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Common Signatures for Text

This page describes common signatures that should be implemented by modules in the TF1 Hub format for tasks that accept text inputs. (For the TF2 SavedModel format, see the analogous SavedModel API.)

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 a float32 tensor of shape [batch_size, N]. This is often called text embedding in dimension N.

Basic usage

  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)

Notes

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.

This interface does not allow fine-tuning of the text representation on TPUs, because it requires the module to instantiate both string processing and the trainable variables at the same time.