NLP with LLMs: Fine-tuning Models for Classification & Question Answering

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Fine-tuning in the context of text-based models refers to the process of taking a pre-trained model and adapting it to a specific task or dataset with additional training. This technique leverages the general language understanding capabilities acquired by the model during its initial extensive training on a large corpus of text and refines its abilities to perform well on a more narrowly defined task or domain-specific data. In this course, you will learn how to fine-tune a model for sentiment analysis, starting with the preparation of datasets optimized for this purpose. You will be guided through setting up your computing environment and preparing a BERT classifier for sentiment analysis. Next, you will discover how to structure text data and align named entity recognition (NER) tags with subword tokenization. You will build on this knowledge to fine-tune a BERT model specifically for NER, training it to accurately identify and classify entities within text. Finally, you will explore the domain of question answering, learning to handle the challenges of long contexts to extract precise answers from extensive texts. You will prepare QnA data for fine-tuning and utilize a DistilBERT model to create an effective QnA system.