Transformers documentation

Utilities for Tokenizers

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Utilities for Tokenizers

This page lists all the utility functions used by the tokenizers, mainly the class PreTrainedTokenizerBase that implements the common methods between PreTrainedTokenizer and PreTrainedTokenizerFast and the mixin SpecialTokensMixin.

Most of those are only useful if you are studying the code of the tokenizers in the library.

PreTrainedTokenizerBase

class transformers.PreTrainedTokenizerBase

< source >

( **kwargs )

Parameters

model_max_length (int, optional) — The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is loaded with from_pretrained(), this will be set to the value stored for the associated model in max_model_input_sizes (see above). If no value is provided, will default to VERY_LARGE_INTEGER (int(1e30)).
padding_side (str, optional) — The side on which the model should have padding applied. Should be selected between [‘right’, ‘left’]. Default value is picked from the class attribute of the same name.
truncation_side (str, optional) — The side on which the model should have truncation applied. Should be selected between [‘right’, ‘left’]. Default value is picked from the class attribute of the same name.
model_input_names (List[string], optional) — The list of inputs accepted by the forward pass of the model (like "token_type_ids" or "attention_mask"). Default value is picked from the class attribute of the same name.
bos_token (str or tokenizers.AddedToken, optional) — A special token representing the beginning of a sentence. Will be associated to self.bos_token and self.bos_token_id.
eos_token (str or tokenizers.AddedToken, optional) — A special token representing the end of a sentence. Will be associated to self.eos_token and self.eos_token_id.
unk_token (str or tokenizers.AddedToken, optional) — A special token representing an out-of-vocabulary token. Will be associated to self.unk_token and self.unk_token_id.
sep_token (str or tokenizers.AddedToken, optional) — A special token separating two different sentences in the same input (used by BERT for instance). Will be associated to self.sep_token and self.sep_token_id.
pad_token (str or tokenizers.AddedToken, optional) — A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation. Will be associated to self.pad_token and self.pad_token_id.
cls_token (str or tokenizers.AddedToken, optional) — A special token representing the class of the input (used by BERT for instance). Will be associated to self.cls_token and self.cls_token_id.
mask_token (str or tokenizers.AddedToken, optional) — A special token representing a masked token (used by masked-language modeling pretraining objectives, like BERT). Will be associated to self.mask_token and self.mask_token_id.
additional_special_tokens (tuple or list of str or tokenizers.AddedToken, optional) — A tuple or a list of additional special tokens. Add them here to ensure they won’t be split by the tokenization process. Will be associated to self.additional_special_tokens and self.additional_special_tokens_ids.

Base class for PreTrainedTokenizer and PreTrainedTokenizerFast.

Handles shared (mostly boiler plate) methods for those two classes.

Class attributes (overridden by derived classes)

vocab_files_names (Dict[str, str]) — A dictionary with, as keys, the __init__ keyword name of each vocabulary file required by the model, and as associated values, the filename for saving the associated file (string).
pretrained_vocab_files_map (Dict[str, Dict[str, str]]) — A dictionary of dictionaries, with the high-level keys being the __init__ keyword name of each vocabulary file required by the model, the low-level being the short-cut-names of the pretrained models with, as associated values, the url to the associated pretrained vocabulary file.
max_model_input_sizes (Dict[str, Optional[int]]) — A dictionary with, as keys, the short-cut-names of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model, or None if the model has no maximum input size.
pretrained_init_configuration (Dict[str, Dict[str, Any]]) — A dictionary with, as keys, the short-cut-names of the pretrained models, and as associated values, a dictionary of specific arguments to pass to the __init__ method of the tokenizer class for this pretrained model when loading the tokenizer with the from_pretrained() method.
model_input_names (List[str]) — A list of inputs expected in the forward pass of the model.
padding_side (str) — The default value for the side on which the model should have padding applied. Should be 'right' or 'left'.
truncation_side (str) — The default value for the side on which the model should have truncation applied. Should be 'right' or 'left'.

call

< source >

( text: typing.Union[str, typing.List[str], typing.List[typing.List[str]]] text_pair: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False max_length: typing.Optional[int] = None stride: int = 0 is_split_into_words: bool = False pad_to_multiple_of: typing.Optional[int] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs ) → BatchEncoding

Parameters

text (str, List[str], List[List[str]]) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
text_pair (str, List[str], List[List[str]]) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
add_special_tokens (bool, optional, defaults to True) — Whether or not to encode the sequences with the special tokens relative to their model.
padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:
- True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
- 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
- False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
truncation (bool, str or TruncationStrategy, optional, defaults to False) — Activates and controls truncation. Accepts the following values:
- True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters.

