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.


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.





NFD unicode normalization


NFKD unicode normalization


NFC unicode normalization


NFKC unicode normalization


Replaces all uppercase to lowercase


Output: hello ὀδυσσεύς


Removes all whitespace characters on the specified sides (left, right or both) of the input

Input: " hi "

Output: "hi"


Removes all accent symbols in unicode (to be used with NFD for consistency)

Input: é

Ouput: e


Replaces a custom string or regexp and changes it with given content

Replace("a", "e") will behave like this:

Input: "banana" Ouput: "benene"


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


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)





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", "?"


Splits on word boundaries (using the following regular expression: \w+|[^\w\s]+

Input: "Hello there!"

Output: "Hello", "there", "!"


Splits on any whitespace character

Input: "Hello there!"

Output: "Hello", "there!"


Will isolate all punctuation characters

Input: "Hello?"

Ouput: "Hello", "?"


Splits on whitespaces and replaces them with a special char “▁” (U+2581)

Input: "Hello there"

Ouput: "Hello", "▁there"


Splits on a given character

Example with x:

Input: "Helloxthere"

Ouput: "Hello", "there"


Splits the numbers from any other characters.

Input: "Hello123there"

Output: `"Hello", "123", "there"`


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?"`


Lets you compose multiple PreTokenizer that will be run in the given order

Sequence([Punctuation(), WhitespaceSplit()])


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




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


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.


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 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.


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.





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]"


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.




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.


Reverts the Metaspace PreTokenizer. This PreTokenizer uses a special identifer to identify whitespaces, and so this Decoder helps with decoding these.


Reverts the WordPiece Model. This model uses a special identifier ## for continuing subwords, and so this Decoder helps with decoding these.