Tokenizer

Tokenizer

A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most of the tokenizers are available in two flavors: a full python implementation and a “Fast” implementation based on the Rust library 🌍 Tokenizers. The “Fast” implementations allows:

a significant speed-up in particular when doing batched tokenization and
additional methods to map between the original string (character and words) and the token space (e.g. getting the index of the token comprising a given character or the span of characters corresponding to a given token).

The base classes PreTrainedTokenizer and PreTrainedTokenizerFast implement the common methods for encoding string inputs in model inputs (see below) and instantiating/saving python and “Fast” tokenizers either from a local file or directory or from a pretrained tokenizer provided by the library (downloaded from BOINC AI’s AWS S3 repository). They both rely on PreTrainedTokenizerBase that contains the common methods, and SpecialTokensMixin.

PreTrainedTokenizer and PreTrainedTokenizerFast thus implement the main methods for using all the tokenizers:

Tokenizing (splitting strings in sub-word token strings), converting tokens strings to ids and back, and encoding/decoding (i.e., tokenizing and converting to integers).
Adding new tokens to the vocabulary in a way that is independent of the underlying structure (BPE, SentencePiece…).
Managing special tokens (like mask, beginning-of-sentence, etc.): adding them, assigning them to attributes in the tokenizer for easy access and making sure they are not split during tokenization.

BatchEncoding holds the output of the PreTrainedTokenizerBase’s encoding methods (__call__, encode_plus and batch_encode_plus) and is derived from a Python dictionary. When the tokenizer is a pure python tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by these methods (input_ids, attention_mask…). When the tokenizer is a “Fast” tokenizer (i.e., backed by BOINC AI tokenizers library), this class provides in addition several advanced alignment methods which can be used to map between the original string (character and words) and the token space (e.g., getting the index of the token comprising a given character or the span of characters corresponding to a given token).

PreTrainedTokenizer

class transformers.PreTrainedTokenizer

( **kwargs )

Parameters

model_max_length (int, optional) — The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is loaded with from_pretrained(), this will be set to the value stored for the associated model in max_model_input_sizes (see above). If no value is provided, will default to VERY_LARGE_INTEGER (int(1e30)).
padding_side (str, optional) — The side on which the model should have padding applied. Should be selected between [‘right’, ‘left’]. Default value is picked from the class attribute of the same name.
truncation_side (str, optional) — The side on which the model should have truncation applied. Should be selected between [‘right’, ‘left’]. Default value is picked from the class attribute of the same name.
chat_template (str, optional) — A Jinja template string that will be used to format lists of chat messages. See https://boincai.com/docs/transformers/chat_templating for a full description.
model_input_names (List[string], optional) — The list of inputs accepted by the forward pass of the model (like "token_type_ids" or "attention_mask"). Default value is picked from the class attribute of the same name.
bos_token (str or tokenizers.AddedToken, optional) — A special token representing the beginning of a sentence. Will be associated to self.bos_token and self.bos_token_id.
eos_token (str or tokenizers.AddedToken, optional) — A special token representing the end of a sentence. Will be associated to self.eos_token and self.eos_token_id.
unk_token (str or tokenizers.AddedToken, optional) — A special token representing an out-of-vocabulary token. Will be associated to self.unk_token and self.unk_token_id.
sep_token (str or tokenizers.AddedToken, optional) — A special token separating two different sentences in the same input (used by BERT for instance). Will be associated to self.sep_token and self.sep_token_id.
pad_token (str or tokenizers.AddedToken, optional) — A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation. Will be associated to self.pad_token and self.pad_token_id.
cls_token (str or tokenizers.AddedToken, optional) — A special token representing the class of the input (used by BERT for instance). Will be associated to self.cls_token and self.cls_token_id.
mask_token (str or tokenizers.AddedToken, optional) — A special token representing a masked token (used by masked-language modeling pretraining objectives, like BERT). Will be associated to self.mask_token and self.mask_token_id.
additional_special_tokens (tuple or list of str or tokenizers.AddedToken, optional) — A tuple or a list of additional special tokens. Add them here to ensure they are skipped when decoding with skip_special_tokens is set to True. If they are not part of the vocabulary, they will be added at the end of the vocabulary.
clean_up_tokenization_spaces (bool, optional, defaults to True) — Whether or not the model should cleanup the spaces that were added when splitting the input text during the tokenization process.
split_special_tokens (bool, optional, defaults to False) — Whether or not the special tokens should be split during the tokenization process. 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', '>']. This argument is only supported for slow tokenizers for the moment.

