def build(self, afm: AuxiliaryFileManager,
corpus: AuxiliaryFile) -> AuxiliaryFile:
subset = self._create_subset_file(afm, corpus)
# Create WordPiece model with a normalizer and pre-tokenizer. Note that
# BERT-specific normalizer and pre-tokenizer are used in this model.
tokenizer = Tokenizer(WordPiece())
tokenizer.normalizer = BertNormalizer(strip_accents=False)
tokenizer.pre_tokenizer = BertPreTokenizer()
# Train tokenizer model with subset of corpus.
trainer = WordPieceTrainer(vocab_size=self.vocab_size,
min_frequency=2,
show_progress=True,
limit_alphabet=self.limit_alphabet,
special_tokens=[self.unk_token] +
self.special_tokens,
continuing_subword_prefix='##')
tokenizer.train(trainer, [subset.name])
# Save trained vocabulary to an auxiliary output file.
vocab = afm.create()
tokenizer.model.save(os.path.dirname(vocab.name))
os.rename(os.path.join(os.path.dirname(vocab.name), 'vocab.txt'),
vocab.name)
return vocab
def train_custom_tokenizer(files: List[str], tokenizer_file: str,
**kwargs) -> BertWordPieceTokenizer:
""" Tokenizerの学習・保存処理：custom PreTokenizer付きのTokenizerを学習・保存する。
"""
tokenizer = BertWordPieceTokenizer(
handle_chinese_chars=False, # for japanese
strip_accents=False, # for japanese
)
tokenizer._tokenizer.pre_tokenizer = PreTokenizer.custom(
MecabPreTokenizer())
# 与えられたコーパスファイル集合からサブワード分割を学習
tokenizer.train(files, **kwargs)
# vocab情報に加えて、前処理等パラメータ情報を含んだトークナイザ設定のJSONを保存
# NOTE: Pythonで書かれたcustom PreTokenizerはシリアライズできないので、RustベースのPreTokenizerをダミー注入してシリアライズ
# JSONにはダミーのPreTokenizerが記録されるので、ロード時にcustom PreTokenizerを再設定する必要がある。
tokenizer._tokenizer.pre_tokenizer = BertPreTokenizer()
tokenizer.save(tokenizer_file)
# (Optional) .txt形式のvocabファイルは f"vocab-{filename}.txt" で保存される（外部の処理で欲しい場合）
filename = "wordpiece"
model_files = tokenizer._tokenizer.model.save(
str(pathlib.Path(tokenizer_file).parent), filename)
return tokenizer
def save_pretrained(self,
dir,
legacy_format=None,
pre=None,
push_to_hub=False,
**kw):
self.backend_tokenizer.pre_tokenizer = BertPreTokenizer()
return super().save_pretrained(dir, legacy_format, pre, push_to_hub,
**kw)
def __init__(
self,
vocab_file: Optional[str] = None,
add_special_tokens: bool = True,
unk_token: str = "[UNK]",
sep_token: str = "[SEP]",
cls_token: str = "[CLS]",
clean_text: bool = True,
handle_chinese_chars: bool = True,
strip_accents: bool = True,
lowercase: bool = True,
wordpieces_prefix: str = "##",
):
if vocab_file is not None:
tokenizer = Tokenizer(
WordPiece.from_files(vocab_file, unk_token=unk_token))
else:
tokenizer = Tokenizer(WordPiece.empty())
tokenizer.add_special_tokens([unk_token, sep_token, cls_token])
tokenizer.normalizer = BertNormalizer(
clean_text=clean_text,
handle_chinese_chars=handle_chinese_chars,
strip_accents=strip_accents,
lowercase=lowercase,
)
tokenizer.pre_tokenizer = BertPreTokenizer()
if add_special_tokens and vocab_file is not None:
sep_token_id = tokenizer.token_to_id(sep_token)
if sep_token_id is None:
raise TypeError("sep_token not found in the vocabulary")
cls_token_id = tokenizer.token_to_id(cls_token)
if cls_token_id is None:
raise TypeError("cls_token not found in the vocabulary")
tokenizer.post_processor = BertProcessing(
(sep_token, sep_token_id), (cls_token, cls_token_id))
tokenizer.decoders = decoders.WordPiece(prefix=wordpieces_prefix)
parameters = {
"model": "BertWordPiece",
"add_special_tokens": add_special_tokens,
"unk_token": unk_token,
"sep_token": sep_token,
"cls_token": cls_token,
"clean_text": clean_text,
"handle_chinese_chars": handle_chinese_chars,
"strip_accents": strip_accents,
"lowercase": lowercase,
"wordpieces_prefix": wordpieces_prefix,
}
super().__init__(tokenizer, parameters)
def save_pretrained(
self,
save_directory,
legacy_format=None,
filename_prefix=None,
push_to_hub=False,
**kwargs,
):
self.backend_tokenizer.pre_tokenizer = BertPreTokenizer()
return super().save_pretrained(save_directory, legacy_format,
filename_prefix, push_to_hub, **kwargs)
def tokenize_corpus(
input_file: str,
output_file: str,
vocab_file: str,
unk_token: str = '<unk>',
control_tokens: List[str] = []):
r"""Tokenize corpus sentences through trained **WordPiece** model.
