hub_sparse_text_embedding_column

Module to construct dense representations from sparse text features.

Description

The input to this feature column is a batch of multiple strings with arbitrary size, assuming the input is a SparseTensor.

Usage

 
hub_sparse_text_embedding_column( 
  key, 
  module_spec, 
  combiner, 
  default_value, 
  trainable = FALSE 
)

Arguments

Arguments	Description
key	A string or feature_column identifying the text feature.
module_spec	A string handle or a _ModuleSpec identifying the module.
combiner	a string specifying reducing op for embeddings in the same Example. Currently, ‘mean’, ‘sqrtn’, ‘sum’ are supported. Using `combiner = NULL` is undefined.
default_value	default value for Examples where the text feature is empty. Note, it’s recommended to have default_value consistent OOV tokens, in case there was special handling of OOV in the text module. If `NULL`, the text feature is assumed be non-empty for each Example.
trainable	Whether or not the Module is trainable. `FALSE` by default, meaning the pre-trained weights are frozen. This is different from the ordinary `tf.feature_column.embedding_column()`, but that one is intended for training from scratch.

Details

This type of feature column is typically suited for modules that operate on pre-tokenized text to produce token level embeddings which are combined with the combiner into a text embedding. The combiner always treats the tokens as a bag of words rather than a sequence. The output (i.e., transformed input layer) is a DenseTensor, with shape [batch_size, num_embedding_dim].