86 lines
4.1 KiB
Markdown
86 lines
4.1 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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# Tokenizer
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A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most
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of the tokenizers are available in two flavors: a full python implementation and a "Fast" implementation based on the
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Rust library [🤗 Tokenizers](https://github.com/huggingface/tokenizers). The "Fast" implementations allows:
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1. a significant speed-up in particular when doing batched tokenization and
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2. additional methods to map between the original string (character and words) and the token space (e.g. getting the
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index of the token comprising a given character or the span of characters corresponding to a given token).
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The base classes [`PreTrainedTokenizer`] and [`PreTrainedTokenizerFast`]
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implement the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python and
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"Fast" tokenizers either from a local file or directory or from a pretrained tokenizer provided by the library
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(downloaded from HuggingFace's AWS S3 repository). They both rely on
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[`~tokenization_utils_base.PreTrainedTokenizerBase`] that contains the common methods, and
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[`~tokenization_utils_base.SpecialTokensMixin`].
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[`PreTrainedTokenizer`] and [`PreTrainedTokenizerFast`] thus implement the main
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methods for using all the tokenizers:
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- Tokenizing (splitting strings in sub-word token strings), converting tokens strings to ids and back, and
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encoding/decoding (i.e., tokenizing and converting to integers).
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- Adding new tokens to the vocabulary in a way that is independent of the underlying structure (BPE, SentencePiece...).
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- Managing special tokens (like mask, beginning-of-sentence, etc.): adding them, assigning them to attributes in the
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tokenizer for easy access and making sure they are not split during tokenization.
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[`BatchEncoding`] holds the output of the
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[`~tokenization_utils_base.PreTrainedTokenizerBase`]'s encoding methods (`__call__`,
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`encode_plus` and `batch_encode_plus`) and is derived from a Python dictionary. When the tokenizer is a pure python
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tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by
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these methods (`input_ids`, `attention_mask`...). When the tokenizer is a "Fast" tokenizer (i.e., backed by
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HuggingFace [tokenizers library](https://github.com/huggingface/tokenizers)), this class provides in addition
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several advanced alignment methods which can be used to map between the original string (character and words) and the
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token space (e.g., getting the index of the token comprising a given character or the span of characters corresponding
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to a given token).
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## PreTrainedTokenizer
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[[autodoc]] PreTrainedTokenizer
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- __call__
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- add_tokens
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- add_special_tokens
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- apply_chat_template
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- batch_decode
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- decode
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- encode
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- push_to_hub
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- all
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## PreTrainedTokenizerFast
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The [`PreTrainedTokenizerFast`] depend on the [tokenizers](https://huggingface.co/docs/tokenizers) library. The tokenizers obtained from the 🤗 tokenizers library can be
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loaded very simply into 🤗 transformers. Take a look at the [Using tokenizers from 🤗 tokenizers](../fast_tokenizers) page to understand how this is done.
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[[autodoc]] PreTrainedTokenizerFast
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- __call__
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- add_tokens
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- add_special_tokens
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- apply_chat_template
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- batch_decode
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- decode
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- encode
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- push_to_hub
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- all
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## BatchEncoding
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[[autodoc]] BatchEncoding
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