Most of those are only useful if you are studying the code of the tokenizers in the library.
class transformers.PreTrainedTokenizerBase < source >( **kwargs )
Parameters
int, optional) — The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is loaded with from_pretrained(), this will be set to the value stored for the associated model in max_model_input_sizes (see above). If no value is provided, will default to VERY_LARGE_INTEGER (int(1e30)). 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. 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. 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. 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. str or tokenizers.AddedToken, optional) — A special token representing the beginning of a sentence. Will be associated to self.bos_token and self.bos_token_id. str or tokenizers.AddedToken, optional) — A special token representing the end of a sentence. Will be associated to self.eos_token and self.eos_token_id. str or tokenizers.AddedToken, optional) — A special token representing an out-of-vocabulary token. Will be associated to self.unk_token and self.unk_token_id. str or tokenizers.AddedToken, optional) — A special token separating two different sentences in the same input (used by BERT for instance). Will be associated to self.sep_token and self.sep_token_id. str or tokenizers.AddedToken, optional) — A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation. Will be associated to self.pad_token and self.pad_token_id. str or tokenizers.AddedToken, optional) — A special token representing the class of the input (used by BERT for instance). Will be associated to self.cls_token and self.cls_token_id. 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. str or tokenizers.AddedToken, optional) — A tuple or a list of additional special tokens. Add them here to ensure they are skipped when decoding with skip_special_tokens is set to True. If they are not part of the vocabulary, they will be added at the end of the vocabulary. bool, optional, defaults to True) — Whether or not the model should cleanup the spaces that were added when splitting the input text during the tokenization process. bool, optional, defaults to False) — Whether or not the special tokens should be split during the tokenization process. Passing will affect the internal state of the tokenizer. The default behavior is to not split special tokens. This means that if <s> is the bos_token, then tokenizer.tokenize("<s>") = ['<s>]. Otherwise, if split_special_tokens=True, then tokenizer.tokenize("<s>") will be give ['<','s', '>']. Base class for PreTrainedTokenizer and PreTrainedTokenizerFast.
Handles shared (mostly boiler plate) methods for those two classes.
Class attributes (overridden by derived classes)
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).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.List[str]) — A list of inputs expected in the forward pass of the model.str) — The default value for the side on which the model should have padding applied. Should be 'right' or 'left'.str) — The default value for the side on which the model should have truncation applied. Should be 'right' or 'left'.( text: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None text_pair: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None text_target: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None text_pair_target: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy, NoneType] = None max_length: typing.Optional[int] = None stride: int = 0 is_split_into_words: bool = False pad_to_multiple_of: typing.Optional[int] = None padding_side: typing.Optional[str] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs ) → BatchEncoding
Parameters
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). 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). 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). 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). 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. 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).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).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.
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. 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. 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). 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. 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.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.
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.
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. bool, optional, defaults to False) — Whether or not to return special tokens mask information. 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.
bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs. bool, optional, defaults to True) — Whether or not to print more information and warnings. self.tokenize() method A BatchEncoding with the following fields:
input_ids — List of token ids to be fed to a model.
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).
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).
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True)
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences.
apply_chat_template < source >( conversation: typing.Union[typing.List[typing.Dict[str, str]], typing.List[typing.List[typing.Dict[str, str]]]] tools: typing.Optional[typing.List[typing.Union[typing.Dict, typing.Callable]]] = None documents: typing.Optional[typing.List[typing.Dict[str, str]]] = None chat_template: typing.Optional[str] = None add_generation_prompt: bool = False continue_final_message: bool = False tokenize: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: bool = False max_length: typing.Optional[int] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_dict: bool = False return_assistant_tokens_mask: bool = False tokenizer_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None **kwargs ) → Union[List[int], Dict]
Parameters
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. 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. 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, defaults to True) — Whether to tokenize the output. If False, the output will be a string. bool, str or PaddingStrategy, optional, defaults to False) — Select a strategy to pad the returned sequences (according to the model’s padding side and padding index) among:
True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).bool, defaults to False) — Whether to truncate sequences at the maximum length. Has no effect if tokenize is False. 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. 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.bool, defaults to False) — Whether to return a dictionary with named outputs. Has no effect if tokenize is False. Dict[str -- Any], optional): Additional kwargs to pass to the tokenizer. 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. Returns
Union[List[int], Dict]
A list of token ids representing the tokenized chat so far, including control tokens. This output is ready to pass to the model, either directly or via methods like generate(). If return_dict is set, will return a dict of tokenizer outputs instead.