If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.
stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned when return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.
is_split_into_words (bool, optional, defaults to False) — Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification.
pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
- 'tf': Return TensorFlow tf.constant objects.
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return Numpy np.ndarray objects.
return_token_type_ids (bool, optional) — Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer’s default, defined by the return_outputs attribute.

What are token type IDs?
return_attention_mask (bool, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer’s default, defined by the return_outputs attribute.

What are attention masks?
return_overflowing_tokens (bool, optional, defaults to False) — Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead of returning overflowing tokens.
return_special_tokens_mask (bool, optional, defaults to False) — Whether or not to return special tokens mask information.
return_offsets_mapping (bool, optional, defaults to False) — Whether or not to return (char_start, char_end) for each token.

This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using Python’s tokenizer, this method will raise NotImplementedError.
return_length (bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs.
verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings. **kwargs — passed to the self.tokenize() method

Returns

BatchEncoding

A BatchEncoding with the following fields:

input_ids — List of token ids to be fed to a model.

What are input IDs?
token_type_ids — List of token type ids to be fed to a model (when return_token_type_ids=True or if “token_type_ids” is in self.model_input_names).

What are token type IDs?
attention_mask — List of indices specifying which tokens should be attended to by the model (when return_attention_mask=True or if “attention_mask” is in self.model_input_names).

What are attention masks?
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True)

Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences.

as_target_tokenizer

< source >

( )

Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to sequence-to-sequence models that need a slightly different processing for the labels.

batch_decode

< source >

( sequences: typing.Union[typing.List[int], typing.List[typing.List[int]], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')] skip_special_tokens: bool = False clean_up_tokenization_spaces: bool = True **kwargs ) → List[str]

Parameters

sequences (Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]) — List of tokenized input ids. Can be obtained using the __call__ method.
skip_special_tokens (bool, optional, defaults to False) — Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (bool, optional, defaults to True) — Whether or not to clean up the tokenization spaces.
kwargs (additional keyword arguments, optional) — Will be passed to the underlying model specific decode method.

Returns

List[str]

The list of decoded sentences.

Convert a list of lists of token ids into a list of strings by calling decode.

batch_encode_plus

< source >

( batch_text_or_text_pairs: typing.Union[typing.List[str], typing.List[typing.Tuple[str, str]], typing.List[typing.List[str]], typing.List[typing.Tuple[typing.List[str], typing.List[str]]], typing.List[typing.List[int]], typing.List[typing.Tuple[typing.List[int], typing.List[int]]]] add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False max_length: typing.Optional[int] = None stride: int = 0 is_split_into_words: bool = False pad_to_multiple_of: typing.Optional[int] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs ) → BatchEncoding

Parameters

batch_text_or_text_pairs (List[str], List[Tuple[str, str]], List[List[str]], List[Tuple[List[str], List[str]]], and for not-fast tokenizers, also List[List[int]], List[Tuple[List[int], List[int]]]) — Batch of sequences or pair of sequences to be encoded. This can be a list of string/string-sequences/int-sequences or a list of pair of string/string-sequences/int-sequence (see details in encode_plus).
add_special_tokens (bool, optional, defaults to True) — Whether or not to encode the sequences with the special tokens relative to their model.
padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:
- True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
- 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
- False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
truncation (bool, str or TruncationStrategy, optional, defaults to False) — Activates and controls truncation. Accepts the following values:
- True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters.

If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.
stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned when return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.
is_split_into_words (bool, optional, defaults to False) — Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification.
pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
- 'tf': Return TensorFlow tf.constant objects.
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return Numpy np.ndarray objects.
return_token_type_ids (bool, optional) — Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer’s default, defined by the return_outputs attribute.

What are token type IDs?
return_attention_mask (bool, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer’s default, defined by the return_outputs attribute.

What are attention masks?
return_overflowing_tokens (bool, optional, defaults to False) — Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead of returning overflowing tokens.
return_special_tokens_mask (bool, optional, defaults to False) — Whether or not to return special tokens mask information.
return_offsets_mapping (bool, optional, defaults to False) — Whether or not to return (char_start, char_end) for each token.

This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using Python’s tokenizer, this method will raise NotImplementedError.
return_length (bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs.
verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings. **kwargs — passed to the self.tokenize() method

Returns

BatchEncoding

A BatchEncoding with the following fields:

input_ids — List of token ids to be fed to a model.

