Welcome to the lesson on Data Augmentation for LLM Training. In this lesson, we will explore how data augmentation can enhance the training of large-scale language models (LLMs). Data augmentation involves creating new data samples from existing ones, which can help improve model performance and generalization. By the end of this lesson, you will understand how to apply various data augmentation techniques to your datasets.

Before we dive into data augmentation, let's briefly recall the importance of having clean and well-prepared data. In previous lessons, we discussed techniques for efficient data storage, deduplication, and filtering. These steps ensure that your dataset is free from duplicates, non-English content, and toxicity, which is crucial for effective augmentation. Remember, clean data is the foundation for successful data augmentation.

One common data augmentation technique is synonym replacement, where words in a sentence are replaced with their synonyms. This can help create diverse training samples. We will use the WordNetAugmenter from the textattack library to perform synonym replacement.

First, let's import the necessary library and create a sample text:

Next, we create an instance of WordNetAugmenter and use it to augment the text:

In this code, WordNetAugmenter is used to replace words in the text with their synonyms. The augment method generates a new version of the text with synonyms. Let's see the output:

Example output:

Easy Data Augmentation (EDA) includes several techniques like synonym replacement, random insertion, and more. These techniques help create diverse training samples with minimal effort. We will use the EasyDataAugmenter class to demonstrate EDA.

First, import the necessary library and create a sample text:

Now, create an instance of EasyDataAugmenter and use it to augment the text:

The EasyDataAugmenter applies various EDA techniques to the text. The augment method generates a new version of the text with these techniques. Let's see the output:

Example output:

Back-translation is a technique where a sentence is translated to another language and then back to the original language. This can create diverse training samples by altering sentence structure while preserving meaning. We will use the BackTranslationAugmenter for this purpose.

First, import the necessary library and create a sample text:

Now, create an instance of BackTranslationAugmenter and use it to augment the text:

The BackTranslationAugmenter translates the text to another language and back to English. The augment method generates a new version of the text. Let's see the output:

Example output:

In this lesson, you learned about three data augmentation techniques: synonym replacement, Easy Data Augmentation (EDA), and back-translation. These techniques help create diverse training samples, improving the performance and generalization of large-scale language models. As you move on to the practice exercises, apply these techniques to see their effects on model training. Experiment with different methods to gain a deeper understanding of data augmentation. Keep up the great work, and enjoy the hands-on practice!