CANINE c (CANINE pre trained with autoregressive character loss) Pretrained CANINE model on 104 languages using a masked language modeling (MLM) objective. It was introduced in the paper CANINE: Pre training an Efficient Tokenization Free Encoder for Language Representation and first released in this repository. What's special about CANINE is that it doesn't require an explicit tokenizer (such as WordPiece or SentencePiece) as other models like BERT and RoBERTa. Instead, it directly operates at a character level: each character is turned into its Unicode code point. This means that input processing is trivial and can typically be accomplished as: The ord() function is part of Python, and turns each character into its Unicode code point. Disclaimer: The team releasing CANINE did not write a model card for this model so this model card has been written by the Hugging Face team. Model description CANINE is a transformers model pretrained on a large corpus of multilingual data in a self supervised fashion, similar to BERT. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automa…
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