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Construct a "fast" RemBert tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
remove_space (`bool`, *optional*, defaults to `True`):
Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).
keep_accents (`bool`, *optional*, defaults to `False`):
Whether or not to keep accents when tokenizing.
bos_token (`str`, *optional*, defaults to `"[CLS]"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
eos_token (`str`, *optional*, defaults to `"[SEP]"`):
The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token
that is used for the end of sequence. The token used is the `sep_token`.
unk_token (`str`, *optional*, defaults to `""`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `""`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
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r r r r r r r r r r kwargs __class__s o/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/rembert/tokenization_rembert_fast.pyr! RemBertTokenizerFast.__init__X s " KUU_adJeJeZ
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RemBERT sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added
token_ids_1 (`List[int]`, *optional*, defaults to `None`):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens.
) sep_token_id cls_token_id)r" r( r) sepclss r% build_inputs_with_special_tokens5RemBertTokenizerFast.build_inputs_with_special_tokens sL & ! ! $s* * 3&4s::r' already_has_special_tokensc U( a> Ub [ S5 eU Vs/ s H" oDU R U R 4; a SOSPM$ sn$ Ub+ S/S/[ U5 - - S/- S/[ U5 - - S/- $ S/S/[ U5 - - S/- $ s snf )ax
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` method.
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional*, defaults to `None`):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Set to True if the token list is already formatted with special tokens for the model
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
zYou should not supply a second sequence if the provided sequence of ids is already formatted with special tokens for the model.r
r )
ValueErrorr, r- len)r" r( r) r2 xs r% get_special_tokens_mask,RemBertTokenizerFast.get_special_tokens_mask s & && R VaaU`PQt 0 0$2C2C DDA!KU`a a "31#K 001QC7A3[AQ;QRVWUXX XsqcC ,,-33 bs )B save_directoryfilename_prefixc [ R R U5 ( d [ R SU S35 g [ R R X( a US- OS[ S - 5 n[ R R U R 5 [ R R U5 :w a [ U R U5 U4$ )NzVocabulary path (z) should be a directory- r )
ospathisdirloggererrorjoinVOCAB_FILES_NAMESabspathr r )r" r9 r: out_vocab_files r% save_vocabulary$RemBertTokenizerFast.save_vocabulary s ww}}^,, LL,^,<rJ zrI z[MASK])N)NF)__name__
__module__ __qualname____firstlineno____doc__rD vocab_files_namesr slow_tokenizer_classr! listintr r0 boolr7 r tuplerG __static_attributes__
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