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Commit 982f181a authored by erenup's avatar erenup
Browse files

Merge remote-tracking branch 'origin/master' into run_multiple_choice_add_doc

parents 603b470a 84b9d1c4
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Showing with 766 additions and 212 deletions
pytorch_transformers/tokenization_gpt2.py
View file @ 982f181a
......@@ -64,13 +64,14 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
Returns list of utf-8 byte and a mapping to unicode strings.
We specifically avoids mapping to whitespace/control characters the bpe code barfs on.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
_chr = unichr if sys.version_info[0] == 2 else chr
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
......@@ -99,7 +100,10 @@ def get_pairs(word):
class GPT2Tokenizer(PreTrainedTokenizer):
"""
GPT-2 BPE tokenizer. Peculiarities:
- Byte-level BPE
- Byte-level Byte-Pair-Encoding
- Requires a space to start the input string => will add a space is there isn't.
As a consequence, this tokenizer `encode` and `decode` method will not conserve
the absence of a space at the beginning of a string: `tokenizer.decode(tokenizer.encode("Hello")) = " Hello"
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
......@@ -111,11 +115,11 @@ class GPT2Tokenizer(PreTrainedTokenizer):
self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens
self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens
self.encoder = json.load(open(vocab_file))
self.decoder = {v:k for k,v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.encoder = json.load(open(vocab_file, encoding="utf-8"))
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v:k for k, v in self.byte_encoder.items()}
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_data]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
......@@ -171,12 +175,13 @@ class GPT2Tokenizer(PreTrainedTokenizer):
def _tokenize(self, text):
""" Tokenize a string. """
text = ' ' + text # GPT-2 (and RoBERTa) tokenizers need at least one space to begin the sentence with.
bpe_tokens = []
for token in re.findall(self.pat, text):
if sys.version_info[0] == 2:
token = ''.join(self.byte_encoder[ord(b)] for b in token)
token = ''.join(self.byte_encoder[ord(b)] for b in token) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
else:
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
return bpe_tokens
......@@ -216,4 +221,4 @@ class GPT2Tokenizer(PreTrainedTokenizer):
writer.write(' '.join(bpe_tokens) + u'\n')
index += 1
return vocab_file, merge_file
return vocab_file, merge_file
\ No newline at end of file
pytorch_transformers/tokenization_roberta.py
View file @ 982f181a
......@@ -23,8 +23,7 @@ import os
import regex as re
from io import open
from .tokenization_gpt2 import bytes_to_unicode, get_pairs
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_gpt2 import GPT2Tokenizer
try:
from functools import lru_cache
......@@ -63,9 +62,13 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
}
class RobertaTokenizer(PreTrainedTokenizer):
class RobertaTokenizer(GPT2Tokenizer):
"""
RoBERTa BPE tokenizer, derived from the GPT-2 tokenizer. Peculiarities: Byte-level BPE
RoBERTa BPE tokenizer, derived from the GPT-2 tokenizer. Peculiarities:
- Byte-level Byte-Pair-Encoding
- Requires a space to start the input string => will add a space is there isn't.
As a consequence, this tokenizer `encode` and `decode` method will not conserve
the absence of a space at the beginning of a string: `tokenizer.decode(tokenizer.encode("Hello")) = " Hello"
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
......@@ -73,132 +76,23 @@ class RobertaTokenizer(PreTrainedTokenizer):
def __init__(self, vocab_file, merges_file, errors='replace', bos_token="<s>", eos_token="</s>", sep_token="</s>",
cls_token="<s>", unk_token="<unk>", pad_token='<pad>', mask_token='<mask>', **kwargs):
super(RobertaTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token,
super(RobertaTokenizer, self).__init__(vocab_file=vocab_file, merges_file=merges_file, errors=errors,
bos_token=bos_token, eos_token=eos_token, unk_token=unk_token,
sep_token=sep_token, cls_token=cls_token, pad_token=pad_token,
mask_token=mask_token, **kwargs)
self.max_len_single_sentence = self.max_len - 2 # take into account special tokens
self.max_len_sentences_pair = self.max_len - 4 # take into account special tokens
self.encoder = json.load(open(vocab_file, encoding="utf-8"))
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_data]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
@property
def vocab_size(self):
return len(self.encoder)
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
new_word.extend(word[i:j])
i = j
except:
new_word.extend(word[i:])
break
if word[i] == first and i < len(word)-1 and word[i+1] == second:
new_word.append(first+second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = ' '.join(word)
self.cache[token] = word
return word
def _tokenize(self, text):
""" Tokenize a string. """
bpe_tokens = []
for token in re.findall(self.pat, text):
if sys.version_info[0] == 2:
token = ''.join(self.byte_encoder[ord(b)] for b in token)
else:
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
return bpe_tokens
def _convert_token_to_id(self, token):
""" Converts a token (str/unicode) in an id using the vocab. """
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (string/unicode) using the vocab."""
return self.decoder.get(index)
def convert_tokens_to_string(self, tokens):
""" Converts a sequence of tokens (string) in a single string. """
text = ''.join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
return text
def add_special_tokens_single_sentence(self, token_ids):
"""
Adds special tokens to a sequence for sequence classification tasks.
A RoBERTa sequence has the following format: <s> X </s>
"""
return [self._convert_token_to_id(self.cls_token)] + token_ids + [self._convert_token_to_id(self.sep_token)]
return [self.cls_token_id] + token_ids + [self.sep_token_id]
def add_special_tokens_sentences_pair(self, token_ids_0, token_ids_1):
"""
Adds special tokens to a sequence pair for sequence classification tasks.
A RoBERTa sequence pair has the following format: <s> A </s></s> B </s>
"""
sep = [self._convert_token_to_id(self.sep_token)]
cls = [self._convert_token_to_id(self.cls_token)]
sep = [self.sep_token_id]
cls = [self.cls_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def save_vocabulary(self, save_directory):
"""Save the tokenizer vocabulary and merge files to a directory."""
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
return
vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file'])
merge_file = os.path.join(save_directory, VOCAB_FILES_NAMES['merges_file'])
with open(vocab_file, 'w', encoding='utf-8') as f:
f.write(json.dumps(self.encoder, ensure_ascii=False))
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write(u'#version: 0.2\n')
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!".format(merge_file))
index = token_index
writer.write(' '.join(bpe_tokens) + u'\n')
index += 1
return vocab_file, merge_file
pytorch_transformers/tokenization_transfo_xl.py
View file @ 982f181a
......@@ -95,7 +95,8 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
# in a library like ours, at all.
vocab_dict = torch.load(pretrained_vocab_file)
for key, value in vocab_dict.items():
self.__dict__[key] = value
if key not in self.__dict__:
self.__dict__[key] = value
if vocab_file is not None:
self.build_vocab()
......
pytorch_transformers/tokenization_utils.py
View file @ 982f181a
......@@ -20,6 +20,7 @@ import logging
import os
import json
import six
import copy
from io import open
from .file_utils import cached_path
......@@ -28,6 +29,7 @@ logger = logging.getLogger(__name__)
SPECIAL_TOKENS_MAP_FILE = 'special_tokens_map.json'
ADDED_TOKENS_FILE = 'added_tokens.json'
TOKENIZER_CONFIG_FILE = 'tokenizer_config.json'
class PreTrainedTokenizer(object):
""" Base class for all tokenizers.
......@@ -40,27 +42,29 @@ class PreTrainedTokenizer(object):
- ``vocab_files_names``: a python ``dict`` with, as keys, the ``__init__`` keyword name of each vocabulary file required by the model, and as associated values, the filename for saving the associated file (string).
- ``pretrained_vocab_files_map``: a python ``dict of dict`` the high-level keys being the ``__init__`` keyword name of each vocabulary file required by the model, the low-level being the `short-cut-names` (string) of the pretrained models with, as associated values, the `url` (string) to the associated pretrained vocabulary file.
- ``max_model_input_sizes``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model, or None if the model has no maximum input size.
- ``pretrained_init_configuration``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained models, and as associated values, a dictionnary of specific arguments to pass to the ``__init__``method of the tokenizer class for this pretrained model when loading the tokenizer with the ``from_pretrained()`` method.
Parameters:
- ``bos_token``: (`Optional`) string: a beginning of sentence token. Will be associated to ``self.bos_token``
- ``bos_token``: (`Optional`) string: a beginning of sentence token. Will be associated to ``self.bos_token`` and ``self.bos_token_id``
- ``eos_token``: (`Optional`) string: an end of sentence token. Will be associated to ``self.eos_token``
- ``eos_token``: (`Optional`) string: an end of sentence token. Will be associated to ``self.eos_token`` and ``self.eos_token_id``
- ``unk_token``: (`Optional`) string: an unknown token. Will be associated to ``self.unk_token``
- ``unk_token``: (`Optional`) string: an unknown token. Will be associated to ``self.unk_token`` and ``self.unk_token_id``
- ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence). Will be associated to ``self.sep_token``
- ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence). Will be associated to ``self.sep_token`` and ``self.sep_token_id``
- ``pad_token``: (`Optional`) string: a padding token. Will be associated to ``self.pad_token``
- ``pad_token``: (`Optional`) string: a padding token. Will be associated to ``self.pad_token`` and ``self.pad_token_id``
- ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model). Will be associated to ``self.cls_token``
- ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model). Will be associated to ``self.cls_token`` and ``self.cls_token_id``
- ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language modeling). Will be associated to ``self.mask_token``
- ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language modeling). Will be associated to ``self.mask_token`` and ``self.mask_token_id``
- ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens. Adding all special tokens here ensure they won't be split by the tokenization process. Will be associated to ``self.additional_special_tokens``
- ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens. Adding all special tokens here ensure they won't be split by the tokenization process. Will be associated to ``self.additional_special_tokens`` and ``self.additional_special_tokens_ids``
"""
vocab_files_names = {}
pretrained_vocab_files_map = {}
pretrained_init_configuration = {}
max_model_input_sizes = {}
SPECIAL_TOKENS_ATTRIBUTES = ["bos_token", "eos_token", "unk_token", "sep_token",
......@@ -155,6 +159,46 @@ class PreTrainedTokenizer(object):
def additional_special_tokens(self, value):
self._additional_special_tokens = value
@property
def bos_token_id(self):
""" Id of the beginning of sentence token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.bos_token)
@property
def eos_token_id(self):
""" Id of the end of sentence token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.eos_token)
@property
def unk_token_id(self):
""" Id of the unknown token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.unk_token)
@property
def sep_token_id(self):
""" Id of the separation token in the vocabulary. E.g. separate context and query in an input sequence. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.sep_token)
@property
def pad_token_id(self):
""" Id of the padding token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.pad_token)
@property
def cls_token_id(self):
""" Id of the classification token in the vocabulary. E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.cls_token)
@property
def mask_token_id(self):
""" Id of the mask token in the vocabulary. E.g. when training a model with masked-language modeling. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.mask_token)
@property
def additional_special_tokens_ids(self):
""" Ids of all the additional special tokens in the vocabulary (list of integers). Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.additional_special_tokens)
def __init__(self, max_len=None, **kwargs):
self._bos_token = None
