def split_word(text):
#Split Augmenter
#Split word to two tokens randomly
aug = naw.SplitAug()
attacked_text = aug.augment(text)
print("Attacked Text:")
print(attacked_text)
def test_split_min_char(self):
texts = ['quick brown']
aug = naw.SplitAug(min_char=6)
for text in texts:
augmented_text = aug.augment(text)
self.assertEqual(text, augmented_text)
def test_split(self):
texts = ['The quick brown fox jumps over the lazy dog']
aug = naw.SplitAug()
for text in texts:
augmented_text = aug.augment(text)
self.assertLess(len(text), len(augmented_text))
def __init__(self):
aug0 = naw.RandomWordAug()
aug1 = naw.ContextualWordEmbsAug(model_path='bert-base-uncased',
action="substitute")
aug2 = naw.SynonymAug(aug_src='wordnet')
aug3 = naw.SplitAug()
aug4 = naw.ContextualWordEmbsAug(model_path='bert-base-uncased',
action="insert")
self.augs = [aug0, aug1, aug2, aug3, aug4]
def __init__(self,
aug_max=2,
aug_p=0.3,
tokenizer=None,
always_apply=False,
p=0.5):
super().__init__(always_apply, p)
self.aug = naw.SplitAug(aug_max=aug_max,
aug_p=aug_p,
tokenizer=tokenizer)
rows = []
for row in csv_reader:
rows.append(row)
for row in list(rows): # Delete character randomly
if row[1] != "flag" and row[1] !='R' and row[1]!="":
counter += 1
if counter != 2:
row[2] = str(character_delete.augment(row[2]))
if counter == 3:
counter = 0
csv_writer.writerow(row)
with open("input_classification_test_data.csv","r") as input:
with open("/Users/wenyaxie/Downloads/negative_data_split_word.csv","w") as output:
split_words = naw.SplitAug()
csv_reader = csv.reader(input)
csv_writer = csv.writer(output)
counter = 0
rows = []
for row in csv_reader:
rows.append(row)
for row in list(rows): # Split word to two tokens randomly
if row[1] != "flag" and row[1] !='R' and row[1]!="":
counter += 1
if counter != 2:
row[2] = str(split_words.augment(row[2]))
if counter == 3: