Tokenizer

Tokenizer

PythonRustNode

Tokenizer

class tokenizers.Tokenizer

( model )

Parameters

• model (Model) — The core algorithm that this Tokenizer should be using.

A Tokenizer works as a pipeline. It processes some raw text as input and outputs an Encoding.

propertydecoder

The optional Decoder in use by the Tokenizer

propertymodel

The Model in use by the Tokenizer

propertynormalizer

The optional Normalizer in use by the Tokenizer

propertypadding

Returns

(dict, optional)

A dict with the current padding parameters if padding is enabled

Get the current padding parameters

Cannot be set, use enable_padding() instead

propertypost_processor

The optional PostProcessor in use by the Tokenizer

propertypre_tokenizer

The optional PreTokenizer in use by the Tokenizer

propertytruncation

Returns

(dict, optional)

A dict with the current truncation parameters if truncation is enabled

Get the currently set truncation parameters

Cannot set, use enable_truncation() instead

add_special_tokens

( tokens ) → int

Parameters

• tokens (A List of AddedToken or str) — The list of special tokens we want to add to the vocabulary. Each token can either be a string or an instance of AddedToken for more customization.

Returns

int

The number of tokens that were created in the vocabulary

Add the given special tokens to the Tokenizer.

If these tokens are already part of the vocabulary, it just let the Tokenizer know about them. If they don’t exist, the Tokenizer creates them, giving them a new id.

These special tokens will never be processed by the model (ie won’t be split into multiple tokens), and they can be removed from the output when decoding.

add_tokens

( tokens ) → int

Parameters

• tokens (A List of AddedToken or str) — The list of tokens we want to add to the vocabulary. Each token can be either a string or an instance of AddedToken for more customization.

Returns

int

The number of tokens that were created in the vocabulary

Add the given tokens to the vocabulary

The given tokens are added only if they don’t already exist in the vocabulary. Each token then gets a new attributed id.

decode

( idsskip_special_tokens = True ) → str

Parameters

• ids (A List/Tuple of int) — The list of ids that we want to decode

• skip_special_tokens (bool, defaults to True) — Whether the special tokens should be removed from the decoded string

Returns

str

The decoded string

Decode the given list of ids back to a string

This is used to decode anything coming back from a Language Model

decode_batch

( sequencesskip_special_tokens = True ) → List[str]

Parameters

• sequences (List of List[int]) — The batch of sequences we want to decode

• skip_special_tokens (bool, defaults to True) — Whether the special tokens should be removed from the decoded strings

Returns

List[str]

A list of decoded strings

Decode a batch of ids back to their corresponding string

enable_padding

( direction = 'right'pad_id = 0pad_type_id = 0pad_token = '[PAD]'length = Nonepad_to_multiple_of = None )

Parameters

• direction (str, optional, defaults to right) — The direction in which to pad. Can be either right or left

• pad_to_multiple_of (int, optional) — If specified, the padding length should always snap to the next multiple of the given value. For example if we were going to pad witha length of 250 but pad_to_multiple_of=8 then we will pad to 256.

• pad_id (int, defaults to 0) — The id to be used when padding

• pad_type_id (int, defaults to 0) — The type id to be used when padding

• pad_token (str, defaults to [PAD]) — The pad token to be used when padding

• length (int, optional) — If specified, the length at which to pad. If not specified we pad using the size of the longest sequence in a batch.

Enable the padding

enable_truncation

( max_lengthstride = 0strategy = 'longest_first'direction = 'right' )

Parameters

• max_length (int) — The max length at which to truncate

• stride (int, optional) — The length of the previous first sequence to be included in the overflowing sequence

• strategy (str, optional, defaults to longest_first) — The strategy used to truncation. Can be one of longest_first, only_first or only_second.

• direction (str, defaults to right) — Truncate direction

Enable truncation

encode

( sequencepair = Noneis_pretokenized = Falseadd_special_tokens = True ) → Encoding

Parameters

• sequence (~tokenizers.InputSequence) — The main input sequence we want to encode. This sequence can be either raw text or pre-tokenized, according to the is_pretokenized argument:

  • If is_pretokenized=False: TextInputSequence

  • If is_pretokenized=True: PreTokenizedInputSequence()

• pair (~tokenizers.InputSequence, optional) — An optional input sequence. The expected format is the same that for sequence.

• is_pretokenized (bool, defaults to False) — Whether the input is already pre-tokenized

• add_special_tokens (bool, defaults to True) — Whether to add the special tokens

Returns

Encoding

The encoded result

Encode the given sequence and pair. This method can process raw text sequences as well as already pre-tokenized sequences.

