def _load_paired_data(self, source_file, target_file):
if self.overlength_strategy == 'drop':
loaded_source_text = load_data(source_file, self.tokenize_strategy,
'none', self.max_source_length,
self.source_language)
loaded_target_text = load_data(target_file, self.tokenize_strategy,
'none', self.max_target_length,
self.target_language)
assert len(loaded_source_text) == len(loaded_target_text)
source_text = []
target_text = []
for src, tgt in zip(loaded_source_text, loaded_target_text):
if (len(src) <= self.max_source_length
and len(tgt) <= self.max_target_length):
source_text.append(src)
target_text.append(tgt)
else:
source_text = load_data(source_file, self.tokenize_strategy,
self.overlength_strategy,
self.max_source_length,
self.source_language)
target_text = load_data(target_file, self.tokenize_strategy,
self.overlength_strategy,
self.max_target_length,
self.target_language)
return source_text, target_text
def _load_source_data(self):
for i, prefix in enumerate(['train', 'valid', 'test']):
filename = os.path.join(self.dataset_path, f'{prefix}.src')
text_data = load_data(filename, self.tokenize_strategy,
self.source_max_length, self.source_language,
self.source_multi_sentence,
self.source_max_num)
assert len(text_data) == len(self.target_text[i])
self.source_text.append(text_data)
def _load_single_data(self, dataset_path):
"""Load full corpus.
This is designed for single sentence format, unconditional task.
Args:
dataset_path (str): path of dataset dir.
"""
dataset_file = os.path.join(dataset_path, 'corpus.txt')
self.text_data = load_data(dataset_file, self.tokenize_strategy,
self.overlength_strategy,
self.max_seq_length, self.language)
self.text_data = split_data([self.text_data], self.split_ratio)[0]
def _load_target_data(self):
"""Load dataset from target file (train, valid, test).
This is designed for single sentence format.
"""
for prefix in ['train', 'valid', 'test']:
filename = os.path.join(self.dataset_path, f'{prefix}.tgt')
text_data = load_data(
filename, self.tokenize_strategy, self.target_max_length, self.target_language,
self.target_multi_sentence, self.target_max_num
)
self.target_text.append(text_data)
def _load_split_data(self, dataset_path):
"""Load dataset from split (train, dev, test).
This is designed for single sentence format, unconditional task.
Args:
dataset_path (str): path of dataset dir.
"""
for prefix in ['train', 'dev', 'test']:
filename = os.path.join(dataset_path, '{}.txt'.format(prefix))
text_data = load_data(filename, self.tokenize_strategy,
self.overlength_strategy,
self.max_seq_length, self.language)
self.text_data.append(text_data)
def _load_source_data(self):
for i, prefix in enumerate(['train', 'valid', 'test']):
filename = os.path.join(self.dataset_path, f'{prefix}.src')
text_data = load_data(filename, self.tokenize_strategy,
self.source_max_length, self.source_language,
True, self.source_max_num)
assert len(text_data) == len(self.target_text[i])
key_data = []
for doc in text_data:
key = []
for kv in doc:
k, kv[0] = kv[0].split('<kv>')
key.append(k)
key_data.append(key)
self.source_value_text.append(text_data)
self.source_key_text.append(key_data)