What are input IDs?
token_type_ids — List of token type ids to be fed to a model (when return_token_type_ids=True or if “token_type_ids” is in self.model_input_names).

What are token type IDs?
attention_mask — List of indices specifying which tokens should be attended to by the model (when return_attention_mask=True or if “attention_mask” is in self.model_input_names).

What are attention masks?
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True)

Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.

This method is deprecated, __call__ should be used instead.

build_inputs_with_special_tokens

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

token_ids_0 (List[int]) — The first tokenized sequence.
token_ids_1 (List[int], optional) — The second tokenized sequence.

Returns

List[int]

The model input with special tokens.

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens.

This implementation does not add special tokens and this method should be overridden in a subclass.

clean_up_tokenization

< source >

( out_string: str ) → str

Parameters

out_string (str) — The text to clean up.

Returns

str

The cleaned-up string.

Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms.

convert_tokens_to_string

< source >

( tokens: typing.List[str] ) → str

Parameters

tokens (List[str]) — The token to join in a string.

Returns

str

The joined tokens.

Converts a sequence of tokens in a single string. The most simple way to do it is " ".join(tokens) but we often want to remove sub-word tokenization artifacts at the same time.

create_token_type_ids_from_sequences

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

token_ids_0 (List[int]) — The first tokenized sequence.
token_ids_1 (List[int], optional) — The second tokenized sequence.

Returns

List[int]

The token type ids.

Create the token type IDs corresponding to the sequences passed. What are token type IDs?

Should be overridden in a subclass if the model has a special way of building those.

decode

< source >

( token_ids: typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')] skip_special_tokens: bool = False clean_up_tokenization_spaces: bool = True **kwargs ) → str

Parameters

token_ids (Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]) — List of tokenized input ids. Can be obtained using the __call__ method.
skip_special_tokens (bool, optional, defaults to False) — Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (bool, optional, defaults to True) — Whether or not to clean up the tokenization spaces.
kwargs (additional keyword arguments, optional) — Will be passed to the underlying model specific decode method.

Returns

str

The decoded sentence.

Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces.

Similar to doing self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids)).

encode

< source >

Parameters

text (str, List[str] or List[int]) — The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the tokenize method) or a list of integers (tokenized string ids using the convert_tokens_to_ids method).
text_pair (str, List[str] or List[int], optional) — Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using the tokenize method) or a list of integers (tokenized string ids using the convert_tokens_to_ids method).
add_special_tokens (bool, optional, defaults to True) — Whether or not to encode the sequences with the special tokens relative to their model.
padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:
- True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
- 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
- False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
truncation (bool, str or TruncationStrategy, optional, defaults to False) — Activates and controls truncation. Accepts the following values:
- True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters.

If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.
stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned when return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.
is_split_into_words (bool, optional, defaults to False) — Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification.
pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
- 'tf': Return TensorFlow tf.constant objects.
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return Numpy np.ndarray objects.
**kwargs — Passed along to the .tokenize() method.

Returns

List[int], torch.Tensor, tf.Tensor or np.ndarray

The tokenized ids of the text.

Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary.

Same as doing self.convert_tokens_to_ids(self.tokenize(text)).

encode_plus

< source >

( text: typing.Union[str, typing.List[str], typing.List[int]] text_pair: typing.Union[str, typing.List[str], typing.List[int], NoneType] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False max_length: typing.Optional[int] = None stride: int = 0 is_split_into_words: bool = False pad_to_multiple_of: typing.Optional[int] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs ) → BatchEncoding

Parameters

text (str, List[str] or List[int] (the latter only for not-fast tokenizers)) — The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the tokenize method) or a list of integers (tokenized string ids using the convert_tokens_to_ids method).
text_pair (str, List[str] or List[int], optional) — Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using the tokenize method) or a list of integers (tokenized string ids using the convert_tokens_to_ids method).
add_special_tokens (bool, optional, defaults to True) — Whether or not to encode the sequences with the special tokens relative to their model.
padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:
- True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
- 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
- False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
truncation (bool, str or TruncationStrategy, optional, defaults to False) — Activates and controls truncation. Accepts the following values:
- True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters.

If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.
stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned when return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.
is_split_into_words (bool, optional, defaults to False) — Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification.
pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
- 'tf': Return TensorFlow tf.constant objects.
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return Numpy np.ndarray objects.
return_token_type_ids (bool, optional) — Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer’s default, defined by the return_outputs attribute.

What are token type IDs?
return_attention_mask (bool, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer’s default, defined by the return_outputs attribute.

