def calc_bleu(preds, targets): assert len(preds) == len(targets), ( 'The length of pred_responses should be equal to the length of ' 'target_responses. But received {} and {}.'.format( len(preds), len(targets))) bleu4 = BLEU(n_size=4) tokenizer = BasicTokenizer() for pred, target in zip(preds, targets): pred_tokens = tokenizer.tokenize(pred) target_token = tokenizer.tokenize(target) bleu4.add_inst(pred_tokens, [target_token]) print('\n' + '*' * 15) print('The auto evaluation result is:') print('BLEU-4:', bleu4.score())
def __init__(self, vocab_file, do_lower_case=True, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]"): if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the " "vocabulary from a pretrained model please use " "`tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(vocab_file)) self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token) self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token)
def __init__(self, vocab_file, bpe_vocab_file=None, bpe_json_file=None, do_lower_case=True, use_bpe_encoder=False, need_token_type_id=True, add_two_sep_token_inter=False, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs): if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the " "vocabulary from a pretrained model please use " "`tokenizer = SkepTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(vocab_file)) self.vocab_file = vocab_file self.bpe_vocab_file = bpe_vocab_file self.bpe_json_file = bpe_json_file self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token, pad_token=pad_token) self.use_bpe_encoder = use_bpe_encoder self.need_token_type_id = need_token_type_id self.add_two_sep_token_inter = add_two_sep_token_inter if not self.use_bpe_encoder: self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token) else: assert (bpe_vocab_file and bpe_json_file) is not None, ( f"bpe_vocab_file and bpe_json_file must be not None.") if os.path.isfile(bpe_vocab_file) and os.path.isfile( bpe_json_file): self.bpe_tokenizer = BpeEncoder(bpe_json_file, bpe_vocab_file)
class NeZhaTokenizer(PretrainedTokenizer): """ Constructs a NeZha tokenizer. It uses a basic tokenizer to do punctuation splitting, lower casing and so on, and follows a WordPiece tokenizer to tokenize as subwords. This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer` which contains most of the main methods. For more information regarding those methods, please refer to this superclass. Args: vocab_file (str): The vocabulary file path (ends with '.txt') required to instantiate a `WordpieceTokenizer`. do_lower_case (bool): Whether or not to lowercase the input when tokenizing. Defaults to`True`. unk_token (str): A special token representing the *unknown (out-of-vocabulary)* token. An unknown token is set to be `unk_token` inorder to be converted to an ID. Defaults to "[UNK]". sep_token (str): A special token separating two different sentences in the same input. Defaults to "[SEP]". pad_token (str): A special token used to make arrays of tokens the same size for batching purposes. Defaults to "[PAD]". cls_token (str): A special token used for sequence classification. It is the last token of the sequence when built with special tokens. Defaults to "[CLS]". mask_token (str): A special token representing a masked token. This is the token used in the masked language modeling task which the model tries to predict the original unmasked ones. Defaults to "[MASK]". Examples: .. code-block:: from paddlenlp.transformers import NeZhaTokenizer tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese') inputs = tokenizer('欢迎使用百度飞桨!') print(inputs) ''' {'input_ids': [101, 3614, 6816, 886, 4500, 4636, 2428, 7607, 3444, 8013, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} ''' """ resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "nezha-base-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/nezha/nezha-chinese-vocab.txt", "nezha-base-wwm-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/nezha/nezha-chinese-vocab.txt", "nezha-large-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/nezha/nezha-chinese-vocab.txt", "nezha-large-wwm-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/nezha/nezha-chinese-vocab.txt", } } pretrained_init_configuration = { "nezha-base-chinese": { "do_lower_case": False }, "nezha-base-wwm-chinese": { "do_lower_case": False }, "nezha-large-chinese": { "do_lower_case": False }, "nezha-large-wwm-chinese": { "do_lower_case": False }, } padding_side = 'right' def __init__(self, vocab_file, do_lower_case=True, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs): if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the " "vocabulary from a pretrained model please use " "`tokenizer = NeZhaTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(vocab_file)) self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token) self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token) @property def vocab_size(self): """ Return the size of vocabulary. Returns: int: The size of vocabulary. """ return len(self.vocab) def _tokenize(self, text): """ End-to-end tokenization for NeZha models. Args: text (str): The text to be tokenized. Returns: list: A list of string representing converted tokens. """ split_tokens = [] for token in self.basic_tokenizer.tokenize(text): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append(sub_token) return split_tokens def convert_tokens_to_string(self, tokens): """ Converts a sequence of tokens (list of string) to a single string. Since the usage of WordPiece introducing `##` to concat subwords, also removes `##` when converting. Args: tokens (list): A list of string representing tokens to be converted. Returns: str: Converted string from tokens. Examples: .. code-block:: from paddlenlp.transformers import NeZhaTokenizer tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese') tokens = tokenizer.tokenize('欢迎使用百度飞桨!') ''' ['欢', '迎', '使', '用', '百', '度', '飞', '桨', '!'] ''' strings = tokenizer.convert_tokens_to_string(tokens) ''' 欢 迎 使 用 百 度 飞 桨 ! ''' """ out_string = " ".join(tokens).replace(" ##", "").strip() return out_string def num_special_tokens_to_add(self, pair=False): """ Returns the number of added tokens when encoding a sequence with special tokens. Args: pair(bool): Whether the input is a sequence pair or a single sequence. Defaults to `False` and the input is a single sequence. Returns: int: Number of tokens added to sequences. """ token_ids_0 = [] token_ids_1 = [] return len( self.build_inputs_with_special_tokens( token_ids_0, token_ids_1 if pair else None)) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A NeZha 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): Optional second list of IDs for sequence pairs. Defaults to `None`. Returns: List[int]: List of input_id with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] _cls = [self.cls_token_id] _sep = [self.sep_token_id] return _cls + token_ids_0 + _sep + token_ids_1 + _sep def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): """ Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. A NeZha offset_mapping has the following format: - single sequence: ``(0,0) X (0,0)`` - pair of sequences: ``(0,0) A (0,0) B (0,0)`` Args: offset_mapping_ids_0 (List[tuple]): List of wordpiece offsets to which the special tokens will be added. offset_mapping_ids_1 (List[tuple], optional): Optional second list of wordpiece offsets for offset mapping pairs. Defaults to `None`. Returns: List[tuple]: A list of wordpiece offsets with the appropriate offsets of special tokens. """ if offset_mapping_1 is None: return [(0, 0)] + offset_mapping_0 + [(0, 0)] return [(0, 0)] + offset_mapping_0 + [(0, 0) ] + offset_mapping_1 + [(0, 0)] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A NeZha sequence pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (List[int]): A list of `inputs_ids` for the first sequence. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to None. Returns: List[int]: List of token_type_id according to the given sequence(s). """ _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) * [0] + len(token_ids_1 + _sep) * [1] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``encode`` methods. Args: token_ids_0 (List[int]): A list of `inputs_ids` for the first sequence. token_ids_1 (List[int], optinal): Optional second list of IDs for sequence pairs. Defaults to `None`. already_has_special_tokens (bool, optional): Whether or not the token list is already formatted with special tokens for the model. Defaults to `False`. Returns: List[int]: The list of integers either be 0 or 1: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: if token_ids_1 is not None: raise ValueError( "You should not supply a second sequence if the provided sequence of " "ids is already formatted with special tokens for the model." ) return list( map( lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0)) if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ( [0] * len(token_ids_1)) + [1] return [1] + ([0] * len(token_ids_0)) + [1]
