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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

< >

( **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.
  • chat_template (str, optional) — A Jinja template string that will be used to format lists of chat messages. See https://huggingface.co/docs/transformers/chat_templating for a full description.
  • 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 are skipped when decoding with skip_special_tokens is set to True. If they are not part of the vocabulary, they will be added at the end of the vocabulary.
  • clean_up_tokenization_spaces (bool, optional, defaults to True) — Whether or not the model should cleanup the spaces that were added when splitting the input text during the tokenization process.
  • split_special_tokens (bool, optional, defaults to False) — Whether or not the special tokens should be split during the tokenization process. Passing will affect the internal state of the tokenizer. The default behavior is to not split special tokens. This means that if <s> is the bos_token, then tokenizer.tokenize("<s>") = ['<s>]. Otherwise, if split_special_tokens=True, then tokenizer.tokenize("<s>") will be give ['<','s', '>'].

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.
  • 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__

< >

( text: typing.Union[str, list[str], list[list[str]], NoneType] = None text_pair: typing.Union[str, list[str], list[list[str]], NoneType] = None text_target: typing.Union[str, list[str], list[list[str]], NoneType] = None text_pair_target: typing.Union[str, list[str], list[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, NoneType] = None max_length: typing.Optional[int] = None stride: int = 0 is_split_into_words: bool = False pad_to_multiple_of: typing.Optional[int] = None padding_side: typing.Optional[str] = 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]], optional) — 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]], optional) — 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_target (str, list[str], list[list[str]], optional) — The sequence or batch of sequences to be encoded as target texts. 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_target (str, list[str], list[list[str]], optional) — The sequence or batch of sequences to be encoded as target texts. 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 add special tokens when encoding the sequences. This will use the underlying PretrainedTokenizerBase.build_inputs_with_special_tokens function, which defines which tokens are automatically added to the input ids. This is useful if you want to add bos or eos tokens automatically.
  • 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 is 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. Requires padding to be activated. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
  • 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.
  • 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.

apply_chat_template

< >

( conversation: typing.Union[list[dict[str, str]], list[list[dict[str, str]]]] tools: typing.Optional[list[typing.Union[dict, typing.Callable]]] = None documents: typing.Optional[list[dict[str, str]]] = None chat_template: typing.Optional[str] = None add_generation_prompt: bool = False continue_final_message: bool = False tokenize: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: bool = False max_length: typing.Optional[int] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_dict: bool = False return_assistant_tokens_mask: bool = False tokenizer_kwargs: typing.Optional[dict[str, typing.Any]] = None **kwargs ) → Union[list[int], Dict]

Parameters

  • conversation (Union[list[dict[str, str]], list[list[dict[str, str]]]]) — A list of dicts with “role” and “content” keys, representing the chat history so far.
  • tools (list[Union[Dict, Callable]], optional) — A list of tools (callable functions) that will be accessible to the model. If the template does not support function calling, this argument will have no effect. Each tool should be passed as a JSON Schema, giving the name, description and argument types for the tool. See our tool use guide for more information.
  • documents (list[dict[str, str]], optional) — A list of dicts representing documents that will be accessible to the model if it is performing RAG (retrieval-augmented generation). If the template does not support RAG, this argument will have no effect. We recommend that each document should be a dict containing “title” and “text” keys.
  • chat_template (str, optional) — A Jinja template to use for this conversion. It is usually not necessary to pass anything to this argument, as the model’s template will be used by default.
  • add_generation_prompt (bool, optional) — If this is set, a prompt with the token(s) that indicate the start of an assistant message will be appended to the formatted output. This is useful when you want to generate a response from the model. Note that this argument will be passed to the chat template, and so it must be supported in the template for this argument to have any effect.
  • continue_final_message (bool, optional) — If this is set, the chat will be formatted so that the final message in the chat is open-ended, without any EOS tokens. The model will continue this message rather than starting a new one. This allows you to “prefill” part of the model’s response for it. Cannot be used at the same time as add_generation_prompt.
  • tokenize (bool, defaults to True) — Whether to tokenize the output. If False, the output will be a string.
  • padding (bool, str or PaddingStrategy, optional, defaults to False) — 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).
  • truncation (bool, defaults to False) — Whether to truncate sequences at the maximum length. Has no effect if tokenize is False.
  • max_length (int, optional) — Maximum length (in tokens) to use for padding or truncation. Has no effect if tokenize is False. If not specified, the tokenizer’s max_length attribute will be used as a default.
  • return_tensors (str or TensorType, optional) — If set, will return tensors of a particular framework. Has no effect if tokenize is False. Acceptable values are:

