`pyabsa.augmentation`

Subpackages

Submodules

pyabsa.augmentation.aug_utils

Package Contents

Functions

`auto_aspect_sentiment_classification_augmentation`(→ None)	Augment the dataset using BoostTextAugmentation tool (https://github.com/yangheng95/BoostTextAugmentation) for aspect
`auto_classification_augmentation`(→ None)	Augment the dataset using BoostTextAugmentation tool (https://github.com/yangheng95/BoostTextAugmentation) for text

pyabsa.augmentation.auto_aspect_sentiment_classification_augmentation(config, dataset, device: str, boosting_fold: int = 4, classifier_training_num: int = 1, augment_num_per_case: int = 10, winner_num_per_case: int = 5, augment_backend: str = 'eda', train_after_aug: bool = True, rewrite_cache: bool = True) → None[source]

Augment the dataset using BoostTextAugmentation tool (https://github.com/yangheng95/BoostTextAugmentation) for aspect sentiment classification.

Parameters:

config (ABSAConfig) – The configuration object for ABSA.
dataset (ABSADataset) – The dataset to be augmented.
device (str) – The device to run the augment on.
boosting_fold (int, optional) – The number of boosting fold. Defaults to 4.
classifier_training_num (int, optional) – The number of classifier training. Defaults to 1.
augment_num_per_case (int, optional) – The number of augmented samples to generate per case. Defaults to 10.
winner_num_per_case (int, optional) – The number of winners per case. Defaults to 5.
augment_backend (str, optional) – The data augment backend to use. Defaults to “eda”.
train_after_aug (bool, optional) – Whether to train the model after the data augmentation. Defaults to True.
rewrite_cache (bool, optional) – Whether to rewrite the cache files. Defaults to True.

Returns:

None

pyabsa.augmentation.auto_classification_augmentation(config, dataset, device: str, boosting_fold: int = 4, classifier_training_num: int = 1, augment_num_per_case: int = 10, winner_num_per_case: int = 5, augment_backend: str = 'eda', train_after_aug: bool = True, rewrite_cache: bool = True) → None[source]

Augment the dataset using BoostTextAugmentation tool (https://github.com/yangheng95/BoostTextAugmentation) for text classification.