class SkepTokenizer(PretrainedTokenizer): r""" Constructs a Skep tokenizer. It uses a basic tokenizer to do punctuation splitting, lower casing and so on, and follows a WordPiece tokenizer to tokenize as subwords. This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer` which contains most of the main methods. For more information regarding those methods, please refer to this superclass. Args: vocab_file (str): The vocabulary file path (ends with '.txt') required to instantiate a `WordpieceTokenizer`. bpe_vocab_file (str, optional): The vocabulary file path of a `BpeTokenizer`. Defaults to `None`. bpe_json_file (str, optional): The json file path of a `BpeTokenizer`. Defaults to `None`. use_bpe_encoder (bool, optional): Whether or not to use BPE Encoder. Defaults to `False`. need_token_type_id (bool, optional): Whether or not to use token type id. Defaults to `True`. add_two_sep_token_inter (bool, optional): Whether or not to add two different `sep_token`. Defaults to `False`. unk_token (str, optional): The special token for unknown words. Defaults to "[UNK]". sep_token (str, optional): The special token for separator token. Defaults to "[SEP]". pad_token (str, optional): The special token for padding. Defaults to "[PAD]". cls_token (str, optional): The special token for cls. Defaults to "[CLS]". mask_token (str, optional): The special token for mask. Defaults to "[MASK]". Examples: .. code-block:: from paddlenlp.transformers import SkepTokenizer tokenizer = SkepTokenizer.from_pretrained('skep_ernie_2.0_large_en') encoded_inputs = tokenizer('He was a puppeteer') # encoded_inputs: # { # 'input_ids': [101, 2002, 2001, 1037, 13997, 11510, 102], # 'token_type_ids': [0, 0, 0, 0, 0, 0, 0] # } """ resource_files_names = { "vocab_file": "vocab.txt", "bpe_vocab_file": "vocab.bpe", "bpe_json_file": "encoder.json" } # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "skep_ernie_1.0_large_ch": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_ernie_1.0_large_ch.vocab.txt", "skep_ernie_2.0_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_ernie_2.0_large_en.vocab.txt", "skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.vocab.txt", }, "bpe_vocab_file": { "skep_ernie_1.0_large_ch": None, "skep_ernie_2.0_large_en": None, "skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.vocab.bpe", }, "bpe_json_file": { "skep_ernie_1.0_large_ch": None, "skep_ernie_2.0_large_en": None, "skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.encoder.json", } } pretrained_init_configuration = { "skep_ernie_1.0_large_ch": { "do_lower_case": True, "use_bpe_encoder": False, "need_token_type_id": True, "add_two_sep_token_inter": False, }, "skep_ernie_2.0_large_en": { "do_lower_case": True, "use_bpe_encoder": False, "need_token_type_id": True, "add_two_sep_token_inter": False, }, "skep_roberta_large_en": { "do_lower_case": True, "use_bpe_encoder": True, "need_token_type_id": False, "add_two_sep_token_inter": True, }, } def __init__(self, vocab_file, bpe_vocab_file=None, bpe_json_file=None, do_lower_case=True, use_bpe_encoder=False, need_token_type_id=True, add_two_sep_token_inter=False, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs): if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the " "vocabulary from a pretrained model please use " "`tokenizer = SkepTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(vocab_file)) self.vocab_file = vocab_file self.bpe_vocab_file = bpe_vocab_file self.bpe_json_file = bpe_json_file self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token, pad_token=pad_token) self.use_bpe_encoder = use_bpe_encoder self.need_token_type_id = need_token_type_id self.add_two_sep_token_inter = add_two_sep_token_inter if not self.use_bpe_encoder: self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token) else: assert (bpe_vocab_file and bpe_json_file) is not None, ( f"bpe_vocab_file and bpe_json_file must be not None.") if os.path.isfile(bpe_vocab_file) and os.path.isfile( bpe_json_file): self.bpe_tokenizer = BpeEncoder(bpe_json_file, bpe_vocab_file) @property def vocab_size(self): r""" Return the size of vocabulary. Returns: int: the size of vocabulary. """ return len(self.vocab) def _tokenize(self, text): r""" End-to-end tokenization for Skep models. Args: text (str): The text to be tokenized. Returns: list: A list of string