Training pipeline will perform following transformation functions.
The text as is--no change to case, punctuation, spelling, tense,
and so on.
Tokenize text to words. Convert each words to a dictionary lookup
index and generate an embedding for each index. Combine the
embedding of all elements into a single embedding using the mean.
Tokenization is based on unicode script boundaries.
Missing values get their own lookup index and resulting embedding.
Stop-words receive no special treatment and are not removed.
Training pipeline will perform following transformation functions.
The text as is--no change to case, punctuation, spelling, tense,
and so on.
Tokenize text to words. Convert each words to a dictionary lookup
index and generate an embedding for each index. Combine the
embedding of all elements into a single embedding using the mean.
Tokenization is based on unicode script boundaries.
Missing values get their own lookup index and resulting embedding.
Stop-words receive no special treatment and are not removed.
Training pipeline will perform following transformation functions.
The text as is--no change to case, punctuation, spelling, tense,
and so on.
Tokenize text to words. Convert each words to a dictionary lookup
index and generate an embedding for each index. Combine the
embedding of all elements into a single embedding using the mean.
Tokenization is based on unicode script boundaries.
Missing values get their own lookup index and resulting embedding.
Stop-words receive no special treatment and are not removed.
[[["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 2026-04-01 UTC."],[],[]]