What are attention masks?
return_overflowing_tokens (bool, optional, defaults to False) — Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead of returning overflowing tokens.
return_special_tokens_mask (bool, optional, defaults to False) — Whether or not to return special tokens mask information.
return_offsets_mapping (bool, optional, defaults to False) — Whether or not to return (char_start, char_end) for each token.

This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using Python’s tokenizer, this method will raise NotImplementedError.
return_length (bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs.
verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings. **kwargs — passed to the self.tokenize() method

Returns

BatchEncoding

A BatchEncoding with the following fields:

input_ids — List of token ids to be fed to a model.

What are input IDs?
token_type_ids — List of token type ids to be fed to a model (when return_token_type_ids=True or if “token_type_ids” is in self.model_input_names).

What are token type IDs?
attention_mask — List of indices specifying which tokens should be attended to by the model (when return_attention_mask=True or if “attention_mask” is in self.model_input_names).

What are attention masks?
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True)

Tokenize and prepare for the model a sequence or a pair of sequences.

This method is deprecated, __call__ should be used instead.

from_pretrained

< source >

( pretrained_model_name_or_path: typing.Union[str, os.PathLike] *init_inputs **kwargs )

Parameters

pretrained_model_name_or_path (str or os.PathLike) — Can be either:
- A string, the model id of a predefined tokenizer hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased.
- A path to a directory containing vocabulary files required by the tokenizer, for instance saved using the save_pretrained() method, e.g., ./my_model_directory/.
- (Deprecated, not applicable to all derived classes) A path or url to a single saved vocabulary file (if and only if the tokenizer only requires a single vocabulary file like Bert or XLNet), e.g., ./my_model_directory/vocab.txt.
cache_dir (str or os.PathLike, optional) — Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.
force_download (bool, optional, defaults to False) — Whether or not to force the (re-)download the vocabulary files and override the cached versions if they exist.
resume_download (bool, optional, defaults to False) — Whether or not to delete incompletely received files. Attempt to resume the download if such a file exists.
proxies (Dict[str, str], optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request.
use_auth_token (str or bool, optional) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running transformers-cli login (stored in ~/.huggingface).
local_files_only (bool, optional, defaults to False) — Whether or not to only rely on local files and not to attempt to download any files.
revision (str, optional, defaults to "main") — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so revision can be any identifier allowed by git.
subfolder (str, optional) — In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for facebook/rag-token-base), specify it here.
inputs (additional positional arguments, optional) — Will be passed along to the Tokenizer __init__ method.
kwargs (additional keyword arguments, optional) — Will be passed to the Tokenizer __init__ method. Can be used to set special tokens like bos_token, eos_token, unk_token, sep_token, pad_token, cls_token, mask_token, additional_special_tokens. See parameters in the __init__ for more details.

Instantiate a PreTrainedTokenizerBase (or a derived class) from a predefined tokenizer.

Passing use_auth_token=True is required when you want to use a private model.

Examples:

# We can't instantiate directly the base class *PreTrainedTokenizerBase* so let's show our examples on a derived class: BertTokenizer
# Download vocabulary from huggingface.co and cache.
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

# Download vocabulary from huggingface.co (user-uploaded) and cache.
tokenizer = BertTokenizer.from_pretrained("dbmdz/bert-base-german-cased")

# If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*)
tokenizer = BertTokenizer.from_pretrained("./test/saved_model/")

# If the tokenizer uses a single vocabulary file, you can point directly to this file
tokenizer = BertTokenizer.from_pretrained("./test/saved_model/my_vocab.txt")

# You can link tokens to special vocabulary when instantiating
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", unk_token="<unk>")
# You should be sure '<unk>' is in the vocabulary when doing that.
# Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)
assert tokenizer.unk_token == "<unk>"

get_special_tokens_mask

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: bool = False ) → A list of integers in the range [0, 1]

Parameters

token_ids_0 (List[int]) — List of ids of the first sequence.
token_ids_1 (List[int], optional) — List of ids of the second sequence.
already_has_special_tokens (bool, optional, defaults to False) — Whether or not the token list is already formatted with special tokens for the model.

Returns

A list of integers in the range [0, 1]

1 for a special token, 0 for a sequence token.

Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model or encode_plus methods.

get_vocab

< source >

( ) → Dict[str, int]

Returns

Dict[str, int]

The vocabulary.

