Tokenizer

property decoder

The optional Decoder in use by the Tokenizer

property model

The Model in use by the Tokenizer

property normalizer

The optional Normalizer in use by the Tokenizer

property padding

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

property post_processor

The optional PostProcessor in use by the Tokenizer

property pre_tokenizer

The optional PreTokenizer in use by the Tokenizer

property truncation

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

( ids skip_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

( sequences skip_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 = 0 pad_type_id = 0 pad_token = '[PAD]' length = None pad_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_length stride = 0 strategy = '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

( sequence pair = None is_pretokenized = False add_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:

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

( input is_pretokenized = False add_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:

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

( identifier revision = 'main' auth_token = None ) → Tokenizer

Parameters

identifier (str) — The identifier of a Model on the Hugging Face 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 Hugging Face Hub

Returns

Tokenizer

The new tokenizer

Instantiate a new Tokenizer from an existing file on the Hugging Face 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

( encoding pair = None add_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:

Truncate according to the set truncation params (provided with enable_truncation())
Apply the PostProcessor
Pad according to the set padding params (provided with enable_padding())

save

( path pretty = 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

( files trainer = 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

( iterator trainer = None length = 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
…

Tokenizers

Tokenizer

Tokenizer

class tokenizers.Tokenizer

add_special_tokens

add_tokens

decode

decode_batch

enable_padding

enable_truncation

encode

encode_batch

from_buffer

from_file

from_pretrained

from_str

get_vocab

get_vocab_size

id_to_token

no_padding

no_truncation

num_special_tokens_to_add

post_process

save

to_str

token_to_id

train

train_from_iterator