def __init__(
self,
vocab_file,
delimiter,
lowercase,
unk_token,
eos_token,
add_eos=False,
add_double_eos=False,
normalization: Optional[str] = None,
):
try:
tokenizer = WordLevel(vocab_file, unk_token=unk_token)
tokenizer = Tokenizer(tokenizer)
except Exception:
raise ValueError(
"Unable to parse file {}. Unknown format. "
"If you tried to load a model saved through TransfoXLTokenizer,"
"please note they are not compatible.".format(vocab_file))
# Create the correct normalization path
normalizer = []
# Include unicode normalization
if normalization:
normalizer += [unicode_normalizer_from_str(normalization)]
# Include case normalization
if lowercase:
normalizer += [Lowercase()]
# Strip normalizer at the end
normalizer += [Strip(left=True, right=True)]
if len(normalizer) > 0:
tokenizer.normalizer = Sequence(
normalizer) if len(normalizer) > 1 else normalizer[0]
# Setup the splitter
tokenizer.pre_tokenizer = CharDelimiterSplit(
delimiter) if delimiter else WhitespaceSplit()
if add_double_eos:
tokenizer.post_processor = BertProcessing(
(eos_token, tokenizer.token_to_id(eos_token)),
(eos_token, tokenizer.token_to_id(eos_token)))
parameters = {
"model": "TransfoXLModel",
"add_eos": add_eos,
"add_double_eos": add_double_eos,
"unk_token": unk_token,
"eos_token": eos_token,
"delimiter": delimiter,
"lowercase": lowercase,
}
super().__init__(tokenizer, parameters)
def __create_tokenizer(self, files):
# Create, train and save the tokenizer.
print("Preparing tokenizer...")
tokenizer = Tokenizer(WordLevel(unk_token="[UNK]"))
tokenizer.pre_tokenizer = WhitespaceSplit()
trainer = WordLevelTrainer(
special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
tokenizer.train(files=files, trainer=trainer)
return tokenizer
def __init__(
self,
vocab_file,
delimiter,
lowercase,
unk_token,
eos_token,
add_eos=False,
add_double_eos=False,
normalization: Optional[str] = None,
):
tokenizer = WordLevel.from_files(vocab_file, unk_token=unk_token)
tokenizer = Tokenizer(tokenizer)
# Create the correct normalization path
normalizer = []
# Include unicode normalization
if normalization:
normalizer += [unicode_normalizer_from_str(normalization)]
# Include case normalization
if lowercase:
normalizer += [Lowercase()]
if len(normalizer) > 0:
tokenizer.normalizer = Sequence(
normalizer) if len(normalizer) > 1 else normalizer[0]
# Setup the splitter
tokenizer.pre_tokenizer = CharDelimiterSplit(
delimiter) if delimiter else WhitespaceSplit()
if add_double_eos:
tokenizer.post_processor = BertProcessing(
(eos_token, tokenizer.token_to_id(eos_token)),
(eos_token, tokenizer.token_to_id(eos_token)))
parameters = {
"model": "TransfoXLModel",
"add_eos": add_eos,
"add_double_eos": add_double_eos,
"unk_token": unk_token,
"eos_token": eos_token,
"delimiter": delimiter,
"lowercase": lowercase,
}
super().__init__(tokenizer, parameters)
def wordpiece_tokenize(line):
tokenizer = Tokenizer(WordPiece(wordpiece_dict3))
tokenizer.enable_padding(length=200)
tokenizer.enable_truncation(max_length=200)
tokenizer.pre_tokenizer = WhitespaceSplit()
tokenizer.post_processor = TemplateProcessing(
single="[CLS] $A [SEP]",
pair="[CLS] $A [SEP] $B:1 [SEP]:1",
special_tokens=[
("[CLS]", 1),
("[SEP]", 2),
],
)
output = tokenizer.encode(line)
return (output.ids)
def configure_tokenizers(self, padding, truncation, max_length, lower):