Arguments:
input_file (str): Input corpus file path.
output_file (str): Output file path.
vocab_file (str): Trained vocabulary file path.
unk_token (str): Unknown token in the vocabulary.
control_tokens (list): Control tokens in the vocabulary.
"""
# Create `WordPiece` model and add special tokens. Note that `unk_token`
# is also a special token.normalizer and pre-tokenizer.
tokenizer = Tokenizer(models.WordPiece(vocab_file, unk_token=unk_token))
tokenizer.add_special_tokens([unk_token] + control_tokens)
# Use BERT-specific normalizer, pre-tokenizer and **WordPiece** decoder.
tokenizer.normalizer = BertNormalizer(strip_accents=False)
tokenizer.pre_tokenizer = BertPreTokenizer()
tokenizer.decoder = decoders.WordPiece(prefix='##')
with open(input_file, 'r', encoding='utf-8') as src, \
open(output_file, 'w', encoding='utf-8') as dst:
# Count total lines in corpus.
total_lines = 0
for _ in src:
total_lines += 1
# Move the corpus file to first.
src.seek(0)
buffer = []
for line in tqdm.tqdm(src,
desc='[*] tokenize corpus',
total=total_lines):
buffer.append(line)
# Tokenize buffered sentences and write to `output_file`.
if len(buffer) > 10000:
for t in tokenizer.encode_batch(buffer):
dst.write(' '.join(t.tokens) + '\n')
buffer.clear()
# Process the remained buffer.
if buffer:
for t in tokenizer.encode_batch(buffer):
dst.write(' '.join(t.tokens) + '\n')
def __init__(self, pretokenizer='moses'):
self.tagger = PerceptronTagger()
self.pretok_type = pretokenizer
if pretokenizer == 'bertpretokenizer':
self.pretokenizer = BertPreTokenizer()
elif pretokenizer == 'moses':
self.pretokenizer = MosesTokenizer()
self.detokenizer = MosesDetokenizer()
elif pretokenizer == 'whitespace':
pass
else:
raise ValueError(
"pretokenizer must be 'bertpretokenizer', 'moses', or 'whitespace'."
)
def __init__(
self,
vocab_file: Optional[str] = None,
merges_file: Optional[str] = None,
unk_token: Optional[str] = "<unk>",
suffix: Optional[str] = "</w>",
dropout: Optional[float] = None,
unicode_normalizer: Optional[str] = None,
):
if vocab_file is not None and merges_file is not None:
tokenizer = Tokenizer(
BPE.from_files(vocab_file,
merges_file,
dropout=dropout,
unk_token=unk_token,
end_of_word_suffix=suffix))
else:
tokenizer = Tokenizer(BPE.empty())
# Check for Unicode normalization first (before everything else)
normalizers = []
if unicode_normalizer:
normalizers += [unicode_normalizer_from_str(unicode_normalizer)]
# OpenAI normalization is the same as Bert
normalizers += [BertNormalizer()]
# Create the normalizer structure
if len(normalizers) > 0:
if len(normalizers) > 1:
tokenizer.normalizer = Sequence(normalizers)
else:
tokenizer.normalizer = normalizers[0]
tokenizer.pre_tokenizer = BertPreTokenizer()
tokenizer.decoder = BPEDecoder(suffix=suffix)
parameters = {
"model": "BPE",
"unk_token": unk_token,
"suffix": suffix,
"dropout": dropout,
}
super().__init__(tokenizer, parameters)
def build(self, afm: AuxiliaryFileManager, corpus: AuxiliaryFile,
vocab: AuxiliaryFile) -> AuxiliaryFile:
total_lines = self._total_lines_in_file(corpus)
