Keras Hub and Universal Sentence Encoder

Keras
,
1

I have a binary classification model that uses Universal Sentence Encoder as a preprocessing layer to convert email subject lines to fixed-length embeddings. (The layer is trainable so that it can learn from my corpus of training data.) I’m currently loading the Keras layer from TensorFlow Hub, but I wonder if I can load it from Keras Hub.

use_layer = tfh.KerasLayer("https://tfhub.dev/google/universal-sentence-encoder/4", trainable=True)
subject_line_featurizer = tf.keras.Sequential([
  tf.keras.layers.Input(shape=(), dtype=tf.string, name="input_subject_line"),
  use_layer,
], name=subject_line_featurizer")

I don’t see Universal Sentence Encoder listed as one of the available models in Keras Hub. Am I missing something?

Also, should I try another prebuilt model for this layer? Gemma, perhaps?

3

I came across this description of the keras_hub.tokenizers.GemmaTokenizer class. Can we use it as a preprocessing layer to convert sentences to fixed-length embeddings?