# Settings
pad_length = None
if padding in {True, "longest"}:
pass
elif padding in {"max_length"}:
pad_length = max_length
elif padding in {False, "do_not_pad"}:
pass
else:
raise ValueError("Unknown padding type")
# SRC tokenizer
tok_normalizers = [NFD(), Strip()]
if lower:
tok_normalizers += [Lowercase()]
self.tokenizer = Tokenizer(tok_model()) # unk_token=... not working
self.tokenizer.add_special_tokens(self.special_tokens)
self.tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
[WhitespaceSplit()])
self.tokenizer.normalizer = normalizers.Sequence(
tok_normalizers) # StripAccents requires NFD
self.tokenizer.decoder = tok_decoder()
# Define template (Needed for the sos/eos tokens)
basic_template = TemplateProcessing(
single=f"{self.SOS_WORD} $A {self.EOS_WORD}",
pair=
f"{self.SOS_WORD} $A {self.EOS_WORD} {self.SOS_WORD} $B {self.EOS_WORD}",
special_tokens=[
(self.SOS_WORD, self.tokenizer.token_to_id(self.SOS_WORD)),
(self.EOS_WORD, self.tokenizer.token_to_id(self.EOS_WORD))
],
)
self.tokenizer.post_processor = basic_template
if padding:
self.tokenizer.enable_padding(pad_id=self.tokenizer.token_to_id(
self.PAD_WORD),
pad_token=self.PAD_WORD,
length=pad_length)
if truncation:
self.tokenizer.enable_truncation(max_length,
stride=0,
strategy='longest_first')
def test_bert_like(self):
pre_tokenizer = Sequence([WhitespaceSplit(), Punctuation()])
assert isinstance(Sequence([]), PreTokenizer)
assert isinstance(Sequence([]), Sequence)
assert isinstance(pickle.loads(pickle.dumps(pre_tokenizer)), Sequence)
result = pre_tokenizer.pre_tokenize_str("Hey friend! How are you?!?")
assert result == [
("Hey", (0, 3)),
("friend", (4, 10)),
("!", (10, 11)),
("How", (16, 19)),
("are", (20, 23)),
("you", (24, 27)),
("?", (27, 28)),
("!", (28, 29)),
("?", (29, 30)),
]
def converted(self):
tokenizer = self.tokenizer(self.proto)
# Tokenizer assemble
tokenizer.normalizer = self.normalizer(self.proto)
replacement = "▁"
add_prefix_space = True
tokenizer.pre_tokenizer = PSequence([
WhitespaceSplit(),
Metaspace(replacement=replacement,
add_prefix_space=add_prefix_space),
])
tokenizer.decoder = decoders.Metaspace(
replacement=replacement, add_prefix_space=add_prefix_space)
post_processor = self.post_processor(tokenizer)
if post_processor:
tokenizer.post_processor = post_processor
# TODO what parameters should we give ?
parameters = {}
return BaseTokenizer(tokenizer, parameters)
def test_instantiate(self):
assert WhitespaceSplit() is not None
assert isinstance(WhitespaceSplit(), PreTokenizer)
assert isinstance(WhitespaceSplit(), WhitespaceSplit)
assert isinstance(pickle.loads(pickle.dumps(WhitespaceSplit())), WhitespaceSplit)
def test_all_tokenizer_on_special_cases(caplog):
caplog.set_level(logging.CRITICAL)
lang_names = ["bert-base-cased", "roberta-base", "xlnet-base-cased"]
tokenizers = []
for lang_name in lang_names:
if "roberta" in lang_name:
add_prefix_space = True
else:
add_prefix_space = False
t = Tokenizer.load(lang_name,
lower_case=False,
add_prefix_space=add_prefix_space)
tokenizers.append(t)
texts = [
"This is a sentence",
"Der entscheidende Pass",
"力加勝北区ᴵᴺᵀᵃছজটডণত",
"Thiso text is included tolod makelio sure Unicodeel is handled properly:",
"This is a sentence...",
"Let's see all on this text and. !23# neverseenwordspossible"
"This is a sentence with multiple spaces",
"""This is a sentence.