self._eos_token = None
......@@ -166,12 +210,15 @@ class PreTrainedTokenizer(object):
self._additional_special_tokens = []
self.max_len = max_len if max_len is not None else int(1e12)
self.max_len_single_sentence = self.max_len
self.max_len_sentences_pair = self.max_len
# Added tokens
self.added_tokens_encoder = {}
self.added_tokens_decoder = {}
# inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``)
self.init_inputs = ()
self.init_kwargs = {}
for key, value in kwargs.items():
if key in self.SPECIAL_TOKENS_ATTRIBUTES:
if key == 'additional_special_tokens':
......@@ -231,17 +278,20 @@ class PreTrainedTokenizer(object):
@classmethod
def _from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
def _from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs):
cache_dir = kwargs.pop('cache_dir', None)
force_download = kwargs.pop('force_download', False)
proxies = kwargs.pop('proxies', None)
s3_models = list(cls.max_model_input_sizes.keys())
vocab_files = {}
init_configuration = {}
if pretrained_model_name_or_path in s3_models:
# Get the vocabulary from AWS S3 bucket
for file_id, map_list in cls.pretrained_vocab_files_map.items():
vocab_files[file_id] = map_list[pretrained_model_name_or_path]
if cls.pretrained_init_configuration and pretrained_model_name_or_path in cls.pretrained_init_configuration:
init_configuration = cls.pretrained_init_configuration[pretrained_model_name_or_path]
else:
# Get the vocabulary from local files
logger.info(
......@@ -264,15 +314,17 @@ class PreTrainedTokenizer(object):
vocab_files[file_id] = full_file_name
# Look for the additional tokens files
all_vocab_files_names = {'added_tokens_file': ADDED_TOKENS_FILE,
'special_tokens_map_file': SPECIAL_TOKENS_MAP_FILE}
additional_files_names = {'added_tokens_file': ADDED_TOKENS_FILE,
'special_tokens_map_file': SPECIAL_TOKENS_MAP_FILE,
'tokenizer_config_file': TOKENIZER_CONFIG_FILE,
}
# If a path to a file was provided, get the parent directory
saved_directory = pretrained_model_name_or_path
if os.path.exists(saved_directory) and not os.path.isdir(saved_directory):
saved_directory = os.path.dirname(saved_directory)
for file_id, file_name in all_vocab_files_names.items():
for file_id, file_name in additional_files_names.items():
full_file_name = os.path.join(saved_directory, file_name)
if not os.path.exists(full_file_name):
logger.info("Didn't find file {}. We won't load it.".format(full_file_name))
......@@ -315,28 +367,46 @@ class PreTrainedTokenizer(object):
logger.info("loading file {} from cache at {}".format(
file_path, resolved_vocab_files[file_id]))
# Prepare tokenizer initialization kwargs
# Did we saved some inputs and kwargs to reload ?
tokenizer_config_file = resolved_vocab_files.pop('tokenizer_config_file', None)
if tokenizer_config_file is not None:
init_kwargs = json.load(open(tokenizer_config_file, encoding="utf-8"))
saved_init_inputs = init_kwargs.pop('init_inputs', ())
if not init_inputs:
init_inputs = saved_init_inputs
else:
init_kwargs = init_configuration
# Update with newly provided kwargs
init_kwargs.update(kwargs)
# Set max length if needed
if pretrained_model_name_or_path in cls.max_model_input_sizes:
# if we're using a pretrained model, ensure the tokenizer
# wont index sequences longer than the number of positional embeddings
max_len = cls.max_model_input_sizes[pretrained_model_name_or_path]
if max_len is not None and isinstance(max_len, (int, float)):
kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
init_kwargs['max_len'] = min(init_kwargs.get('max_len', int(1e12)), max_len)
# Merge resolved_vocab_files arguments in kwargs.
# Merge resolved_vocab_files arguments in init_kwargs.
added_tokens_file = resolved_vocab_files.pop('added_tokens_file', None)
special_tokens_map_file = resolved_vocab_files.pop('special_tokens_map_file', None)
for args_name, file_path in resolved_vocab_files.items():
if args_name not in kwargs:
kwargs[args_name] = file_path
if args_name not in init_kwargs:
init_kwargs[args_name] = file_path
if special_tokens_map_file is not None:
special_tokens_map = json.load(open(special_tokens_map_file, encoding="utf-8"))
for key, value in special_tokens_map.items():
if key not in kwargs:
kwargs[key] = value
if key not in init_kwargs:
init_kwargs[key] = value
# Instantiate tokenizer.
tokenizer = cls(*inputs, **kwargs)
tokenizer = cls(*init_inputs, **init_kwargs)
# Save inputs and kwargs for saving and re-loading with ``save_pretrained``
tokenizer.init_inputs = init_inputs
tokenizer.init_kwargs = init_kwargs
# Add supplementary tokens.
if added_tokens_file is not None:
......@@ -349,8 +419,13 @@ class PreTrainedTokenizer(object):
def save_pretrained(self, save_directory):
""" Save the tokenizer vocabulary files (with added tokens) and the
special-tokens-to-class-attributes-mapping to a directory.
""" Save the tokenizer vocabulary files together with:
- added tokens,
- special-tokens-to-class-attributes-mapping,
- tokenizer instantiation positional and keywords inputs (e.g. do_lower_case for Bert).
This won't save modifications other than (added tokens and special token mapping) you may have
applied to the tokenizer after the instantion (e.g. modifying tokenizer.do_lower_case after creation).
This method make sure the full tokenizer can then be re-loaded using the :func:`~pytorch_transformers.PreTrainedTokenizer.from_pretrained` class method.
"""
......@@ -360,6 +435,15 @@ class PreTrainedTokenizer(object):
special_tokens_map_file = os.path.join(save_directory, SPECIAL_TOKENS_MAP_FILE)
added_tokens_file = os.path.join(save_directory, ADDED_TOKENS_FILE)
tokenizer_config_file = os.path.join(save_directory, TOKENIZER_CONFIG_FILE)
tokenizer_config = copy.deepcopy(self.init_kwargs)
tokenizer_config['init_inputs'] = copy.deepcopy(self.init_inputs)
for file_id in self.vocab_files_names.keys():
tokenizer_config.pop(file_id, None)
with open(tokenizer_config_file, 'w', encoding='utf-8') as f:
f.write(json.dumps(tokenizer_config, ensure_ascii=False))
with open(special_tokens_map_file, 'w', encoding='utf-8') as f:
f.write(json.dumps(self.special_tokens_map, ensure_ascii=False))
......@@ -441,6 +525,13 @@ class PreTrainedTokenizer(object):
to class attributes. If special tokens are NOT in the vocabulary, they are added
to it (indexed starting from the last index of the current vocabulary).
Using `add_special_tokens` will ensure your special tokens can be used in several ways:
- special tokens are carefully handled by the tokenizer (they are never split)
- you can easily refer to special tokens using tokenizer class attributes like `tokenizer.cls_token`. This makes it easy to develop model-agnostic training and fine-tuning scripts.
When possible, special tokens are already registered for provided pretrained models (ex: BertTokenizer cls_token is already registered to be '[CLS]' and XLM's one is also registered to be '</s>')
Args:
special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes:
[``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``,
......@@ -546,6 +637,9 @@ class PreTrainedTokenizer(object):
""" Converts a single token, or a sequence of tokens, (str/unicode) in a single integer id
(resp. a sequence of ids), using the vocabulary.
"""
if tokens is None:
return None
if isinstance(tokens, str) or (six.PY2 and isinstance(tokens, unicode)):
return self._convert_token_to_id_with_added_voc(tokens)
......@@ -559,6 +653,9 @@ class PreTrainedTokenizer(object):
return ids
def _convert_token_to_id_with_added_voc(self, token):
if token is None:
return None
if token in self.added_tokens_encoder:
return self.added_tokens_encoder[token]
return self._convert_token_to_id(token)
......@@ -566,7 +663,7 @@ class PreTrainedTokenizer(object):
def _convert_token_to_id(self, token):
raise NotImplementedError
def encode(self, text, text_pair=None, add_special_tokens=False):
def encode(self, text, text_pair=None, add_special_tokens=False, **kwargs):
"""
Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
......@@ -577,15 +674,16 @@ class PreTrainedTokenizer(object):
text_pair: Optional second sequence to be encoded.
add_special_tokens: if set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
**kwargs: passed to the `self.tokenize()` method
"""
if text_pair is None:
if add_special_tokens:
return self.add_special_tokens_single_sentence(self.convert_tokens_to_ids(self.tokenize(text)))
return self.add_special_tokens_single_sentence(self.convert_tokens_to_ids(self.tokenize(text, **kwargs)))
else:
return self.convert_tokens_to_ids(self.tokenize(text))
return self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text)]
second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair)]
first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text, **kwargs)]
second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)]
if add_special_tokens:
return self.add_special_tokens_sentences_pair(first_sentence_tokens, second_sentence_tokens)
......@@ -614,7 +712,7 @@ class PreTrainedTokenizer(object):
return self._convert_id_to_token(ids)
tokens = []
for index in ids:
if index in self.all_special_ids and skip_special_tokens:
if skip_special_tokens and index in self.all_special_ids:
continue
if index in self.added_tokens_decoder:
tokens.append(self.added_tokens_decoder[index])
......@@ -639,7 +737,25 @@ class PreTrainedTokenizer(object):
Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``.
"""
filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
text = self.convert_tokens_to_string(filtered_tokens)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separatly for added tokens and byte-level tokens
# cf. https://github.com/huggingface/pytorch-transformers/issues/1133
sub_texts = []
current_sub_text = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
current_sub_text = []
sub_texts.append(" " + token)
else:
current_sub_text.append(token)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
text = ''.join(sub_texts)
if self._sep_token is not None and self._sep_token in text:
text = text.replace(self._cls_token, self._sep_token)
......@@ -676,7 +792,7 @@ class PreTrainedTokenizer(object):
all_toks = []
set_attr = self.special_tokens_map
for attr_value in set_attr.values():
all_toks = all_toks + (attr_value if isinstance(attr_value, (list, tuple)) else [attr_value])
all_toks = all_toks + (list(attr_value) if isinstance(attr_value, (list, tuple)) else [attr_value])
all_toks = list(set(all_toks))
return all_toks
......
