This page describes common signatures that should be implemented by modules for tasks that accept text inputs.
Text feature vector
A text feature vector module creates a dense vector representation
from text features.
It accepts a batch of strings of shape
[batch_size] and maps them to
float32 tensor of shape
[batch_size, N]. This is often called
text embedding in dimension
embed = hub.Module("path/to/module") representations = embed([ "A long sentence.", "single-word", "http://example.com"])
Feature column usage
feature_columns = [ hub.text_embedding_column("comment", "path/to/module", trainable=False), ] input_fn = tf.estimator.inputs.numpy_input_fn(features, labels, shuffle=True) estimator = tf.estimator.DNNClassifier(hidden_units, feature_columns) estimator.train(input_fn, max_steps=100)
Modules have been pre-trained on different domains and/or tasks, and therefore not every text feature vector module would be suitable for your problem. E.g.: some modules could have been trained on a single language.