    • 'tf': Return TensorFlow tf.Tensor objects.
    • 'pt': Return PyTorch torch.Tensor objects.
    • 'np': Return NumPy np.ndarray objects.
    • 'jax': Return JAX jnp.ndarray objects.
  • return_dict (bool, defaults to False) — Whether to return a dictionary with named outputs. Has no effect if tokenize is False.
  • tokenizer_kwargs (dict[str -- Any], optional): Additional kwargs to pass to the tokenizer.
  • return_assistant_tokens_mask (bool, defaults to False) — Whether to return a mask of the assistant generated tokens. For tokens generated by the assistant, the mask will contain 1. For user and system tokens, the mask will contain 0. This functionality is only available for chat templates that support it via the {% generation %} keyword.
  • **kwargs — Additional kwargs to pass to the template renderer. Will be accessible by the chat template.

Returns

Union[list[int], Dict]

A list of token ids representing the tokenized chat so far, including control tokens. This output is ready to pass to the model, either directly or via methods like generate(). If return_dict is set, will return a dict of tokenizer outputs instead.

Converts a list of dictionaries with "role" and "content" keys to a list of token ids. This method is intended for use with chat models, and will read the tokenizer’s chat_template attribute to determine the format and control tokens to use when converting.

as_target_tokenizer

< >

( )

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

< >

( sequences: typing.Union[list[int], list[list[int]], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')] skip_special_tokens: bool = False clean_up_tokenization_spaces: typing.Optional[bool] = None **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) — Whether or not to clean up the tokenization spaces. If None, will default to self.clean_up_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

< >

( batch_text_or_text_pairs: typing.Union[list[str], list[tuple[str, str]], list[list[str]], list[tuple[list[str], list[str]]], list[list[int]], list[tuple[list[int], 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, NoneType] = None max_length: typing.Optional[int] = None stride: int = 0 is_split_into_words: bool = False pad_to_multiple_of: typing.Optional[int] = None padding_side: typing.Optional[str] = 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 split_special_tokens: bool = False **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 add special tokens when encoding the sequences. This will use the underlying PretrainedTokenizerBase.build_inputs_with_special_tokens function, which defines which tokens are automatically added to the input ids. This is useful if you want to add bos or eos tokens automatically.
  • 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 is 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. Requires padding to be activated. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
  • 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.
  • 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

< >

( token_ids_0: list token_ids_1: typing.Optional[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

< >

( 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

< >

( tokens: list ) → 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

< >

( token_ids_0: list token_ids_1: typing.Optional[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

< >

( token_ids: typing.Union[int, list[int], numpy.ndarray, ForwardRef('torch.Tensor')] skip_special_tokens: bool = False clean_up_tokenization_spaces: typing.Optional[bool] = None **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) — Whether or not to clean up the tokenization spaces. If None, will default to self.clean_up_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

< >

( text: typing.Union[str, list[str], list[int]] text_pair: typing.Union[str, list[str], 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, NoneType] = None max_length: typing.Optional[int] = None stride: int = 0 padding_side: typing.Optional[str] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None **kwargs ) → list[int], torch.Tensor, tf.Tensor or np.ndarray

Parameters