convert the category name to a dictionary lookup index and
generate an embedding for each index.
Categories that appear less than 5 times in the training dataset
are treated as the "unknown" category. The "unknown" category gets
its own special lookup index and resulting embedding.
Training pipeline will perform following transformation functions.
The categorical string as is--no change to case, punctuation,
spelling, tense, and so on.
Convert the category name to a dictionary lookup index and
generate an embedding for each index.
Categories that appear less than 5 times in the training dataset
are treated as the "unknown" category. The "unknown" category gets
its own special lookup index and resulting embedding.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2026-05-08 UTC."],[],[]]