# Source: https://huggingface.co/docs/transformers/v5.0.0/internal/tokenization_utils.md

# Utilities for Tokenizers

This page lists all the utility functions used by the tokenizers, mainly the class
[PreTrainedTokenizerBase](/docs/transformers/v5.0.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase) that implements the common methods between
[PreTrainedTokenizer](/docs/transformers/v5.0.0/en/main_classes/tokenizer#transformers.PythonBackend) and [PreTrainedTokenizerFast](/docs/transformers/v5.0.0/en/main_classes/tokenizer#transformers.TokenizersBackend).

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

## PreTrainedTokenizerBase[[transformers.PreTrainedTokenizerBase]]

#### transformers.PreTrainedTokenizerBase[[transformers.PreTrainedTokenizerBase]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L959)

Base class for all tokenizer backends.

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__transformers.PreTrainedTokenizerBase.__call__https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L2464[{"name": "text", "val": ": TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None"}, {"name": "text_pair", "val": ": TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None"}, {"name": "text_target", "val": ": TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None"}, {"name": "text_pair_target", "val": ": TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None"}, {"name": "add_special_tokens", "val": ": bool = True"}, {"name": "padding", "val": ": bool | str | PaddingStrategy = False"}, {"name": "truncation", "val": ": bool | str | TruncationStrategy | None = None"}, {"name": "max_length", "val": ": int | None = None"}, {"name": "stride", "val": ": int = 0"}, {"name": "is_split_into_words", "val": ": bool = False"}, {"name": "pad_to_multiple_of", "val": ": int | None = None"}, {"name": "padding_side", "val": ": str | None = None"}, {"name": "return_tensors", "val": ": str | TensorType | None = None"}, {"name": "return_token_type_ids", "val": ": bool | None = None"}, {"name": "return_attention_mask", "val": ": bool | None = None"}, {"name": "return_overflowing_tokens", "val": ": bool = False"}, {"name": "return_special_tokens_mask", "val": ": bool = False"}, {"name": "return_offsets_mapping", "val": ": bool = False"}, {"name": "return_length", "val": ": bool = False"}, {"name": "verbose", "val": ": bool = True"}, {"name": "tokenizer_kwargs", "val": ": dict[str, Any] | None = None"}, {"name": "**kwargs", "val": ""}]- **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).
- **tokenizer_kwargs** (`dict[str, Any]`, *optional*) --
  Additional kwargs to pass to the tokenizer. These will be merged with the explicit parameters and
  other kwargs, with explicit parameters taking precedence.

- **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](/docs/transformers/v5.0.0/en/internal/file_utils#transformers.utils.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](/docs/transformers/v5.0.0/en/internal/tokenization_utils#transformers.tokenization_utils_base.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](/docs/transformers/v5.0.0/en/internal/file_utils#transformers.TensorType), *optional*) --
  If set, will return tensors instead of list of python integers. Acceptable values are:

  - `'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?](../glossary#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?](../glossary#attention-mask)
- **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](/docs/transformers/v5.0.0/en/main_classes/tokenizer#transformers.TokenizersBackend), 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()` method0[BatchEncoding](/docs/transformers/v5.0.0/en/main_classes/tokenizer#transformers.BatchEncoding)A [BatchEncoding](/docs/transformers/v5.0.0/en/main_classes/tokenizer#transformers.BatchEncoding) with the following fields:

- **input_ids** -- List of token ids to be fed to a model.

  [What are input IDs?](../glossary#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?](../glossary#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?](../glossary#attention-mask)

- **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.

**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()](/docs/transformers/v5.0.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.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.

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). Will be associated to `self.mask_token` and `self.mask_token_id`.

extra_special_tokens (list of `str` or `tokenizers.AddedToken`, *optional*) : A list of extra model-specific 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.

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 `` is the `bos_token`, then `tokenizer.tokenize("") = ['`]. Otherwise, if `split_special_tokens=True`, then `tokenizer.tokenize("")` will be give `['']`.

**Returns:**

`[BatchEncoding](/docs/transformers/v5.0.0/en/main_classes/tokenizer#transformers.BatchEncoding)`

A [BatchEncoding](/docs/transformers/v5.0.0/en/main_classes/tokenizer#transformers.BatchEncoding) with the following fields:

- **input_ids** -- List of token ids to be fed to a model.

  [What are input IDs?](../glossary#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?](../glossary#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?](../glossary#attention-mask)

- **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`)
#### add_special_tokens[[transformers.PreTrainedTokenizerBase.add_special_tokens]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L1100)

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).

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()](/docs/transformers/v5.0.0/en/main_classes/model#transformers.PreTrainedModel.resize_token_embeddings) method.

