This article demonstrates how to perform text summarization using the scikit-llm library, which provides a simple interface for utilizing large language models within a scikit-learn style workflow. The guide walks through installing the necessary dependencies and implementing both extractive and abstractive summarization techniques on sample text data.
Key topics include:
- Introduction to the scikit-llm library
- Implementing abstractive summarization using LLMs
- Using scikit-llm for text classification and clustering tasks
- Practical code examples for integrating LLM capabilities into machine learning pipelines
Key topics include:
- Introduction to the scikit-llm library
- Implementing abstractive summarization using LLMs
- Using scikit-llm for text classification and clustering tasks
- Practical code examples for integrating LLM capabilities into machine learning pipelines
In this post, we'll explore how to use Hugging Face's Pipeline API to generate summaries with a zero-shot model and train a summarization model on the arXiv dataset. We'll also evaluate the trained model and compare it to the simple heuristic we developed in the previous post.