pytorch_transformers/tokenization_xlm.py
View file @ 982f181a
......@@ -20,8 +20,12 @@ import json
import logging
import os
import re
import sys
import unicodedata
from io import open
import sacremoses as sm
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_bert import BasicTokenizer
......@@ -43,6 +47,8 @@ PRETRAINED_VOCAB_FILES_MAP = {
'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-vocab.json",
'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-enfr-1024-vocab.json",
'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-clm-ende-1024-vocab.json",
'xlm-mlm-17-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-vocab.json",
'xlm-mlm-100-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-vocab.json",
},
'merges_file':
{
......@@ -54,6 +60,8 @@ PRETRAINED_VOCAB_FILES_MAP = {
'xlm-mlm-xnli15-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-xnli15-1024-merges.txt",
'xlm-clm-enfr-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-enfr-1024-merges.txt",
'xlm-clm-ende-1024': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-ende-1024-merges.txt",
'xlm-mlm-17-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-merges.txt",
'xlm-mlm-100-1280': "https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-100-1280-merges.txt",
},
}
......@@ -66,6 +74,342 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'xlm-mlm-xnli15-1024': 512,
'xlm-clm-enfr-1024': 512,
'xlm-clm-ende-1024': 512,
'xlm-mlm-17-1280': 512,
'xlm-mlm-100-1280': 512,
}
PRETRAINED_INIT_CONFIGURATION = {
'xlm-mlm-en-2048': {"do_lowercase_and_remove_accent": True},
'xlm-mlm-ende-1024': { "do_lowercase_and_remove_accent": True,
"id2lang": { "0": "de",
"1": "en"},
"lang2id": { "de": 0,
"en": 1 }},
'xlm-mlm-enfr-1024': { "do_lowercase_and_remove_accent": True,
"id2lang": { "0": "en",
"1": "fr"},
"lang2id": { "en": 0,
"fr": 1 }},
'xlm-mlm-enro-1024': { "do_lowercase_and_remove_accent": True,
"id2lang": { "0": "en",
"1": "ro"},
"lang2id": { "en": 0,
"ro": 1 }},
'xlm-mlm-tlm-xnli15-1024': { "do_lowercase_and_remove_accent": True,
"id2lang": { "0": "ar",
"1": "bg",
"2": "de",
"3": "el",
"4": "en",
"5": "es",
"6": "fr",
"7": "hi",
"8": "ru",
"9": "sw",
"10": "th",
"11": "tr",
"12": "ur",
"13": "vi",
"14": "zh"},
"lang2id": { "ar": 0,
"bg": 1,
"de": 2,
"el": 3,
"en": 4,
"es": 5,
"fr": 6,
"hi": 7,
"ru": 8,
"sw": 9,
"th": 10,
"tr": 11,
"ur": 12,
"vi": 13,
"zh": 14 }},
'xlm-mlm-xnli15-1024': { "do_lowercase_and_remove_accent": True,
"id2lang": { "0": "ar",
"1": "bg",
"2": "de",
"3": "el",
"4": "en",
"5": "es",
"6": "fr",
"7": "hi",
"8": "ru",
"9": "sw",
"10": "th",
"11": "tr",
"12": "ur",
"13": "vi",
"14": "zh"},
"lang2id": { "ar": 0,
"bg": 1,
"de": 2,
"el": 3,
"en": 4,
"es": 5,
"fr": 6,
"hi": 7,
"ru": 8,
"sw": 9,
"th": 10,
"tr": 11,
"ur": 12,
"vi": 13,
"zh": 14 }},
'xlm-clm-enfr-1024': { "do_lowercase_and_remove_accent": True,
"id2lang": { "0": "en",
"1": "fr"},
"lang2id": { "en": 0,
"fr": 1 }},
'xlm-clm-ende-1024': { "do_lowercase_and_remove_accent": True,
"id2lang": { "0": "de",
"1": "en"},
"lang2id": { "de": 0,
"en": 1 }},
'xlm-mlm-17-1280': {"do_lowercase_and_remove_accent": False,
"id2lang": {
"0": "ar",
"1": "de",
"2": "en",
"3": "es",
"4": "fr",
"5": "hi",
"6": "it",
"7": "ja",
"8": "ko",
"9": "nl",
"10": "pl",
"11": "pt",
"12": "ru",
"13": "sv",
"14": "tr",
"15": "vi",
"16": "zh"
},
"lang2id": {
"ar": 0,
"de": 1,
"en": 2,
"es": 3,
"fr": 4,
"hi": 5,
"it": 6,
"ja": 7,
"ko": 8,
"nl": 9,
"pl": 10,
"pt": 11,
"ru": 12,
"sv": 13,
"tr": 14,
"vi": 15,
"zh": 16}},
'xlm-mlm-100-1280': {"do_lowercase_and_remove_accent": False,
"id2lang": {
"0": "af",
"1": "als",
"2": "am",
"3": "an",
"4": "ang",
"5": "ar",
"6": "arz",
"7": "ast",
"8": "az",
"9": "bar",
"10": "be",
"11": "bg",
"12": "bn",
"13": "br",
"14": "bs",
"15": "ca",
"16": "ceb",
"17": "ckb",
"18": "cs",
"19": "cy",
"20": "da",
"21": "de",
"22": "el",
"23": "en",
"24": "eo",
"25": "es",
"26": "et",
"27": "eu",
"28": "fa",
"29": "fi",
"30": "fr",
"31": "fy",
"32": "ga",
"33": "gan",
"34": "gl",
"35": "gu",
"36": "he",
"37": "hi",
"38": "hr",
"39": "hu",
"40": "hy",
"41": "ia",
"42": "id",
"43": "is",
"44": "it",
"45": "ja",
"46": "jv",
"47": "ka",
"48": "kk",
"49": "kn",
"50": "ko",
"51": "ku",
"52": "la",
"53": "lb",
"54": "lt",
"55": "lv",
"56": "mk",
"57": "ml",
"58": "mn",
"59": "mr",
"60": "ms",
"61": "my",
"62": "nds",
"63": "ne",
"64": "nl",
"65": "nn",
"66": "no",
"67": "oc",
"68": "pl",
"69": "pt",
"70": "ro",
"71": "ru",
"72": "scn",
"73": "sco",
"74": "sh",
"75": "si",
"76": "simple",
"77": "sk",
"78": "sl",
"79": "sq",
"80": "sr",
"81": "sv",
"82": "sw",
"83": "ta",
"84": "te",
"85": "th",
"86": "tl",
"87": "tr",
"88": "tt",
"89": "uk",
"90": "ur",
"91": "uz",
"92": "vi",
"93": "war",
"94": "wuu",
"95": "yi",
"96": "zh",
"97": "zh_classical",
"98": "zh_min_nan",
"99": "zh_yue"
},
