text-augmentation

Text Augmentation

Welcome to the Text Augmentation project! This project focuses on augmenting text data using various techniques to increase the size and diversity of training datasets for NLP models. By generating new samples from existing data, we can improve the performance and robustness of our models. Let's explore different text augmentation methods and apply them to real-world datasets! 📈

Key Techniques 🔧

• Synonym Replacement 🔄: Replace words with their synonyms to generate new text.
• Random Insertion ➕: Insert random words into sentences to create variations.
• Random Deletion ➖: Remove words randomly to simulate noise in data.
• Random Swap ↔️: Swap words within sentences to change their order.
• Back Translation 🔄: Translate text to another language and back to the original language for augmentation.
• TextAttack 🛡️: Use the TextAttack library for advanced text augmentation techniques.

Lets dive into the notebook 📔