paddlenlp.transformers.ernie.tokenizer 源代码

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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.

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import os
import pickle
import six
import shutil

import sentencepiece as spm

from paddlenlp.utils.env import MODEL_HOME

from .. import BasicTokenizer, PretrainedTokenizer, WordpieceTokenizer

__all__ = ['ErnieTokenizer', 'ErnieTinyTokenizer']


[文档]class ErnieTokenizer(PretrainedTokenizer): r""" Constructs an ERNIE 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 (str, optional): Whether or not to lowercase the input when tokenizing. Defaults to`True`. unk_token (str, optional): 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, optional): A special token separating two different sentences in the same input. Defaults to "[SEP]". pad_token (str, optional): A special token used to make arrays of tokens the same size for batching purposes. Defaults to "[PAD]". cls_token (str, optional): 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, optional): 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 ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') encoded_inputs = tokenizer('He was a puppeteer') # encoded_inputs: # { 'input_ids': [1, 4444, 4385, 1545, 6712, 10062, 9568, 9756, 9500, 2], # 'token_type_ids': [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": { # Deprecated, alias for ernie-1.0-base-zh "ernie-1.0": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie/vocab.txt", "ernie-1.0-base-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie/vocab.txt", "ernie-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_tiny/vocab.txt", "ernie-2.0-en": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_base/vocab.txt", "ernie-2.0-en-finetuned-squad": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_base/vocab.txt", "ernie-2.0-large-en": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_large/vocab.txt", "ernie-gen-base-en": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie-gen-base-en/vocab.txt", "ernie-gen-large-en": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie-gen-large/vocab.txt", "ernie-gen-large-en-430g": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie-gen-large-430g/vocab.txt", "rocketqa-zh-dureader-query-encoder": "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa-zh-dureader-vocab.txt", "rocketqa-zh-dureader-para-encoder": "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa-zh-dureader-vocab.txt", "rocketqa-v1-marco-query-encoder": "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa-v1-marco-vocab.txt", "rocketqa-v1-marco-para-encoder": "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa-v1-marco-vocab.txt", "rocketqa-zh-dureader-cross-encoder": "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa-zh-dureader-vocab.txt", "rocketqa-v1-marco-cross-encoder": "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa-v1-marco-vocab.txt", "ernie-3.0-base-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_base_zh_vocab.txt", "ernie-3.0-xbase-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_xbase_zh_vocab.txt", "ernie-3.0-medium-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_medium_zh_vocab.txt", "ernie-3.0-mini-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_mini_zh_vocab.txt", "ernie-3.0-micro-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_micro_zh_vocab.txt", "ernie-3.0-nano-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_nano_zh_vocab.txt", } } pretrained_init_configuration = { "ernie-1.0": { "do_lower_case": True }, "ernie-1.0-base-zh": { "do_lower_case": True }, "ernie-tiny": { "do_lower_case": True }, "ernie-2.0-en": { "do_lower_case": True }, "ernie-2.0-en-finetuned-squad": { "do_lower_case": True }, "ernie-2.0-large-en": { "do_lower_case": True }, "ernie-gen-base-en": { "do_lower_case": True }, "ernie-gen-large-en": { "do_lower_case": True }, "ernie-gen-large-en-430g": { "do_lower_case": True }, "rocketqa-zh-dureader-query-encoder": { "do_lower_case": True }, "rocketqa-zh-dureader-para-encoder": { "do_lower_case": True }, "rocketqa-v1-marco-query-encoder": { "do_lower_case": True }, "rocketqa-v1-marco-para-encoder": { "do_lower_case": True }, "rocketqa-zh-dureader-cross-encoder": { "do_lower_case": True }, "rocketqa-v1-marco-cross-encoder": { "do_lower_case": True }, "ernie-3.0-base-zh": { "do_lower_case": True }, "ernie-3.0-xbase-zh": { "do_lower_case": True }, "ernie-3.0-medium-zh": { "do_lower_case": True }, "ernie-3.0-mini-zh": { "do_lower_case": True }, "ernie-3.0-micro-zh": { "do_lower_case": True }, "ernie-3.0-nano-zh": { "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 = ErnieTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(vocab_file)) self.do_lower_case = do_lower_case 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): r""" End-to-end tokenization for ERNIE models. Args: text (str): The text to be tokenized. Returns: List[str]: 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
[文档] def convert_tokens_to_string(self, tokens): r""" 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[str]): A list of string representing tokens to be converted. Returns: str: Converted string from tokens. Examples: .. code-block:: from paddlenlp.transformers import ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') tokens = tokenizer.tokenize('He was a puppeteer') strings = tokenizer.convert_tokens_to_string(tokens) #he was a puppeteer """ out_string = " ".join(tokens).replace(" ##", "").strip() return out_string
[文档] def num_special_tokens_to_add(self, pair=False): r""" 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 (bool, optional): 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))
[文档] 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. An Ernie 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
[文档] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): r""" Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. An ERNIE 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 char 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)]
