# Define early stopping callback
early_stop = keras.callbacks.EarlyStopping(
    monitor="loss", patience=3, min_delta=0.001
)

# Define TensorBoard callback
logdir = os.path.join(".", "logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S"))
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)

# Model Input
left_inputs, right_inputs, similarity = process_model_input(data)

# Train the encoder model
history = encoder.fit(
    [left_inputs, right_inputs],
    similarity,
    batch_size=8,
    epochs=20,
    validation_split=0.2,
    callbacks=[early_stop, tensorboard_callback],
)

# Define model input
inputs = keras.Input(shape=[], dtype=tf.string)

# Pass the input through the embedding layer
embedding = hub.KerasLayer(embedding_layer)(inputs)

# Create the tuned model
tuned_model = keras.Model(inputs=inputs, outputs=embedding)