"lang2id": {
"af": 0,
"als": 1,
"am": 2,
"an": 3,
"ang": 4,
"ar": 5,
"arz": 6,
"ast": 7,
"az": 8,
"bar": 9,
"be": 10,
"bg": 11,
"bn": 12,
"br": 13,
"bs": 14,
"ca": 15,
"ceb": 16,
"ckb": 17,
"cs": 18,
"cy": 19,
"da": 20,
"de": 21,
"el": 22,
"en": 23,
"eo": 24,
"es": 25,
"et": 26,
"eu": 27,
"fa": 28,
"fi": 29,
"fr": 30,
"fy": 31,
"ga": 32,
"gan": 33,
"gl": 34,
"gu": 35,
"he": 36,
"hi": 37,
"hr": 38,
"hu": 39,
"hy": 40,
"ia": 41,
"id": 42,
"is": 43,
"it": 44,
"ja": 45,
"jv": 46,
"ka": 47,
"kk": 48,
"kn": 49,
"ko": 50,
"ku": 51,
"la": 52,
"lb": 53,
"lt": 54,
"lv": 55,
"mk": 56,
"ml": 57,
"mn": 58,
"mr": 59,
"ms": 60,
"my": 61,
"nds": 62,
"ne": 63,
"nl": 64,
"nn": 65,
"no": 66,
"oc": 67,
"pl": 68,
"pt": 69,
"ro": 70,
"ru": 71,
"scn": 72,
"sco": 73,
"sh": 74,
"si": 75,
"simple": 76,
"sk": 77,
"sl": 78,
"sq": 79,
"sr": 80,
"sv": 81,
"sw": 82,
"ta": 83,
"te": 84,
"th": 85,
"tl": 86,
"tr": 87,
"tt": 88,
"uk": 89,
"ur": 90,
"uz": 91,
"vi": 92,
"war": 93,
"wuu": 94,
"yi": 95,
"zh": 96,
"zh_classical": 97,
"zh_min_nan": 98,
"zh_yue": 99
}},
}
def get_pairs(word):
......@@ -80,62 +424,145 @@ def get_pairs(word):
prev_char = char
return pairs
def text_standardize(text):
def lowercase_and_remove_accent(text):
"""
fixes some issues the spacy tokenizer had on books corpus
also does some whitespace standardization
Lowercase and strips accents from a piece of text based on
https://github.com/facebookresearch/XLM/blob/master/tools/lowercase_and_remove_accent.py
"""
text = text.replace('—', '-')
text = text.replace('–', '-')
text = text.replace('―', '-')
text = ' '.join(text)
text = text.lower()
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output).lower().split(' ')
def replace_unicode_punct(text):
'''
Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl
'''
text = text.replace('，', ',')
text = re.sub(r'。\s*', '. ', text)
text = text.replace('、', ',')
text = text.replace('”', '"')
text = text.replace('“', '"')
text = text.replace('∶', ':')
text = text.replace('：', ':')
text = text.replace('？', '?')
text = text.replace('《', '"')
text = text.replace('》', '"')
text = text.replace('）', ')')
text = text.replace('！', '!')
text = text.replace('（', '(')
text = text.replace('；', ';')
text = text.replace('１', '"')
text = text.replace('」', '"')
text = text.replace('「', '"')
text = text.replace('０', '0')
text = text.replace('３', '3')
text = text.replace('２', '2')
text = text.replace('５', '5')
text = text.replace('６', '6')
text = text.replace('９', '9')
text = text.replace('７', '7')
text = text.replace('８', '8')
text = text.replace('４', '4')
text = re.sub(r'．\s*', '. ', text)
text = text.replace('～', '~')
text = text.replace('’', '\'')
text = text.replace('…', '...')
text = text.replace('´', "'")
text = re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text)
text = re.sub(r'\s*\n\s*', ' \n ', text)
text = re.sub(r'[^\S\n]+', ' ', text)
return text.strip()
text = text.replace('━', '-')
text = text.replace('〈', '<')
text = text.replace('〉', '>')
text = text.replace('【', '[')
text = text.replace('】', ']')
text = text.replace('％', '%')
return text
def remove_non_printing_char(text):
'''
Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl
'''
output = []
for char in text:
cat = unicodedata.category(char)
if cat.startswith('C'):
continue
output.append(char)
return "".join(output)
def romanian_preprocessing(text):
'''Sennrich's WMT16 scripts for Romanian preprocessing, used by model `xlm-mlm-enro-1024`'''
# https://github.com/rsennrich/wmt16-scripts/blob/master/preprocess/normalise-romanian.py
text = text.replace("\u015e", "\u0218").replace("\u015f", "\u0219")
text = text.replace("\u0162", "\u021a").replace("\u0163", "\u021b")
# https://github.com/rsennrich/wmt16-scripts/blob/master/preprocess/remove-diacritics.py
text = text.replace("\u0218", "S").replace("\u0219", "s") #s-comma
text = text.replace("\u021a", "T").replace("\u021b", "t") #t-comma
text = text.replace("\u0102", "A").replace("\u0103", "a")
text = text.replace("\u00C2", "A").replace("\u00E2", "a")
text = text.replace("\u00CE", "I").replace("\u00EE", "i")
return text
class XLMTokenizer(PreTrainedTokenizer):
"""
BPE tokenizer for XLM, adapted from OpenAI BPE tokenizer. Peculiarities:
BPE tokenizer for XLM
- lower case all inputs
- Moses preprocessing & tokenization for most supported languages
- uses `SpaCy tokenizer <https://spacy.io/api/tokenizer/>`_ and \
`ftfy <https://ftfy.readthedocs.io/en/latest/>`_ for pre-BPE tokenization if they are installed, \
fallback to BERT's BasicTokenizer if not.
- Language specific tokenization for Chinese (Jieba), Japanese (KyTea) and Thai (PyThaiNLP)
- (optionally) lower case & normalize all inputs text
- argument ``special_tokens`` and function ``set_special_tokens``, can be used to add additional symbols \
(ex: "__classify__") to a vocabulary.
