def test_bert_padding(train_raw):
preprocessor = preprocessors.BertPreprocessor()
data_preprocessed = preprocessor.transform(train_raw, verbose=0)
dataset = Dataset(data_preprocessed, mode='point')
pre_fixed_padding = callbacks.BertPadding(fixed_length_left=5,
fixed_length_right=5,
pad_mode='pre')
dataloader = DataLoader(dataset, batch_size=5, callback=pre_fixed_padding)
for batch in dataloader:
assert batch[0]['text_left'].shape == (5, 7)
assert batch[0]['text_right'].shape == (5, 6)
post_padding = callbacks.BertPadding(pad_mode='post')
dataloader = DataLoader(dataset, batch_size=5, callback=post_padding)
for batch in dataloader:
max_left_len = max(batch[0]['length_left'].detach().cpu().numpy())
max_right_len = max(batch[0]['length_right'].detach().cpu().numpy())
assert batch[0]['text_left'].shape == (5, max_left_len + 2)
assert batch[0]['text_right'].shape == (5, max_right_len + 1)
def get_default_preprocessor(cls,
mode: str = 'bert-base-uncased'
) -> BasePreprocessor:
""":return: Default preprocessor."""
return preprocessors.BertPreprocessor(mode=mode)
def get_default_preprocessor(cls) -> BasePreprocessor:
""":return: Default preprocessor."""
return preprocessors.BertPreprocessor()
