def use_embedding_augmenter():
from textattack.augmentation import EmbeddingAugmenter
augmenter = EmbeddingAugmenter(transformations_per_example=64)
s = 'There is nothing either good or bad, but thinking makes it so.'
augmented_text_list = augmenter.augment(s)
augmented_s = 'There is nothing either good or unfavourable, but thinking makes it so.'
assert augmented_s in augmented_text_list
def test_embedding_augmenter():
from textattack.augmentation import EmbeddingAugmenter
augmenter = EmbeddingAugmenter(pct_words_to_swap=0.01,
transformations_per_example=64)
s = "There is nothing either good or bad, but thinking makes it so."
augmented_text_list = augmenter.augment(s)
augmented_s = (
"There is nothing either good or unfavourable, but thinking makes it so."
)
assert augmented_s in augmented_text_list
from textattack.augmentation import EmbeddingAugmenter
augmenter = EmbeddingAugmenter(pct_words_to_swap=0.2,
transformations_per_example=2)
with open('dataset/test.txt', 'r') as r, open('augmentation_test.txt',
'w') as w:
for line in r:
list_labels_id = line.split(';')
aug = augmenter.augment(list_labels_id[0])
w.write(list_labels_id[0] + ';' + list_labels_id[1])
for aug_text in aug:
w.write(aug_text + ';' + list_labels_id[1])
def augment_examples(sentences: List[str], num_transformations: int = 1):
emb_augmenter = EmbeddingAugmenter(transformations_per_example=num_transformations)
augmented = emb_augmenter.augment_many(sentences, show_progress=True)
augmented = [element[0] for element in augmented]
return augmented
## WordNetAugmenter Augments the text by replacing it with synonyms from the WordNet thesaurus (thesaurus). WordNet is a lexical database for English. Nouns, verbs, adjectives, and adverbs are grouped into sets of cognitive synonyms (synsets), each of which expresses a different concept. WordNet superficially resembles a thesaurus in that it groups words based on their meaning. """ aug = WordNetAugmenter(); aug.augment(text) """## EmbeddingAugmenter Augment text by transforming words with their inlays, with a constraint to ensure that their cosine similarity is at least 0.8. """ aug = EmbeddingAugmenter(); aug.augment(text) """## CharSwapAugmenter Augment text by substituting, deleting, inserting, and swapping adjacent characters. """ aug = CharSwapAugmenter() aug.augment(text) """## EasyDataAugmenter This augments the text with a combination of Wordnet synonym replacement, word deletion, word order changes, and synonym insertions. All these 4 functionalities occur randomly. So we will get different results every time we run the code. This returns 4 augmented results, unlike the other methods. """ aug = EasyDataAugmenter() aug.augment(text)