Converts a list of dictionaries with "role" and "content" keys to a list of token ids. This method is intended for use with chat models, and will read the tokenizer’s chat_template attribute to determine the format and control tokens to use when converting.
Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to sequence-to-sequence models that need a slightly different processing for the labels.
batch_decode < source >( sequences: typing.Union[typing.List[int], typing.List[typing.List[int]], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')] skip_special_tokens: bool = False clean_up_tokenization_spaces: typing.Optional[bool] = None **kwargs ) → List[str]
Parameters
Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]) — List of tokenized input ids. Can be obtained using the __call__ method. bool, optional, defaults to False) — Whether or not to remove special tokens in the decoding. bool, optional) — Whether or not to clean up the tokenization spaces. If None, will default to self.clean_up_tokenization_spaces. The list of decoded sentences.
Convert a list of lists of token ids into a list of strings by calling decode.
batch_encode_plus < source >( batch_text_or_text_pairs: typing.Union[typing.List[str], typing.List[typing.Tuple[str, str]], typing.List[typing.List[str]], typing.List[typing.Tuple[typing.List[str], typing.List[str]]], typing.List[typing.List[int]], typing.List[typing.Tuple[typing.List[int], typing.List[int]]]] add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy, NoneType] = None max_length: typing.Optional[int] = None stride: int = 0 is_split_into_words: bool = False pad_to_multiple_of: typing.Optional[int] = None padding_side: typing.Optional[str] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True split_special_tokens: bool = False **kwargs ) → BatchEncoding
Parameters
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). 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. 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).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).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.
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. 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. 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). 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. 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.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.
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.
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. bool, optional, defaults to False) — Whether or not to return special tokens mask information. 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.
bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs. bool, optional, defaults to True) — Whether or not to print more information and warnings. self.tokenize() method A BatchEncoding with the following fields:
input_ids — List of token ids to be fed to a model.
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).
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).
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True)
Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.
This method is deprecated, __call__ should be used instead.
( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]
Parameters
List[int]) — The first tokenized sequence. List[int], optional) — The second tokenized sequence. The model input with special tokens.
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens.
This implementation does not add special tokens and this method should be overridden in a subclass.
clean_up_tokenization < source >( out_string: str ) → str
Parameters
The cleaned-up string.
Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms.
convert_tokens_to_string < source >( tokens: typing.List[str] ) → str
Parameters
The joined tokens.
Converts a sequence of tokens in a single string. The most simple way to do it is " ".join(tokens) but we often want to remove sub-word tokenization artifacts at the same time.
( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]
Parameters
List[int]) — The first tokenized sequence. List[int], optional) — The second tokenized sequence. The token type ids.
Create the token type IDs corresponding to the sequences passed. What are token type IDs?
Should be overridden in a subclass if the model has a special way of building those.
decode < source >( token_ids: typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')] skip_special_tokens: bool = False clean_up_tokenization_spaces: typing.Optional[bool] = None **kwargs ) → str
Parameters
Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]) — List of tokenized input ids. Can be obtained using the __call__ method. bool, optional, defaults to False) — Whether or not to remove special tokens in the decoding. bool, optional) — Whether or not to clean up the tokenization spaces. If None, will default to self.clean_up_tokenization_spaces. The decoded sentence.
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces.
Similar to doing self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids)).
( text: typing.Union[str, typing.List[str], typing.List[int]] text_pair: typing.Union[str, typing.List[str], typing.List[int], NoneType] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy, NoneType] = None max_length: typing.Optional[int] = None stride: int = 0 padding_side: typing.Optional[str] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None **kwargs ) → List[int], torch.Tensor, tf.Tensor or np.ndarray
Parameters
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). 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). 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. 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).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).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.
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. 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. 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). 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. 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..tokenize() method. Returns
List[int], torch.Tensor, tf.Tensor or np.ndarray
The tokenized ids of the text.
Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary.
Same as doing self.convert_tokens_to_ids(self.tokenize(text)).
