Use this when your sparse features are in string or integer format, and you
want to distribute your inputs into a finite number of buckets by hashing.
output_id = Hash(input_feature_string) % bucket_size for string type input.
For int type input, the value is converted to its string representation first
and then hashed by the same formula.
For input dictionary features, features[key] is either Tensor or
SparseTensor. If Tensor, missing values can be represented by -1 for int
and '' for string, which will be dropped by this feature column.
A unique string identifying the input feature. It is used as the column
name and the dictionary key for feature parsing configs, feature Tensor
objects, and feature columns.
hash_bucket_size
An int > 1. The number of buckets.
dtype
The type of features. Only string and integer types are supported.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[]]