def samples_to_features_bert_lm(sample,
max_seq_len,
tokenizer,
next_sent_pred=True):
"""
Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with
IDs, LM labels, padding_mask, CLS and SEP tokens etc.
:param sample: Sample, containing sentence input as strings and is_next label
:type sample: Sample
:param max_seq_len: Maximum length of sequence.
:type max_seq_len: int
:param tokenizer: Tokenizer
:return: InputFeatures, containing all inputs and labels of one sample as IDs (as used for model training)
"""
if next_sent_pred:
tokens_a = sample.tokenized["text_a"]["tokens"]
tokens_b = sample.tokenized["text_b"]["tokens"]
# mask random words
tokens_a, t1_label = mask_random_words(
tokens_a,
tokenizer.vocab,
token_groups=sample.tokenized["text_a"]["start_of_word"])
tokens_b, t2_label = mask_random_words(
tokens_b,
tokenizer.vocab,
token_groups=sample.tokenized["text_b"]["start_of_word"])
# convert lm labels to ids
t1_label_ids = [
-1 if tok == '' else tokenizer.vocab[tok] for tok in t1_label
]
t2_label_ids = [
-1 if tok == '' else tokenizer.vocab[tok] for tok in t2_label
]
lm_label_ids = t1_label_ids + t2_label_ids
# Convert is_next_label: Note that in Bert, is_next_labelid = 0 is used for next_sentence=true!
if sample.clear_text["nextsentence_label"]:
is_next_label_id = [0]
else:
is_next_label_id = [1]
else:
tokens_a = sample.tokenized["text_a"]["tokens"]
tokens_b = None
tokens_a, t1_label = mask_random_words(
tokens_a,
tokenizer.vocab,
token_groups=sample.tokenized["text_a"]["start_of_word"])
# convert lm labels to ids
lm_label_ids = [
-1 if tok == '' else tokenizer.vocab[tok] for tok in t1_label
]
# encode string tokens to input_ids and add special tokens
inputs = tokenizer.encode_plus(text=tokens_a,
text_pair=tokens_b,
add_special_tokens=True,
max_length=max_seq_len,
truncation_strategy='do_not_truncate'
# We've already truncated our tokens before
)
input_ids, segment_ids, special_tokens_mask = inputs["input_ids"], inputs[
"token_type_ids"], inputs["special_tokens_mask"]
# account for special tokens (CLS, SEP, SEP..) in lm_label_ids
lm_label_ids = insert_at_special_tokens_pos(lm_label_ids,
special_tokens_mask,
insert_element=-1)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
padding_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
# Padding up to the sequence length.
# Normal case: adding multiple 0 to the right
# Special cases:
# a) xlnet pads on the left and uses "4" for padding token_type_ids
if tokenizer.__class__.__name__ == "XLNetTokenizer":
pad_on_left = True
segment_ids = pad(segment_ids, max_seq_len, 4, pad_on_left=pad_on_left)
else:
pad_on_left = False
segment_ids = pad(segment_ids, max_seq_len, 0, pad_on_left=pad_on_left)
input_ids = pad(input_ids,
max_seq_len,
tokenizer.pad_token_id,
pad_on_left=pad_on_left)
padding_mask = pad(padding_mask, max_seq_len, 0, pad_on_left=pad_on_left)
lm_label_ids = pad(lm_label_ids, max_seq_len, -1, pad_on_left=pad_on_left)
feature_dict = {
"input_ids": input_ids,
"padding_mask": padding_mask,
"segment_ids": segment_ids,
"lm_label_ids": lm_label_ids,
}
if next_sent_pred:
feature_dict["nextsentence_label_ids"] = is_next_label_id
assert len(input_ids) == max_seq_len
assert len(padding_mask) == max_seq_len
assert len(segment_ids) == max_seq_len
assert len(lm_label_ids) == max_seq_len
return [feature_dict]
def samples_to_features_bert_lm(sample, max_seq_len, tokenizer, next_sent_pred=True):
"""
Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with
IDs, LM labels, padding_mask, CLS and SEP tokens etc.
