Tokenizer¶
A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most of the tokenizers are available in two flavors: a full python implementation and a “Fast” implementation based on the Rust library tokenizers. The “Fast” implementations allows:
a significant speed-up in particular when doing batched tokenization and
additional methods to map between the original string (character and words) and the token space (e.g. getting the index of the token comprising a given character or the span of characters corresponding to a given token). Currently no “Fast” implementation is available for the SentencePiece-based tokenizers (for T5, ALBERT, CamemBERT, XLMRoBERTa and XLNet models).
The base classes PreTrainedTokenizer and PreTrainedTokenizerFast
implement the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python and
“Fast” tokenizers either from a local file or directory or from a pretrained tokenizer provided by the library
(downloaded from HuggingFace’s AWS S3 repository). They both rely on
PreTrainedTokenizerBase that contains the common methods, and
SpecialTokensMixin.
PreTrainedTokenizer and PreTrainedTokenizerFast thus implement the main
methods for using all the tokenizers:
Tokenizing (splitting strings in sub-word token strings), converting tokens strings to ids and back, and encoding/decoding (i.e., tokenizing and converting to integers).
Adding new tokens to the vocabulary in a way that is independent of the underlying structure (BPE, SentencePiece…).
Managing special tokens (like mask, beginning-of-sentence, etc.): adding them, assigning them to attributes in the tokenizer for easy access and making sure they are not split during tokenization.
BatchEncoding holds the output of the tokenizer’s encoding methods (__call__,
encode_plus and batch_encode_plus) and is derived from a Python dictionary. When the tokenizer is a pure python
tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by
these methods (input_ids, attention_mask…). When the tokenizer is a “Fast” tokenizer (i.e., backed by
HuggingFace tokenizers library), this class provides in addition
several advanced alignment methods which can be used to map between the original string (character and words) and the
token space (e.g., getting the index of the token comprising a given character or the span of characters corresponding
to a given token).