When should I use embedding-based classification instead of fine-tuned models?

Use this approach when you need a quick text classification prototype for multi-class prediction tasks like sentiment scoring or rating prediction, especially when you have clear categorical distinctions. However, fine-tuned models typically outperform embeddings for maximum classification accuracy.

What data do I need to provide, and what will I get back?

You provide a dataset of food reviews with text and corresponding 1-5 star scores that must be prepared using the Get_embeddings_from_dataset Notebook. You receive a trained classification model that predicts review scores from text, along with performance metrics showing how well the model distinguishes between different ratings.

Prompt Chain

Classify Text Using Embeddings

Name: Classification Using Embeddings
Availability: OnlineOnly
Author: OpenAI Cookbook

Jupyter notebook demonstrating text classification using embeddings to predict food review scores (1-5 stars) from review text, with training/testing split for

Copy chain

OpenAI Cookbook

Own this? Claim it

Spark score

out of 100

Updated last month

Version 1.0.0

Add to Favorites

Why it matters

Leverage text embeddings to predict the score of food reviews. This asset demonstrates a practical application of embeddings for text classification tasks.

Outcomes

What it gets done

Generate embeddings for text data.

Train a classifier using generated embeddings.

Evaluate classification performance on unseen data.

Predict review scores based on text content.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/oai-classificationusingembeddings | bash

Overview

Classification Using Embeddings

This Jupyter notebook demonstrates text classification using embeddings to predict food review scores from 1 to 5 stars based on review text. It splits data into training and testing sets to evaluate performance on unseen data, showing that extreme ratings (5-star and 1-star) are easier to predict than mid-range scores. Use this when prototyping embedding-based text classification for rating prediction or sentiment analysis tasks. Consider fine-tuned models instead when you need better accuracy, and ensure you have more training examples than embedding dimensions for optimal results.

What it does

This notebook demonstrates how to build a text classification system using embeddings to predict food review scores on a 1-to-5 scale based on review text. It walks through splitting a dataset into training and testing sets, applying embeddings to classify text, and evaluating model performance on unseen data to realistically assess accuracy across different star ratings.

When to use - and when NOT to

Use this approach when you need a quick text classification prototype using embeddings for multi-class prediction tasks like sentiment scoring or rating prediction. It works well when you have clear categorical distinctions (like 5-star vs 1-star reviews) that are easier to predict.

Do not use this method when you need maximum classification accuracy, as fine-tuned models typically outperform embeddings for many text classification tasks. Also avoid this approach when you have fewer examples than embedding dimensions, as the source notes this limitation affects the notebook's own dataset.

Inputs and outputs

You provide a dataset of food reviews with text content and corresponding scores (1-5 stars). The dataset must be prepared using the Get_embeddings_from_dataset Notebook to generate embeddings. You receive a trained classification model that predicts review scores from text, along with performance metrics showing how well the model distinguishes between different star ratings. The output includes evaluation results showing that 5-star and 1-star reviews are easier to predict, while 2-4 star reviews show more subjectivity and require more data for accurate prediction.

Who it's for

This notebook is for data scientists and machine learning practitioners exploring text classification approaches using embeddings. It suits those prototyping sentiment analysis or rating prediction systems who want to understand the performance characteristics of embedding-based classification, particularly the trade-offs between ease of implementation and accuracy compared to fine-tuned models. Users should be comfortable with Jupyter notebooks and have datasets where extreme categories (highest and lowest ratings) are more common, as these show the best performance in this approach.

Source README

Classification using embeddings

There are many ways to classify text. This notebook shares an example of text classification using embeddings. For many text classification tasks, we've seen fine-tuned models do better than embeddings. See an example of fine-tuned models for classification in Fine-tuned_classification.ipynb. We also recommend having more examples than embedding dimensions, which we don't quite achieve here.

In this text classification task, we predict the score of a food review (1 to 5) based on the embedding of the review's text. We split the dataset into a training and a testing set for all the following tasks, so we can realistically evaluate performance on unseen data. The dataset is created in the Get_embeddings_from_dataset Notebook.

We can see that the model has learnt to distinguish between the categories decently. 5-star reviews show the best performance overall, and this is not too surprising, since they are the most common in the dataset.

Unsurprisingly 5-star and 1-star reviews seem to be easier to predict. Perhaps with more data, the nuances between 2-4 stars could be better predicted, but there's also probably more subjectivity in how people use the inbetween scores.

FAQ

Common questions

Discussion