:param sample: Sample, containing sentence input as strings and is_next label
:type sample: Sample
:param max_seq_len: Maximum length of sequence.
:type max_seq_len: int
:param tokenizer: Tokenizer
:return: InputFeatures, containing all inputs and labels of one sample as IDs (as used for model training)
"""
if next_sent_pred:
tokens_a = sample.tokenized["text_a"]["tokens"]
tokens_b = sample.tokenized["text_b"]["tokens"]
# mask random words
tokens_a, t1_label = mask_random_words(tokens_a, tokenizer.vocab,
token_groups=sample.tokenized["text_a"]["start_of_word"])
tokens_b, t2_label = mask_random_words(tokens_b, tokenizer.vocab,
token_groups=sample.tokenized["text_b"]["start_of_word"])
if tokenizer.is_fast:
# Detokenize input as fast tokenizer can't handle tokenized input
tokens_a = " ".join(tokens_a)
tokens_a = re.sub(r"(^|\s)(##)", "", tokens_a)
tokens_b = " ".join(tokens_b)
tokens_b = re.sub(r"(^|\s)(##)", "", tokens_b)
# convert lm labels to ids
t1_label_ids = [-1 if tok == '' else tokenizer.convert_tokens_to_ids(tok) for tok in t1_label]
t2_label_ids = [-1 if tok == '' else tokenizer.convert_tokens_to_ids(tok) for tok in t2_label]
lm_label_ids = t1_label_ids + t2_label_ids
# Convert is_next_label: Note that in Bert, is_next_labelid = 0 is used for next_sentence=true!
if sample.clear_text["nextsentence_label"]:
is_next_label_id = [0]
else:
is_next_label_id = [1]
else:
tokens_a = sample.tokenized["text_a"]["tokens"]
tokens_b = None
tokens_a, t1_label = mask_random_words(tokens_a, tokenizer.vocab,
token_groups=sample.tokenized["text_a"]["start_of_word"])
if tokenizer.is_fast:
# Detokenize input as fast tokenizer can't handle tokenized input
tokens_a = " ".join(tokens_a)
tokens_a = re.sub(r"(^|\s)(##)", "", tokens_a)
# convert lm labels to ids
lm_label_ids = [-1 if tok == '' else tokenizer.convert_tokens_to_ids(tok) for tok in t1_label]
if tokenizer.is_fast:
inputs = tokenizer(text=tokens_a,
text_pair=tokens_b,
add_special_tokens=True,
return_special_tokens_mask=True,
return_token_type_ids=True)
seq_b_len = len(sample.tokenized["text_b"]["tokens"]) if "text_b" in sample.tokenized else 0
if (len(inputs["input_ids"]) - inputs["special_tokens_mask"].count(1)) != \
(len(sample.tokenized["text_a"]["tokens"]) + seq_b_len):
logger.error(f"FastTokenizer encoded sample {sample.clear_text['text']} to "
f"{len(inputs['input_ids']) - inputs['special_tokens_mask'].count(1)} tokens, which differs "
f"from number of tokens produced in tokenize_with_metadata(). \n"
f"Further processing is likely to be wrong.")
else:
# encode string tokens to input_ids and add special tokens
inputs = tokenizer.encode_plus(text=tokens_a,
text_pair=tokens_b,
add_special_tokens=True,
truncation=False,
truncation_strategy='do_not_truncate',
# We've already truncated our tokens before
return_special_tokens_mask=True,
return_token_type_ids=True
)
input_ids, segment_ids, special_tokens_mask = inputs["input_ids"], inputs["token_type_ids"], inputs[
"special_tokens_mask"]
# account for special tokens (CLS, SEP, SEP..) in lm_label_ids
lm_label_ids = insert_at_special_tokens_pos(lm_label_ids, special_tokens_mask, insert_element=-1)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