# Create WordPiece model and add special tokens. Note that `unk_token`
# is also a special token.
tokenizer = Tokenizer(WordPiece(vocab.name, unk_token=self.unk_token))
tokenizer.add_special_tokens(self.special_tokens + [self.unk_token])
# Use BERT-specific normalizer, pre-tokenizer and decoder.
tokenizer.normalizer = BertNormalizer(strip_accents=False)
tokenizer.pre_tokenizer = BertPreTokenizer()
tokenizer.decoder = WordPieceDecoder(prefix='##')
tokenized = afm.create()
with corpus.open('r') as src, tokenized.open('w') as dst:
# Create tqdm progress bar with colorful description.
tqdm_iter = tqdm.tqdm(src,
desc=colorful.render(
'<r>[*]</r> tokenize sentences with '
'<g>WordPiece</g> model'),
total=total_lines)
batch_lines = []
for line in tqdm_iter:
batch_lines.append(line)
# Encode the grouped batch sentences and write the tokenized
# sentences to the auxiliary output file.
if len(batch_lines) > self.batch_size:
for t in tokenizer.encode_batch(batch_lines):
dst.write(' '.join(t.tokens) + '\n')
batch_lines.clear()
# Encode the remainders and write to the output file.
if batch_lines:
for t in tokenizer.encode_batch(batch_lines):
dst.write(' '.join(t.tokens) + '\n')
return tokenized
def __init__(
self,
vocab: Optional[Union[str, Dict[str, int]]] = None,
unk_token: Union[str, AddedToken] = "[UNK]",
sep_token: Union[str, AddedToken] = "[SEP]",
cls_token: Union[str, AddedToken] = "[CLS]",
pad_token: Union[str, AddedToken] = "[PAD]",
mask_token: Union[str, AddedToken] = "[MASK]",
clean_text: bool = True,
handle_chinese_chars: bool = True,
strip_accents: Optional[bool] = None,
lowercase: bool = True,
wordpieces_prefix: str = "##",
):
if vocab is not None:
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(unk_token)))
else:
tokenizer = Tokenizer(WordPiece(unk_token=str(unk_token)))
# Let the tokenizer know about special tokens if they are part of the vocab
if tokenizer.token_to_id(str(unk_token)) is not None:
tokenizer.add_special_tokens([str(unk_token)])
if tokenizer.token_to_id(str(sep_token)) is not None:
tokenizer.add_special_tokens([str(sep_token)])
if tokenizer.token_to_id(str(cls_token)) is not None:
tokenizer.add_special_tokens([str(cls_token)])
if tokenizer.token_to_id(str(pad_token)) is not None:
tokenizer.add_special_tokens([str(pad_token)])
if tokenizer.token_to_id(str(mask_token)) is not None:
tokenizer.add_special_tokens([str(mask_token)])
tokenizer.normalizer = BertNormalizer(
clean_text=clean_text,
handle_chinese_chars=handle_chinese_chars,
strip_accents=strip_accents,
lowercase=lowercase,
)
tokenizer.pre_tokenizer = BertPreTokenizer()
if vocab is not None:
sep_token_id = tokenizer.token_to_id(str(sep_token))
if sep_token_id is None:
raise TypeError("sep_token not found in the vocabulary")
cls_token_id = tokenizer.token_to_id(str(cls_token))
if cls_token_id is None:
raise TypeError("cls_token not found in the vocabulary")
tokenizer.post_processor = BertProcessing(
(str(sep_token), sep_token_id), (str(cls_token), cls_token_id))
tokenizer.decoder = decoders.WordPiece(prefix=wordpieces_prefix)
parameters = {
"model": "BertWordPiece",
"unk_token": unk_token,
"sep_token": sep_token,
"cls_token": cls_token,
"pad_token": pad_token,
"mask_token": mask_token,
"clean_text": clean_text,
"handle_chinese_chars": handle_chinese_chars,
"strip_accents": strip_accents,
"lowercase": lowercase,
"wordpieces_prefix": wordpieces_prefix,
}
super().__init__(tokenizer, parameters)
def __getstate__(self):
state = self.__dict__.copy()
state["_tokenizer"].pre_tokenizer = BertPreTokenizer()
return state
def test_instantiate(self):
assert BertPreTokenizer() is not None
assert isinstance(BertPreTokenizer(), PreTokenizer)
def train_tokenizer(
input_file: str,
vocab_file: str,
temporary: str,
subset_size: int = 512000000,
vocab_size: int = 8000,
limit_alphabet: int = 6000,
unk_token: str = '<unk>',
control_tokens: List[str] = []):
r"""Train **WordPiece** tokenizer and save trained subword vocabulary.