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

- Special tokens can be skipped when decoding using `skip_special_tokens = True`.
- Special tokens are carefully handled by the tokenizer (they are never split), similar to `AddedTokens`.
- 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](/docs/transformers/v5.0.0/en/model_doc/bert#transformers.BertTokenizer) `cls_token` is already registered to be `'[CLS]'` and XLM's one is also registered to be
`''`).

Examples:

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

special_tokens_dict = {"cls_token": ""}

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 == ""
```

**Parameters:**

special_tokens_dict (dictionary *str* to *str*, `tokenizers.AddedToken`, or `Sequence[Union[str, 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`, `extra_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).

replace_extra_special_tokens (`bool`, *optional*, defaults to `True`) : If `True`, the existing list of extra special tokens will be replaced by the list provided in `special_tokens_dict`. Otherwise, `extra_special_tokens` will be extended. In the former case, the tokens will NOT be removed from the tokenizer's full vocabulary - they are only being flagged as non-special tokens. Remember, this only affects which tokens are skipped during decoding, not the `added_tokens_encoder` and `added_tokens_decoder`. This means that the previous `extra_special_tokens` are still added tokens, and will not be split by the model.

**Returns:**

``int``

Number of tokens added to the vocabulary.
#### add_tokens[[transformers.PreTrainedTokenizerBase.add_tokens]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L1205)

#TODO remove this from here! PreTrainedTOkeniuzerBase should be agnostic of AddedToken.

Add a list of new tokens. If the new tokens are not in the vocabulary, they are added to the end. Added tokens and
tokens from the vocabulary of the tokenization algorithm are therefore not treated in the same way.

Examples:

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

**Parameters:**

new_tokens (`str`, `tokenizers.AddedToken` or a sequence 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`) : Specifies if the token is special. This mostly changes the normalization behavior See details for `tokenizers.AddedToken` in HuggingFace tokenizers library.

**Returns:**

``int``

Number of tokens added to the vocabulary.
#### apply_chat_template[[transformers.PreTrainedTokenizerBase.apply_chat_template]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L3017)

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.

**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](https://huggingface.co/docs/transformers/en/chat_extras#passing-tools) 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](/docs/transformers/v5.0.0/en/internal/file_utils#transformers.utils.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](/docs/transformers/v5.0.0/en/internal/file_utils#transformers.TensorType), *optional*) : If set, will return tensors of a particular framework. Has no effect if tokenize is `False`. Acceptable values are: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects.

return_dict (`bool`, defaults to `True`) : 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.
#### batch_decode[[transformers.PreTrainedTokenizerBase.batch_decode]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L2929)

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

This method is provided for backwards compatibility. The `decode` method now handles batched input natively,
so you can use `decode` directly instead of `batch_decode`.

**Parameters:**

sequences (`Union[list[int], list[list[int]], np.ndarray, torch.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_ids_to_tokens[[transformers.PreTrainedTokenizerBase.convert_ids_to_tokens]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L1489)

Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
added tokens.

**Parameters:**

ids (`int` or `list[int]`) : The token id (or token ids) to convert to tokens.

skip_special_tokens (`bool`, *optional*, defaults to `False`) : Whether or not to remove special tokens in the decoding.

**Returns:**

``str` or `list[str]``

The decoded token(s).
#### convert_tokens_to_ids[[transformers.PreTrainedTokenizerBase.convert_tokens_to_ids]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L1473)

Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
vocabulary.

**Parameters:**

tokens (`str` or `list[str]`) : One or several token(s) to convert to token id(s).

**Returns:**

``int` or `list[int]``

The token id or list of token ids.
#### convert_tokens_to_string[[transformers.PreTrainedTokenizerBase.convert_tokens_to_string]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L2868)

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.

**Parameters:**

tokens (`list[str]`) : The token to join in a string.

**Returns:**

``str``

The joined tokens.
#### decode[[transformers.PreTrainedTokenizerBase.decode]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L2881)

Converts a sequence of ids into a string, or a list of sequences into a list of strings,
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))`.

**Parameters:**

token_ids (`Union[int, list[int], list[list[int]], np.ndarray, torch.Tensor]`) : A single sequence or a batch (list of sequences) 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.

kwargs (additional keyword arguments, *optional*) : Will be passed to the underlying model specific decode method.

**Returns:**

``Union[str, list[str]]``

The decoded string for a single sequence, or a list of decoded strings for a
batch of sequences.
#### encode[[transformers.PreTrainedTokenizerBase.encode]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L2278)

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))`.

**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 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](/docs/transformers/v5.0.0/en/internal/file_utils#transformers.utils.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](/docs/transformers/v5.0.0/en/internal/tokenization_utils#transformers.tokenization_utils_base.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](/docs/transformers/v5.0.0/en/internal/file_utils#transformers.TensorType), *optional*) : If set, will return tensors instead of list of python integers. Acceptable values are:  - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. 

- ****kwargs** : Passed along to the `.tokenize()` method.

**Returns:**

``list[int]`, `torch.Tensor`, or `np.ndarray``

The tokenized ids of the text.
#### encode_message_with_chat_template[[transformers.PreTrainedTokenizerBase.encode_message_with_chat_template]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L3189)

Tokenize a single message. This method is a convenience wrapper around `apply_chat_template` that allows you
to tokenize messages one by one. This is useful for things like token-by-token streaming.
This method is not guaranteed to be perfect. For some models, it may be impossible to robustly tokenize
single messages. For example, if the chat template adds tokens after each message, but also has a prefix that
is added to the entire chat, it will be impossible to distinguish a chat-start-token from a message-start-token.
In these cases, this method will do its best to find the correct tokenization, but it may not be perfect.
**Note:** This method does not support `add_generation_prompt`. If you want to add a generation prompt,
you should do it separately after tokenizing the conversation.

**Parameters:**

message (`dict`) : A dictionary with "role" and "content" keys, representing the message to tokenize.

conversation_history (`list[dict]`, *optional*) : A list of dicts with "role" and "content" keys, representing the chat history so far. If you are tokenizing messages one by one, you should pass the previous messages in the conversation here.

- ****kwargs** : Additional kwargs to pass to the `apply_chat_template` method.

**Returns:**

``list[int]``

A list of token ids representing the tokenized message.
#### from_pretrained[[transformers.PreTrainedTokenizerBase.from_pretrained]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L1505)

Instantiate a [PreTrainedTokenizerBase](/docs/transformers/v5.0.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase) (or a derived class) from a predefined
tokenizer.

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

Examples:

