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this is a good Guide to the sentence piece izer and why itamp;#39;s such a powerful tool in order to feed language into a neural network you first have to convert that language into discrete numbers usually integers there are different strategies to do that and thereamp;#39;s pros and cons to each so one of them is character encodings so basically you give each character in the alphabet a specific number e will be one B will be two and you convert the entire text into a list of numbers this leads to a small vocabulary but moral sometimes has trouble learning all the associations and can be prone to just saying gibberish another strategy is Word level encodings the vocabularies quickly become very very large the advantage is the model wonamp;#39;t see any gibberish because it can only speak in terms of existing words but thereamp;#39;s also inconveniences the large vocabulary makes the need for a very very very large model and another inconvenience is that once the vocabulary has be