Note:
Since tokenizers_ reads whole file data in training, this function
could occur memory errors if `input_file` is too large. Under the
assumption that `input_file` is shuffled randomly, the subset of input
corpus will be used in training.
Caution:
The subset of input corpus is saved in `temporary` directory. Please be
careful not to delete the file while executing this function.
Arguments:
input_file (str): Input file path.
vocab_file (str): Output vocabulary file path.
temporary (str): Temporary directory where the subset of corpus would
be saved.
subset_size (int): The maximum number of lines in the subset.
vocab_size (int): The number of subwords in the vocabulary.
limit_alphabet (int): The maximum number of alphabets in vocabulary.
unk_tokens (str): Unknown token in the vocabulary.
control_tokens (list): Control tokens in the vocabulary.
.. _tokenizers: https://github.com/huggingface/tokenizers
"""
# Create **WordPiece** model and add normalizer and pre-tokenizer.
# BERT-specific normalizer and pre-tokenizer are used.
tokenizer = Tokenizer(models.WordPiece())
tokenizer.normalizer = BertNormalizer(strip_accents=False)
tokenizer.pre_tokenizer = BertPreTokenizer()
# Split the head of input corpus file and save in `temporary` directory.
subset_file = random_filename(temporary)
_split_subset_from_file(input_file, subset_file, subset_size)
# Train the model with splitted subset of corpus.
trainer = WordPieceTrainer(vocab_size=vocab_size,
min_frequency=2,
show_progress=True,
limit_alphabet=limit_alphabet,
special_tokens=[unk_token] + control_tokens,
continuing_subword_prefix='##')
tokenizer.train(trainer, [subset_file])
# Save trained subword vocabulary in `temporary` directory and rename to
# `vocab_file`.
tokenizer.model.save(temporary)
os.rename(os.path.join(temporary, 'vocab.txt'), vocab_file)
# Remove temporary subset corpus.
os.remove(subset_file)
def preprocess_data(args):
label_counter = Counter([])
examples_per_file = Counter()
print("Reading all files for labels.")
for input_file in args.input_files:
with xopen(input_file, "rt") as f:
for example, labels in input_readers[args.task](f):
examples_per_file[input_file] += 1
label_counter.update(labels)
if args.top_n_labels > 0:
mlb_full = MultiLabelBinarizer(sparse_output=True)
mlb_full = mlb_full.fit(label_counter.keys())
label_counter = dict(label_counter.most_common(args.top_n_labels))
mlb = MultiLabelBinarizer(sparse_output=True)
# Passing a list in a list because that's what the function wants.
if args.labels_in:
labels = json.load(open(args.labels_in))
mlb = mlb.fit([labels])
else:
mlb = mlb.fit([[pair for pair in label_counter]])
# Save list of partial -> full mapping if doing top N labels.
if args.top_n_labels > 0:
label_mapping = np.where(np.in1d(mlb_full.classes_,
mlb.classes_))[0].tolist()
with xopen(args.label_mapping, "wt") as f:
f.write(json.dumps(label_mapping))
# Also save the full labels.
with xopen(args.full_labels, "wt") as f:
f.write(json.dumps(list(mlb_full.classes_)))
# Save list of labels.
with xopen(args.labels_out, "wt") as f:
f.write(json.dumps(list(mlb.classes_)))
# Set parallel tokenization thread count.
os.environ["RAYON_NUM_THREADS"] = str(args.processes)
from tokenizers import Tokenizer, decoders, trainers
from tokenizers.models import WordPiece
from tokenizers.normalizers import BertNormalizer
from tokenizers.pre_tokenizers import BertPreTokenizer
from tokenizers.processors import BertProcessing
if args.task == 'cafa':
