157 lines
6.7 KiB
Python
157 lines
6.7 KiB
Python
# coding=utf-8
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# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes for RoBERTa."""
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import logging
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from .tokenization_gpt2 import GPT2Tokenizer
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logger = logging.getLogger(__name__)
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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}
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PRETRAINED_VOCAB_FILES_MAP = {
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"vocab_file": {
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"roberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-vocab.json",
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"roberta-large": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-vocab.json",
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"roberta-large-mnli": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-vocab.json",
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"distilroberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-vocab.json",
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"roberta-base-openai-detector": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-vocab.json",
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"roberta-large-openai-detector": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-vocab.json",
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},
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"merges_file": {
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"roberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-merges.txt",
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"roberta-large": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-merges.txt",
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"roberta-large-mnli": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-merges.txt",
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"distilroberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-merges.txt",
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"roberta-base-openai-detector": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-merges.txt",
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"roberta-large-openai-detector": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-merges.txt",
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},
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}
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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"roberta-base": 512,
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"roberta-large": 512,
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"roberta-large-mnli": 512,
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"distilroberta-base": 512,
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"roberta-base-openai-detector": 512,
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"roberta-large-openai-detector": 512,
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}
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class RobertaTokenizer(GPT2Tokenizer):
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"""
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RoBERTa BPE tokenizer, derived from the GPT-2 tokenizer. Peculiarities:
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- Byte-level Byte-Pair-Encoding
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- Requires a space to start the input string => the encoding methods should be called with the
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``add_prefix_space`` flag set to ``True``.
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Otherwise, this tokenizer ``encode`` and ``decode`` method will not conserve
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the absence of a space at the beginning of a string: `tokenizer.decode(tokenizer.encode("Hello")) = " Hello"`
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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def __init__(
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self,
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vocab_file,
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merges_file,
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errors="replace",
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bos_token="<s>",
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eos_token="</s>",
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sep_token="</s>",
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cls_token="<s>",
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unk_token="<unk>",
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pad_token="<pad>",
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mask_token="<mask>",
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**kwargs
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):
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super().__init__(
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vocab_file=vocab_file,
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merges_file=merges_file,
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errors=errors,
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bos_token=bos_token,
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eos_token=eos_token,
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unk_token=unk_token,
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sep_token=sep_token,
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cls_token=cls_token,
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pad_token=pad_token,
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mask_token=mask_token,
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**kwargs,
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)
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self.max_len_single_sentence = self.max_len - 2 # take into account special tokens
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self.max_len_sentences_pair = self.max_len - 4 # take into account special tokens
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks
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by concatenating and adding special tokens.
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A RoBERTa sequence has the following format:
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single sequence: <s> X </s>
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pair of sequences: <s> A </s></s> B </s>
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"""
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if token_ids_1 is None:
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return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
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cls = [self.cls_token_id]
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sep = [self.sep_token_id]
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return cls + token_ids_0 + sep + sep + token_ids_1 + sep
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def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
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"""
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Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.
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Args:
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token_ids_0: list of ids (must not contain special tokens)
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token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids
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for sequence pairs
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already_has_special_tokens: (default False) Set to True if the token list is already formated with
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special tokens for the model
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Returns:
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A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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if token_ids_1 is not None:
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raise ValueError(
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"You should not supply a second sequence if the provided sequence of "
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"ids is already formated with special tokens for the model."
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)
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return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
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if token_ids_1 is None:
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return [1] + ([0] * len(token_ids_0)) + [1]
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return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
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def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
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"""
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Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
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RoBERTa does not make use of token type ids, therefore a list of zeros is returned.
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if token_ids_1 is None, only returns the first portion of the mask (0's).
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"""
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sep = [self.sep_token_id]
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cls = [self.cls_token_id]
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if token_ids_1 is None:
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return len(cls + token_ids_0 + sep) * [0]
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return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
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