With linebreak""",
"""Sentence with multiple
newlines
""",
"and another one\n\n\nwithout space",
"This is a sentence with multiple tabs",
]
expected_to_fail = [(1, 1), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (2, 1),
(2, 5)]
for i_tok, tokenizer in enumerate(tokenizers):
for i_text, text in enumerate(texts):
# Important: we don't assume to preserve whitespaces after tokenization.
# This means: \t, \n " " etc will all resolve to a single " ".
# This doesn't make a difference for BERT + XLNet but it does for roBERTa
test_passed = True
# 1. original tokenize function from transformer repo on full sentence
standardized_whitespace_text = ' '.join(
text.split()) # remove multiple whitespaces
tokenized = tokenizer.tokenize(standardized_whitespace_text)
# 2. Our tokenization method using a pretokenizer which can normalize multiple white spaces
# This approach is used in NER
pre_tokenizer = WhitespaceSplit()
words_and_spans = pre_tokenizer.pre_tokenize_str(text)
words = [x[0] for x in words_and_spans]
word_spans = [x[1] for x in words_and_spans]
encoded = tokenizer.encode_plus(
words, is_split_into_words=True,
add_special_tokens=False).encodings[0]
# verify that tokenization on full sequence is the same as the one on "whitespace tokenized words"
if encoded.tokens != tokenized:
test_passed = False
# token offsets are originally relative to the beginning of the word
# These lines convert them so they are relative to the beginning of the sentence
token_offsets = []
for (start, end), w_index, in zip(encoded.offsets, encoded.words):
word_start_ch = word_spans[w_index][0]
token_offsets.append(
(start + word_start_ch, end + word_start_ch))
if getattr(tokenizer, "add_prefix_space", None):
token_offsets = [(start - 1, end)
for start, end in token_offsets]
# verify that offsets align back to original text
if text == "力加勝北区ᴵᴺᵀᵃছজটডণত":
# contains [UNK] that are impossible to match back to original text space
continue
for tok, (start, end) in zip(encoded.tokens, token_offsets):
#subword-tokens have special chars depending on model type. In order to align with original text we need to get rid of them
tok = re.sub(r"^(##|Ġ|▁)", "", tok)
#tok = tokenizer.decode(tokenizer.convert_tokens_to_ids(tok))
original_tok = text[start:end]
if tok != original_tok:
test_passed = False
if (i_tok, i_text) in expected_to_fail:
assert not test_passed, f"Behaviour of {tokenizer.__class__.__name__} has changed on text {text}'"
else:
assert test_passed, f"Behaviour of {tokenizer.__class__.__name__} has changed on text {text}'"
def test_instantiate(self):
assert WhitespaceSplit() is not None
assert isinstance(WhitespaceSplit(), PreTokenizer)
from itertools import product
from tokenizers import Tokenizer, Regex
from tokenizers.models import WordLevel
from tokenizers.normalizers import NFD
from tokenizers.trainers import WordLevelTrainer
from tokenizers.pre_tokenizers import Split, WhitespaceSplit
## Loop which creates and loads the tokenizer
def stackTraceTokenizer(tokens:tuple, events:tuple, vocab_size=2_000, min_freq=3):
for norm, event in product(tokens, events):
print(norm, event)
tokenizer = Tokenizer(WordLevel(unk_token="[UNK]"))
tokenizer.normalizer = NFD()
if norm == 'white':
tokenizer.pre_tokenizer = WhitespaceSplit()
else:
tokenizer.pre_tokenizer = Split(pattern=Regex("[A-Z]+[a-z0-9]+|[.A-Z]+|[a-z0-9]+"),
behavior='isolated')
trainer = WordLevelTrainer(vocab_size=vocab_size, min_frequency=min_freq,
special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
tokenizer.train(["vocab-{}.txt".format(event)], trainer)
print(f"Trained tokenizer for {norm}:{event}")