Base class for all slow tokenizers.

Inherits from PreTrainedTokenizerBase.

Handle all the shared methods for tokenization and special tokens as well as methods downloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.

This class also contain the added tokens in a unified way on top of all tokenizers so we don’t have to handle the specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece…).

Class attributes (overridden by derived classes)

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

__call__

( text: typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = Nonetext_pair: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = Nonetext_target: typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = Nonetext_pair_target: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = Noneadd_special_tokens: bool = Truepadding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = Falsetruncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = Nonemax_length: typing.Optional[int] = Nonestride: int = 0is_split_into_words: bool = Falsepad_to_multiple_of: typing.Optional[int] = Nonereturn_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = Nonereturn_token_type_ids: typing.Optional[bool] = Nonereturn_attention_mask: typing.Optional[bool] = Nonereturn_overflowing_tokens: bool = Falsereturn_special_tokens_mask: bool = Falsereturn_offsets_mapping: bool = Falsereturn_length: bool = Falseverbose: bool = True**kwargs ) → BatchEncoding

Parameters

text (str, List[str], List[List[str]], optional) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
text_pair (str, List[str], List[List[str]], optional) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
text_target (str, List[str], List[List[str]], optional) — The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
text_pair_target (str, List[str], List[List[str]], optional) — The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
add_special_tokens (bool, optional, defaults to True) — Whether or not to add special tokens when encoding the sequences. This will use the underlying PretrainedTokenizerBase.build_inputs_with_special_tokens function, which defines which tokens are automatically added to the input ids. This is 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).
return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
- 'tf': Return TensorFlow tf.constant objects.
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return Numpy np.ndarray objects.
return_token_type_ids (bool, optional) — Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer’s default, defined by the return_outputs attribute.
What are token type IDs?
return_attention_mask (bool, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer’s default, defined by the return_outputs attribute.
What are attention masks?
return_overflowing_tokens (bool, optional, defaults to False) — Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead of returning overflowing tokens.
return_special_tokens_mask (bool, optional, defaults to False) — Whether or not to return special tokens mask information.
return_offsets_mapping (bool, optional, defaults to False) — Whether or not to return (char_start, char_end) for each token.
This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using Python’s tokenizer, this method will raise NotImplementedError.
return_length (bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs.
verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings. **kwargs — passed to the self.tokenize() method

Returns

A BatchEncoding with the following fields:

input_ids — List of token ids to be fed to a model.
What are input IDs?
token_type_ids — List of token type ids to be fed to a model (when return_token_type_ids=True or if “token_type_ids” is in self.model_input_names).
What are token type IDs?
attention_mask — List of indices specifying which tokens should be attended to by the model (when return_attention_mask=True or if “attention_mask” is in self.model_input_names).
What are attention masks?
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True)

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

apply_chat_template

( conversation: typing.Union[typing.List[typing.Dict[str, str]], ForwardRef('Conversation')]chat_template: typing.Optional[str] = Noneadd_generation_prompt: bool = Falsetokenize: bool = Truepadding: bool = Falsetruncation: bool = Falsemax_length: typing.Optional[int] = Nonereturn_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None**tokenizer_kwargs ) → List[int]

Parameters

conversation (Union[List[Dict[str, str]], “Conversation”]) — A Conversation object or list of dicts with “role” and “content” keys, representing the chat history so far.
chat_template (str, optional) — A Jinja template to use for this conversion. If this is not passed, the model’s default chat template will be used instead.
add_generation_prompt (bool, optional) — Whether to end the prompt with the token(s) that indicate the start of an assistant message. 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.
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. **tokenizer_kwargs — Additional kwargs to pass to the tokenizer.

Returns

List[int]

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().

Converts a Conversation object or 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. When chat_template is None, it will fall back to the default_chat_template specified at the class level.

batch_decode

( sequences: typing.Union[typing.List[int], typing.List[typing.List[int]], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]skip_special_tokens: bool = Falseclean_up_tokenization_spaces: bool = None**kwargs ) → List[str]

Parameters

sequences (Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]) — List of tokenized input ids. Can be obtained using the __call__ method.
skip_special_tokens (bool, optional, defaults to False) — Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (bool, optional) — Whether or not to clean up the tokenization spaces. If None, will default to self.clean_up_tokenization_spaces.
kwargs (additional keyword arguments, optional) — Will be passed to the underlying model specific decode method.

Returns

List[str]

The list of decoded sentences.

