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From the documentation of TextVectorization:

max_tokens: Maximum size of the vocabulary for this layer. This should only be specified when adapting a vocabulary or when setting pad_to_max_tokens=True. Note that this vocabulary contains 1 OOV token, so the effective number of tokens is (max_tokens - 1 - (1 if output_mode == "int" else 0)).

output_mode="int": Outputs integer indices, one integer index per split string token. When output_mode == "int", 0 is reserved for masked locations; this reduces the vocab size to max_tokens - 2 instead of max_tokens - 1

The above quotes explain that in the typical encoding scenario (output_mode==int), there are two reserved tokens (OOV and masking), so the actual tokens (are num_tokens-2).

Question 1: It is mentioned that masking is encoded as integer 0, so to which integer is OOV encoded?

Question 2: In the following popular tutorial from keras.io (https://keras.io/examples/nlp/text_classification_from_scratch/):

vectorize_layer = keras.layers.TextVectorization(
    standardize=custom_standardization,
    max_tokens=max_features,
    output_mode="int",
    output_sequence_length=sequence_length,
)
...
text_input = keras.Input(shape=(1,), dtype=tf.string, name='text')
x = vectorize_layer(text_input)
x = layers.Embedding(max_features + 1, embedding_dim)(x)

Can someone explain why +1 is added in the embedding? The TextVectorization has already accounted for the two reserved keywords. Also, assuming the TextVectorization can only generate |max_features| different outputs from (0 to max_features-1), it seems impossible to generate |max_features + 1| as requested in the embedding parameter.

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