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:

input_ids = [ord(char) for char in text]

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 automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:

• Masked language modeling (MLM): one randomly masks part of the inputs, which the model needs to predict. This model (CANINE-c) is trained with an autoregressive character loss. One masks several character spans within each sequence, which the model then autoregressively predicts.
• Next sentence prediction (NSP): the model concatenates two sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.

This way, the model learns an inner representation of multiple languages that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the CANINE model as inputs.

Intended uses & limitations

You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the to look for fine-tuned versions on a task that interests you.