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

decode

( token_ids: typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]skip_special_tokens: bool = Falseclean_up_tokenization_spaces: bool = None**kwargs ) → str

Parameters

token_ids (Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]) — List of tokenized input ids. Can be obtained using the __call__ method.
skip_special_tokens (bool, optional, defaults to False) — Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (bool, optional) — Whether or not to clean up the tokenization spaces. If None, will default to self.clean_up_tokenization_spaces.
kwargs (additional keyword arguments, optional) — Will be passed to the underlying model specific decode method.

Returns

str

The decoded sentence.

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

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

encode

( text: typing.Union[str, typing.List[str], typing.List[int]]text_pair: typing.Union[str, typing.List[str], typing.List[int], NoneType] = Noneadd_special_tokens: bool = Truepadding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = Falsetruncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = Nonemax_length: typing.Optional[int] = Nonestride: int = 0return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None**kwargs ) → List[int], torch.Tensor, tf.Tensor or np.ndarray

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).
return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
- 'tf': Return TensorFlow tf.constant objects.
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return Numpy np.ndarray objects.
**kwargs — Passed along to the .tokenize() method.

Returns

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

The tokenized ids of the text.

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

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

push_to_hub

( repo_id: struse_temp_dir: typing.Optional[bool] = Nonecommit_message: typing.Optional[str] = Noneprivate: typing.Optional[bool] = Nonetoken: typing.Union[bool, str, NoneType] = Nonemax_shard_size: typing.Union[int, str, NoneType] = '10GB'create_pr: bool = Falsesafe_serialization: bool = Falserevision: str = None**deprecated_kwargs )

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 boincai-cli login (stored in ~/.boincai). Will default to True if repo_url is not specified.
max_shard_size (int or str, optional, defaults to "10GB") — 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").
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 False) — Whether or not to convert the model weights in safetensors format for safer serialization.
revision (str, optional) — Branch to push the uploaded files to.

Upload the tokenizer files to the 🌍 Model Hub.

Examples:

Copied

from transformers import AutoTokenizer

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

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

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

convert_ids_to_tokens

( ids: typing.Union[int, typing.List[int]]skip_special_tokens: bool = False ) → str or List[str]

Parameters

ids (int or List[int]) — The token id (or token ids) to convert to tokens.
skip_special_tokens (bool, optional, defaults to False) — Whether or not to remove special tokens in the decoding.

Returns

str or List[str]

The decoded token(s).

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

convert_tokens_to_ids

( tokens: typing.Union[str, typing.List[str]] ) → int or List[int]

Parameters

tokens (str or List[str]) — One or several token(s) to convert to token id(s).

Returns

int or List[int]

The token id or list of token ids.

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

get_added_vocab

( ) → Dict[str, int]

Returns

Dict[str, int]

The added tokens.

Returns the added tokens in the vocabulary as a dictionary of token to index. Results might be different from the fast call because for now we always add the tokens even if they are already in the vocabulary. This is something we should change.

num_special_tokens_to_add

( pair: bool = False ) → int

Parameters

pair (bool, optional, defaults to False) — Whether the number of added tokens should be computed in the case of a sequence pair or a single sequence.

Returns

int

Number of special tokens added to sequences.

Returns the number of added tokens when encoding a sequence with special tokens.

This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not put this inside your training loop.

prepare_for_tokenization

( text: stris_split_into_words: bool = False**kwargs ) → Tuple[str, Dict[str, Any]]

Parameters

text (str) — The text to prepare.
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.
kwargs (Dict[str, Any], optional) — Keyword arguments to use for the tokenization.

Returns

Tuple[str, Dict[str, Any]]

The prepared text and the unused kwargs.

Performs any necessary transformations before tokenization.

This method should pop the arguments from kwargs and return the remaining kwargs as well. We test the kwargs at the end of the encoding process to be sure all the arguments have been used.

tokenize

( text: str**kwargs ) → List[str]

Parameters

text (str) — The sequence to be encoded.
**kwargs (additional keyword arguments) — Passed along to the model-specific prepare_for_tokenization preprocessing method.

Returns

List[str]

The list of tokens.

Converts a string in a sequence of tokens, using the tokenizer.

Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces). Takes care of added tokens.

