Is there a faster way to convert sentences to TFHUB embeddings?

Question

So I am involved in a project that involves feeding a combination of text embeddings and image vectors into a DNN to arrive at the result. Now for the word embedding part, I am using TFHUB's Electra while for the image part I am using a NASNet Mobile network.

However, the issue I am facing is that while running the word embedding part, using the code shown below, the code just keeps running nonstop. It has been over 2 hours now and my training dataset has just 14900 rows of tweets.

Note - The input to the function is just a list of 14900 tweets.

tfhub_handle_encoder="https://tfhub.dev/google/electra_small/2" 
tfhub_handle_preprocess="https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3

# Load Models 
bert_preprocess_model = hub.KerasLayer(tfhub_handle_preprocess) 
bert_model = hub.KerasLayer(tfhub_handle_encoder)

def get_text_embedding(text):

  preprocessing_layer = hub.KerasLayer(tfhub_handle_preprocess, name='Preprocessor')   
  encoder_inputs = preprocessing_layer(text)   encoder = 
  hub.KerasLayer(tfhub_handle_encoder, trainable=True, name='Embeddings')   outputs = 
  encoder(encoder_inputs)   text_repr = outputs['pooled_output']   text_repr = 
  tf.keras.layers.Dense(128, activation='relu')(text_repr)

  return text_repr

text_repr = get_text_embedding(train_text)

Is there a faster way to get text representation using these models?

Thanks for the help!