(ex: "__classify__") to a vocabulary
- `lang2id` attribute maps the languages supported by the model with their ids if provided (automatically set for pretrained vocabularies)
- `id2lang` attributes does reverse mapping if provided (automatically set for pretrained vocabularies)
- `do_lowercase_and_remove_accent` controle lower casing and accent (automatically set for pretrained vocabularies)
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self, vocab_file, merges_file, unk_token="<unk>", bos_token="<s>",
sep_token="</s>", pad_token="<pad>", cls_token="</s>",
mask_token="<special1>", additional_special_tokens=["<special0>",
"<special1>", "<special2>", "<special3>", "<special4>", "<special5>",
"<special6>", "<special7>", "<special8>", "<special9>"], **kwargs):
"<special6>", "<special7>", "<special8>", "<special9>"],
lang2id=None, id2lang=None, do_lowercase_and_remove_accent=True,
**kwargs):
super(XLMTokenizer, self).__init__(unk_token=unk_token, bos_token=bos_token,
sep_token=sep_token, pad_token=pad_token,
cls_token=cls_token, mask_token=mask_token,
additional_special_tokens=additional_special_tokens,
**kwargs)
self.max_len_single_sentence = self.max_len - 2 # take into account special tokens
self.max_len_sentences_pair = self.max_len - 3 # take into account special tokens
# cache of sm.MosesPunctNormalizer instance
self.cache_moses_punct_normalizer = dict()
# cache of sm.MosesTokenizer instance
self.cache_moses_tokenizer = dict()
self.lang_with_custom_tokenizer = set(['zh', 'th', 'ja'])
# True for current supported model (v1.2.0), False for XLM-17 & 100
self.do_lowercase_and_remove_accent = do_lowercase_and_remove_accent
self.lang2id = lang2id
self.id2lang = id2lang
if lang2id is not None and id2lang is not None:
assert len(lang2id) == len(id2lang)
try:
import ftfy
from spacy.lang.en import English
_nlp = English()
self.nlp = _nlp.Defaults.create_tokenizer(_nlp)
self.fix_text = ftfy.fix_text
except ImportError:
logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
self.nlp = BasicTokenizer(do_lower_case=True)
self.fix_text = None
self.ja_word_tokenizer = None
self.zh_word_tokenizer = None
self.encoder = json.load(open(vocab_file, encoding="utf-8"))
self.decoder = {v:k for k,v in self.encoder.items()}
......@@ -144,6 +571,43 @@ class XLMTokenizer(PreTrainedTokenizer):
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {}
def moses_punct_norm(self, text, lang):
if lang not in self.cache_moses_punct_normalizer:
punct_normalizer = sm.MosesPunctNormalizer(lang=lang)
self.cache_moses_punct_normalizer[lang] = punct_normalizer
else:
punct_normalizer = self.cache_moses_punct_normalizer[lang]
return punct_normalizer.normalize(text)
def moses_tokenize(self, text, lang):
if lang not in self.cache_moses_tokenizer:
moses_tokenizer = sm.MosesTokenizer(lang=lang)
self.cache_moses_tokenizer[lang] = moses_tokenizer
else:
moses_tokenizer = self.cache_moses_tokenizer[lang]
return moses_tokenizer.tokenize(text, return_str=False, escape=False)
def moses_pipeline(self, text, lang):
text = replace_unicode_punct(text)
text = self.moses_punct_norm(text, lang)
text = remove_non_printing_char(text)
return text
def ja_tokenize(self, text):
if self.ja_word_tokenizer is None:
try:
import Mykytea
self.ja_word_tokenizer = Mykytea.Mykytea('-model %s/local/share/kytea/model.bin' % os.path.expanduser('~'))
except (AttributeError, ImportError) as e:
logger.error("Make sure you install KyTea (https://github.com/neubig/kytea) and it's python wrapper (https://github.com/chezou/Mykytea-python) with the following steps")
logger.error("1. git clone git@github.com:neubig/kytea.git && cd kytea")
logger.error("2. autoreconf -i")
logger.error("3. ./configure --prefix=$HOME/local")
logger.error("4. make && make install")
logger.error("5. pip install kytea")
raise e
return list(self.ja_word_tokenizer.getWS(text))
@property
def vocab_size(self):
return len(self.encoder)
......@@ -191,19 +655,90 @@ class XLMTokenizer(PreTrainedTokenizer):
self.cache[token] = word
return word
def _tokenize(self, text):
""" Tokenize a string. """
def _tokenize(self, text, lang='en', bypass_tokenizer=False):
"""
Tokenize a string given language code. For Chinese, Japanese and Thai, we use a language specific tokenizerself. Otherwise, we use Moses.
Details of tokenization:
- [sacremoses](https://github.com/alvations/sacremoses): port of Moses
- Install with `pip install sacremoses`
- [pythainlp](https://github.com/PyThaiNLP/pythainlp): Thai tokenizer
- Install with `pip install pythainlp`
- [kytea](https://github.com/chezou/Mykytea-python): Japanese tokenizer, wrapper of [KyTea](https://github.com/neubig/kytea)
- Install with the following steps:
```
git clone git@github.com:neubig/kytea.git && cd kytea
autoreconf -i
./configure --prefix=$HOME/local
make && make install
pip install kytea
```
- [jieba](https://github.com/fxsjy/jieba): Chinese tokenizer *
- Install with `pip install jieba`
\* The original XLM used [Stanford Segmenter](https://nlp.stanford.edu/software/stanford-segmenter-2018-10-16.zip).
However, the wrapper (`nltk.tokenize.stanford_segmenter`) is slow due to JVM overhead, and it will be deprecated.
Jieba is a lot faster and pip-installable. Note there is some mismatch with the Stanford Segmenter. It should be fine
if you fine-tune the model with Chinese supervisionself. If you want the same exact behaviour, use the original XLM
[preprocessing script](https://github.com/facebookresearch/XLM/tree/master/tools) to tokenize the sentence externally,
and set `bypass_tokenizer=True` to bypass the tokenizer.
Args:
- lang: ISO language code (default = 'en') (string). Languages should belong of the model supported languages. However, we don't enforce it.
- bypass_tokenizer: Allow users to preprocess and tokenize the sentences externally (default = False) (bool). If True, we only apply BPE.