PreTrainedTokenizerFast

The PreTrainedTokenizerFast depend on the tokenizers library. The tokenizers obtained from the 🌍 tokenizers library can be loaded very simply into 🌍 transformers. Take a look at the Using tokenizers from 🌍 tokenizers page to understand how this is done.

class transformers.PreTrainedTokenizerFast

( *args**kwargs )

Parameters

model_max_length (int, optional) — The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is loaded with from_pretrained(), this will be set to the value stored for the associated model in max_model_input_sizes (see above). If no value is provided, will default to VERY_LARGE_INTEGER (int(1e30)).
padding_side (str, optional) — The side on which the model should have padding applied. Should be selected between [‘right’, ‘left’]. Default value is picked from the class attribute of the same name.
truncation_side (str, optional) — The side on which the model should have truncation applied. Should be selected between [‘right’, ‘left’]. Default value is picked from the class attribute of the same name.
chat_template (str, optional) — A Jinja template string that will be used to format lists of chat messages. See https://boincai.com/docs/transformers/chat_templating for a full description.
model_input_names (List[string], optional) — The list of inputs accepted by the forward pass of the model (like "token_type_ids" or "attention_mask"). Default value is picked from the class attribute of the same name.
bos_token (str or tokenizers.AddedToken, optional) — A special token representing the beginning of a sentence. Will be associated to self.bos_token and self.bos_token_id.
eos_token (str or tokenizers.AddedToken, optional) — A special token representing the end of a sentence. Will be associated to self.eos_token and self.eos_token_id.
unk_token (str or tokenizers.AddedToken, optional) — A special token representing an out-of-vocabulary token. Will be associated to self.unk_token and self.unk_token_id.
sep_token (str or tokenizers.AddedToken, optional) — A special token separating two different sentences in the same input (used by BERT for instance). Will be associated to self.sep_token and self.sep_token_id.
pad_token (str or tokenizers.AddedToken, optional) — A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation. Will be associated to self.pad_token and self.pad_token_id.
cls_token (str or tokenizers.AddedToken, optional) — A special token representing the class of the input (used by BERT for instance). Will be associated to self.cls_token and self.cls_token_id.
mask_token (str or tokenizers.AddedToken, optional) — A special token representing a masked token (used by masked-language modeling pretraining objectives, like BERT). Will be associated to self.mask_token and self.mask_token_id.
additional_special_tokens (tuple or list of str or tokenizers.AddedToken, optional) — A tuple or a list of additional special tokens. Add them here to ensure they are skipped when decoding with skip_special_tokens is set to True. If they are not part of the vocabulary, they will be added at the end of the vocabulary.
clean_up_tokenization_spaces (bool, optional, defaults to True) — Whether or not the model should cleanup the spaces that were added when splitting the input text during the tokenization process.
split_special_tokens (bool, optional, defaults to False) — Whether or not the special tokens should be split during the tokenization process. 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', '>']. This argument is only supported for slow tokenizers for the moment.
tokenizer_object (tokenizers.Tokenizer) — A tokenizers.Tokenizer object from 🌍tokenizers to instantiate from. See Using tokenizers from 🌍 tokenizers for more information.
tokenizer_file (str) — A path to a local JSON file representing a previously serialized tokenizers.Tokenizer object from 🌍 tokenizers.

Base class for all fast tokenizers (wrapping BOINC AI tokenizers library).

Inherits from PreTrainedTokenizerBase.

Handles all the shared methods for tokenization and special tokens, as well as methods for downloading/caching/loading pretrained tokenizers, as well as adding tokens to the vocabulary.

This class also contains the added tokens in a unified way on top of all tokenizers so we don’t have to handle the specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece…).

Class attributes (overridden by derived classes)

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

__call__

( text: typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = Nonetext_pair: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = Nonetext_target: typing.Union[str, typing.List[str], typing.List[typing.List[str]]] = Nonetext_pair_target: typing.Union[str, typing.List[str], typing.List[typing.List[str]], NoneType] = Noneadd_special_tokens: bool = Truepadding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = Falsetruncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = Nonemax_length: typing.Optional[int] = Nonestride: int = 0is_split_into_words: bool = Falsepad_to_multiple_of: typing.Optional[int] = Nonereturn_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = Nonereturn_token_type_ids: typing.Optional[bool] = Nonereturn_attention_mask: typing.Optional[bool] = Nonereturn_overflowing_tokens: bool = Falsereturn_special_tokens_mask: bool = Falsereturn_offsets_mapping: bool = Falsereturn_length: bool = Falseverbose: bool = True**kwargs ) → BatchEncoding