```python
# 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("google-bert/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("google-bert/bert-base-uncased", unk_token="")
# You should be sure '' is in the vocabulary when doing that.
# Otherwise use tokenizer.add_special_tokens({'unk_token': ''}) instead)
assert tokenizer.unk_token == ""
```

**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. - A path to a *directory* containing vocabulary files required by the tokenizer, for instance saved using the [save_pretrained()](/docs/transformers/v5.0.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.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.

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.

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 `hf auth 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.

trust_remote_code (`bool`, *optional*, defaults to `False`) : Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.

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`, `extra_special_tokens`. See parameters in the `__init__` for more details.
#### get_chat_template[[transformers.PreTrainedTokenizerBase.get_chat_template]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L3249)

Retrieve the chat template string used for tokenizing chat messages. This template is used
internally by the `apply_chat_template` method and can also be used externally to retrieve the model's chat
template for better generation tracking.

**Parameters:**

chat_template (`str`, *optional*) : A Jinja template or the name of a 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.

tools (`list[Dict]`, *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 [chat templating guide](https://huggingface.co/docs/transformers/main/en/chat_templating#automated-function-conversion-for-tool-use) for more information.

**Returns:**

``str``

The chat template string.
#### get_special_tokens_mask[[transformers.PreTrainedTokenizerBase.get_special_tokens_mask]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L1330)

Retrieve sequence ids from a token list that has no special tokens added.

For fast tokenizers, data collators call this with `already_has_special_tokens=True` to build a mask over an
already-formatted sequence. In that case, we compute the mask by checking membership in `all_special_ids`.

**Parameters:**

token_ids_0 : List of IDs for the (possibly already formatted) sequence.

token_ids_1 : Unused when `already_has_special_tokens=True`. Must be None in that case.

already_has_special_tokens : Whether the sequence is already formatted with special tokens.

**Returns:**

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

1 for a special token, 0 for a sequence token.
#### get_vocab[[transformers.PreTrainedTokenizerBase.get_vocab]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L1461)

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.

**Returns:**

``dict[str, int]``

The vocabulary.
#### pad[[transformers.PreTrainedTokenizerBase.pad]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L2615)

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`).

Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the
text followed by a call to the `pad` method to get a padded encoding.

If the `encoded_inputs` passed are dictionary of numpy arrays, or PyTorch 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.

**Parameters:**

encoded_inputs ([BatchEncoding](/docs/transformers/v5.0.0/en/main_classes/tokenizer#transformers.BatchEncoding), list of [BatchEncoding](/docs/transformers/v5.0.0/en/main_classes/tokenizer#transformers.BatchEncoding), `dict[str, list[int]]`, `dict[str, list[list[int]]` or `list[dict[str, list[int]]]`) : Tokenized inputs. Can represent one input ([BatchEncoding](/docs/transformers/v5.0.0/en/main_classes/tokenizer#transformers.BatchEncoding) or `dict[str, list[int]]`) or a batch of tokenized inputs (list of [BatchEncoding](/docs/transformers/v5.0.0/en/main_classes/tokenizer#transformers.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, or PyTorch tensors), see the note above for the return type.

padding (`bool`, `str` or [PaddingStrategy](/docs/transformers/v5.0.0/en/internal/file_utils#transformers.utils.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'` (default): 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'`: 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).

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_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?](../glossary#attention-mask)

return_tensors (`str` or [TensorType](/docs/transformers/v5.0.0/en/internal/file_utils#transformers.TensorType), *optional*) : If set, will return tensors instead of list of python integers. Acceptable values are:  - `'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.
#### parse_response[[transformers.PreTrainedTokenizerBase.parse_response]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L3358)

Converts an output string created by generating text from a model into a parsed message dictionary.
This method is intended for use with chat models, and will read the tokenizer's `response_schema` attribute to
control parsing, although this can be overridden by passing a `response_schema` argument directly.

This method is currently **highly experimental** and the schema specification is likely to change in future!
We recommend not building production code on top of it just yet.

**Parameters:**

response (`str`) : The output string generated by the model. This can be either a decoded string or list of strings, or token IDs as a list/array.

schema (`Union[list, dict]`, *optional*) : A response schema that indicates the expected output format and how parsing should be performed. If not provided, the tokenizer's `response_schema` attribute will be used.
#### push_to_hub[[transformers.PreTrainedTokenizerBase.push_to_hub]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/utils/hub.py#L711)

Upload the tokenizer files to the 🤗 Model Hub.

Examples:

