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# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
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"""Fast Tokenization classes for RoBERTa."""
from typing import List, Optional
from ...tokenization_utils_base import AddedToken
from ...utils import logging
from ..gpt2.tokenization_gpt2_fast import GPT2TokenizerFast
from .tokenization_roberta import RobertaTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
"roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json",
},
"merges_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
"roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt",
},
"tokenizer_file": {
"roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
"roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
"roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
"distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
"roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json",
"roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"roberta-base": 512,
"roberta-large": 512,
"roberta-large-mnli": 512,
"distilroberta-base": 512,
"roberta-base-openai-detector": 512,
"roberta-large-openai-detector": 512,
}
[docs]class RobertaTokenizerFast(GPT2TokenizerFast):
"""
Construct a "fast" RoBERTa tokenizer (backed by HuggingFace's `tokenizers` library), derived from the GPT-2
tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
::
>>> from transformers import RobertaTokenizerFast
>>> tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
>>> tokenizer("Hello world")['input_ids']
[0, 31414, 232, 328, 2]
>>> tokenizer(" Hello world")['input_ids']
[0, 20920, 232, 2]
You can get around that behavior by passing ``add_prefix_space=True`` when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
.. note::
When used with ``is_split_into_words=True``, this tokenizer needs to be instantiated with
``add_prefix_space=True``.
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the main
methods. Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (:obj:`str`):
Path to the vocabulary file.
merges_file (:obj:`str`):
Path to the merges file.
errors (:obj:`str`, `optional`, defaults to :obj:`"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 (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
.. note::
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 :obj:`cls_token`.
eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
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 :obj:`sep_token`.
sep_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
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 (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
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 (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
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 (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (:obj:`str`, `optional`, defaults to :obj:`"<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.
add_prefix_space (:obj:`bool`, `optional`, defaults to :obj:`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).
trim_offsets (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether the post processing step should trim offsets to avoid including whitespaces.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = RobertaTokenizer
def __init__(
self,
vocab_file,
merges_file,
tokenizer_file=None,
errors="replace",
bos_token="<s>",
eos_token="</s>",
sep_token="</s>",
cls_token="<s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
add_prefix_space=False,
**kwargs
):
super().__init__(
vocab_file,
merges_file,
tokenizer_file=tokenizer_file,
errors=errors,
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
cls_token=cls_token,
unk_token=unk_token,
pad_token=pad_token,
mask_token=mask_token,
add_prefix_space=add_prefix_space,
**kwargs,
)
@property
def mask_token(self) -> str:
"""
:obj:`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while
not having been set.
Roberta tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
comprise the space before the `<mask>`.
"""
if self._mask_token is None and self.verbose:
logger.error("Using mask_token, but it is not set yet.")
return None
return str(self._mask_token)
@mask_token.setter
def mask_token(self, value):
"""
Overriding the default behavior of the mask token to have it eat the space before it.
This is needed to preserve backward compatibility with all the previously used models based on Roberta.
"""
# Mask token behave like a normal word, i.e. include the space before it
# So we set lstrip to True
value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value
self._mask_token = value
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
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 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]