Parameters

text (str, List[str], List[List[str]], optional) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
text_pair (str, List[str], List[List[str]], optional) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
text_target (str, List[str], List[List[str]], optional) — The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
text_pair_target (str, List[str], List[List[str]], optional) — The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
add_special_tokens (bool, optional, defaults to True) — Whether or not to add special tokens when encoding the sequences. This will use the underlying PretrainedTokenizerBase.build_inputs_with_special_tokens function, which defines which tokens are automatically added to the input ids. This is 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).
return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
- 'tf': Return TensorFlow tf.constant objects.
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return Numpy np.ndarray objects.
return_token_type_ids (bool, optional) — Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer’s default, defined by the return_outputs attribute.
What are token type IDs?
return_attention_mask (bool, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer’s default, defined by the return_outputs attribute.
What are attention masks?
return_overflowing_tokens (bool, optional, defaults to False) — Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead of returning overflowing tokens.
return_special_tokens_mask (bool, optional, defaults to False) — Whether or not to return special tokens mask information.
return_offsets_mapping (bool, optional, defaults to False) — Whether or not to return (char_start, char_end) for each token.
This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using Python’s tokenizer, this method will raise NotImplementedError.
return_length (bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs.
verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings. **kwargs — passed to the self.tokenize() method

Returns

A BatchEncoding with the following fields:

input_ids — List of token ids to be fed to a model.
What are input IDs?
token_type_ids — List of token type ids to be fed to a model (when return_token_type_ids=True or if “token_type_ids” is in self.model_input_names).
What are token type IDs?
attention_mask — List of indices specifying which tokens should be attended to by the model (when return_attention_mask=True or if “attention_mask” is in self.model_input_names).
What are attention masks?
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True)

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

apply_chat_template

( conversation: typing.Union[typing.List[typing.Dict[str, str]], ForwardRef('Conversation')]chat_template: typing.Optional[str] = Noneadd_generation_prompt: bool = Falsetokenize: bool = Truepadding: bool = Falsetruncation: bool = Falsemax_length: typing.Optional[int] = Nonereturn_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None**tokenizer_kwargs ) → List[int]

Parameters

conversation (Union[List[Dict[str, str]], “Conversation”]) — A Conversation object or list of dicts with “role” and “content” keys, representing the chat history so far.
chat_template (str, optional) — A Jinja template to use for this conversion. If this is not passed, the model’s default chat template will be used instead.
add_generation_prompt (bool, optional) — Whether to end the prompt with the token(s) that indicate the start of an assistant message. 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.
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. **tokenizer_kwargs — Additional kwargs to pass to the tokenizer.

Returns

List[int]

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().

Converts a Conversation object or 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. When chat_template is None, it will fall back to the default_chat_template specified at the class level.

batch_decode

( sequences: typing.Union[typing.List[int], typing.List[typing.List[int]], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]skip_special_tokens: bool = Falseclean_up_tokenization_spaces: bool = None**kwargs ) → List[str]

Parameters

sequences (Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]) — List of tokenized input ids. Can be obtained using the __call__ method.
skip_special_tokens (bool, optional, defaults to False) — Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (bool, optional) — Whether or not to clean up the tokenization spaces. If None, will default to self.clean_up_tokenization_spaces.
kwargs (additional keyword arguments, optional) — Will be passed to the underlying model specific decode method.

Returns

List[str]

The list of decoded sentences.

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

decode

( token_ids: typing.Union[int, typing.List[int], ForwardRef('np.ndarray'), ForwardRef('torch.Tensor'), ForwardRef('tf.Tensor')]skip_special_tokens: bool = Falseclean_up_tokenization_spaces: bool = None**kwargs ) → str

Parameters

token_ids (Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]) — List of tokenized input ids. Can be obtained using the __call__ method.
skip_special_tokens (bool, optional, defaults to False) — Whether or not to remove special tokens in the decoding.
clean_up_tokenization_spaces (bool, optional) — Whether or not to clean up the tokenization spaces. If None, will default to self.clean_up_tokenization_spaces.
kwargs (additional keyword arguments, optional) — Will be passed to the underlying model specific decode method.

Returns

str

The decoded sentence.

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

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

encode

( text: typing.Union[str, typing.List[str], typing.List[int]]text_pair: typing.Union[str, typing.List[str], typing.List[int], NoneType] = Noneadd_special_tokens: bool = Truepadding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = Falsetruncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = Nonemax_length: typing.Optional[int] = Nonestride: int = 0return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None**kwargs ) → List[int], torch.Tensor, tf.Tensor or np.ndarray

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).
return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
- 'tf': Return TensorFlow tf.constant objects.
- 'pt': Return PyTorch torch.Tensor objects.
- 'np': Return Numpy np.ndarray objects.
**kwargs — Passed along to the .tokenize() method.