Returns the vocabulary as a dictionary of token to index.

tokenizer.get_vocab()[token] is equivalent to tokenizer.convert_tokens_to_ids(token) when token is in the vocab.

pad

< source >

( encoded_inputs: typing.Union[transformers.tokenization_utils_base.BatchEncoding, typing.List[transformers.tokenization_utils_base.BatchEncoding], typing.Dict[str, typing.List[int]], typing.Dict[str, typing.List[typing.List[int]]], typing.List[typing.Dict[str, typing.List[int]]]] padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = True max_length: typing.Optional[int] = None pad_to_multiple_of: typing.Optional[int] = None return_attention_mask: typing.Optional[bool] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None verbose: bool = True )

Parameters

encoded_inputs (BatchEncoding, list of BatchEncoding, Dict[str, List[int]], Dict[str, List[List[int]] or List[Dict[str, List[int]]]) — Tokenized inputs. Can represent one input (BatchEncoding or Dict[str, List[int]]) or a batch of tokenized inputs (list of BatchEncoding, Dict[str, List[List[int]]] or List[Dict[str, List[int]]]) so you can use this method during preprocessing as well as in a PyTorch Dataloader collate function.

Instead of List[int] you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors), see the note above for the return type.
padding (bool, str or PaddingStrategy, optional, defaults to True) — Select a strategy to pad the returned sequences (according to the model’s padding side and padding index) among:
- True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
- 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
- False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
max_length (int, optional) — Maximum length of the returned list and optionally padding length (see above).
pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value.

This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability

= 7.5 (Volta).
return_attention_mask (bool, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer’s default, defined by the return_outputs attribute.

What are attention masks?
return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
- 'tf': Return TensorFlow tf.constant objects.
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return Numpy np.ndarray objects.
verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings.

Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length in the batch.

Padding side (left/right) padding token ids are defined at the tokenizer level (with self.padding_side, self.pad_token_id and self.pad_token_type_id)

If the encoded_inputs passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the result will use the same type unless you provide a different tensor type with return_tensors. In the case of PyTorch tensors, you will lose the specific device of your tensors however.

prepare_for_model

< source >

( ids: typing.List[int] pair_ids: typing.Optional[typing.List[int]] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = False max_length: typing.Optional[int] = None stride: int = 0 pad_to_multiple_of: typing.Optional[int] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True prepend_batch_axis: bool = False **kwargs ) → BatchEncoding

Parameters

ids (List[int]) — Tokenized input ids of the first sequence. Can be obtained from a string by chaining the tokenize and convert_tokens_to_ids methods.
pair_ids (List[int], optional) — Tokenized input ids of the second sequence. Can be obtained from a string by chaining the tokenize and convert_tokens_to_ids methods.
add_special_tokens (bool, optional, defaults to True) — Whether or not to encode the sequences with the special tokens relative to their model.
padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:
- True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
- 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
- False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
truncation (bool, str or TruncationStrategy, optional, defaults to False) — Activates and controls truncation. Accepts the following values:
- True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters.

If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.
stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned when return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.
is_split_into_words (bool, optional, defaults to False) — Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification.
pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
- 'tf': Return TensorFlow tf.constant objects.
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return Numpy np.ndarray objects.
return_token_type_ids (bool, optional) — Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer’s default, defined by the return_outputs attribute.

What are token type IDs?
return_attention_mask (bool, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer’s default, defined by the return_outputs attribute.

What are attention masks?
return_overflowing_tokens (bool, optional, defaults to False) — Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead of returning overflowing tokens.
return_special_tokens_mask (bool, optional, defaults to False) — Whether or not to return special tokens mask information.
return_offsets_mapping (bool, optional, defaults to False) — Whether or not to return (char_start, char_end) for each token.

This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using Python’s tokenizer, this method will raise NotImplementedError.
return_length (bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs.
verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings. **kwargs — passed to the self.tokenize() method

Returns

BatchEncoding

A BatchEncoding with the following fields:

input_ids — List of token ids to be fed to a model.

What are input IDs?
token_type_ids — List of token type ids to be fed to a model (when return_token_type_ids=True or if “token_type_ids” is in self.model_input_names).

What are token type IDs?
attention_mask — List of indices specifying which tokens should be attended to by the model (when return_attention_mask=True or if “attention_mask” is in self.model_input_names).

