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 usefull 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 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. 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

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)

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[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 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. Please see the RAG section of the chat templating guide for examples of passing documents with chat templates.
• 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, defaults to False) — Whether to pad sequences to the maximum length. Has no effect if tokenize is False.
• 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.

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.

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.

The list of decoded sentences.

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 usefull 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 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. 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

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)

Parameters

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

The model input with special tokens.

Parameters

• out_string (str) — The text to clean up.

The cleaned-up string.

Parameters

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

The joined tokens.

Parameters

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

The token type ids.

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.

The decoded sentence.

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 usefull 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 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. 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.

  **kwargs — Passed along to the .tokenize() method.

The tokenized ids of the text.

Parameters

• text (str, List[str] or (for non-fast tokenizers) 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 usefull 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 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. 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

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)

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() 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 — Deprecated and ignored. All downloads are now resumed by default when possible. Will be removed in v5 of Transformers.
• 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 huggingface-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.
• 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, additional_special_tokens. See parameters in the __init__ for more details.

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 for more information.

The chat template string.

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.

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

The vocabulary.

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).

• 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?

• 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.

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 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 usefull 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 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. 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

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)

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__.

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.

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.
• use_temp_dir (bool, optional) — Whether or not to use a temporary directory to store the files saved before they are pushed to the Hub. Will default to True if there is no directory named like repo_id, False otherwise.
• commit_message (str, optional) — Message to commit while pushing. Will default to "Upload tokenizer".
• private (bool, optional) — Whether or not the repository created should be private.
• 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 huggingface-cli login (stored in ~/.huggingface). Will default to True if repo_url is not specified.
• max_shard_size (int or str, optional, defaults to "5GB") — 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"). We default it to "5GB" so that users can easily load models on free-tier Google Colab instances without any CPU OOM issues.
• create_pr (bool, optional, defaults to False) — Whether or not to create a PR with the uploaded files or directly commit.
• safe_serialization (bool, optional, defaults to True) — Whether or not to convert the model weights in safetensors format for safer serialization.
• revision (str, optional) — Branch to push the uploaded files to.
• commit_description (str, optional) — The description of the commit that will be created
• tags (List[str], optional) — List of tags to push on the Hub.

Parameters

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

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() method.

The files saved.

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.

Paths to the files saved.

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()

The list of tokens.

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 'longest_first') — 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.

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.

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).

• replace_additional_special_tokens (bool, optional,, defaults to True) — If True, the existing list of additional special tokens will be replaced by the list provided in special_tokens_dict. Otherwise, self._additional_special_tokens is just 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 additional_special_tokens are still added tokens, and will not be split by the model.

Number of tokens added to the vocabulary.

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.

Number of tokens added to the vocabulary.