# Define our custom tokenizer.
# It is exactly the same as the default BERT tokenizer, except for max_input_chars_per_word
# being 20000 instead of 100. This tokenizer is very slow on the long protein sequences.
tokenizer = WordPiece.from_files(args.vocab,
unk_token="[UNK]",
max_input_chars_per_word=20000)
tokenizer = Tokenizer(tokenizer)
tokenizer.add_special_tokens(["[UNK]", "[SEP]", "[CLS]"])
tokenizer.normalizer = BertNormalizer(lowercase=args.do_lower_case)
tokenizer.pre_tokenizer = BertPreTokenizer()
tokenizer.post_processor = BertProcessing(
("[SEP]", tokenizer.token_to_id("[SEP]")),
("[CLS]", tokenizer.token_to_id("[CLS]")))
tokenizer.decoder = decoders.WordPiece(prefix='##')
else:
tokenizer = BertWordPieceTokenizer(args.vocab,
lowercase=args.do_lower_case)
tokenizer.enable_padding(max_length=args.seq_len)
tokenizer.enable_truncation(max_length=args.seq_len)
for input_file in args.input_files:
with xopen(input_file, 'rt') as in_f:
file_name = generate_out_filename(input_file, args)
with xopen(file_name, "wt") as out_f:
print("Processing to: ", file_name)
# Write the shape as the first row, useful for the finetuning.
if args.labels_in:
n_labels = len(json.load(open(args.labels_in)))
else:
n_labels = len(label_counter)
out_f.write(
json.dumps((examples_per_file[input_file], n_labels)) +
'\n')
batch_size = min(examples_per_file[input_file],
args.processes * 100)
example_batch = []
labels_batch = []
doc_idx_batch = []
with ParallelGenerator(input_readers[args.task](in_f),
max_lookahead=batch_size) as g:
START_POS = int(args.window_start) / 100
for doc_idx, (example, labels) in enumerate(g):
#example = ' '.join(example.split(' ')[-510:])
example_batch.append(example)
labels_batch.append(labels)
doc_idx_batch.append(doc_idx)
if len(example_batch) == batch_size:
example_batch = tokenizer.encode_batch(
example_batch)
labels_batch = mlb.transform(labels_batch)
for example, labels, doc_idx in zip(
example_batch, labels_batch,
doc_idx_batch):
# Convert sparse arrays to python lists for json dumping.
# print(labels);input()
labels = labels.nonzero()[1].tolist()
"""try:
[][0]
print("DOC_LEN:",len(example.overflowing)+1)
mid = len(example.overflowing)//2
out_f.write(json.dumps( [example.overflowing[mid].ids, labels, len(example.overflowing)+1] ) + '\n')
except IndexError:
out_f.write(json.dumps( [example.ids, labels, len(example.overflowing)+1] ) + '\n')"""
if args.all_blocks or args.n_blocks > 0:
blocks = [example.ids] + [
blk.ids for blk in example.overflowing
]
#print("BLOCKS:%d,TOKENS:%d" % (len(list(blocks)), sum([len(list(tokens)) for tokens in blocks])))
for b, block in enumerate(blocks, 2):
if b > args.n_blocks and args.n_blocks > 0:
break
out_f.write(
json.dumps(
[block, labels, doc_idx]) +
'\n')
else:
window = get_window(example, START_POS)
assert len(window) == 512
assert all(
[type(y) is int for y in window])
out_f.write(
json.dumps([window, labels]) + '\n')
example_batch = []
labels_batch = []
# Write out whatever is left in the last smaller batch.
example_batch = tokenizer.encode_batch(example_batch)
labels_batch = mlb.transform(labels_batch)
for example, labels, doc_idx in zip(
example_batch, labels_batch, doc_idx_batch):