What are attention masks?
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True)

Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Please Note, for pair_ids different than None and truncation_strategy = longest_first or True, it is not possible to return overflowing tokens. Such a combination of arguments will raise an error.

prepare_seq2seq_batch

< source >

( src_texts: typing.List[str] tgt_texts: typing.Optional[typing.List[str]] = None max_length: typing.Optional[int] = None max_target_length: typing.Optional[int] = None padding: str = 'longest' return_tensors: str = None truncation: bool = True **kwargs ) → BatchEncoding

Parameters

src_texts (List[str]) — List of documents to summarize or source language texts.
tgt_texts (list, optional) — List of summaries or target language texts.
max_length (int, optional) — Controls the maximum length for encoder inputs (documents to summarize or source language texts) If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.
max_target_length (int, optional) — Controls the maximum length of decoder inputs (target language texts or summaries) If left unset or set to None, this will use the max_length value.
padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:
- True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
- 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
- False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
- 'tf': Return TensorFlow tf.constant objects.
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return Numpy np.ndarray objects.
truncation (bool, str or TruncationStrategy, optional, defaults to True) — Activates and controls truncation. Accepts the following values:
- True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). **kwargs — Additional keyword arguments passed along to self.__call__.

Returns

BatchEncoding

A BatchEncoding with the following fields:

input_ids — List of token ids to be fed to the encoder.
attention_mask — List of indices specifying which tokens should be attended to by the model.
labels — List of token ids for tgt_texts.

The full set of keys [input_ids, attention_mask, labels], will only be returned if tgt_texts is passed. Otherwise, input_ids, attention_mask will be the only keys.

Prepare model inputs for translation. For best performance, translate one sentence at a time.

push_to_hub

< source >

( repo_path_or_name: typing.Optional[str] = None repo_url: typing.Optional[str] = None use_temp_dir: bool = False commit_message: typing.Optional[str] = None organization: typing.Optional[str] = None private: typing.Optional[bool] = None use_auth_token: typing.Union[bool, str, NoneType] = None max_shard_size: typing.Union[int, str, NoneType] = '10GB' **model_card_kwargs ) → str

Parameters

repo_path_or_name (str, optional) — Can either be a repository name for your tokenizer in the Hub or a path to a local folder (in which case the repository will have the name of that local folder). If not specified, will default to the name given by repo_url and a local directory with that name will be created.
repo_url (str, optional) — Specify this in case you want to push to an existing repository in the hub. If unspecified, a new repository will be created in your namespace (unless you specify an organization) with repo_name.
use_temp_dir (bool, optional, defaults to False) — Whether or not to clone the distant repo in a temporary directory or in repo_path_or_name inside the current working directory. This will slow things down if you are making changes in an existing repo since you will need to clone the repo before every push.
commit_message (str, optional) — Message to commit while pushing. Will default to "add tokenizer".
organization (str, optional) — Organization in which you want to push your tokenizer (you must be a member of this organization).
private (bool, optional) — Whether or not the repository created should be private (requires a paying subscription).
use_auth_token (bool or str, optional) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running transformers-cli login (stored in ~/.huggingface). Will default to True if repo_url is not specified.

Returns

str

The url of the commit of your tokenizer in the given repository.

Upload the tokenizer files to the 🤗 Model Hub while synchronizing a local clone of the repo in repo_path_or_name.

Examples:

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")

# Push the tokenizer to your namespace with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
tokenizer.push_to_hub("my-finetuned-bert")

# Push the tokenizer to your namespace with the name "my-finetuned-bert" with no local clone.
tokenizer.push_to_hub("my-finetuned-bert", use_temp_dir=True)

# Push the tokenizer to an organization with the name "my-finetuned-bert" and have a local clone in the
# *my-finetuned-bert* folder.
tokenizer.push_to_hub("my-finetuned-bert", organization="huggingface")

# Make a change to an existing repo that has been cloned locally in *my-finetuned-bert*.
tokenizer.push_to_hub("my-finetuned-bert", repo_url="https://huggingface.co/sgugger/my-finetuned-bert")

register_for_auto_class

< source >

( auto_class = 'AutoTokenizer' )

Parameters

auto_class (str or type, optional, defaults to "AutoTokenizer") — The auto class to register this new tokenizer with.

Register this class with a given auto class. This should only be used for custom tokenizers as the ones in the library are already mapped with AutoTokenizer.

This API is experimental and may have some slight breaking changes in the next releases.

save_pretrained

< source >

( save_directory: typing.Union[str, os.PathLike] legacy_format: typing.Optional[bool] = None filename_prefix: typing.Optional[str] = None push_to_hub: bool = False **kwargs ) → A tuple of str

Parameters

save_directory (str or os.PathLike) — The path to a directory where the tokenizer will be saved.
legacy_format (bool, optional) — Only applicable for a fast tokenizer. If unset (default), will save the tokenizer in the unified JSON format as well as in legacy format if it exists, i.e. with tokenizer specific vocabulary and a separate added_tokens files.

