Enhancing NLP Tasks Using LLMs with spacy-llm
In this chapter, we will build upon the knowledge gained in Chapter 6, and explore how to integrate large language models (LLMs) into spaCy pipelines using the spacy-llm library. We will start by understanding the basics of LLMs and prompt engineering, and how these powerful models can be leveraged to perform a wide range of NLP tasks within spaCy. We’ll demonstrate how to configure and use pre-built LLM tasks such as text summarization, and then take a step further by creating a custom task to extract contextual information from text. This will involve using Jinja templates for prompt creation and writing custom spaCy components that can efficiently handle complex NLP tasks. By the end of this chapter, you will have a deeper understanding of how to enhance traditional NLP pipelines with the flexibility and power of LLMs.
In this chapter, we’re going to cover the following main topics:
- LLMs and prompt engineering...
