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add_special_tokens (`bool`, *optional*, defaults to `True`):
Whether or not to encode the sequences with the special tokens relative to their model.
padding (`bool`, `str` or [`~tokenization_utils_base.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 [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.
- `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to
`None`, this will use the predefined model maximum length if a maximum length is required by one of the
truncation/padding parameters. If the model has no specific maximum input length (like XLNet)
truncation/padding to a maximum length will be deactivated.
stride (`int`, *optional*, defaults to 0):
If set to a number along with `max_length`, the overflowing tokens returned when
`return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.
is_split_into_words (`bool`, *optional*, defaults to `False`):
Whether or not the input is already pretokenized (e.g. split into words). Set this to `True` if you are
passing pretokenized inputs to avoid additional tokenization.
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).
return_tensors (`str` or [`~tokenization_utils_base.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
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Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).
[`MarkupLMTokenizer`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`,
`token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`. This tokenizer inherits from [`TokenizersBackend`] which
contains most of the main methods and ensures a `tokenizers` backend is always instantiated.
Users should refer to this superclass for more information regarding those methods.
Args:
vocab (`str` or `dict[str, int]`, *optional*):
Custom vocabulary dictionary. If not provided, the vocabulary is loaded from `vocab_file`.
merges (`str` or `list[str]`, *optional*):
Custom merges list. If not provided, merges are loaded from `merges_file`.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
bos_token (`str`, *optional*, defaults to `""`):
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 `""`):
The end of sequence token.
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`.
sep_token (`str`, *optional*, defaults to `""`):
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.
cls_token (`str`, *optional*, defaults to `""`):
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.
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.
pad_token (`str`, *optional*, defaults to `""`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `""`):
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.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
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text (`str`, `list[str]`, `list[list[str]]`):
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words).
text_pair (`list[str]`, `list[list[str]]`):
The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
(pretokenized string).
xpaths (`list[list[int]]`, `list[list[list[int]]]`):
Node-level xpaths. Each bounding box should be normalized to be on a 0-1000 scale.
node_labels (`list[int]`, `list[list[int]]`, *optional*):
Node-level integer labels (for token classification tasks).
is_split_into_words (`bool`, *optional*):
Set to `True` if the inputs are already provided as pretokenized word lists.
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Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
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Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
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Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
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Dictionary of tokenized inputs (`list[int]`) or batch of tokenized inputs (`list[list[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
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`>= 7.5` (Volta).
padding_side:
The side on which the model should have padding applied. Should be selected between ['right', 'left'].
Default value is picked from the class attribute of the same name.
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
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differences
rX _padzMarkupLMTokenizer._padV s B ! ($48N8N$N !'(>(>q(AB 6 6 6 ^,J !&8&D*WiJimnJn%);;q@DVVJ - 1K1K K qPSTbPcgqPq !%5^%K01sS 5H/HN+ , #c.&99J+7+C<IZIZLw&(7EFV7W[\Z]`jZj7jN#34#~5&'78DC* X `
- pair of sequences: ` A B `
Args:
token_ids_0 (`list[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
)rI rJ )rR r r clsseps rX build_inputs_with_special_tokensz2MarkupLMTokenizer.build_inputs_with_special_tokens sb " %%&48I8I7JJ J ! ![ 3&4s::rY c | j g}| j g}|t ||z |z dgz S t ||z |z |z |z dgz S )a
Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`list[int]`):
List of IDs.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of zeros.
r )rJ rI rM )rR r r r r s rX $create_token_type_ids_from_sequencesz6MarkupLMTokenizer.create_token_type_ids_from_sequences sh ! ! s[(3./1#5 53$s*[83>?1#EErY save_directoryfilename_prefixc f | j j j || }t | S )N)name)rF modelsaver )rR r r filess rX save_vocabularyz!MarkupLMTokenizer.save_vocabulary s+ %%**>*PU|rY )NNreplace