```python
from transformers import AutoTokenizer

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

# Push the tokenizer to your namespace with the name "my-finetuned-bert".
tokenizer.push_to_hub("my-finetuned-bert")

# Push the tokenizer to an organization with the name "my-finetuned-bert".
tokenizer.push_to_hub("huggingface/my-finetuned-bert")
```

**Parameters:**

repo_id (`str`) : The name of the repository you want to push your tokenizer to. It should contain your organization name when pushing to a given organization.

commit_message (`str`, *optional*) : Message to commit while pushing. Will default to `"Upload tokenizer"`.

commit_description (`str`, *optional*) : The description of the commit that will be created

private (`bool`, *optional*) : Whether to make the repo private. If `None` (default), the repo will be public unless the organization's default is private. This value is ignored if the repo already exists.

token (`bool` or `str`, *optional*) : The token to use as HTTP bearer authorization for remote files. If `True` (default), will use the token generated when running `hf auth login` (stored in `~/.huggingface`).

revision (`str`, *optional*) : Branch to push the uploaded files to.

create_pr (`bool`, *optional*, defaults to `False`) : Whether or not to create a PR with the uploaded files or directly commit.

max_shard_size (`int` or `str`, *optional*, defaults to `"50GB"`) : Only applicable for models. The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5MB"`).

tags (`list[str]`, *optional*) : List of tags to push on the Hub.
#### register_for_auto_class[[transformers.PreTrainedTokenizerBase.register_for_auto_class]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L2997)

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`.

**Parameters:**

auto_class (`str` or `type`, *optional*, defaults to `"AutoTokenizer"`) : The auto class to register this new tokenizer with.
#### save_chat_templates[[transformers.PreTrainedTokenizerBase.save_chat_templates]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L3303)

Writes chat templates out to the save directory if we're using the new format, and removes them from
the tokenizer config if present. If we're using the legacy format, it doesn't write any files, and instead
writes the templates to the tokenizer config in the correct format.
#### save_pretrained[[transformers.PreTrainedTokenizerBase.save_pretrained]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L2047)

Save the full tokenizer state.

This method make sure the full tokenizer can then be re-loaded using the
`~tokenization_utils_base.PreTrainedTokenizer.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).

**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. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace).

kwargs (`dict[str, Any]`, *optional*) : Additional key word arguments passed along to the [push_to_hub()](/docs/transformers/v5.0.0/en/main_classes/model#transformers.utils.PushToHubMixin.push_to_hub) method.

**Returns:**

`A tuple of `str``

The files saved.
#### save_vocabulary[[transformers.PreTrainedTokenizerBase.save_vocabulary]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L2240)

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.

**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.
#### tokenize[[transformers.PreTrainedTokenizerBase.tokenize]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L2258)

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

**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__()](/docs/transformers/v5.0.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__)

**Returns:**

``list[str]``

The list of tokens.

## Enums and namedtuples[[transformers.tokenization_utils_base.TruncationStrategy]]

#### transformers.tokenization_utils_base.TruncationStrategy[[transformers.tokenization_utils_base.TruncationStrategy]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L153)

Possible values for the `truncation` argument in [PreTrainedTokenizerBase.__call__()](/docs/transformers/v5.0.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__). Useful for tab-completion in
an IDE.

#### transformers.CharSpan[[transformers.CharSpan]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L165)

Character span in the original string.

**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.

#### transformers.TokenSpan[[transformers.TokenSpan]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/tokenization_utils_base.py#L178)

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

**Parameters:**

start (`int`) : Index of the first token in the span.

end (`int`) : Index of the token following the last token in the span.