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hello everyone today were taking a look at by t5 towards the token-free future with pre-trained by tobite models majority of existing pre-trained language models typically operate on the subword level meaning that they operate on sequences of tokens representing sub-word units for example if we take a look at a sentence such as in japan cloisonne and amelies are animals are well known as shipoyaki what a typical pre-processing model such as sentence piece will do is its going to split the tokens into sub-word units so some words which are occurring frequently in the data set such as in and japan are going to be represented as a single entity or single single representation whereas more rare words such as close on uh here will be represented in this case with three tokens or three subword units and the model is then trained to process inputs and outputs in such a way using those subword units this has been very effective however has a number of limitations um practically so most nota