def __call__(self, instance):
input_tokens, input_pos, input_dep, target_tokens, target_pos, target_dep = instance
# -3 for special tokens [CLS], [SEP], [SEP]
truncate_tokens_pair(input_tokens, target_tokens, self.max_len - 3)
truncate_tokens_pair(input_pos, target_pos, self.max_len - 3)
truncate_tokens_pair(input_dep, target_dep, self.max_len - 3)
target_tokens = truncate_tokens(target_tokens, self.max_len)
target_pos = truncate_tokens(target_pos, self.max_len)
target_dep = truncate_tokens(target_dep, self.max_len)
# Add Special Tokens
origin_word_tokens = ['[CLS]'] + input_tokens + [
'[SEP]'
] + target_tokens + ['[SEP]']
if rand() < 0.5:
word_tokens = origin_word_tokens
else:
word_tokens = ['[CLS]'] + input_tokens + ['[SEP]'] + (
['[MASK]'] * len(target_tokens)) + ['[SEP]']
#word_tokens = ['[CLS]'] + input_tokens + ['[SEP]'] + target_tokens + ['[SEP]']
pos_tokens = ['[CLS]'] + input_pos + ['[SEP]'] + target_pos + ['[SEP]']
dep_tokens = ['[CLS]'] + input_dep + ['[SEP]'] + target_dep + ['[SEP]']
input_segment_ids = [0] * (len(input_tokens) +
2) + [1] * (len(target_tokens) + 1)
input_mask = [1] * len(word_tokens)
target_mask = [1] * (len(target_tokens) + 1)
input_len = len(input_tokens) + 2
target_len = len(target_tokens) + 1
input_word_ids, input_pos_ids, input_dep_ids = self.indexer(
word_tokens, pos_tokens, dep_tokens)
origin_input_word_ids, _, _ = self.indexer(origin_word_tokens, [], [])
target_word_ids, target_pos_ids, target_dep_ids = self.indexer(
target_tokens + ['[SEP]'], target_pos + ['[SEP]'],
target_dep + ['[SEP]'])
# Zero Padding
input_n_pad = self.max_len - len(input_word_ids)
origin_input_word_ids.extend([0] * input_n_pad)
input_word_ids.extend([0] * input_n_pad)
input_pos_ids.extend([0] * input_n_pad)
input_dep_ids.extend([0] * input_n_pad)
input_segment_ids.extend([0] * input_n_pad)
input_mask.extend([0] * input_n_pad)
target_n_pad = self.max_len - len(target_word_ids)
target_word_ids.extend([0] * target_n_pad)
target_pos_ids.extend([0] * target_n_pad)
target_dep_ids.extend([0] * target_n_pad)
target_mask.extend([0] * target_n_pad)
return (origin_input_word_ids, input_word_ids, input_pos_ids,
input_dep_ids, input_segment_ids, input_mask, target_word_ids,
target_pos_ids, target_dep_ids, target_mask, input_len,
target_len)
def __call__(self, instance):
input_tokens, input_pos, input_dep, target_tokens, target_pos, target_dep = instance
# -3 for special tokens [CLS], [SEP], [SEP]
truncate_tokens_pair(input_tokens, target_tokens, self.max_len - 3)
truncate_tokens_pair(input_pos, target_pos, self.max_len - 3)
truncate_tokens_pair(input_dep, target_dep, self.max_len - 3)
target_tokens = truncate_tokens(target_tokens, self.max_len)
target_pos = truncate_tokens(target_pos, self.max_len)
target_dep = truncate_tokens(target_dep, self.max_len)
# Add Special Tokens
word_tokens = ['[CLS]'] + input_tokens + ['[SEP]'] + target_tokens + [
'[SEP]'
]
pos_tokens = ['[CLS]'] + input_pos + ['[SEP]'] + target_pos + ['[SEP]']
dep_tokens = ['[CLS]'] + input_dep + ['[SEP]'] + target_dep + ['[SEP]']
input_segment_ids = [0] * (len(input_tokens) +
2) + [1] * (len(target_tokens) + 1)
input_mask = [1] * len(word_tokens)
target_mask = [1] * len(target_tokens)
# For masked Language Models
masked_word_tokens, masked_pos_tokens, masked_dep_tokens, masked_pos = [], [], [], []
# the number of prediction is sometimes less than max_pred when sequence is short
n_pred = min(self.max_pred,
max(1, int(round(len(word_tokens) * self.mask_prob))))
# candidate positions of masked tokens
#
#cand_pos = [i for i, token in enumerate(word_tokens)
# if word_tokens != '[CLS]' and word_tokens != '[SEP]']
#Detect SEP for summary
cand_pos = [
i for i, token in enumerate(word_tokens) if word_tokens != '[CLS]'
]
shuffle(cand_pos)
for pos in cand_pos[:n_pred]:
masked_word_tokens.append(word_tokens[pos])
masked_pos_tokens.append(pos_tokens[pos])
masked_dep_tokens.append(dep_tokens[pos])
masked_pos.append(pos)
if rand() < 0.8: # 80%
word_tokens[pos] = '[MASK]'
pos_tokens[pos] = '[MASK]'
dep_tokens[pos] = '[MASK]'
#elif rand() < 0.5: # 10%
# word_tokens[pos] = get_random_word(self.vocab_words)
# pos_tokens[pos] = get_random_word(self.vocab_pos)
# dep_tokens[pos] = get_random_word(self.vocab_dep)
# when n_pred < max_pred, we only calculate loss within n_pred
masked_weights = [1] * len(masked_pos_tokens)
#replace right as mask for summary
#if rand() < 0.1:
# word_tokens = word_tokens[:len(input_tokens)+2] + ['[MASK]']*len(self.max_len - len(input_tokens)+2)
# pos_tokens = pos_tokens[:len(input_pos)+2] + ['[MASK]']*len(self.max_len - len(input_pos)+2)
# dep_tokens = dep_tokens[:len(input_dep)+2] + ['[MASK]']*len(self.max_len - len(input_dep)+2)
input_word_ids, input_pos_ids, input_dep_ids = self.indexer(
word_tokens, pos_tokens, dep_tokens)
masked_word_ids, masked_pos_ids, masked_dep_ids = self.indexer(
masked_word_tokens, masked_pos_tokens, masked_dep_tokens)
target_word_ids, target_pos_ids, target_dep_ids = self.indexer(
target_tokens, target_pos, target_dep)
# Zero Padding
input_n_pad = self.max_len - len(input_word_ids)
input_word_ids.extend([0] * input_n_pad)
input_pos_ids.extend([0] * input_n_pad)
input_dep_ids.extend([0] * input_n_pad)
input_segment_ids.extend([0] * input_n_pad)
input_mask.extend([0] * input_n_pad)
target_n_pad = self.max_len - len(target_word_ids)
target_word_ids.extend([0] * target_n_pad)
target_pos_ids.extend([0] * target_n_pad)
target_dep_ids.extend([0] * target_n_pad)
target_mask.extend([0] * target_n_pad)
# Zero Padding for masked target
if self.max_pred > n_pred:
n_pad = self.max_pred - n_pred
masked_word_ids.extend([0] * n_pad)
masked_pos_ids.extend([0] * n_pad)
masked_dep_ids.extend([0] * n_pad)
masked_pos.extend([0] * n_pad)
masked_weights.extend([0] * n_pad)
return (input_word_ids, input_pos_ids, input_dep_ids,
input_segment_ids, input_mask, masked_word_ids, masked_pos_ids,
masked_dep_ids, masked_pos, masked_weights, target_word_ids,
target_pos_ids, target_dep_ids, target_mask)