Tokenizers documentation

Components

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# Components

When building a Tokenizer, you can attach various types of components to this Tokenizer in order to customize its behavior. This page lists most provided components.

## Normalizers

A Normalizer is in charge of pre-processing the input string in order to normalize it as relevant for a given use case. Some common examples of normalization are the Unicode normalization algorithms (NFD, NFKD, NFC & NFKC), lowercasing etc… The specificity of tokenizers is that we keep track of the alignment while normalizing. This is essential to allow mapping from the generated tokens back to the input text.

The Normalizer is optional.

Python
Rust
Node
Name Description Example
NFD NFD unicode normalization
NFKD NFKD unicode normalization
NFC NFC unicode normalization
NFKC NFKC unicode normalization
Lowercase Replaces all uppercase to lowercase Input: HELLO ὈΔΥΣΣΕΎΣ
Output: helloὀδυσσεύς
Strip Removes all whitespace characters on the specified sides (left, right or both) of the input Input: "hi"
Output: "hi"
StripAccents Removes all accent symbols in unicode (to be used with NFD for consistency) Input: é
Ouput: e
Replace Replaces a custom string or regexp and changes it with given content Replace("a", "e") will behave like this:
Input: "banana"
Ouput: "benene"
BertNormalizer Provides an implementation of the Normalizer used in the original BERT. Options that can be set are:
• clean_text
• handle_chinese_chars
• strip_accents
• lowercase
Sequence Composes multiple normalizers that will run in the provided order Sequence([NFKC(), Lowercase()])

## Pre-tokenizers

The PreTokenizer takes care of splitting the input according to a set of rules. This pre-processing lets you ensure that the underlying Model does not build tokens across multiple “splits”. For example if you don’t want to have whitespaces inside a token, then you can have a PreTokenizer that splits on these whitespaces.

You can easily combine multiple PreTokenizer together using a Sequence (see below). The PreTokenizer is also allowed to modify the string, just like a Normalizer does. This is necessary to allow some complicated algorithms that require to split before normalizing (e.g. the ByteLevel)

Python
Rust
Node
Name Description Example
ByteLevel Splits on whitespaces while remapping all the bytes to a set of visible characters. This technique as been introduced by OpenAI with GPT-2 and has some more or less nice properties:
• Since it maps on bytes, a tokenizer using this only requires 256 characters as initial alphabet (the number of values a byte can have), as opposed to the 130,000+ Unicode characters.
• A consequence of the previous point is that it is absolutely unnecessary to have an unknown token using this since we can represent anything with 256 tokens (Youhou!! 🎉🎉)
• For non ascii characters, it gets completely unreadable, but it works nonetheless!
Input: "Hello my friend, how are you?"
Ouput: "Hello", "Ġmy", Ġfriend", ",", "Ġhow", "Ġare", "Ġyou", "?"
Whitespace Splits on word boundaries (using the following regular expression: \w+|[^\w\s]+ Input: "Hello there!"
Output: "Hello", "there", "!"
WhitespaceSplit Splits on any whitespace character Input: "Hello there!"
Output: "Hello", "there!"
Punctuation Will isolate all punctuation characters Input: "Hello?"
Ouput: "Hello", "?"
Metaspace Splits on whitespaces and replaces them with a special char “▁” (U+2581) Input: "Hello there"
Ouput: "Hello", "▁there"
CharDelimiterSplit Splits on a given character Example with x:
Input: "Helloxthere"
Ouput: "Hello", "there"
Digits Splits the numbers from any other characters. Input: "Hello123there"
Output: "Hello", "123", "there"
Split Versatile pre-tokenizer that splits on provided pattern and according to provided behavior. The pattern can be inverted if necessary.
• pattern should be either a custom string or regexp.
• behavior should be one of:
• removed
• isolated
• merged_with_previous
• merged_with_next
• contiguous
• invert should be a boolean flag.
Example with pattern = , behavior = "isolated", invert = False:
Input: "Hello, how are you?"
Output: "Hello,", " ", "how", " ", "are", " ", "you?"
Sequence Lets you compose multiple PreTokenizer that will be run in the given order Sequence([Punctuation(), WhitespaceSplit()])

## Models

Models are the core algorithms used to actually tokenize, and therefore, they are the only mandatory component of a Tokenizer.

Name Description
WordLevel This is the “classic” tokenization algorithm. It let’s you simply map words to IDs without anything fancy. This has the advantage of being really simple to use and understand, but it requires extremely large vocabularies for a good coverage. Using this Model requires the use of a PreTokenizer. No choice will be made by this model directly, it simply maps input tokens to IDs.
BPE One of the most popular subword tokenization algorithm. The Byte-Pair-Encoding works by starting with characters, while merging those that are the most frequently seen together, thus creating new tokens. It then works iteratively to build new tokens out of the most frequent pairs it sees in a corpus. BPE is able to build words it has never seen by using multiple subword tokens, and thus requires smaller vocabularies, with less chances of having “unk” (unknown) tokens.
WordPiece This is a subword tokenization algorithm quite similar to BPE, used mainly by Google in models like BERT. It uses a greedy algorithm, that tries to build long words first, splitting in multiple tokens when entire words don’t exist in the vocabulary. This is different from BPE that starts from characters, building bigger tokens as possible. It uses the famous ## prefix to identify tokens that are part of a word (ie not starting a word).
Unigram Unigram is also a subword tokenization algorithm, and works by trying to identify the best set of subword tokens to maximize the probability for a given sentence. This is different from BPE in the way that this is not deterministic based on a set of rules applied sequentially. Instead Unigram will be able to compute multiple ways of tokenizing, while choosing the most probable one.

## Post-Processors

After the whole pipeline, we sometimes want to insert some special tokens before feed a tokenized string into a model like ”[CLS] My horse is amazing [SEP]”. The PostProcessor is the component doing just that.

Name Description Example
TemplateProcessing Let’s you easily template the post processing, adding special tokens, and specifying the type_id for each sequence/special token. The template is given two strings representing the single sequence and the pair of sequences, as well as a set of special tokens to use. Example, when specifying a template with these values:
• single: "[CLS] $A [SEP]" • pair: "[CLS]$A [SEP] \$B [SEP]"
• special tokens:
• "[CLS]"
• "[SEP]"

Input: ("I like this", "but not this")
Output: "[CLS] I like this [SEP] but not this [SEP]"

## Decoders

The Decoder knows how to go from the IDs used by the Tokenizer, back to a readable piece of text. Some Normalizer and PreTokenizer use special characters or identifiers that need to be reverted for example.

Name Description
ByteLevel Reverts the ByteLevel PreTokenizer. This PreTokenizer encodes at the byte-level, using a set of visible Unicode characters to represent each byte, so we need a Decoder to revert this process and get something readable again.
Metaspace Reverts the Metaspace PreTokenizer. This PreTokenizer uses a special identifer ▁ to identify whitespaces, and so this Decoder helps with decoding these.
WordPiece Reverts the WordPiece Model. This model uses a special identifier ##` for continuing subwords, and so this Decoder helps with decoding these.