If False, will only save the tokenizer in the unified JSON format. This format is incompatible with “slow” tokenizers (not powered by the tokenizers library), so the tokenizer will not be able to be loaded in the corresponding “slow” tokenizer.

If True, will save the tokenizer in legacy format. If the “slow” tokenizer doesn’t exits, a value error is raised. filename_prefix — (str, optional): A prefix to add to the names of the files saved by the tokenizer.
push_to_hub (bool, optional, defaults to False) — Whether or not to push your model to the Hugging Face model hub after saving it.

Using push_to_hub=True will synchronize the repository you are pushing to with save_directory, which requires save_directory to be a local clone of the repo you are pushing to if it’s an existing folder. Pass along temp_dir=True to use a temporary directory instead.

Returns

A tuple of str

The files saved.

Save the full tokenizer state.

This method make sure the full tokenizer can then be re-loaded using the from_pretrained class method..

Warning,None This won’t save modifications you may have applied to the tokenizer after the instantiation (for instance, modifying tokenizer.do_lower_case after creation).

save_vocabulary

< source >

( save_directory: str filename_prefix: typing.Optional[str] = None ) → Tuple(str)

Parameters

save_directory (str) — The directory in which to save the vocabulary.
filename_prefix (str, optional) — An optional prefix to add to the named of the saved files.

Returns

Tuple(str)

Paths to the files saved.

Save only the vocabulary of the tokenizer (vocabulary + added tokens).

This method won’t save the configuration and special token mappings of the tokenizer. Use _save_pretrained() to save the whole state of the tokenizer.

tokenize

< source >

( text: str pair: typing.Optional[str] = None add_special_tokens: bool = False **kwargs ) → List[str]

Parameters

text (str) — The sequence to be encoded.
pair (str, optional) — A second sequence to be encoded with the first.
add_special_tokens (bool, optional, defaults to False) — Whether or not to add the special tokens associated with the corresponding model.
kwargs (additional keyword arguments, optional) — Will be passed to the underlying model specific encode method. See details in call()

Returns

List[str]

The list of tokens.

Converts a string in a sequence of tokens, replacing unknown tokens with the unk_token.

truncate_sequences

< source >

( ids: typing.List[int] pair_ids: typing.Optional[typing.List[int]] = None num_tokens_to_remove: int = 0 truncation_strategy: typing.Union[str, transformers.tokenization_utils_base.TruncationStrategy] = 'longest_first' stride: int = 0 ) → Tuple[List[int], List[int], List[int]]

Parameters

ids (List[int]) — Tokenized input ids of the first sequence. Can be obtained from a string by chaining the tokenize and convert_tokens_to_ids methods.
pair_ids (List[int], optional) — Tokenized input ids of the second sequence. Can be obtained from a string by chaining the tokenize and convert_tokens_to_ids methods.
num_tokens_to_remove (int, optional, defaults to 0) — Number of tokens to remove using the truncation strategy.
truncation_strategy (str or TruncationStrategy, optional, defaults to False) — The strategy to follow for truncation. Can be:
- 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
stride (int, optional, defaults to 0) — If set to a positive number, the overflowing tokens returned will contain some tokens from the main sequence returned. The value of this argument defines the number of additional tokens.

Returns

Tuple[List[int], List[int], List[int]]

The truncated ids, the truncated pair_ids and the list of overflowing tokens. Note: The longest_first strategy returns empty list of overflowing tokens if a pair of sequences (or a batch of pairs) is provided.

Truncates a sequence pair in-place following the strategy.

SpecialTokensMixin

class transformers.SpecialTokensMixin

< source >

( verbose = True **kwargs )

Parameters

bos_token (str or tokenizers.AddedToken, optional) — A special token representing the beginning of a sentence.
eos_token (str or tokenizers.AddedToken, optional) — A special token representing the end of a sentence.
unk_token (str or tokenizers.AddedToken, optional) — A special token representing an out-of-vocabulary token.
sep_token (str or tokenizers.AddedToken, optional) — A special token separating two different sentences in the same input (used by BERT for instance).
pad_token (str or tokenizers.AddedToken, optional) — A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation.
cls_token (str or tokenizers.AddedToken, optional) — A special token representing the class of the input (used by BERT for instance).
mask_token (str or tokenizers.AddedToken, optional) — A special token representing a masked token (used by masked-language modeling pretraining objectives, like BERT).
additional_special_tokens (tuple or list of str or tokenizers.AddedToken, optional) — A tuple or a list of additional special tokens.