Returns

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

The tokenized ids of the text.

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

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

push_to_hub

( repo_id: struse_temp_dir: typing.Optional[bool] = Nonecommit_message: typing.Optional[str] = Noneprivate: typing.Optional[bool] = Nonetoken: typing.Union[bool, str, NoneType] = Nonemax_shard_size: typing.Union[int, str, NoneType] = '10GB'create_pr: bool = Falsesafe_serialization: bool = Falserevision: str = None**deprecated_kwargs )

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 boincai-cli login (stored in ~/.boincai). Will default to True if repo_url is not specified.
max_shard_size (int or str, optional, defaults to "10GB") — 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").
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 False) — Whether or not to convert the model weights in safetensors format for safer serialization.
revision (str, optional) — Branch to push the uploaded files to.

Upload the tokenizer files to the 🌍 Model Hub.

Examples:

Copied

from transformers import AutoTokenizer

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

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

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

convert_ids_to_tokens

( ids: typing.Union[int, typing.List[int]]skip_special_tokens: bool = False ) → str or List[str]

Parameters

ids (int or List[int]) — The token id (or token ids) to convert to tokens.
skip_special_tokens (bool, optional, defaults to False) — Whether or not to remove special tokens in the decoding.

Returns

str or List[str]

The decoded token(s).

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

convert_tokens_to_ids

( tokens: typing.Union[str, typing.List[str]] ) → int or List[int]

Parameters

tokens (str or List[str]) — One or several token(s) to convert to token id(s).

Returns

int or List[int]

The token id or list of token ids.

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

get_added_vocab

( ) → Dict[str, int]

Returns

Dict[str, int]

The added tokens.

Returns the added tokens in the vocabulary as a dictionary of token to index.

num_special_tokens_to_add

( pair: bool = False ) → int

Parameters

pair (bool, optional, defaults to False) — Whether the number of added tokens should be computed in the case of a sequence pair or a single sequence.

Returns

int

Number of special tokens added to sequences.

Returns the number of added tokens when encoding a sequence with special tokens.

This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not put this inside your training loop.

set_truncation_and_padding

( padding_strategy: PaddingStrategytruncation_strategy: TruncationStrategymax_length: intstride: intpad_to_multiple_of: typing.Optional[int] )

Parameters

padding_strategy (PaddingStrategy) — The kind of padding that will be applied to the input
truncation_strategy (TruncationStrategy) — The kind of truncation that will be applied to the input
max_length (int) — The maximum size of a sequence.
stride (int) — The stride to use when handling overflow.
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).

Define the truncation and the padding strategies for fast tokenizers (provided by BOINC AI tokenizers library) and restore the tokenizer settings afterwards.

The provided tokenizer has no padding / truncation strategy before the managed section. If your tokenizer set a padding / truncation strategy before, then it will be reset to no padding / truncation when exiting the managed section.

train_new_from_iterator

( text_iteratorvocab_sizelength = Nonenew_special_tokens = Nonespecial_tokens_map = None**kwargs ) → PreTrainedTokenizerFast

Parameters

text_iterator (generator of List[str]) — The training corpus. Should be a generator of batches of texts, for instance a list of lists of texts if you have everything in memory.
vocab_size (int) — The size of the vocabulary you want for your tokenizer.
length (int, optional) — The total number of sequences in the iterator. This is used to provide meaningful progress tracking
new_special_tokens (list of str or AddedToken, optional) — A list of new special tokens to add to the tokenizer you are training.
special_tokens_map (Dict[str, str], optional) — If you want to rename some of the special tokens this tokenizer uses, pass along a mapping old special token name to new special token name in this argument.
kwargs (Dict[str, Any], optional) — Additional keyword arguments passed along to the trainer from the 🌍 Tokenizers library.

Returns

PreTrainedTokenizerFast

A new tokenizer of the same type as the original one, trained on text_iterator.

Trains a tokenizer on a new corpus with the same defaults (in terms of special tokens or tokenization pipeline) as the current one.

