<jupyter_start><jupyter_text>Character Augmenter 1. keyboard : Augmenter that apply typo error simulation to textual input.<jupyter_code>aug = nac.KeyboardAug(name='Keyboard_Aug', aug_char_min=1, aug_char_max=10, aug_char_p=0.3, aug_word_p=0.3, 
                      aug_word_min=1, aug_word_max=10, stopwords=None, tokenizer=None, reverse_tokenizer=None, 
                      include_special_char=True, include_numeric=True, include_upper_case=True, lang='en', verbose=0, 
                      stopwords_regex=None, model_path=None, min_char=4)

test_sentence_aug = aug.augment(test_sentence)
print(test_sentence)
print(test_sentence_aug)<jupyter_output>I genuinely have no idea what the output of this sequence of words will be - it will be interesting to find out what nlpaug can do with this!
I geJuonelJ have no iEeW 1haY the outouG of this sequence of wo4dE qil/ be - it will be interesting to find out @yat nlpaug can do w7Fh YhiW!<jupyter_text>2. ocr : Augmenter that apply ocr error simulation to textual input.<jupyter_code>aug = nac.OcrAug(name='OCR_Aug', aug_char_min=1, aug_char_max=10, aug_char_p=0.3, aug_word_p=0.3, aug_word_min=1, 
                 aug_word_max=10, stopwords=None, tokenizer=None, reverse_tokenizer=None, verbose=0, stopwords_regex=None, 
                 min_char=1)

test_sentence_aug = aug.augment(test_sentence)
print(test_sentence)
print(test_sentence_aug)<jupyter_output>I genuinely have no idea what the output of this sequence of words will be - it will be interesting to find out what nlpaug can do with this!
I genuinely have no idea what the output of this 8e9oence of words wi11 6e - it will 6e interesting to find oot what nlpaug can d0 with thi8!<jupyter_text>3. random : Augmenter that apply random character error to textual input.<jupyter_code>aug = nac.RandomCharAug(action='substitute', name='RandomChar_Aug', aug_char_min=1, aug_char_max=10, aug_char_p=0.3, 
                        aug_word_p=0.3, aug_word_min=1, aug_word_max=10, include_upper_case=True, include_lower_case=True, 
                        include_numeric=True, min_char=4, swap_mode='adjacent', spec_char='!@#$%^&*()_+', stopwords=None, 
                        tokenizer=None, reverse_tokenizer=None, verbose=0, stopwords_regex=None, candidiates=None)

test_sentence_aug = aug.augment(test_sentence)
print(test_sentence)
print(test_sentence_aug)<jupyter_output>I genuinely have no idea what the output of this sequence of words will be - it will be interesting to find out what nlpaug can do with this!
I gYnu%nBly hG1e no idea dhaF the oetbut of thg( seQugnre of words will be - it @Tll be interesting to find out DXat nlpaug can do XGth this!<jupyter_text>Word Augmenter 1. antonym : Augmenter that apply semantic meaning based to textual input.<jupyter_code>aug = naw.AntonymAug(name='Antonym_Aug', aug_min=1, aug_max=10, aug_p=0.3, lang='eng', stopwords=None, tokenizer=None, 
                     reverse_tokenizer=None, stopwords_regex=None, verbose=0)

test_sentence_aug = aug.augment(test_sentence)
print(test_sentence)
print(test_sentence_aug)<jupyter_output>I genuinely have no idea what the output of this sequence of words will be - it will be interesting to find out what nlpaug can do with this!
I genuinely lack no idea what the output of this sequence of words will differ - it will differ uninteresting to lose out what nlpaug can unmake with this!<jupyter_text>3. context_word_embedding : Augmenter that apply operation (word level) to textual input based on contextual word embeddings.<jupyter_code>aug = naw.ContextualWordEmbsAug(model_path='bert-base-uncased', model_type='', action='substitute', # temperature=1.0, 
                                top_k=100,
                                # top_p=None, 
                                name='ContextualWordEmbs_Aug', aug_min=1, aug_max=10, aug_p=0.3, 
                                stopwords=None, device='cpu', force_reload=False,
                                # optimize=None, 
                                stopwords_regex=None, 
                                verbose=0, silence=True)

test_sentence_aug = aug.augment(test_sentence)
print(test_sentence)
print(test_sentence_aug)<jupyter_output><empty_output><jupyter_text>4. random : Augmenter that apply random word operation to textual input.<jupyter_code>aug = naw.RandomWordAug(action='delete', name='RandomWord_Aug', aug_min=1, aug_max=10, aug_p=0.3, stopwords=None, 
                        target_words=None, tokenizer=None, reverse_tokenizer=None, stopwords_regex=None, verbose=0)

test_sentence_aug = aug.augment(test_sentence)
print(test_sentence)
print(test_sentence_aug)<jupyter_output>I genuinely have no idea what the output of this sequence of words will be - it will be interesting to find out what nlpaug can do with this!
I have idea what of this words be - it will be interesting find out what nlpaug do with this!<jupyter_text>5. spelling : Augmenter that apply spelling error simulation to textual input.<jupyter_code>aug = naw.SpellingAug(dict_path=None, name='Spelling_Aug', aug_min=1, aug_max=10, aug_p=0.3, stopwords=None, 
                      tokenizer=None, reverse_tokenizer=None, include_reverse=True, stopwords_regex=None, verbose=0)

test_sentence_aug = aug.augment(test_sentence)
print(test_sentence)
print(test_sentence_aug)<jupyter_output>I genuinely have no idea what the output of this sequence of words will be - it will be interesting to find out what nlpaug can do with this!
I genuinely have no aidea wehat zhe output of this sequence of worlds w'll be - it will be interesting do fined out what nlpaug canottle do witw this!<jupyter_text>6. split : Augmenter that apply word splitting operation to textual input.<jupyter_code>aug = naw.SplitAug(name='Split_Aug', aug_min=1, aug_max=10, aug_p=0.3, min_char=4, stopwords=None, tokenizer=None, 
                   reverse_tokenizer=None, stopwords_regex=None, verbose=0)

test_sentence_aug = aug.augment(test_sentence)
print(test_sentence)
print(test_sentence_aug)<jupyter_output>I genuinely have no idea what the output of this sequence of words will be - it will be interesting to find out what nlpaug can do with this!
I genuinely ha ve no idea w hat the output of this sequ ence of w ords w ill be - it w ill be in teresting to find out w hat nlpa ug can do with this!<jupyter_text>7. synonym : Augmenter that apply semantic meaning based to textual input.<jupyter_code>aug = naw.SynonymAug(aug_src='wordnet', model_path=None, name='Synonym_Aug', aug_min=1, aug_max=10, aug_p=0.3, lang='eng', 
                     stopwords=None, tokenizer=None, reverse_tokenizer=None, stopwords_regex=None, force_reload=False, 
                     verbose=0)

test_sentence_aug = aug.augment(test_sentence)
print(test_sentence)
print(test_sentence_aug)<jupyter_output>I genuinely have no idea what the output of this sequence of words will be - it will be interesting to find out what nlpaug can do with this!
Ace genuinely sustain no melodic theme what the output of this chronological sequence of words bequeath equal - information technology testament be interesting to find out what nlpaug can do with this!
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