Augmenter that apply random word operation to textual input.
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)¶
Augmenter that apply randomly behavior for augmentation.
- action (str) – ‘substitute’, ‘swap’, ‘delete’ or ‘crop’. If value is ‘swap’, adjacent words will be swapped randomly. If value is ‘delete’, word will be removed randomly. If value is ‘crop’, a set of contunous word will be removed randomly.
- aug_p (float) – Percentage of word will be augmented.
- aug_min (int) – Minimum number of word will be augmented.
- aug_max (int) – Maximum number of word will be augmented. If None is passed, number of augmentation is calculated via aup_p. If calculated result from aug_p is smaller than aug_max, will use calculated result from aug_p. Otherwise, using aug_max.
- stopwords (list) – List of words which will be skipped from augment operation. Not effective if action is ‘crop’
- stopwords_regex (str) – Regular expression for matching words which will be skipped from augment operation. Not effective if action is ‘crop’
- target_words (list) – List of word for replacement (used for substitute operation only). Default value is _.
- tokenizer (func) – Customize tokenization process
- reverse_tokenizer (func) – Customize reverse of tokenization process
- name (str) – Name of this augmenter
>>> import nlpaug.augmenter.word as naw >>> aug = naw.RandomWordAug()
augment(data, n=1, num_thread=1)¶
- data (object/list) – Data for augmentation. It can be list of data (e.g. list of string or numpy) or single element (e.g. string or numpy). Numpy format only supports audio or spectrogram data. For text data, only support string or list of string.
- n (int) – Default is 1. Number of unique augmented output. Will be force to 1 if input is list of data
- num_thread (int) – Number of thread for data augmentation. Use this option when you are using CPU and n is larger than 1
>>> augmented_data = aug.augment(data)