[文档] 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 ERNIE 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). 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]
[文档]class ErnieTinyTokenizer(PretrainedTokenizer): r""" Constructs a ErnieTiny tokenizer. It uses the `dict.wordseg.pickle` cut the text to words, and use the `sentencepiece` tools to cut the words to sub-words. Examples: .. code-block:: from paddlenlp.transformers import ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') encoded_inputs = tokenizer('He was a puppeteer') # encoded_inputs: # { 'input_ids': [1, 4444, 4385, 1545, 6712, 10062, 9568, 9756, 9500, 2], # 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # } Args: vocab_file (str): The file path of the vocabulary. sentencepiece_model_file (str): The file path of sentencepiece model. word_dict(str): The file path of word vocabulary, which is used to do chinese word segmentation. do_lower_case (str, optional): Whether or not to lowercase the input when tokenizing. Defaults to`True`. unk_token (str, optional): 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, optional): A special token separating two different sentences in the same input. Defaults to "[SEP]". pad_token (str, optional): A special token used to make arrays of tokens the same size for batching purposes. Defaults to "[PAD]". cls_token (str, optional): 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, optional): 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 ErnieTinyTokenizer tokenizer = ErnieTinyTokenizer.from_pretrained('ernie-tiny') inputs = tokenizer('He was a puppeteer') ''' {'input_ids': [3, 941, 977, 16690, 269, 11346, 11364, 1337, 13742, 1684, 5], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} ''' """ resource_files_names = { "sentencepiece_model_file": "spm_cased_simp_sampled.model", "vocab_file": "vocab.txt", "word_dict": "dict.wordseg.pickle" } # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "ernie-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_tiny/vocab.txt" }, "sentencepiece_model_file": { "ernie-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_tiny/spm_cased_simp_sampled.model" }, "word_dict": { "ernie-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_tiny/dict.wordseg.pickle" }, } pretrained_init_configuration = {"ernie-tiny": {"do_lower_case": True}} def __init__(self, vocab_file, sentencepiece_model_file, word_dict, do_lower_case=True, encoding="utf8", unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs): self.sp_model = spm.SentencePieceProcessor() self.word_dict = word_dict self.do_lower_case = do_lower_case self.encoding = encoding 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 = ErnieTinyTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(vocab_file)) if not os.path.isfile(word_dict): raise ValueError( "Can't find a file at path '{}'. To load the " "word dict from a pretrained model please use " "`tokenizer = ErnieTinyTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" .format(word_dict)) self.dict = pickle.load(open(word_dict, 'rb')) self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token) # if the sentencepiece_model_file is not exists, just the default sentence-piece model if os.path.isfile(sentencepiece_model_file): self.sp_model.Load(sentencepiece_model_file) @property def vocab_size(self): r""" Return the size of vocabulary. Returns: int: The size of vocabulary. """ return len(self.vocab) def cut(self, chars): words = [] idx = 0 window_size = 5 while idx < len(chars): matched = False for i in range(window_size, 0, -1): cand = chars[idx:idx + i] if cand in self.dict: words.append(cand) matched = True break if not matched: i = 1 words.append(chars[idx]) idx += i return words def _tokenize(self, text): r""" End-to-end tokenization for ErnieTiny models. Args: text (str): The text to be tokenized. Returns: List(str): A list of string representing converted tokens. """ if len(text) == 0: return [] if not isinstance(text, six.string_types): text = text.decode(self.encoding) text = [s for s in self.cut(text) if s != ' '] text = ' '.join(text) text = text.lower() tokens = self.sp_model.EncodeAsPieces(text) in_vocab_tokens = [] unk_token = self.vocab.unk_token for token in tokens: if token in self.vocab: in_vocab_tokens.append(token) else: in_vocab_tokens.append(unk_token) return in_vocab_tokens
[文档] def convert_tokens_to_string(self, tokens): r""" 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 ErnieTinyTokenizer tokenizer = ErnieTinyTokenizer.from_pretrained('ernie-tiny') inputs = tokenizer.tokenize('He was a puppeteer') #['▁h', '▁e', '▁was', '▁a', '▁pu', 'pp', 'e', '▁te', 'er'] strings = tokenizer.convert_tokens_to_string(tokens) """ out_string = " ".join(tokens).replace(" ##", "").strip() return out_string
[文档] def save_resources(self, save_directory): r""" 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(): ### TODO: make the name 'ernie-tiny' as a variable source_path = os.path.join(MODEL_HOME, 'ernie-tiny', file_name) save_path = os.path.join(save_directory, self.resource_files_names[name]) shutil.copyfile(source_path, save_path)
[文档] def num_special_tokens_to_add(self, pair=False): r""" 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 (bool, optional): 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))
[文档] 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. An ERNIE 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
[文档] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): r""" Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. An ERNIE 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 char 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]: 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)]
[文档] 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 ERNIE 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). 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]
[文档] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): r""" 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): Optional second list of IDs for sequence pairs. Defaults to `None`. already_has_special_tokens (str, 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 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]