padding_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
# Padding up to the sequence length.
# Normal case: adding multiple 0 to the right
# Special cases:
# a) xlnet pads on the left and uses "4" for padding token_type_ids
if tokenizer.__class__.__name__ == "XLNetTokenizer":
pad_on_left = True
segment_ids = pad(segment_ids, max_seq_len, 4, pad_on_left=pad_on_left)
else:
pad_on_left = False
segment_ids = pad(segment_ids, max_seq_len, 0, pad_on_left=pad_on_left)
input_ids = pad(input_ids, max_seq_len, tokenizer.pad_token_id, pad_on_left=pad_on_left)
padding_mask = pad(padding_mask, max_seq_len, 0, pad_on_left=pad_on_left)
lm_label_ids = pad(lm_label_ids, max_seq_len, -1, pad_on_left=pad_on_left)
feature_dict = {
"input_ids": input_ids,
"padding_mask": padding_mask,
"segment_ids": segment_ids,
"lm_label_ids": lm_label_ids,
}
if next_sent_pred:
feature_dict["nextsentence_label_ids"] = is_next_label_id
assert len(input_ids) == max_seq_len
assert len(padding_mask) == max_seq_len
assert len(segment_ids) == max_seq_len
assert len(lm_label_ids) == max_seq_len
return [feature_dict]
def samples_to_features_bert_lm(sample,
max_seq_len,
tokenizer,
next_sent_pred=True):
"""
Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with
IDs, LM labels, padding_mask, CLS and SEP tokens etc.
:param sample: Sample, containing sentence input as strings and is_next label
:type sample: Sample
:param max_seq_len: Maximum length of sequence.
:type max_seq_len: int
:param tokenizer: Tokenizer
:return: InputFeatures, containing all inputs and labels of one sample as IDs (as used for model training)
"""
tokens_a = sample.tokenized["text_a"]["tokens"]
tokens_b = sample.tokenized["text_b"]["tokens"]
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] or [CLS], [SEP]
if not next_sent_pred:
n_special_tokens = 2
else:
n_special_tokens = 3
truncate_seq_pair(tokens_a, tokens_b, max_seq_len - n_special_tokens)
tokens_a, t1_label = mask_random_words(
tokens_a,
tokenizer.vocab,
token_groups=sample.tokenized["text_a"]["start_of_word"])
# convert lm labels to ids
t1_label_ids = [
-1 if tok == '' else tokenizer.vocab[tok] for tok in t1_label
]
if next_sent_pred:
tokens_b, t2_label = mask_random_words(
tokens_b,
tokenizer.vocab,
token_groups=sample.tokenized["text_b"]["start_of_word"])
t2_label_ids = [
-1 if tok == '' else tokenizer.vocab[tok] for tok in t2_label
]
# concatenate lm labels and account for CLS, SEP, SEP
lm_label_ids = [-1] + t1_label_ids + [-1] + t2_label_ids + [-1]
else:
lm_label_ids = [-1] + t1_label_ids + [-1]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambigiously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in tokens_a:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
if next_sent_pred:
assert len(tokens_b) > 0
for token in tokens_b:
tokens.append(token)
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
padding_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_len:
input_ids.append(0)
padding_mask.append(0)
segment_ids.append(0)
lm_label_ids.append(-1)
# Convert is_next_label: Note that in Bert, is_next_labelid = 0 is used for next_sentence=true!
if next_sent_pred:
if sample.clear_text["nextsentence_label"]:
is_next_label_id = [0]
else:
is_next_label_id = [1]
assert len(input_ids) == max_seq_len
assert len(padding_mask) == max_seq_len
assert len(segment_ids) == max_seq_len
assert len(lm_label_ids) == max_seq_len
feature_dict = {
"input_ids": input_ids,
"padding_mask": padding_mask,
"segment_ids": segment_ids,
"lm_label_ids": lm_label_ids,
}
if next_sent_pred:
feature_dict["nextsentence_label_ids"] = is_next_label_id
return [feature_dict]
