Zero-Shot and Few-Shot Text Classification with SCIKIT-LLM

Analyzing customer feedback and identifying key themes in textual data is traditionally a laborious process. It involves data gathering, manual labeling, and the fine-tuning of specialized models. Zero-shot text classification, however, offers a streamlined approach, leveraging the power of Large Language Models (LLMs) to bypass the need for extensive model training. This article explores how zero-shot classification simplifies sentiment analysis using the SKLLM library (combining scikit-learn and LLMs), demonstrating its application on the Kaggle Women’s E-Commerce Clothing Reviews dataset.

Key Learning Outcomes

This tutorial will cover:

• The conventional sentiment analysis workflow and its limitations.
• The principles and benefits of zero-shot text classification with LLMs.
• An introduction to the SKLLM library and its integration with scikit-learn.
• Practical application of zero-shot classification to the Women’s E-Commerce Clothing Reviews dataset.
• Hands-on experience with zero-shot classification for real-world scenarios.

*This article is part of the***Data Science Blogathon.

Table of contents

• What is Zero-Shot Text Classification?
• Why is Zero-Shot So Efficient?
• Dataset Overview
• Step-by-Step Guide
• Potential Drawbacks
• Few-Shot Text Classification
• Chain-of-Thought Text Classification
• Summary
• Frequently Asked Questions

What is Zero-Shot Text Classification?

Analyzing the large volume of customer reviews received by online retailers presents a significant challenge for efficient sentiment analysis and theme identification. Traditional methods involve:

• Gathering and cleaning review data.
• Manually labeling thousands of samples (e.g., "positive," "negative," "neutral").
• Fine-tuning a specialized classification model using this labeled data.

This process is time-consuming and resource-intensive. Zero-shot text classification offers a solution: using LLMs directly to classify text without the need for custom training. By providing descriptive labels (e.g., "positive," "negative," "neutral"), the model infers the correct class.

Why is Zero-Shot So Efficient?

The efficiency of zero-shot classification stems from:

• Elimination of Fine-Tuning: The costly process of fine-tuning LLMs like GPT-4 is avoided. Pre-trained LLMs are used directly, providing immediate high-quality classification.
• Easy Label Adaptation: Changing the label set (e.g., from general sentiments to more specific ones) only requires updating the label list; no model retraining is needed.
• Reduced Data Requirements: Unlike supervised learning, zero-shot classification requires only descriptive labels, making it suitable for situations with limited or unlabeled data.
• Faster Deployment: By skipping data annotation and model training, deployment is significantly accelerated.

Dataset Overview

The Women’s E-Commerce Clothing Reviews dataset from Kaggle is used in this tutorial.

[Link to Dataset]

Key dataset characteristics:

• Contains thousands of customer reviews on women's clothing.
• The "Review Text" column contains the main text data.
• Additional metadata ("Title," "Rating," "Recommended IND," etc.) is available but not essential for zero-shot classification.

Step-by-Step Guide

This section details how to perform sentiment analysis and theme detection using zero-shot classification with LLMs and the SKLLM library.

Step 1: Installation and Setup

Ensure Python 3.7 is installed and install SKLLM:

pip install scikit-llm

Obtain a valid API key for an LLM provider (e.g., OpenAI) and set it in your environment:

from skllm.config import SKLLMConfig

# Replace with your OpenAI API key
SKLLMConfig.set_openai_key("your_openai_api_key")

Step 2: Import Libraries and Load Data

import pandas as pd
from skllm.models.gpt.classification.zero_shot import ZeroShotGPTClassifier

# Load dataset
df = pd.read_csv("Womens Clothing E-Commerce Reviews.csv")

# Handle missing review texts
df = df.dropna(subset=["Review Text"]).reset_index(drop=True)
X = df["Review Text"].tolist()

Step 3: Define Labels

For sentiment classification, use: ["positive", "negative", "neutral"]. This can be customized as needed.

Step 4: Zero-Shot Classification

Instantiate ZeroShotGPTClassifier (using gpt-4o or another suitable model):

clf = ZeroShotGPTClassifier(model="gpt-4o")
clf.fit(None, ["positive", "negative", "neutral"])

fit(None, labels) indicates that no training data is required; the classifier is initialized with the label set.

Step 5: Classify Reviews

predictions = clf.predict(X)

for review_text, sentiment in zip(X[:5], predictions[:5]):
    print(f"Review: {review_text}")
    print(f"Predicted Sentiment: {sentiment}")
    print("-" * 50)

This displays the first five reviews and their predicted sentiments.

Results Comparison

Traditional ML approaches require labeling, model training, validation, and continuous updates. Zero-shot significantly reduces this overhead, offering immediate results without labeled data and easy label refinement.

Potential Drawbacks

• Accuracy Fluctuations: Accuracy can vary depending on the complexity of the text and the model's ability to interpret domain-specific jargon.
• Cost Considerations: Using powerful models like GPT-4 incurs API costs.
• Data Privacy: Ensure compliance with data privacy regulations when sending data to external APIs.

Few-Shot Text Classification

Few-shot classification uses a small number of labeled examples per class to guide the model. The SKLLM estimators use the entire training set to create few-shot examples. For large datasets, consider splitting the data and using a small training subset (e.g., no more than 10 examples per class) and shuffling the examples.

pip install scikit-llm

Chain-of-Thought Text Classification

Chain-of-thought classification generates intermediate reasoning steps, potentially improving accuracy but increasing token usage and cost.

from skllm.config import SKLLMConfig

# Replace with your OpenAI API key
SKLLMConfig.set_openai_key("your_openai_api_key")

Experimenting with few-shot and chain-of-thought approaches may yield better results than the baseline zero-shot method.

Summary

The SKLLM library provides a fast and efficient alternative to building custom sentiment analysis pipelines. Zero-shot classification enables rapid analysis of customer feedback without the need for manual labeling or model training. This is particularly valuable for iterative tasks and label expansion.

Key Points

• Zero-shot classification simplifies sentiment analysis without manual labeling or model training.
• SKLLM integrates scikit-learn with LLMs for efficient text classification.
• LLMs like GPT-4 provide high-quality classification results immediately.
• Zero-shot classification is fast, adaptable, and requires minimal data.

Frequently Asked Questions

Q1. Choosing between zero-shot, few-shot, and chain-of-thought: Zero-shot is ideal for quick prototyping and limited data; few-shot improves accuracy with a small labeled dataset; chain-of-thought enhances performance but increases cost.

Q2. Number of examples for few-shot: Up to 10 examples per class are recommended; shuffle examples to avoid bias.

Q3. Chain-of-thought impact on accuracy: Not guaranteed to improve accuracy; effectiveness depends on task complexity and prompt clarity.

Q4. Cost at scale: Cost depends on token usage, model choice, prompt length, and dataset size. Chain-of-thought increases cost due to longer prompts.

Note: The image used in this article is not owned by the author and is used with permission.

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