BatchEncoding

class transformers.BatchEncoding

( data: typing.Union[typing.Dict[str, typing.Any], NoneType] = Noneencoding: typing.Union[tokenizers.Encoding, typing.Sequence[tokenizers.Encoding], NoneType] = Nonetensor_type: typing.Union[NoneType, str, transformers.utils.generic.TensorType] = Noneprepend_batch_axis: bool = Falsen_sequences: typing.Optional[int] = None )

Parameters

data (dict) — Dictionary of lists/arrays/tensors returned by the __call__/encode_plus/batch_encode_plus methods (‘input_ids’, ‘attention_mask’, etc.).
encoding (tokenizers.Encoding or Sequence[tokenizers.Encoding], optional) — If the tokenizer is a fast tokenizer which outputs additional information like mapping from word/character space to token space the tokenizers.Encoding instance or list of instance (for batches) hold this information.
tensor_type (Union[None, str, TensorType], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at initialization.
prepend_batch_axis (bool, optional, defaults to False) — Whether or not to add a batch axis when converting to tensors (see tensor_type above).
n_sequences (Optional[int], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at initialization.

Holds the output of the call(), encode_plus() and batch_encode_plus() methods (tokens, attention_masks, etc).

This class is derived from a python dictionary and can be used as a dictionary. In addition, this class exposes utility methods to map from word/character space to token space.

char_to_token

( batch_or_char_index: intchar_index: typing.Optional[int] = Nonesequence_index: int = 0 ) → int

Parameters

batch_or_char_index (int) — Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of the word in the sequence
char_index (int, optional) — If a batch index is provided in batch_or_token_index, this can be the index of the word in the sequence.
sequence_index (int, optional, defaults to 0) — If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0 or 1) the provided character index belongs to.

Returns

int

Index of the token.

Get the index of the token in the encoded output comprising a character in the original string for a sequence of the batch.

Can be called as:

self.char_to_token(char_index) if batch size is 1
self.char_to_token(batch_index, char_index) if batch size is greater or equal to 1

This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized words.

char_to_word

( batch_or_char_index: intchar_index: typing.Optional[int] = Nonesequence_index: int = 0 ) → int or List[int]

Parameters

batch_or_char_index (int) — Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of the character in the original string.
char_index (int, optional) — If a batch index is provided in batch_or_token_index, this can be the index of the character in the original string.
sequence_index (int, optional, defaults to 0) — If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0 or 1) the provided character index belongs to.

Returns

int or List[int]

Index or indices of the associated encoded token(s).

Get the word in the original string corresponding to a character in the original string of a sequence of the batch.

Can be called as:

self.char_to_word(char_index) if batch size is 1
self.char_to_word(batch_index, char_index) if batch size is greater than 1

This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized words.

convert_to_tensors

( tensor_type: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = Noneprepend_batch_axis: bool = False )

Parameters

tensor_type (str or TensorType, optional) — The type of tensors to use. If str, should be one of the values of the enum TensorType. If None, no modification is done.
prepend_batch_axis (int, optional, defaults to False) — Whether or not to add the batch dimension during the conversion.

Convert the inner content to tensors.

sequence_ids

( batch_index: int = 0 ) → List[Optional[int]]

Parameters

batch_index (int, optional, defaults to 0) — The index to access in the batch.

Returns

List[Optional[int]]

A list indicating the sequence id corresponding to each token. Special tokens added by the tokenizer are mapped to None and other tokens are mapped to the index of their corresponding sequence.

Return a list mapping the tokens to the id of their original sentences:

None for special tokens added around or between sequences,
0 for tokens corresponding to words in the first sequence,
1 for tokens corresponding to words in the second sequence when a pair of sequences was jointly encoded.

to

( device: typing.Union[str, ForwardRef('torch.device')] ) → BatchEncoding

Parameters

device (str or torch.device) — The device to put the tensors on.

Returns

The same instance after modification.

Send all values to device by calling v.to(device) (PyTorch only).

token_to_chars

( batch_or_token_index: inttoken_index: typing.Optional[int] = None ) → CharSpan

Parameters

batch_or_token_index (int) — Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of the token in the sequence.
token_index (int, optional) — If a batch index is provided in batch_or_token_index, this can be the index of the token or tokens in the sequence.

Returns

Span of characters in the original string, or None, if the token (e.g. , ) doesn’t correspond to any chars in the origin string.

Get the character span corresponding to an encoded token in a sequence of the batch.

Character spans are returned as a CharSpan with:

start — Index of the first character in the original string associated to the token.
end — Index of the character following the last character in the original string associated to the token.

Can be called as:

self.token_to_chars(token_index) if batch size is 1
self.token_to_chars(batch_index, token_index) if batch size is greater or equal to 1

token_to_sequence

( batch_or_token_index: inttoken_index: typing.Optional[int] = None ) → int

Parameters

batch_or_token_index (int) — Index of the sequence in the batch. If the batch only comprises one sequence, this can be the index of the token in the sequence.
token_index (int, optional) — If a batch index is provided in batch_or_token_index, this can be the index of the token in the sequence.

