NLP with LLMs: Hugging Face Classification QnA & Text Generation Pipelines

“Sentiment analysis named entity recognition (NER) question answering and text generation are pivotal tasks in the realm of Natural Language Processing (NLP) that enable machines to interpret and understand human language in a nuanced manner.

In this course you will be introduced to the concept of Hugging Face pipelines a streamlined approach to applying pre-trAIned models to a variety of NLP tasks. Through hands-on exploration you will learn how to classify text using zero-shot classification techniques perform sentiment analysis with DistilBERT and apply models to specialized tasks utilizing the power of NLP to adapt to niche domAIns.

Next you will discover how to employ models to accurately answer questions based on provided contexts and understand the mechanics behind model-based answers including their limitations and capabilities.

Finally you will discover various text generation strategies such as greedy search and beam search learning how to balance predictability with creativity in generated text. You will also explore text generation through sampling techniques and the application of mask filling with BERT models.”