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Within the context of neural networks for NLP, tokenization is vital for changing raw textual content details right into a structure suitable for input towards the model.
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Every single token is often represented for a numerical vector, and your complete sequence of tokens kinds the input for the neural network for jobs like language modeling, text classification, equipment translation, plus much more.
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