#textvectorization

TextVectorization: adapt() with max_tokens vs. a fixed vocabulary for reproducible token indices

I'm designing a text preprocessing pipeline with tf.keras.layers.TextVectorization where token indices must stay identical across training sessions, because downstream checkpoints and a transfer-learning step depend on a stable mapping. Assume a recent TensorFlow 2.x release. My understanding is that adapt() builds the vocabulary from token frequencies, and

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kerasreproducibilitypreprocessing