TXTGenie

TextGenie is a Python library for text data augmentation. It can be used to generate similar kinds of samples from a text dataset, thus generating a more robust dataset to train better models. It can also take care of labeled datasets while generating similar samples keeping their labels in memory. TextGenie uses various Natural Language Processing methods such as paraphrase generation, BERT mask filling, and converting text to active voice if found in passive voices. This library currently supports the English language.

TextGenie is a valuable tool for anyone who wants to improve the quality and size of their text dataset. It is easy to use and offers a variety of features to help users generate similar kinds of samples from their text dataset.

Here are some potential use cases for TextGenie:

• Improving the quality of text datasets for machine learning models. TextGenie can be used to generate similar kinds of samples from a text dataset, which can help to improve the performance of machine learning models trained on that dataset.
• Creating new text datasets for tasks such as natural language generation and translation. TextGenie can be used to generate new text datasets from existing datasets, which can be useful for tasks such as natural language generation and translation.
• Augmenting existing text datasets for tasks such as sentiment analysis and question answering. TextGenie can be used to augment existing text datasets with new samples, which can help to improve the performance of machine learning models trained on those datasets for tasks such as sentiment analysis and question answering.

Here are some of the pros and cons of using TextGenie:

Pros:

• Easy to use
• Variety of features
• Supports labeled datasets
• Can generate similar kinds of samples from a text dataset
• Can be used to improve the quality and size of text datasets

Cons:

• Currently only supports the English language
• Some features may not be fully functional

Overall, TextGenie is a promising new tool for text data augmentation. It is easy to use and offers a variety of features that can help users generate similar kinds of samples from their text dataset.

Here are some tips for using TextGenie:

• Start by installing the TextGenie library.
• Create a new project and load your text dataset into TextGenie.
• Choose the text augmentation methods that you want to use.
• Generate new samples from your text dataset.
• Evaluate the new samples and make adjustments to your text augmentation parameters as needed.

I hope this helps!