( text: typing.Union[str, typing.List[str], typing.List[int]] text_pair: typing.Union[str, typing.List[str], typing.List[int], NoneType] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy, NoneType] = None max_length: typing.Optional[int] = None stride: int = 0 is_split_into_words: bool = False pad_to_multiple_of: typing.Optional[int] = None padding_side: typing.Optional[str] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs ) → BatchEncoding
Parameters
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). 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). 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. 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).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).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.
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. 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. 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). 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. 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.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.
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.
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. bool, optional, defaults to False) — Whether or not to return special tokens mask information. 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.
bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs. bool, optional, defaults to True) — Whether or not to print more information and warnings. self.tokenize() method A BatchEncoding with the following fields:
input_ids — List of token ids to be fed to a model.
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).
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).
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True)
Tokenize and prepare for the model a sequence or a pair of sequences.
This method is deprecated, __call__ should be used instead.
( pretrained_model_name_or_path: typing.Union[str, os.PathLike] *init_inputs cache_dir: typing.Union[str, os.PathLike, NoneType] = None force_download: bool = False local_files_only: bool = False token: typing.Union[bool, str, NoneType] = None revision: str = 'main' trust_remote_code = False **kwargs )
Parameters
str or os.PathLike) — Can be either:
./my_model_directory/../my_model_directory/vocab.txt.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. bool, optional, defaults to False) — Whether or not to force the (re-)download the vocabulary files and override the cached versions if they exist. 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. 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). bool, optional, defaults to False) — Whether or not to only rely on local files and not to attempt to download any files. 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. 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. __init__ method. 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. __init__ method. Can be used to set special tokens like bos_token, eos_token, unk_token, sep_token, pad_token, cls_token, mask_token, additional_special_tokens. See parameters in the __init__ for more details. Instantiate a PreTrainedTokenizerBase (or a derived class) from a predefined tokenizer.
Passing token=True is required when you want to use a private model.
Examples:
tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
tokenizer = BertTokenizer.from_pretrained("dbmdz/bert-base-german-cased")
tokenizer = BertTokenizer.from_pretrained("./test/saved_model/")
tokenizer = BertTokenizer.from_pretrained("./test/saved_model/my_vocab.txt")
tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased", unk_token="<unk>")
assert tokenizer.unk_token == "<unk>" get_chat_template < source >
( chat_template: typing.Optional[str] = None tools: typing.Optional[typing.List[typing.Dict]] = None ) → str
Parameters
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. 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.
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.
( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: bool = False ) → A list of integers in the range [0, 1]
Parameters
List[int]) — List of ids of the first sequence. List[int], optional) — List of ids of the second sequence. bool, optional, defaults to False) — Whether or not the token list is already formatted with special tokens for the model. Returns
A list of integers in the range [0, 1]
1 for a special token, 0 for a sequence token.
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model or encode_plus methods.
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.
( encoded_inputs: typing.Union[transformers.tokenization_utils_base.BatchEncoding, typing.List[transformers.tokenization_utils_base.BatchEncoding], typing.Dict[str, typing.List[int]], typing.Dict[str, typing.List[typing.List[int]]], typing.List[typing.Dict[str, typing.List[int]]]] padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = True max_length: typing.Optional[int] = None pad_to_multiple_of: typing.Optional[int] = None padding_side: typing.Optional[str] = None return_attention_mask: typing.Optional[bool] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None verbose: bool = True )
Parameters
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.
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' (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).int, optional) — Maximum length of the returned list and optionally padding length (see above). 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).
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. 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.
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.bool, optional, defaults to True) — Whether or not to print more information and warnings. Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length in the batch.
Padding side (left/right) padding token ids are defined at the tokenizer level (with self.padding_side, self.pad_token_id and self.pad_token_type_id).
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, PyTorch tensors or TensorFlow tensors, the result will use the same type unless you provide a different tensor type with return_tensors. In the case of PyTorch tensors, you will lose the specific device of your tensors however.
( ids: typing.List[int] pair_ids: typing.Optional[typing.List[int]] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy, NoneType] = None max_length: typing.Optional[int] = None stride: int = 0 pad_to_multiple_of: typing.Optional[int] = None padding_side: typing.Optional[str] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True prepend_batch_axis: bool = False **kwargs ) → BatchEncoding
Parameters
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. 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. 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. 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).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).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.
