Models

Models

PythonRustNode

BPE

class tokenizers.models.BPE

( vocab = Nonemerges = Nonecache_capacity = Nonedropout = Noneunk_token = Nonecontinuing_subword_prefix = Noneend_of_word_suffix = Nonefuse_unk = Nonebyte_fallback = False )

Parameters

• vocab (Dict[str, int], optional) — A dictionnary of string keys and their ids {"am": 0,...}

• merges (List[Tuple[str, str]], optional) — A list of pairs of tokens (Tuple[str, str]) [("a", "b"),...]

• cache_capacity (int, optional) — The number of words that the BPE cache can contain. The cache allows to speed-up the process by keeping the result of the merge operations for a number of words.

• dropout (float, optional) — A float between 0 and 1 that represents the BPE dropout to use.

• unk_token (str, optional) — The unknown token to be used by the model.

• continuing_subword_prefix (str, optional) — The prefix to attach to subword units that don’t represent a beginning of word.

• end_of_word_suffix (str, optional) — The suffix to attach to subword units that represent an end of word.

• fuse_unk (bool, optional) — Whether to fuse any subsequent unknown tokens into a single one

• byte_fallback (bool, optional) — Whether to use spm byte-fallback trick (defaults to False)

An implementation of the BPE (Byte-Pair Encoding) algorithm

from_file

( vocabmerge**kwargs ) → BPE

Parameters

• vocab (str) — The path to a vocab.json file

• merges (str) — The path to a merges.txt file

Returns

BPE

An instance of BPE loaded from these files

Instantiate a BPE model from the given files.

This method is roughly equivalent to doing:

Copied

vocab, merges = BPE.read_file(vocab_filename, merges_filename)
bpe = BPE(vocab, merges)

If you don’t need to keep the vocab, merges values lying around, this method is more optimized than manually calling read_file() to initialize a BPE

read_file

( vocabmerges ) → A Tuple with the vocab and the merges

Parameters

• vocab (str) — The path to a vocab.json file

• merges (str) — The path to a merges.txt file

Returns

A Tuple with the vocab and the merges

The vocabulary and merges loaded into memory

Read a vocab.json and a merges.txt files

This method provides a way to read and parse the content of these files, returning the relevant data structures. If you want to instantiate some BPE models from memory, this method gives you the expected input from the standard files.

Model

class tokenizers.models.Model

( )

Base class for all models

The model represents the actual tokenization algorithm. This is the part that will contain and manage the learned vocabulary.

This class cannot be constructed directly. Please use one of the concrete models.

get_trainer

( ) → Trainer

Returns

Trainer

The Trainer used to train this model

Get the associated Trainer

Retrieve the Trainer associated to this Model.

id_to_token

( id ) → str

Parameters

• id (int) — An ID to convert to a token

Returns

str

The token associated to the ID

Get the token associated to an ID

save

( folderprefix ) → List[str]

Parameters

• folder (str) — The path to the target folder in which to save the various files

• prefix (str, optional) — An optional prefix, used to prefix each file name

Returns

List[str]

The list of saved files

Save the current model

Save the current model in the given folder, using the given prefix for the various files that will get created. Any file with the same name that already exists in this folder will be overwritten.

token_to_id

( tokens ) → int

Parameters

• token (str) — A token to convert to an ID

Returns

int

The ID associated to the token

Get the ID associated to a token

tokenize

( sequence ) → A List of Token

Parameters

• sequence (str) — A sequence to tokenize

Returns

A List of Token

The generated tokens

Tokenize a sequence

Unigram

class tokenizers.models.Unigram

( vocabunk_idbyte_fallback )

Parameters

• vocab (List[Tuple[str, float]], optional, optional) — A list of vocabulary items and their relative score [(“am”, -0.2442),…]

An implementation of the Unigram algorithm

WordLevel

class tokenizers.models.WordLevel

( vocabunk_token )

Parameters

• vocab (str, optional) — A dictionnary of string keys and their ids {"am": 0,...}

• unk_token (str, optional) — The unknown token to be used by the model.

An implementation of the WordLevel algorithm

Most simple tokenizer model based on mapping tokens to their corresponding id.

from_file

( vocabunk_token ) → WordLevel

Parameters

• vocab (str) — The path to a vocab.json file

Returns

WordLevel

An instance of WordLevel loaded from file

Instantiate a WordLevel model from the given file

This method is roughly equivalent to doing:

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vocab = WordLevel.read_file(vocab_filename)
wordlevel = WordLevel(vocab)

If you don’t need to keep the vocab values lying around, this method is more optimized than manually calling read_file() to initialize a WordLevel

read_file

( vocab ) → Dict[str, int]

Parameters

• vocab (str) — The path to a vocab.json file

Returns

Dict[str, int]

The vocabulary as a dict

Read a vocab.json

This method provides a way to read and parse the content of a vocabulary file, returning the relevant data structures. If you want to instantiate some WordLevel models from memory, this method gives you the expected input from the standard files.

WordPiece

class tokenizers.models.WordPiece

( vocabunk_tokenmax_input_chars_per_word )

Parameters

• vocab (Dict[str, int], optional) — A dictionnary of string keys and their ids {"am": 0,...}

• unk_token (str, optional) — The unknown token to be used by the model.

• max_input_chars_per_word (int, optional) — The maximum number of characters to authorize in a single word.

An implementation of the WordPiece algorithm

from_file

( vocab**kwargs ) → WordPiece

Parameters

• vocab (str) — The path to a vocab.txt file

Returns

WordPiece

An instance of WordPiece loaded from file

Instantiate a WordPiece model from the given file

This method is roughly equivalent to doing:

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vocab = WordPiece.read_file(vocab_filename)
wordpiece = WordPiece(vocab)

If you don’t need to keep the vocab values lying around, this method is more optimized than manually calling read_file() to initialize a WordPiece

read_file

( vocab ) → Dict[str, int]

Parameters

• vocab (str) — The path to a vocab.txt file

Returns

Dict[str, int]

The vocabulary as a dict

Read a vocab.txt file

This method provides a way to read and parse the content of a standard vocab.txt file as used by the WordPiece Model, returning the relevant data structures. If you want to instantiate some WordPiece models from memory, this method gives you the expected input from the standard files.