def tensorize_random_mask(self, batch_text, bsize=None):
assert type(batch_text) in [list, tuple], (type(batch_text))
# add placehold for the [Q] marker
batch_text = ['. ' + x for x in batch_text]
batch_ids = []
batch_masks = []
for text in batch_text:
tokens = self.tok.tokenize(text)
pos = nltk.pos_tag(tokens)
new_tokens = []
new_masks = []
for i in range(len(tokens)):
p = pos[i]
token = tokens[i]
# if token is Noun or adj and is not stop words and not subword token
if ('NN' in p[1] or 'JJ' in p[1]) and (
token not in self.nltk_stopwords) and '#' not in token:
new_tokens.append(token)
new_masks.append(1)
# add 1 means let BERT do self attention with mask token (mask_v2), and zero is not(mask_v1)
# 不讓bert做self attention代表不讓他破壞句法結構? 最後一層layer再猜
new_tokens.append('[MASK]')
new_masks.append(0)
else:
new_tokens.append(token)
new_masks.append(1)
# padding or truncate
if len(new_tokens) >= self.query_maxlen - 2:
new_tokens = ['[CLS]'] + new_tokens[:self.query_maxlen -
2] + ['[SEP]']
new_masks = [1] + new_masks[:self.query_maxlen - 2] + [0]
else:
new_tokens = ['[CLS]'] + new_tokens + ['[MASK]'] * (
self.query_maxlen - 2 - len(new_tokens)) + ['[SEP]']
new_masks = new_masks + [0] * (self.query_maxlen -
len(new_masks))
new_ids = self.tok.convert_tokens_to_ids(new_tokens)
new_ids[1] = self.Q_marker_token_id
# print(text)
# print(new_tokens , len(new_tokens))
# print(new_ids , len(new_ids))
# print(new_masks , len(new_masks))
batch_ids.append(new_ids)
batch_masks.append(new_masks)
ids = torch.tensor(batch_ids)
mask = torch.tensor(batch_masks)
if bsize:
batches = _split_into_batches(ids, mask, bsize)
return batches
return ids, mask
def tensorize(self, batch_text, bsize=None):
assert type(batch_text) in [list, tuple], (type(batch_text))
# add placehold for the [Q] marker
batch_text = ['. ' + x for x in batch_text]
obj = self.tok(batch_text,
padding='max_length',
truncation=True,
return_tensors='pt',
max_length=self.query_maxlen)
ids, mask = obj['input_ids'], obj['attention_mask']
# postprocess for the [Q] marker and the [MASK] augmentation
ids[:, 1] = self.Q_marker_token_id
ids[ids == 0] = self.mask_token_id
if bsize:
batches = _split_into_batches(ids, mask, bsize)
return batches
return ids, mask
def tensorize(self, batch_text, bsize=None):
assert type(batch_text) in [list, tuple], (type(batch_text))
# add placehold for the [D] marker
batch_text = ['. ' + x for x in batch_text]
obj = self.tok(batch_text,
padding='longest',
truncation='longest_first',
return_tensors='pt',
max_length=self.doc_maxlen)
ids, mask = obj['input_ids'], obj['attention_mask']
# postprocess for the [D] marker
ids[:, 1] = self.D_marker_token_id
if bsize:
ids, mask, reverse_indices = _sort_by_length(ids, mask, bsize)
batches = _split_into_batches(ids, mask, bsize)
return batches, reverse_indices
return ids, mask
