The question was closed without an answer, so I will reopen it. I had a model that I got around 90% accuracy, but was unable to submit it when I was taking the exam and didn’t pass because I wasn’t able to submit my working model and failed the entire exam when I passed the rest of it.
"When I try to do model.save(‘filename.h5’) for a model with text vectorizer & embedding involved, I get below error msg: Save or restore weights that is not an instance of tf.Variable is not supported in h5, use save_format='tf' instead. Received a model or layer TextVectorization with weights
However, given that in the TF cert exam we need to upload .h5 models (i.e., tf format might not help), how do we handle this situation?"*
Unfortunately, model with Text Vectorization layer does not support saving in .h5 format.
Going forward, the ideal way to use it would be to use the new saving format with model.save("model.keras", save_format="keras").
We will bring it to Tensorflow certification team’s attention, till then the workaround would be to use the Text Vectorization in your data pipeline to something like Load text | TensorFlow Core and then use the normal Keras Sequential API for the model part.