Set up your first classification model

This article walks you through creating a classification model from scratch, from naming it to measuring its accuracy. If you are not yet sure whether classification is the right model type, read the "What is a classification model?" article first.

Prerequisites

Before you begin, make sure you have:

• Access to the Models section of the Tekst platform.
• A clear idea of the categories (labels) you want the model to choose from.
• A few real example messages in mind that you can use to check the model's behavior.

Step 1: Create the model

1. Go to the Models section and select Create model.
2. Choose Classification model as the type.
3. Select Let's get started.
4. Enter a Model name and an optional Model description.
5. Choose which inboxes the model should be linked to. By default it can apply to all of your inboxes, or you can limit it to specific ones.
6. Select Create Model.

Tekst creates the model and opens its detail page, where you will configure the rest.

Step 2: Add your labels

On the model detail page you will see the label configuration table. This is where you define the categories the model can assign.

1. Select Add label.
2. Enter a clear name for the label.
3. Add a description that explains, in plain language, when this label should apply. Good descriptions make a large difference to accuracy, so be specific.
4. Repeat for each category you need.

If your categories naturally nest, you can add child labels under a parent label to build a hierarchy.

Step 3: Choose single-label or multi-label

Decide whether each message should receive exactly one label (single-label) or can receive several at once (multi-label), and set the model accordingly. Choose multi-label only when a message can genuinely belong to more than one category at the same time.

Step 4: Build a test set

A test set is a collection of real messages for which you have confirmed the correct label. It is how Tekst measures the model's accuracy and how the model learns your business.

1. Add conversations to the test set.
2. For each one, confirm the correct label (the "ground truth").
3. Aim for a representative spread across all of your labels, including the ones that occur less often.

Step 5: Review accuracy

Once you have a test set, Tekst evaluates the model and reports its accuracy. Open the model's accuracy view to see how it is performing overall and per label, and to spot which labels need clearer descriptions or more examples.

For a full explanation of how the score is calculated, see the "How classification accuracy is measured" article.

Step 6: Publish the model

When you are happy with the model, publish it so it starts running on live messages. The model's status badge shows whether it is up to date or still processing your latest changes.

What happens next

As your team accepts or corrects the model's predictions, those corrections feed back into the model and improve it over time. Tekst continuously monitors performance and retrains as needed - see How often are the AI models retrained?.