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. 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. 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). 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. 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.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.
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.
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. bool, optional, defaults to False) — Whether or not to return special tokens mask information. 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.
bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs. bool, optional, defaults to True) — Whether or not to print more information and warnings. self.tokenize() method A BatchEncoding with the following fields:
input_ids — List of token ids to be fed to a model.
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).
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).
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True)
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and manages a moving window (with user defined stride) for overflowing tokens. Please Note, for pair_ids different than None and truncation_strategy = longest_first or True, it is not possible to return overflowing tokens. Such a combination of arguments will raise an error.
( src_texts: typing.List[str] tgt_texts: typing.Optional[typing.List[str]] = None max_length: typing.Optional[int] = None max_target_length: typing.Optional[int] = None padding: str = 'longest' return_tensors: typing.Optional[str] = None truncation: bool = True **kwargs ) → BatchEncoding
Parameters
List[str]) — List of documents to summarize or source language texts. list, optional) — List of summaries or target language texts. 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. 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. 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).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.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).self.__call__. A BatchEncoding with the following fields:
The full set of keys [input_ids, attention_mask, labels], will only be returned if tgt_texts is passed. Otherwise, input_ids, attention_mask will be the only keys.
Prepare model inputs for translation. For best performance, translate one sentence at a time.
push_to_hub < source >( repo_id: str use_temp_dir: typing.Optional[bool] = None commit_message: typing.Optional[str] = None private: typing.Optional[bool] = None token: typing.Union[bool, str, NoneType] = None max_shard_size: typing.Union[int, str, NoneType] = '5GB' create_pr: bool = False safe_serialization: bool = True revision: typing.Optional[str] = None commit_description: typing.Optional[str] = None tags: typing.Optional[list[str]] = None **deprecated_kwargs )
Parameters
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. 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. str, optional) — Message to commit while pushing. Will default to "Upload tokenizer". 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. 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. 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. bool, optional, defaults to False) — Whether or not to create a PR with the uploaded files or directly commit. bool, optional, defaults to True) — Whether or not to convert the model weights in safetensors format for safer serialization. str, optional) — Branch to push the uploaded files to. str, optional) — The description of the commit that will be created List[str], optional) — List of tags to push on the Hub. Upload the tokenizer files to the 🤗 Model Hub.
Examples:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
tokenizer.push_to_hub("my-finetuned-bert")
tokenizer.push_to_hub("huggingface/my-finetuned-bert") register_for_auto_class < source >
( auto_class = 'AutoTokenizer' )
Parameters
str or type, optional, defaults to "AutoTokenizer") — The auto class to register this new tokenizer with. Register this class with a given auto class. This should only be used for custom tokenizers as the ones in the library are already mapped with AutoTokenizer.
This API is experimental and may have some slight breaking changes in the next releases.
save_pretrained < source >( save_directory: typing.Union[str, os.PathLike] legacy_format: typing.Optional[bool] = None filename_prefix: typing.Optional[str] = None push_to_hub: bool = False **kwargs ) → A tuple of str
Parameters
str or os.PathLike) — The path to a directory where the tokenizer will be saved. 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.
str, optional) — A prefix to add to the names of the files saved by the tokenizer. 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). Dict[str, Any], optional) — Additional key word arguments passed along to the push_to_hub() method. The files saved.
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).
( save_directory: str filename_prefix: typing.Optional[str] = None ) → Tuple(str)
Parameters
str) — The directory in which to save the vocabulary. str, optional) — An optional prefix to add to the named of the saved files. Paths to the files saved.
Save only the vocabulary of the tokenizer (vocabulary + added tokens).
This method won’t save the configuration and special token mappings of the tokenizer. Use _save_pretrained() to save the whole state of the tokenizer.
( text: str pair: typing.Optional[str] = None add_special_tokens: bool = False **kwargs ) → List[str]
Parameters
str) — The sequence to be encoded. str, optional) — A second sequence to be encoded with the first. bool, optional, defaults to False) — Whether or not to add the special tokens associated with the corresponding model. The list of tokens.
