Using LLM Summarization in Your Scikit-Learn Pipelines with Scikit-LLM

Introduction

Using traditional, lightweight machine learning classifiers on text frequently requires condensing lengthy, often noisy raw text into clean signals to succeed. Therefore, it wouldn't be crazy to think of using a large language model (LLM) for text summarization as a preprocessing stage before feeding cleaner texts to the downstream classification model. What if we could do this seamlessly as part of a traditional, scikit-learn-based pipeline where LLM-driven summarization can coexist with traditional data preprocessing, training, and inference? And what if we could do all of this using exclusively free tools? Look no further.

In this article, we will learn how to build an architecture that blends Scikit-LLM estimators for text summarization with traditional scikit-learn methods into a single, unified pipeline for summarizing and classifying raw, messy text, bringing together the best of both worlds: LLMs and classical machine learning models.

Integrating LLM-Driven Summarization in a Machine Learning Pipeline

This example can be run entirely in a Google Colab notebook. It uses Groq's LLM API to fully rely on free models. A prerequisite is to access the Groq website, register, and generate your own API key. Once it's generated, carefully copy your API key (it is shown only once) and paste it as a new "Secret" variable in Google Colab, found at the key icon on the left-hand side of Colab. Name the secret variable GROQ_API_KEY.

After that, we start our notebook by installing Scikit-LLM:

!pip install scikit-llm -q

Now, into the Python code. The first step is to configure the environment surrounding Scikit-LLM, routing our requests through Groq's API instead of the default OpenAI API. This will help us leverage Groq's free, friendly, and efficient pre-trained models, bypassing per-token costs.

Next, we take a look at the summaries produced for the three training examples to verify they make sense:

Output:

And these are the final results we obtain — not bad at all!