representing converted tokens. """ split_tokens = [] if not self.use_bpe_encoder: for token in self.basic_tokenizer.tokenize(text): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append(sub_token) else: for token in self.bpe_tokenizer.encode(text): split_tokens.append(str(token)) return split_tokens def num_special_tokens_to_add(self, pair=False): r""" Returns the number of added tokens when encoding a sequence with special tokens. Args: pair (bool, optional): Returns the number of added tokens in the case of a sequence pair if set to True, returns the number of added tokens in the case of a single sequence if set to False. Defaults to False. Returns: int: Number of tokens added to sequences """ token_ids_0 = [] token_ids_1 = [] return len( self.build_inputs_with_special_tokens( token_ids_0, token_ids_1 if pair else None)) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): r""" Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence has the following format: - single sequence: ``[CLS] X [SEP]`` - pair of sequences: ``[CLS] A [SEP] B [SEP]`` A skep_roberta_large_en sequence has the following format: - single sequence: ``[CLS] X [SEP]`` - pair of sequences: ``[CLS] A [SEP] [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): Optional second list of IDs for sequence pairs. Defaults to `None`. Returns: list[int]: List of input_id with the appropriate special tokens. """ if not self.add_two_sep_token_inter: if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] _cls = [self.cls_token_id] _sep = [self.sep_token_id] return _cls + token_ids_0 + _sep + token_ids_1 + _sep else: if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] _cls = [self.cls_token_id] _sep = [self.sep_token_id] return _cls + token_ids_0 + _sep + _sep + token_ids_1 + _sep def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): r""" Create a mask from the two sequences passed to be used in a sequence-pair classification task. A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). note: There is no need token type ids for skep_roberta_large_ch model. Args: token_ids_0 (List[int]): List of IDs. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to `None`. Returns: List[int]: List of token_type_id according to the given sequence(s). """ if self.need_token_type_id: _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) * [0] + len(token_ids_1 + _sep) * [1] else: # For model skep-roberta-large-en, token type ids is no need. return None def save_resources(self, save_directory): """ Save tokenizer related resources to files under `save_directory`. Args: save_directory (str): Directory to save files into. """ for name, file_name in self.resource_files_names.items(): save_path = os.path.join(save_directory, file_name) source_file = getattr(self, name) if source_file is not None: shutil.copyfile(source_file, save_path)
class SqueezeBertTokenizer(PretrainedTokenizer): """ Constructs a SqueezeBert tokenizer. It uses a basic tokenizer to do punctuation splitting, lower casing and so on, and follows a WordPiece tokenizer to tokenize as subwords. Args: vocab_file (str): file path of the vocabulary do_lower_case (bool): Whether the text strips accents and convert to lower case. Default: `True`. Default: True. unk_token (str): The special token for unkown words. Default: "[UNK]". sep_token (str): The special token for separator token . Default: "[SEP]". pad_token (str): The special token for padding. Default: "[PAD]". cls_token (str): The special token for cls. Default: "[CLS]". mask_token (str): The special token for mask. Default: "[MASK]". Examples: .. code-block:: python from paddlenlp.transformers import SqueezeBertTokenizer tokenizer = SqueezeBertTokenizer.from_pretrained('squeezebert-uncased') # the following line get: ['he', 'was', 'a', 'puppet', '##eer'] tokens = tokenizer('He was a puppeteer') # the following line get: 'he was a puppeteer' tokenizer.convert_tokens_to_string(tokens) """ resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "squeezebert-uncased": "http://bj.bcebos.com/paddlenlp/models/transformers/squeezebert/squeezebert-uncased/vocab.txt", "squeezebert-mnli": "http://bj.bcebos.com/paddlenlp/models/transformers/squeezebert/squeezebert-mnli/vocab.txt", "squeezebert-mnli-headless": "http://bj.bcebos.com/paddlenlp/models/transformers/squeezebert/squeezebert-mnli-headless/vocab.txt", } } pretrained_init_configuration = { "squeezebert-uncased": { "do_lower_case": True }, "squeezebert-mnli": { "do_lower_case": True }, "squeezebert-mnli-headless": { "do_lower_case": True } } def __init__(self, vocab_file, do_lower_case=True, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs): if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the " "vocabulary from a pretrained model please use " "`tokenizer = SqueezeBertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(vocab_file)) self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token) self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=unk_token) @property def vocab_size(self): """ return the size of vocabulary. Returns: int: the size of vocabulary. """ return len(self.vocab) def _tokenize(self, text): """ End-to-end tokenization for SqueezeBert models. Args: text (str): The text to be tokenized. Returns: list: A list of string representing converted tokens. """ split_tokens = [] for token in self.basic_tokenizer.tokenize(text): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append(sub_token) return split_tokens def convert_tokens_to_string(self, tokens): """ Converts a sequence of tokens (list of string) in a single string. Since the usage of WordPiece introducing `##` to concat subwords, also remove `##` when converting. Args: tokens (list): A list of string representing tokens to be converted. Returns: str: Converted string from tokens. """ out_string = " ".join(tokens).replace(" ##", "").strip() return out_string def num_special_tokens_to_add(self, pair=False): """ Returns the number of added tokens when encoding a sequence with special tokens. Note: This encodes inputs and checks the number of added tokens, and is therefore not efficient. Do not put this inside your training loop. Args: pair: Returns the number of added tokens in the case of a sequence pair if set to True, returns the number of added tokens in the case of a single sequence if set to False. Returns: Number of tokens added to sequences """ token_ids_0 = [] token_ids_1 = [] return len( self.build_inputs_with_special_tokens( token_ids_0, token_ids_1 if pair else None)) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A SqueezeBert sequence has the following format: :: - single sequence: ``[CLS] X [SEP]`` - pair of sequences: ``[CLS] A [SEP] B [SEP]`` Args: token_ids_0 (:obj:`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: :obj:`List[int]`: List of input_id with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] _cls = [self.cls_token_id] _sep = [self.sep_token_id] return _cls + token_ids_0 + _sep + token_ids_1 + _sep def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): """ Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. A SqueezeBert offset_mapping has the following format: :: - single sequence: ``(0,0) X (0,0)`` - pair of sequences: `(0,0) A (0,0) B (0,0)`` Args: offset_mapping_ids_0 (:obj:`List[tuple]`): List of char offsets to which the special tokens will be added. offset_mapping_ids_1 (:obj:`List[tuple]`, `optional`): Optional second list of char offsets for offset mapping pairs. Returns: :obj:`List[tuple]`: List of char offsets with the appropriate offsets of special tokens. """ if offset_mapping_1 is None: return [(0, 0)] + offset_mapping_0 + [(0, 0)] return [(0, 0)] + offset_mapping_0 + [(0, 0) ] + offset_mapping_1 + [(0, 0)] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A SqueezeBert sequence pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s). 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 token_type_id according to the given sequence(s). """ _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) * [0] + len(token_ids_1 + _sep) * [1] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``encode`` methods. Args: token_ids_0 (List[int]): List of ids of the first sequence. token_ids_1 (List[int], optinal): List of ids of the second sequence. already_has_special_tokens (bool, optional): Whether or not the token list is already formatted with special tokens for the model. Defaults to None. Returns: results (List[int]): The list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: if token_ids_1 is not None: raise ValueError( "You should not supply a second sequence if the provided sequence of " "ids is already formatted with special tokens for the model." ) return list( map( lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0)) if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ( [0] * len(token_ids_1)) + [1] return [1] + ([0] * len(token_ids_0)) + [1]