Returns:
List of tokens.
"""
if lang and self.lang2id and lang not in self.lang2id:
logger.error("Supplied language code not found in lang2id mapping. Please check that your language is supported by the loaded pretrained model.")
if bypass_tokenizer:
text = text.split()
elif lang not in self.lang_with_custom_tokenizer:
text = self.moses_pipeline(text, lang=lang)
# TODO: make sure we are using `xlm-mlm-enro-1024`, since XLM-100 doesn't have this step
if lang == 'ro':
text = romanian_preprocessing(text)
text = self.moses_tokenize(text, lang=lang)
elif lang == 'th':
text = self.moses_pipeline(text, lang=lang)
try:
if 'pythainlp' not in sys.modules:
from pythainlp.tokenize import word_tokenize as th_word_tokenize
else:
th_word_tokenize = sys.modules['pythainlp'].word_tokenize
except (AttributeError, ImportError) as e:
logger.error("Make sure you install PyThaiNLP (https://github.com/PyThaiNLP/pythainlp) with the following steps")
logger.error("1. pip install pythainlp")
raise e
text = th_word_tokenize(text)
elif lang == 'zh':
try:
if 'jieba' not in sys.modules:
import jieba
else:
jieba = sys.modules['jieba']
except (AttributeError, ImportError) as e:
logger.error("Make sure you install Jieba (https://github.com/fxsjy/jieba) with the following steps")
logger.error("1. pip install jieba")
raise e
text = ' '.join(jieba.cut(text))
text = self.moses_pipeline(text, lang=lang)
text = text.split()
elif lang == 'ja':
text = self.moses_pipeline(text, lang=lang)
text = self.ja_tokenize(text)
else:
raise ValueError('It should not reach here')
if self.do_lowercase_and_remove_accent and not bypass_tokenizer:
text = lowercase_and_remove_accent(text)
split_tokens = []
if self.fix_text is None:
# Using BERT's BasicTokenizer
text = self.nlp.tokenize(text)
for token in text:
for token in text:
if token:
split_tokens.extend([t for t in self.bpe(token).split(' ')])
else:
# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
text = self.nlp(text_standardize(self.fix_text(text)))
for token in text:
split_tokens.extend([t for t in self.bpe(token.text.lower()).split(' ')])
return split_tokens
def _convert_token_to_id(self, token):
......@@ -224,15 +759,15 @@ class XLMTokenizer(PreTrainedTokenizer):
Adds special tokens to a sequence for sequence classification tasks.
An XLM sequence has the following format: [CLS] X [SEP]
"""
return [self._convert_token_to_id(self.cls_token)] + token_ids + [self._convert_token_to_id(self.sep_token)]
return [self.cls_token_id] + token_ids + [self.sep_token_id]
def add_special_tokens_sentences_pair(self, token_ids_0, token_ids_1):
"""
Adds special tokens to a sequence pair for sequence classification tasks.
An XLM sequence pair has the following format: [CLS] A [SEP] B [SEP]
"""
sep = [self._convert_token_to_id(self.sep_token)]
cls = [self._convert_token_to_id(self.cls_token)]
sep = [self.sep_token_id]
cls = [self.cls_token_id]
return cls + token_ids_0 + sep + token_ids_1 + sep
def save_vocabulary(self, save_directory):
......
pytorch_transformers/tokenization_xlnet.py
View file @ 982f181a
......@@ -61,7 +61,7 @@ class XLNetTokenizer(PreTrainedTokenizer):
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self, vocab_file, max_len=None,
def __init__(self, vocab_file,
do_lower_case=False, remove_space=True, keep_accents=False,
bos_token="<s>", eos_token="</s>", unk_token="<unk>", sep_token="<sep>",
pad_token="<pad>", cls_token="<cls>", mask_token="<mask>",
......@@ -186,8 +186,8 @@ class XLNetTokenizer(PreTrainedTokenizer):
Adds special tokens to a sequence pair for sequence classification tasks.
An XLNet sequence pair has the following format: A [SEP] B [SEP][CLS]
"""
sep = [self._convert_token_to_id(self.sep_token)]
cls = [self._convert_token_to_id(self.cls_token)]
sep = [self.sep_token_id]
cls = [self.cls_token_id]
return token_ids + sep + cls
def add_special_tokens_sentences_pair(self, token_ids_0, token_ids_1):
......@@ -195,8 +195,8 @@ class XLNetTokenizer(PreTrainedTokenizer):
Adds special tokens to a sequence for sequence classification tasks.
An XLNet sequence has the following format: X [SEP][CLS]
"""
sep = [self._convert_token_to_id(self.sep_token)]
cls = [self._convert_token_to_id(self.cls_token)]
sep = [self.sep_token_id]
cls = [self.cls_token_id]
return token_ids_0 + sep + token_ids_1 + sep + cls
def save_vocabulary(self, save_directory):
......
requirements.txt
View file @ 982f181a
......@@ -9,4 +9,6 @@ requests
# For OpenAI GPT
regex
# For XLNet
sentencepiece
\ No newline at end of file
sentencepiece
# For XLM
sacremoses
\ No newline at end of file
setup.py
View file @ 982f181a
......@@ -38,7 +38,7 @@ from setuptools import find_packages, setup
setup(
name="pytorch_transformers",
version="1.1.0",
version="1.2.0",
author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Google AI Language Team Authors, Open AI team Authors",
author_email="thomas@huggingface.co",
description="Repository of pre-trained NLP Transformer models: BERT & RoBERTa, GPT & GPT-2, Transformer-XL, XLNet and XLM",
......@@ -55,7 +55,8 @@ setup(
'requests',
'tqdm',
'regex',
'sentencepiece'],
'sentencepiece',
'sacremoses'],
entry_points={
'console_scripts': [
"pytorch_transformers=pytorch_transformers.__main__:main",
......
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