Choosing the Best Approach for Multi-Class Text Classification: A Comprehensive Guide

Text classification is one of the foundational tasks in natural language processing (NLP), widely used for spam detection, sentiment analysis, intent recognition, and categorizing large datasets. In this article, we’ll explore and compare four prominent approaches for multi-class text classification based on scalability, cost, performance, and accuracy. These methods include:

1. Text Embeddings with Cosine Similarity
2. GPT Models with Few-Shot Prompting
3. Fine-Tuning GPT Models
4. Fine-Tuning BERT or Sentence Transformer Models

Let’s dive into the nuances of each method, highlighting their strengths, weaknesses, and suitability for different scenarios.

1. Text Embeddings with Cosine Similarity

This approach involves converting both the text and predefined categories into vector representations using a model like OpenAI’s text-embedding-ada-002. Classification is achieved by calculating the cosine similarity between the text embedding and category embeddings, assigning the category with the highest similarity.

Example Code:

# Text Embeddings with Cosine Similarity

from sklearn.metrics.pairwise 
import cosine_similarity 
import openai import numpy as np
Example categories and Usertext
categories = ["Technology", "Support & Helpdesk", "Project Management"] 
usertext = "Share…