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The TextVectorization in tf.keras enables you to do text tokenization and token indexing, to turn text into sequences of integers or into a categorical of bags of ngrams. Guide: https://colab.research.google.com/drive/1XcMJqKcTLjduIqjysRd1jGdRaKdetc3X …pic.twitter.com/UOkCVLotj8
You can use it either as part of a http://tf.data pipeline (for async CPU preprocessing with prefetching, when training the main model on GPU) or as part of your model itself -- to obtain an end-to-end model that expects raw strings as input.
Sounds kinda like a Gödel numbering scheme!
can I use it for word embedding?
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