# Convert sparse arrays to python lists for json dumping.
# print(labels);input()
labels = labels.nonzero()[1].tolist()
"""try:
[][0]
print("DOC_LEN:",len(example.overflowing)+1)
mid = len(example.overflowing)//2
out_f.write(json.dumps( [example.overflowing[mid].ids, labels, len(example.overflowing)+1] ) + '\n')
except IndexError:
out_f.write(json.dumps( [example.ids, labels, len(example.overflowing)+1] ) + '\n')"""
if args.all_blocks or args.n_blocks > 0:
blocks = [example.ids] + [
blk.ids for blk in example.overflowing
]
#print("BLOCKS:%d,TOKENS:%d" % (len(list(blocks)), sum([len(list(tokens)) for tokens in blocks])))
for b, block in enumerate(blocks, 2):
if b > args.n_blocks and args.n_blocks > 0:
break
out_f.write(
json.dumps([block, labels, doc_idx]) +
'\n')
else:
out_f.write(
json.dumps(
[get_window(example, START_POS), labels]) +
'\n')
def test_instantiate(self):
assert BertPreTokenizer() is not None
assert isinstance(BertPreTokenizer(), PreTokenizer)
assert isinstance(BertPreTokenizer(), BertPreTokenizer)
assert isinstance(pickle.loads(pickle.dumps(BertPreTokenizer())), BertPreTokenizer)
def preprocess_data(args):
label_counter = Counter([])
examples_per_file = Counter()
print("Reading all files for labels.")
for input_file in args.input_files:
with xopen(input_file, "rt") as f:
for example, labels in input_readers[args.task](f):
examples_per_file[input_file] += 1
label_counter.update(labels)
if args.top_n_labels > 0:
mlb_full = MultiLabelBinarizer(sparse_output=True)
mlb_full = mlb_full.fit(label_counter.keys())
label_counter = dict(label_counter.most_common(args.top_n_labels))
mlb = MultiLabelBinarizer(sparse_output=True)
# Passing a list in a list because that's what the function wants.
mlb = mlb.fit([[pair for pair in label_counter]])
# Save list of partial -> full mapping if doing top N labels.
if args.top_n_labels > 0:
label_mapping = np.where(np.in1d(mlb_full.classes_,
mlb.classes_))[0].tolist()
with xopen(args.label_mapping, "wt") as f:
f.write(json.dumps(label_mapping))
# Also save the full labels.
with xopen(args.full_labels, "wt") as f:
f.write(json.dumps(list(mlb_full.classes_)))
# Save list of labels.
with xopen(args.labels_out, "wt") as f:
f.write(json.dumps(list(mlb.classes_)))
# Set parallel tokenization thread count.
os.environ["RAYON_NUM_THREADS"] = str(args.processes)
from tokenizers import Tokenizer, decoders, trainers
from tokenizers.models import WordPiece
from tokenizers.normalizers import BertNormalizer
from tokenizers.pre_tokenizers import BertPreTokenizer
from tokenizers.processors import BertProcessing
if args.task == 'cafa':
# Define our custom tokenizer.
# It is exactly the same as the default BERT tokenizer, except for max_input_chars_per_word
# being 20000 instead of 100. This tokenizer is very slow on the long protein sequences.
tokenizer = WordPiece.from_files(args.vocab,
unk_token="[UNK]",
max_input_chars_per_word=20000)
tokenizer = Tokenizer(tokenizer)
tokenizer.add_special_tokens(["[UNK]", "[SEP]", "[CLS]"])
tokenizer.normalizer = BertNormalizer(lowercase=args.do_lower_case)
tokenizer.pre_tokenizer = BertPreTokenizer()
tokenizer.post_processor = BertProcessing(
("[SEP]", tokenizer.token_to_id("[SEP]")),
("[CLS]", tokenizer.token_to_id("[CLS]")))
tokenizer.decoder = decoders.WordPiece(prefix='##')
else:
tokenizer = BertWordPieceTokenizer(args.vocab,
lowercase=args.do_lower_case)
tokenizer.enable_padding(max_length=args.seq_len)
tokenizer.enable_truncation(max_length=args.seq_len)
for input_file in args.input_files:
with xopen(input_file, 'rt') as in_f:
file_name = generate_out_filename(input_file, args)
with xopen(file_name, "wt") as out_f:
print("Processing to: ", file_name)
# Write the shape as the first row, useful for the finetuning.
out_f.write(
json.dumps((examples_per_file[input_file],
len(label_counter))) + '\n')
batch_size = min(examples_per_file[input_file],
args.processes * 100)
example_batch = []
labels_batch = []
with ParallelGenerator(input_readers[args.task](in_f),
max_lookahead=batch_size) as g:
for example, labels in g:
example_batch.append(example)
labels_batch.append(labels)
if len(example_batch) == batch_size:
example_batch = tokenizer.encode_batch(
example_batch)
labels_batch = mlb.transform(labels_batch)
for example, labels in zip(example_batch,
labels_batch):
