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.