Example:

Here are some examples of the inputs that are accepted:

Copied

encode("A single sequence")*
encode("A sequence", "And its pair")*
encode([ "A", "pre", "tokenized", "sequence" ], is_pretokenized=True)`
encode(
[ "A", "pre", "tokenized", "sequence" ], [ "And", "its", "pair" ],
is_pretokenized=True
)

encode_batch

( inputis_pretokenized = Falseadd_special_tokens = True ) → A List of [`~tokenizers.Encoding“]

Parameters

• input (A List/`Tuple of ~tokenizers.EncodeInput) — A list of single sequences or pair sequences to encode. Each sequence can be either raw text or pre-tokenized, according to the is_pretokenized argument:

  • If is_pretokenized=False: TextEncodeInput()

  • If is_pretokenized=True: PreTokenizedEncodeInput()

• is_pretokenized (bool, defaults to False) — Whether the input is already pre-tokenized

• add_special_tokens (bool, defaults to True) — Whether to add the special tokens

Returns

A List of [`~tokenizers.Encoding“]

The encoded batch

Encode the given batch of inputs. This method accept both raw text sequences as well as already pre-tokenized sequences.

Example:

Here are some examples of the inputs that are accepted:

Copied

encode_batch([
"A single sequence",
("A tuple with a sequence", "And its pair"),
[ "A", "pre", "tokenized", "sequence" ],
([ "A", "pre", "tokenized", "sequence" ], "And its pair")
])

from_buffer

( buffer ) → Tokenizer

Parameters

• buffer (bytes) — A buffer containing a previously serialized Tokenizer

Returns

Tokenizer

The new tokenizer

Instantiate a new Tokenizer from the given buffer.

from_file

( path ) → Tokenizer

Parameters

• path (str) — A path to a local JSON file representing a previously serialized Tokenizer

Returns

Tokenizer

The new tokenizer

Instantiate a new Tokenizer from the file at the given path.

from_pretrained

( identifierrevision = 'main'auth_token = None ) → Tokenizer

Parameters

• identifier (str) — The identifier of a Model on the BOINC AI Hub, that contains a tokenizer.json file

• revision (str, defaults to main) — A branch or commit id

• auth_token (str, optional, defaults to None) — An optional auth token used to access private repositories on the BOINC AI Hub

Returns

Tokenizer

The new tokenizer

Instantiate a new Tokenizer from an existing file on the BOINC AI Hub.

from_str

( json ) → Tokenizer

Parameters

• json (str) — A valid JSON string representing a previously serialized Tokenizer

Returns

Tokenizer

The new tokenizer

Instantiate a new Tokenizer from the given JSON string.

get_vocab

( with_added_tokens = True ) → Dict[str, int]

Parameters

• with_added_tokens (bool, defaults to True) — Whether to include the added tokens

Returns

Dict[str, int]

The vocabulary

Get the underlying vocabulary

get_vocab_size

( with_added_tokens = True ) → int

Parameters

• with_added_tokens (bool, defaults to True) — Whether to include the added tokens

Returns

int

The size of the vocabulary

Get the size of the underlying vocabulary

id_to_token

( id ) → Optional[str]

Parameters

• id (int) — The id to convert

Returns

Optional[str]

An optional token, None if out of vocabulary

Convert the given id to its corresponding token if it exists

no_padding

( )

Disable padding

no_truncation

( )

Disable truncation

num_special_tokens_to_add

( is_pair )

Return the number of special tokens that would be added for single/pair sentences. :param is_pair: Boolean indicating if the input would be a single sentence or a pair :return:

post_process

( encodingpair = Noneadd_special_tokens = True ) → Encoding

Parameters

• encoding (Encoding) — The Encoding corresponding to the main sequence.

• pair (Encoding, optional) — An optional Encoding corresponding to the pair sequence.

• add_special_tokens (bool) — Whether to add the special tokens

Returns

Encoding

The final post-processed encoding

Apply all the post-processing steps to the given encodings.

The various steps are:

1. Truncate according to the set truncation params (provided with enable_truncation())

2. Apply the PostProcessor

3. Pad according to the set padding params (provided with enable_padding())

save

( pathpretty = True )

Parameters

• path (str) — A path to a file in which to save the serialized tokenizer.

• pretty (bool, defaults to True) — Whether the JSON file should be pretty formatted.

Save the Tokenizer to the file at the given path.

to_str

( pretty = False ) → str

Parameters

• pretty (bool, defaults to False) — Whether the JSON string should be pretty formatted.

Returns

str

A string representing the serialized Tokenizer

Gets a serialized string representing this Tokenizer.

token_to_id

( token ) → Optional[int]

Parameters

• token (str) — The token to convert

Returns

Optional[int]

An optional id, None if out of vocabulary

Convert the given token to its corresponding id if it exists

train

( filestrainer = None )

Parameters

• files (List[str]) — A list of path to the files that we should use for training

• trainer (~tokenizers.trainers.Trainer, optional) — An optional trainer that should be used to train our Model

Train the Tokenizer using the given files.

Reads the files line by line, while keeping all the whitespace, even new lines. If you want to train from data store in-memory, you can check train_from_iterator()

train_from_iterator

( iteratortrainer = Nonelength = None )

Parameters

• iterator (Iterator) — Any iterator over strings or list of strings

• trainer (~tokenizers.trainers.Trainer, optional) — An optional trainer that should be used to train our Model

• length (int, optional) — The total number of sequences in the iterator. This is used to provide meaningful progress tracking

Train the Tokenizer using the provided iterator.

You can provide anything that is a Python Iterator

• A list of sequences List[str]

• A generator that yields str or List[str]

• A Numpy array of strings

• …