Converts a string into a sequence of tokens, replacing unknown tokens with the unk_token.
( ids: typing.List[int] pair_ids: typing.Optional[typing.List[int]] = None num_tokens_to_remove: int = 0 truncation_strategy: typing.Union[str, transformers.tokenization_utils_base.TruncationStrategy] = 'longest_first' stride: int = 0 ) → Tuple[List[int], List[int], List[int]]
Parameters
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. 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. int, optional, defaults to 0) — Number of tokens to remove using the 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).int, optional, defaults to 0) — If set to a positive number, the overflowing tokens returned will contain some tokens from the main sequence returned. The value of this argument defines the number of additional tokens. Returns
Tuple[List[int], List[int], List[int]]
The truncated ids, the truncated pair_ids and the list of overflowing tokens. Note: The longest_first strategy returns empty list of overflowing tokens if a pair of sequences (or a batch of pairs) is provided.
Truncates a sequence pair in-place following the strategy.
class transformers.SpecialTokensMixin < source >( verbose = False **kwargs )
Parameters
str or tokenizers.AddedToken, optional) — A special token representing the beginning of a sentence. str or tokenizers.AddedToken, optional) — A special token representing the end of a sentence. str or tokenizers.AddedToken, optional) — A special token representing an out-of-vocabulary token. str or tokenizers.AddedToken, optional) — A special token separating two different sentences in the same input (used by BERT for instance). 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. str or tokenizers.AddedToken, optional) — A special token representing the class of the input (used by BERT for instance). str or tokenizers.AddedToken, optional) — A special token representing a masked token (used by masked-language modeling pretraining objectives, like BERT). str or tokenizers.AddedToken, optional) — A tuple or a list of additional tokens, which will be marked as special, meaning that they will be skipped when decoding if skip_special_tokens is set to True. A mixin derived by PreTrainedTokenizer and PreTrainedTokenizerFast to handle specific behaviors related to special tokens. In particular, this class hold the attributes which can be used to directly access these special tokens in a model-independent manner and allow to set and update the special tokens.
add_special_tokens < source >( special_tokens_dict: typing.Dict[str, typing.Union[str, tokenizers.AddedToken]] replace_additional_special_tokens = True ) → int
Parameters
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).
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._special_tokens_map["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.
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() method.
Using add_special_tokens will ensure your special tokens can be used in several ways:
skip_special_tokens = True.AddedTokens.tokenizer.cls_token. This makes it easy to develop model-agnostic training and fine-tuning scripts.When possible, special tokens are already registered for provided pretrained models (for instance BertTokenizer cls_token is already registered to be :obj’[CLS]’ and XLM’s one is also registered to be '</s>').
Examples:
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
model = GPT2Model.from_pretrained("openai-community/gpt2")
special_tokens_dict = {"cls_token": "<CLS>"}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print("We have added", num_added_toks, "tokens")
model.resize_token_embeddings(len(tokenizer))
assert tokenizer.cls_token == "<CLS>" add_tokens < source >
( new_tokens: typing.Union[str, tokenizers.AddedToken, typing.List[typing.Union[str, tokenizers.AddedToken]]] special_tokens: bool = False ) → int
Parameters
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. 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.
Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to it with indices starting from length of the current vocabulary and will be isolated before the tokenization algorithm is applied. Added tokens and tokens from the vocabulary of the tokenization algorithm are therefore not treated in the same way.
Note, when adding new tokens to the vocabulary, you should make sure to also resize the token embedding matrix of the model so that its embedding matrix matches the tokenizer.
In order to do that, please use the resize_token_embeddings() method.
Examples:
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")
model.resize_token_embeddings(len(tokenizer))
The sanitize_special_tokens is now deprecated kept for backward compatibility and will be removed in transformers v5.
( value names = None module = None qualname = None type = None start = 1 )
Possible values for the truncation argument in PreTrainedTokenizerBase.call(). Useful for tab-completion in an IDE.
( start: int end: int )
Parameters
int) — Index of the first character in the original string. int) — Index of the character following the last character in the original string. Character span in the original string.
class transformers.TokenSpan < source >( start: int end: int )
Parameters
int) — Index of the first token in the span. int) — Index of the token following the last token in the span. Token span in an encoded string (list of tokens).
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