What happens when the vocab size of an embedded layer is larger than the text corpus used in training?
tf.keras.layers.Embedding(..., embeddings_initializer=&quot;uniform&quot;*,..., *kwargs)


All the weights are initialized with the init strategy
All learn the optimum values  with the backprop
Weights for which there is no input will have zero output every time, hence no learning.
Hence these extra weights will remain at their initialization value

You may check these extra weights before and after.
weight = model.layers[0].get_weights() # Save before training
history = model.fit(x, y)

# These two should be same
weight[0][-1]  # Last weight - Before
model.layers[0].get_weights()[0][-1] # Last weight - After