yield tokenizer, event, norm
def dataset_from_dicts(self, dicts, indices=None, return_baskets=False, non_initial_token="X"):
self.baskets = []
self.pre_tokenizer = WhitespaceSplit()
texts = [x["text"] for x in dicts]
words_and_spans = [self.pre_tokenizer.pre_tokenize_str(x) for x in texts]
words = [[x[0] for x in y] for y in words_and_spans]
word_spans_batch = [[x[1] for x in y] for y in words_and_spans]
tokenized_batch = self.tokenizer.batch_encode_plus(
words,
return_offsets_mapping=True,
return_special_tokens_mask=True,
return_token_type_ids=True,
return_attention_mask=True,
truncation=True,
max_length=self.max_seq_len,
padding="max_length",
is_split_into_words=True,
)
for i in range(len(dicts)):
tokenized = tokenized_batch[i]
d = dicts[i]
id_external = self._id_from_dict(d)
if indices:
id_internal = indices[i]
else:
id_internal = i
input_ids = tokenized.ids
segment_ids = tokenized.type_ids
initial_mask = self._get_start_of_word(tokenized.words)
assert len(initial_mask) == len(input_ids)
padding_mask = tokenized.attention_mask
if return_baskets:
token_to_word_map = tokenized.words
word_spans = word_spans_batch[i]
tokenized_dict = {
"tokens": tokenized.tokens,
"word_spans": word_spans,
"token_to_word_map": token_to_word_map,
"start_of_word": initial_mask
}
else:
tokenized_dict = {}
feature_dict = {
"input_ids": input_ids,
"padding_mask": padding_mask,
"segment_ids": segment_ids,
"initial_mask": initial_mask,
}
for task_name, task in self.tasks.items():
try:
label_name = task["label_name"]
labels_word = d[label_name]
label_list = task["label_list"]
label_tensor_name = task["label_tensor_name"]
if task["task_type"] == "classification":
label_ids = [label_list.index(labels_word)]
elif task["task_type"] == "ner":
labels_token = expand_labels(labels_word, initial_mask, non_initial_token)
label_ids = [label_list.index(lt) for lt in labels_token]
except ValueError:
label_ids = None
problematic_labels = set(labels_token).difference(set(label_list))
print(f"[Task: {task_name}] Could not convert labels to ids via label_list!"
f"\nWe found a problem with labels {str(problematic_labels)}")
except KeyError:
label_ids = None
# print(f"[Task: {task_name}] Could not convert labels to ids via label_list!"
# "\nIf your are running in *inference* mode: Don't worry!"
# "\nIf you are running in *training* mode: Verify you are supplying a proper label list to your processor and check that labels in input data are correct.")
if label_ids:
feature_dict[label_tensor_name] = label_ids
curr_sample = Sample(id=None,
clear_text=d,
tokenized=tokenized_dict,
features=[feature_dict])
curr_basket = SampleBasket(id_internal=id_internal,
raw=d,
id_external=id_external,
samples=[curr_sample])
self.baskets.append(curr_basket)
if indices and 0 not in indices:
pass
else:
self._log_samples(1)
dataset, tensor_names = self._create_dataset()
ret = [dataset, tensor_names, self.problematic_sample_ids]
if return_baskets:
ret.append(self.baskets)
return tuple(ret)
class MTLProcessor(Processor):
def __init__(
self,
tokenizer,
max_seq_len,
data_dir,
train_filename,
test_filename,
delimiter,
dev_split=0.0,
dev_filename=None,
label_list=None,
metric=None,
proxies=None,
**kwargs
):
self.delimiter = delimiter
super(MTLProcessor, self).__init__(
tokenizer=tokenizer,
max_seq_len=max_seq_len,
train_filename=train_filename,
dev_filename=dev_filename,
test_filename=test_filename,
dev_split=dev_split,
data_dir=data_dir,
tasks={},
proxies=proxies
)
def file_to_dicts(self, file: str) -> [dict]:
dicts = list()
df = pd.read_csv(file)
for text, label, tokens in zip(df.sentence.values, df.label.values, df.trigger.values):
