unified tokenizer api and serialization + tests
This commit is contained in:
thomwolf
@@ -22,7 +22,6 @@ import os
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import unicodedata
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from io import open
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from .file_utils import cached_path
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from .tokenization_utils import PreTrainedTokenizer, clean_up_tokenization
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logger = logging.getLogger(__name__)
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@@ -32,20 +31,21 @@ VOCAB_FILES_NAMES = {'vocab_file': 'vocab.txt'}
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PRETRAINED_VOCAB_FILES_MAP = {
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'vocab_file':
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{
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'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
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'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
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'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
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'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
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'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
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'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
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'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
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'bert-base-german-cased': "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-vocab.txt",
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'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-vocab.txt",
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'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-vocab.txt",
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'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-vocab.txt",
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'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-vocab.txt",
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'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-vocab.txt",
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}}
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'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
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'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
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'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
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'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
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'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt",
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'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
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'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt",
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'bert-base-german-cased': "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-vocab.txt",
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'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-vocab.txt",
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'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-vocab.txt",
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'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-vocab.txt",
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'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-vocab.txt",
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'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-vocab.txt",
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}
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}
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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'bert-base-uncased': 512,
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@@ -93,8 +93,9 @@ class BertTokenizer(PreTrainedTokenizer):
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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__(self, vocab_file, do_lower_case=True, max_len=None, do_basic_tokenize=True,
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never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
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def __init__(self, vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None,
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unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]",
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mask_token="[MASK]", **kwargs):
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"""Constructs a BertTokenizer.
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Args:
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@@ -102,17 +103,18 @@ class BertTokenizer(PreTrainedTokenizer):
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do_lower_case: Whether to lower case the input
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Only has an effect when do_wordpiece_only=False
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do_basic_tokenize: Whether to do basic tokenization before wordpiece.
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max_len: An artificial maximum length to truncate tokenized sequences to;
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Effective maximum length is always the minimum of this
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value (if specified) and the underlying BERT model's
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sequence length.
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never_split: List of tokens which will never be split during tokenization.
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Only has an effect when do_wordpiece_only=False
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"""
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super(BertTokenizer, self).__init__(unk_token=unk_token, sep_token=sep_token,
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pad_token=pad_token, cls_token=cls_token,
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mask_token=mask_token, **kwargs)
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if not os.path.isfile(vocab_file):
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raise ValueError(
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"Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
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"model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
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if never_split is None:
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never_split = self.all_special_tokens
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self.vocab = load_vocab(vocab_file)
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self.ids_to_tokens = collections.OrderedDict(
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[(ids, tok) for tok, ids in self.vocab.items()])
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@@ -120,90 +122,34 @@ class BertTokenizer(PreTrainedTokenizer):
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if do_basic_tokenize:
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self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
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never_split=never_split)
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
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self.max_len = max_len if max_len is not None else int(1e12)
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)
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@property
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def UNK_TOKEN(self):
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return "[UNK]"
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def vocab_size(self):
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return len(self.vocab)
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@property
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def SEP_TOKEN(self):
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return "[SEP]"
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@property
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def PAD_TOKEN(self):
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return "[PAD]"
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@property
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def CLS_TOKEN(self):
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return "[CLS]"
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@property
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def MASK_TOKEN(self):
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return "[MASK]"
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@property
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def UNK_ID(self):
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return self.vocab["[UNK]"]
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@property
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def SEP_ID(self):
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return self.vocab["[SEP]"]
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@property
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def PAD_ID(self):
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return self.vocab["[PAD]"]
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@property
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def CLS_ID(self):
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return self.vocab["[CLS]"]
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@property
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def MASK_ID(self):
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return self.vocab["[MASK]"]
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def tokenize(self, text):
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def _tokenize(self, text):
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split_tokens = []
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if self.do_basic_tokenize:
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for token in self.basic_tokenizer.tokenize(text):
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for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
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for sub_token in self.wordpiece_tokenizer.tokenize(token):
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split_tokens.append(sub_token)
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else:
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split_tokens = self.wordpiece_tokenizer.tokenize(text)
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return split_tokens
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def convert_tokens_to_ids(self, tokens):
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"""Converts a sequence of tokens into ids using the vocab."""
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ids = []
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for token in tokens:
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ids.append(self.vocab[token])
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if len(ids) > self.max_len:
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logger.warning(
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"Token indices sequence length is longer than the specified maximum "
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" sequence length for this BERT model ({} > {}). Running this"
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" sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
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)
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return ids
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def _convert_token_to_id(self, token):
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""" Converts a token (str/unicode) in an id using the vocab. """
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return self.vocab.get(token, self.vocab.get(self.unk_token))
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def convert_ids_to_tokens(self, ids):
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"""Converts a sequence of ids in wordpiece tokens using the vocab."""
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tokens = []
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for i in ids:
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tokens.append(self.ids_to_tokens[i])
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return tokens
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (string/unicode) using the vocab."""
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return self.ids_to_tokens.get(index, self.unk_token)
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def encode(self, text):
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return self.convert_tokens_to_ids(self.tokenize(text))
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def decode(self, token_ids, clean_up_tokenization_spaces=True):
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def _convert_ids_to_string(self, tokens_ids):
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"""Converts a sequence of ids in a string."""
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tokens = self.convert_ids_to_tokens(token_ids)
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tokens = self.convert_ids_to_tokens(tokens_ids)
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out_string = ''.join(tokens).replace(' ##', '').strip()
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if clean_up_tokenization_spaces:
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for special_tok in (self.UNK_TOKEN, self.SEP_TOKEN, self.PAD_TOKEN, self.CLS_TOKEN, self.MASK_TOKEN):
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out_string = out_string.replace(special_tok, '')
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out_string = clean_up_tokenization(out_string)
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return out_string
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def save_vocabulary(self, vocab_path):
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@@ -245,17 +191,20 @@ class BasicTokenizer(object):
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def __init__(self,
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do_lower_case=True,
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never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
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never_split=None):
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"""Constructs a BasicTokenizer.
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Args:
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do_lower_case: Whether to lower case the input.
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"""
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if never_split is None:
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never_split = []
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self.do_lower_case = do_lower_case
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self.never_split = never_split
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def tokenize(self, text):
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def tokenize(self, text, never_split=None):
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"""Tokenizes a piece of text."""
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never_split = self.never_split + (never_split if never_split is not None else [])
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text = self._clean_text(text)
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# This was added on November 1st, 2018 for the multilingual and Chinese
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# models. This is also applied to the English models now, but it doesn't
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@@ -267,7 +216,7 @@ class BasicTokenizer(object):
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orig_tokens = whitespace_tokenize(text)
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split_tokens = []
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for token in orig_tokens:
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if self.do_lower_case and token not in self.never_split:
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if self.do_lower_case and token not in never_split:
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token = token.lower()
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token = self._run_strip_accents(token)
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split_tokens.extend(self._run_split_on_punc(token))
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@@ -286,9 +235,9 @@ class BasicTokenizer(object):
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output.append(char)
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return "".join(output)
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def _run_split_on_punc(self, text):
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def _run_split_on_punc(self, text, never_split=None):
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"""Splits punctuation on a piece of text."""
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if text in self.never_split:
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if never_split is not None and text in never_split:
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return [text]
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chars = list(text)
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i = 0
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@@ -360,7 +309,7 @@ class BasicTokenizer(object):
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class WordpieceTokenizer(object):
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"""Runs WordPiece tokenization."""
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def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
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def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
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self.vocab = vocab
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self.unk_token = unk_token
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self.max_input_chars_per_word = max_input_chars_per_word
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