Returns

int

Index of the word in the input sequence.

Get the index of the sequence represented by the given token. In the general use case, this method returns 0 for a single sequence or the first sequence of a pair, and 1 for the second sequence of a pair

Can be called as:

self.token_to_sequence(token_index) if batch size is 1
self.token_to_sequence(batch_index, token_index) if batch size is greater than 1

This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e., words are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized words.

token_to_word

( batch_or_token_index: inttoken_index: typing.Optional[int] = None ) → int

Parameters

batch_or_token_index (int) — Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of the token in the sequence.
token_index (int, optional) — If a batch index is provided in batch_or_token_index, this can be the index of the token in the sequence.

Returns

int

Index of the word in the input sequence.

Get the index of the word corresponding (i.e. comprising) to an encoded token in a sequence of the batch.

Can be called as:

self.token_to_word(token_index) if batch size is 1
self.token_to_word(batch_index, token_index) if batch size is greater than 1

This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e., words are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized words.

tokens

( batch_index: int = 0 ) → List[str]

Parameters

batch_index (int, optional, defaults to 0) — The index to access in the batch.

Returns

List[str]

The list of tokens at that index.

Return the list of tokens (sub-parts of the input strings after word/subword splitting and before conversion to integer indices) at a given batch index (only works for the output of a fast tokenizer).

word_ids

( batch_index: int = 0 ) → List[Optional[int]]

Parameters

batch_index (int, optional, defaults to 0) — The index to access in the batch.

Returns

List[Optional[int]]

A list indicating the word corresponding to each token. Special tokens added by the tokenizer are mapped to None and other tokens are mapped to the index of their corresponding word (several tokens will be mapped to the same word index if they are parts of that word).

Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer.

word_to_chars

( batch_or_word_index: intword_index: typing.Optional[int] = Nonesequence_index: int = 0 ) → CharSpan or List[CharSpan]

Parameters

batch_or_word_index (int) — Index of the sequence in the batch. If the batch only comprise one sequence, this can be the index of the word in the sequence
word_index (int, optional) — If a batch index is provided in batch_or_token_index, this can be the index of the word in the sequence.
sequence_index (int, optional, defaults to 0) — If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0 or 1) the provided word index belongs to.

Returns

CharSpan or List[CharSpan]

Span(s) of the associated character or characters in the string. CharSpan are NamedTuple with:

start: index of the first character associated to the token in the original string
end: index of the character following the last character associated to the token in the original string

Get the character span in the original string corresponding to given word in a sequence of the batch.

Character spans are returned as a CharSpan NamedTuple with:

start: index of the first character in the original string
end: index of the character following the last character in the original string

Can be called as:

self.word_to_chars(word_index) if batch size is 1
self.word_to_chars(batch_index, word_index) if batch size is greater or equal to 1

word_to_tokens

( batch_or_word_index: intword_index: typing.Optional[int] = Nonesequence_index: int = 0 ) → (TokenSpan, optional)

Parameters

batch_or_word_index (int) — Index of the sequence in the batch. If the batch only comprises one sequence, this can be the index of the word in the sequence.
word_index (int, optional) — If a batch index is provided in batch_or_token_index, this can be the index of the word in the sequence.
sequence_index (int, optional, defaults to 0) — If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0 or 1) the provided word index belongs to.

Returns

(TokenSpan, optional)

Span of tokens in the encoded sequence. Returns None if no tokens correspond to the word. This can happen especially when the token is a special token that has been used to format the tokenization. For example when we add a class token at the very beginning of the tokenization.

Get the encoded token span corresponding to a word in a sequence of the batch.

Token spans are returned as a TokenSpan with:

start — Index of the first token.
end — Index of the token following the last token.

Can be called as:

self.word_to_tokens(word_index, sequence_index: int = 0) if batch size is 1
self.word_to_tokens(batch_index, word_index, sequence_index: int = 0) if batch size is greater or equal to 1

This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized words.

words

( batch_index: int = 0 ) → List[Optional[int]]

Parameters

batch_index (int, optional, defaults to 0) — The index to access in the batch.

Returns

List[Optional[int]]

A list indicating the word corresponding to each token. Special tokens added by the tokenizer are mapped to None and other tokens are mapped to the index of their corresponding word (several tokens will be mapped to the same word index if they are parts of that word).

Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer.

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