Multi-Label Text Classification with Scikit-LLM

2026-06-11 · Source: MachineLearningMastery.com - Machinelearningmastery.com · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

This article demonstrates multi-label text classification using large language models (LLMs) and the scikit-LLM library, eliminating the need for labeled training data or complex model training. It details setting up scikit-LLM with a free, open-source LLM from Groq, specifically "llama-3.3-70b-versatile", for zero-shot inference. The process involves configuring an API key, setting a custom endpoint, and initializing a MultiLabelZeroShotGPTClassifier. The guide then shows how to load the "google-research-datasets/go_emotions" dataset, using 100 comments, and define a set of candidate labels like "admiration" and "anger". The LLM is "fitted" by simply exposing it to these labels, enabling it to assign multiple sentiments to a single text, such as "amusement" and "joy" for a sample comment. The article highlights that inference can be computationally intensive despite the lack of traditional training.

Key takeaway

For Machine Learning Engineers or Data Scientists building text classification systems, you can now implement multi-label sentiment analysis without extensive labeled datasets. Utilize scikit-LLM with a free LLM like Groq's "llama-3.3-70b-versatile" to quickly deploy zero-shot classifiers. Consider expanding your candidate label set or experimenting with few-shot strategies to sharpen predictions, and always build an evaluation loop for production use cases.

Key insights

Scikit-LLM enables zero-shot multi-label text classification using LLMs with a scikit-learn-like workflow.

Principles

• LLMs can perform multi-label classification without labeled training data.
• Zero-shot reasoning simplifies complex text categorization tasks.
• Wrapper libraries streamline LLM integration into existing ML workflows.

Method

Configure scikit-LLM with an LLM API key and endpoint, initialize a MultiLabelZeroShotGPTClassifier, then "fit" it by providing only the candidate labels for zero-shot inference.

In practice

• Use Groq's free LLMs for fast, quota-free inference.
• Apply "go_emotions" dataset for multi-label sentiment analysis.
• Expand candidate label sets to refine domain-specific predictions.

Topics

• Multi-label Text Classification
• Large Language Models
• scikit-LLM
• Zero-shot Learning
• Groq
• Sentiment Analysis

Best for: Machine Learning Engineer, NLP Engineer, Data Scientist

Related on AIssential

• Using a Local LLM as a Zero-Shot Classifier — Locally hosted LLMs can effectively perform zero-shot classification on short, semantically complex texts where traditional methods fail.
• Text Summarization with Scikit-LLM — Integrate LLM-driven text summarization into scikit-learn pipelines to manage large text volumes efficiently.
• Scikit-LLM vs. Traditional Text Classifiers: When Should You Use an LLM? — LLMs offer superior zero-shot text classification accuracy and competitive latency, especially with limited data.
• On GATE, Text and Social Media Analysis, and Detecting Misinformation Online — A multi-layer analytical pipeline effectively extracts and categorizes digital identity aspects of agrifood products from online discourse.
• Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? — Zero-shot learning offers a viable solution for sentiment analysis in software engineering, mitigating annotated data scarcity.
• BanglaBERT vs. Frontier LLMs: Diagnosing Zero-Shot Collapse in Bangla NLP — Frontier LLMs experience zero-shot collapse on low-resource Bangla NLP tasks, necessitating fine-tuned models or few-shot scaling for nuanced sentiment.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by MachineLearningMastery.com - Machinelearningmastery.com.