columns = dict()
text = ast.literal_eval(text)
tokens = ast.literal_eval(tokens)
columns["text"] = " ".join(text)
columns["document_level_task_label"] = label # Key hard-coded
columns["token_level_task_label"] = list(map(str, tokens)) # Key hard-coded
dicts.append(columns)
return dicts
@staticmethod
def _get_start_of_word(word_ids):
words = np.array(word_ids)
words[words == None] = -1
start_of_word_single = [0] + list(np.ediff1d(words) > 0)
start_of_word_single = [int(x) for x in start_of_word_single]
return start_of_word_single
# Most of the code is copied from NERProcessor - dataset_from_dicts()
def dataset_from_dicts(self, dicts, indices=None, return_baskets=False, non_initial_token="X"):
self.baskets = []
self.pre_tokenizer = WhitespaceSplit()
texts = [x["text"] for x in dicts]
words_and_spans = [self.pre_tokenizer.pre_tokenize_str(x) for x in texts]
words = [[x[0] for x in y] for y in words_and_spans]
word_spans_batch = [[x[1] for x in y] for y in words_and_spans]
tokenized_batch = self.tokenizer.batch_encode_plus(
words,
return_offsets_mapping=True,
return_special_tokens_mask=True,
return_token_type_ids=True,
return_attention_mask=True,
truncation=True,
max_length=self.max_seq_len,
padding="max_length",
is_split_into_words=True,
)
for i in range(len(dicts)):
tokenized = tokenized_batch[i]
d = dicts[i]
id_external = self._id_from_dict(d)
if indices:
id_internal = indices[i]
else:
id_internal = i
input_ids = tokenized.ids
segment_ids = tokenized.type_ids
initial_mask = self._get_start_of_word(tokenized.words)
assert len(initial_mask) == len(input_ids)
padding_mask = tokenized.attention_mask
if return_baskets:
token_to_word_map = tokenized.words
word_spans = word_spans_batch[i]
tokenized_dict = {
"tokens": tokenized.tokens,
"word_spans": word_spans,
"token_to_word_map": token_to_word_map,
"start_of_word": initial_mask
}
else:
tokenized_dict = {}
feature_dict = {
"input_ids": input_ids,
"padding_mask": padding_mask,
"segment_ids": segment_ids,
"initial_mask": initial_mask,
}
for task_name, task in self.tasks.items():
try:
label_name = task["label_name"]
labels_word = d[label_name]
label_list = task["label_list"]
label_tensor_name = task["label_tensor_name"]
if task["task_type"] == "classification":
label_ids = [label_list.index(labels_word)]
elif task["task_type"] == "ner":
labels_token = expand_labels(labels_word, initial_mask, non_initial_token)
label_ids = [label_list.index(lt) for lt in labels_token]
except ValueError:
label_ids = None
problematic_labels = set(labels_token).difference(set(label_list))
print(f"[Task: {task_name}] Could not convert labels to ids via label_list!"
f"\nWe found a problem with labels {str(problematic_labels)}")
except KeyError:
label_ids = None
# print(f"[Task: {task_name}] Could not convert labels to ids via label_list!"
# "\nIf your are running in *inference* mode: Don't worry!"
# "\nIf you are running in *training* mode: Verify you are supplying a proper label list to your processor and check that labels in input data are correct.")
if label_ids:
feature_dict[label_tensor_name] = label_ids
curr_sample = Sample(id=None,
clear_text=d,
tokenized=tokenized_dict,
features=[feature_dict])
curr_basket = SampleBasket(id_internal=id_internal,
raw=d,
id_external=id_external,
samples=[curr_sample])
self.baskets.append(curr_basket)
if indices and 0 not in indices:
pass
else:
self._log_samples(1)
dataset, tensor_names = self._create_dataset()
ret = [dataset, tensor_names, self.problematic_sample_ids]
if return_baskets:
ret.append(self.baskets)
return tuple(ret)
