What does this notebook help me build?

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, including dataset splitting, embedding application, and model evaluation.

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.

Which review ratings are easiest and hardest to predict with this method?

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.

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Classify Text Using Embeddings

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

This notebook classifies food reviews into 1-5 star ratings using text embeddings, evaluated on a held-out test set.

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Source checked Aug 19, 2026

Version 1.0.0

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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.

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Overview

Classification Using Embeddings

This notebook classifies food reviews into 1-5 star ratings using text embeddings, splitting data into training and test sets for an honest evaluation. It notes that fine-tuned models often outperform embeddings for classification tasks. Use it as a starting example for embedding-based text classification before reaching for fine-tuning. Best on extreme categories; middle scores are noisier due to subjective labeling.

What it does

This notebook demonstrates text classification using embeddings: it predicts a food review's star rating (1 to 5) from the embedding of the review's text, splitting the dataset into training and test sets to evaluate performance honestly on unseen data.

When to use - and when NOT to

Use it as a starting point for embedding-based text classification, or to see a worked example of train/test evaluation on embeddings. The notebook itself flags that fine-tuned models generally outperform embeddings for many classification tasks, pointing to a companion fine-tuning notebook for that alternative, and recommends having more training examples than embedding dimensions - a condition this particular example doesn't fully meet.

Inputs and outputs

Input is the pre-computed review-text embeddings from the companion embeddings-dataset notebook, paired with their 1-5 star labels. Output is a model that predicts the star rating from the embedding; in this run it distinguished categories decently overall, performing best on 5-star reviews (the most common in the dataset) and doing better on the extremes, 5-star and 1-star, than on the middle scores, where more subjective labeling likely adds noise.

Integrations

Part of the OpenAI Cookbook's embeddings examples, alongside the notebook that builds the embedding dataset and the fine-tuning notebook it's compared against.

Who it's for

Developers exploring whether embeddings are sufficient for a text classification task before reaching for fine-tuning.

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 in-between scores.

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