Easy Text Augmenter is a Python package for augmenting text data directly on your pandas dataframe using various NLP techniques. There are only 3 techniques for now :
- augment_random_word
- augment_random_character
- augment_word_bert
!pip install easy-nlp-augmentation
import easy_text_augmenter
easy_text_augmenter.info()import pandas as pd
from easy_text_augmenter import augment_random_word
df = pd.DataFrame({
'text': ['This is a test', 'Another test data ', 'Of course we need more data', 'Newton does not like apple', 'Hello world I am a human'],
'label': ['A', 'A', 'B', 'B', 'A']
})
classes_to_augment = ['A', 'B']
augmented_df = augment_random_word(df, classes_to_augment, augmentation_percentage=0.8, text_column='text')
print(augmented_df)Result :
text label
0 This is a test A
1 Another test data A
2 Of course we need more data B
3 Newton does not like apple B
4 Hello world I am a human A
5 Th is is a te st A
6 Another data A
7 Does not newton like apple B
from easy_text_augmenter import augment_random_word
classes_to_augment = ['A', 'B']
augmented_df = augment_random_character(df, classes_to_augment, augmentation_percentage=0.8, text_column='text')
print(augmented_df)Result :
text label
0 This is a test A
1 Another test data A
2 Of course we need more data B
3 Newton does not like apple B
4 Hello world I am a human A
5 This is a estt A
6 Another te8t data A
7 Newtun d0e8 not like apple B
from easy_text_augmenter import augment_word_bert
classes_to_augment = ['A', 'B']
augmented_df = augment_word_bert(df, classes_to_augment, augmentation_percentage=0.8, text_column='text', model_path='bert-base-uncased', random_state=70)
print(augmented_df)Result :
text label
0 This is a test A
1 Another test data A
2 Of course we need more data B
3 Newton does not like apple B
4 Hello world I am a human A
5 another test of data A
6 this term is not a test A
7 newton does absolutely not like every apple B
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augment_random_word
Description:
The augment_random_word function augments a specified percentage of samples in given classes of a DataFrame by randomly applying one of three augmentation techniques (swap, delete, split) to the text column.
augment_random_word(df, classes_to_augment, augmentation_percentage, text_column, random_state=42, weights=[0.5, 0.3, 0.2])
Parameters:
df(pandas.DataFrame): The input DataFrame containing the text data and labels.classes_to_augment(list): A list of class labels that need to be augmented.augmentation_percentage(float): The percentage of samples to augment from each specified class.text_column(str): The name of the column in the DataFrame that contains the text data.random_state(int, optional): A random seed used for specify which rows to augment. Default is 42.weights(list, optional): A list of weights to determine the probability of selecting each augmentation type. Default is [0.5, 0.3, 0.2] for swap, delete, and split, respectively.
weights techniques :
- swap: randomly swap word in text.
- delete: randomly delete word in text.
- split: randomly split word in text.
Returns:
- pandas.DataFrame: A new DataFrame with the augmented data appended to the original data.
augment_random_character
Description:
The augment_random_character function performs random character-based augmentations on specific classes of text data within a DataFrame. It uses several augmentation techniques to randomly alter characters in the text, increasing the diversity of the dataset.
augment_random_character(df, classes_to_augment, augmentation_percentage, text_column, random_state=42, weights=[0.2, 0.2, 0.2, 0.2, 0.2])
Parameters:
df(pd.DataFrame): The input DataFrame containing text data and their corresponding labels.classes_to_augment(list): A list of class labels indicating which classes should be augmented.augmentation_percentage(float): The percentage of samples in each class that should be augmented.text_column(str): The column name in the DataFrame that contains the text data to be augmented.random_state(int, optional): A random seed used for specify which rows to augment. Default is 42.weights(list, optional): A list of weights for each augmentation technique, used to determine the probability of choosing each technique. Default is [0.2, 0.2, 0.2, 0.2, 0.2].
weights techniques :
- aug_ocr: OCR-based augmentation.
- aug_keyboard: Keyboard error simulation.
- aug_insert: Random character insertion.
- aug_swap: Random character swapping.
- aug_delete: Random character deletion.
Returns:
- pandas.DataFrame: A new DataFrame with the augmented data appended to the original data.
augment_word_bert
Description:
The augment_word_bert function augments text data in a DataFrame using a BERT-based word augmentation technique. It inserts or substitutes words in the specified text column for a given percentage of samples in the specified classes.
def augment_word_bert(df, classes_to_augment, augmentation_percentage, text_column, model_path, random_state=42, weights=[0.7, 0.3])
Parameters:
df(pandas.DataFrame): The DataFrame containing the data to be augmented.classes_to_augment(list): A list of class labels indicating which classes should be augmented.augmentation_percentage(float): The percentage of samples within each class to augment (e.g., 0.2 for 20%).text_column(str): The name of the column in the DataFrame that contains the text to be augmented.model_path(str): The path to the pre-trained BERT model used for augmentation.random_state(int, optional): A random seed used for specify which rows to augment. Default is 42.weights(list, optional): The weights for choosing between the insertion and substitution augmentation techniques (default is [0.7, 0.3]).
Returns:
- pandas.DataFrame: The original DataFrame with additional augmented samples.