# Convert sparse arrays to python lists for json dumping.
# print(labels);input()
labels = labels.nonzero()[1].tolist()
out_f.write(
json.dumps([example.ids, labels]) + '\n')
example_batch = []
labels_batch = []
# Write out whatever is left in the last smaller batch.
example_batch = tokenizer.encode_batch(example_batch)
labels_batch = mlb.transform(labels_batch)
for example, labels in zip(example_batch, labels_batch):
# Convert sparse arrays to python lists for json dumping.
# print(labels);input()
labels = labels.nonzero()[1].tolist()
out_f.write(json.dumps([example.ids, labels]) + '\n')
def __init__(self,
vocab_file: Optional[str] = None,
unk_token: Union[str, AddedToken] = "<unk>",
sep_token: Union[str, AddedToken] = "</s>",
cls_token: Union[str, AddedToken] = "<s>",
nl_token: Union[str, AddedToken] = "<nl>",
pad_token: Union[str, AddedToken] = "<pad>",
mask_token: Union[str, AddedToken] = "<mask>",
clean_text: bool = True,
handle_chinese_chars: bool = True,
separate_numbers: bool = True,
strip_accents: bool = True,
lowercase: bool = True,
wordpieces_prefix: str = "##",
special_chars: str = SPECIAL_CHARS,
zh_norm: bool = True,
handle_simpl: bool = True,
do_postprocess: bool = False):
if vocab_file is not None:
tokenizer = Tokenizer(
WordPiece(vocab_file, unk_token=str(unk_token)))
else:
tokenizer = Tokenizer(WordPiece())
# Let the tokenizer know about special tokens if they are part of the vocab
if tokenizer.token_to_id(str(unk_token)) is not None:
tokenizer.add_special_tokens([str(unk_token)])
if tokenizer.token_to_id(str(sep_token)) is not None:
tokenizer.add_special_tokens([str(sep_token)])
if tokenizer.token_to_id(str(cls_token)) is not None:
tokenizer.add_special_tokens([str(cls_token)])
if tokenizer.token_to_id(str(pad_token)) is not None:
tokenizer.add_special_tokens([str(pad_token)])
if tokenizer.token_to_id(str(nl_token)) is not None:
tokenizer.add_special_tokens([str(nl_token)])
if tokenizer.token_to_id(str(mask_token)) is not None:
tokenizer.add_special_tokens([str(mask_token)])
if tokenizer.token_to_id(str(mask_token)) is not None:
tokenizer.add_special_tokens([str(mask_token)])
tokenizer.normalizer = Sequence([
NFKC(),
BertNormalizer(clean_text=clean_text,
handle_chinese_chars=handle_chinese_chars,
separate_numbers=separate_numbers,
strip_accents=strip_accents,
lowercase=lowercase,
special_chars=special_chars,
zh_norm=zh_norm,
handle_simpl=handle_simpl)
])
tokenizer.pre_tokenizer = BertPreTokenizer()
if vocab_file is not None and do_postprocess:
sep_token_id = tokenizer.token_to_id(str(sep_token))
if sep_token_id is None:
raise TypeError("sep_token not found in the vocabulary")
cls_token_id = tokenizer.token_to_id(str(cls_token))
if cls_token_id is None:
raise TypeError("cls_token not found in the vocabulary")
tokenizer.post_processor = BertProcessing(
(str(sep_token), sep_token_id), (str(cls_token), cls_token_id))
tokenizer.decoder = decoders.WordPiece(prefix=wordpieces_prefix)
parameters = {
"model": "BertWordPiece",
"unk_token": unk_token,
"sep_token": sep_token,
"cls_token": cls_token,
"nl_token": nl_token,
"pad_token": pad_token,
"mask_token": mask_token,
"clean_text": clean_text,
"handle_chinese_chars": handle_chinese_chars,
"separate_numbers": separate_numbers,
"strip_accents": strip_accents,
"lowercase": lowercase,
"special_chars": special_chars,
"zh_norm": zh_norm,
"handle_simpl": handle_simpl,
"wordpieces_prefix": wordpieces_prefix,
}
super().__init__(tokenizer, parameters)