A mixin derived by PreTrainedTokenizer and PreTrainedTokenizerFast to handle specific behaviors related to special tokens. In particular, this class hold the attributes which can be used to directly access these special tokens in a model-independent manner and allow to set and update the special tokens.

add_special_tokens

< source >

( special_tokens_dict: typing.Dict[str, typing.Union[str, tokenizers.AddedToken]] ) → int

Parameters

special_tokens_dict (dictionary str to str or tokenizers.AddedToken) — Keys should be in the list of predefined special attributes: [bos_token, eos_token, unk_token, sep_token, pad_token, cls_token, mask_token, additional_special_tokens].

Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the unk_token to them).

Returns

int

Number of tokens added to the vocabulary.

Add a dictionary of special tokens (eos, pad, cls, etc.) to the encoder and link them to class attributes. If special tokens are NOT in the vocabulary, they are added to it (indexed starting from the last index of the current vocabulary).

Note,None When adding new tokens to the vocabulary, you should make sure to also resize the token embedding matrix of the model so that its embedding matrix matches the tokenizer.

In order to do that, please use the resize_token_embeddings() method.

Using add_special_tokens will ensure your special tokens can be used in several ways:

Special tokens are carefully handled by the tokenizer (they are never split).
You can easily refer to special tokens using tokenizer class attributes like tokenizer.cls_token. This makes it easy to develop model-agnostic training and fine-tuning scripts.

When possible, special tokens are already registered for provided pretrained models (for instance BertTokenizer cls_token is already registered to be :obj’[CLS]’ and XLM’s one is also registered to be '</s>').

Examples:

# Let's see how to add a new classification token to GPT-2
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2Model.from_pretrained("gpt2")

special_tokens_dict = {"cls_token": "<CLS>"}

num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print("We have added", num_added_toks, "tokens")
# Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer.
model.resize_token_embeddings(len(tokenizer))

assert tokenizer.cls_token == "<CLS>"

add_tokens

< source >

( new_tokens: typing.Union[str, tokenizers.AddedToken, typing.List[typing.Union[str, tokenizers.AddedToken]]] special_tokens: bool = False ) → int

Parameters

new_tokens (str, tokenizers.AddedToken or a list of str or tokenizers.AddedToken) — Tokens are only added if they are not already in the vocabulary. tokenizers.AddedToken wraps a string token to let you personalize its behavior: whether this token should only match against a single word, whether this token should strip all potential whitespaces on the left side, whether this token should strip all potential whitespaces on the right side, etc.
special_tokens (bool, optional, defaults to False) — Can be used to specify if the token is a special token. This mostly change the normalization behavior (special tokens like CLS or [MASK] are usually not lower-cased for instance).

See details for tokenizers.AddedToken in HuggingFace tokenizers library.

Returns

int

Number of tokens added to the vocabulary.

Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to it with indices starting from length of the current vocabulary.

Note,None When adding new tokens to the vocabulary, you should make sure to also resize the token embedding matrix of the model so that its embedding matrix matches the tokenizer.

In order to do that, please use the resize_token_embeddings() method.

Examples:

# Let's see how to increase the vocabulary of Bert model and tokenizer
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
model = BertModel.from_pretrained("bert-base-uncased")

num_added_toks = tokenizer.add_tokens(["new_tok1", "my_new-tok2"])
print("We have added", num_added_toks, "tokens")
# Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e., the length of the tokenizer.
model.resize_token_embeddings(len(tokenizer))

sanitize_special_tokens

< source >

( ) → int

Returns

int

The number of tokens added in the vocabulary during the operation.

Make sure that all the special tokens attributes of the tokenizer (tokenizer.mask_token, tokenizer.cls_token, etc.) are in the vocabulary.

Add the missing ones to the vocabulary if needed.

Enums and namedtuples

class transformers.tokenization_utils_base.TruncationStrategy

< source >

( value names = None module = None qualname = None type = None start = 1 )

Possible values for the truncation argument in PreTrainedTokenizerBase.call(). Useful for tab-completion in an IDE.

class transformers.CharSpan

< source >

( start: int end: int )

Parameters

start (int) — Index of the first character in the original string.
end (int) — Index of the character following the last character in the original string.

Character span in the original string.

class transformers.TokenSpan

< source >

( start: int end: int )

Parameters

start (int) — Index of the first token in the span.
end (int) — Index of the token following the last token in the span.

Token span in an encoded string (list of tokens).

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