Tokenization is an essential component of NLP systems. Although a number of tokenization schemes exist, they all have some limitations. For example, it is difficult to deal with
CANINE is a tokenization-free model with a large language encoder with a transformer stack at its core Its input is a sequence of unicode characters. This input is model agnostic and covers a wide range (>900) of languages.
The authors present three motives for CANINE.
In the model, the authors use hashing to reduce the number of parameters. Across the model, it is further reduced (downsampled) using strided convolutions. CANINE is trained on the NSP and MLM tasks (just like BERT). For the evalution, it uses the following three tasks