Hello text data: Train an AutoML text classification model

Starting on September 15, 2024, you can only customize classification, entity extraction, and sentiment analysis objectives by moving to Vertex AI Gemini prompts and tuning. Training or updating models for Vertex AI AutoML for Text classification, entity extraction, and sentiment analysis objectives will no longer be available. You can continue using existing Vertex AI AutoML Text models until June 15, 2025. For a comparison of AutoML text and Gemini, see Gemini for AutoML text users. For more information about how Gemini offers enhanced user experience through improved prompting capabilities, see Introduction to tuning. To get started with tuning, see Model tuning for Gemini text models

To learn more, run the "Create, train, and deploy an AutoML text classification model" Jupyter notebook in one of the following environments:

Open in Colab | Open in Colab Enterprise | Open in Vertex AI Workbench user-managed notebooks | View on GitHub

Use the Vertex AI console to train an AutoML text classification model. After your dataset is created and data is imported, use the Vertex AI console to start model training.

This tutorial has several pages:

1. Setting up your project and environment.

2. Creating a text classification dataset .

3. Training an AutoML text classification model.

4. Deploy model to an endpoint and send a prediction.

5. Cleaning up your project.

Each page assumes that you have already performed the instructions from the previous pages of the tutorial.

Train an AutoML model

1. In the Google Cloud console, go to the Model Registry page.

   Go to the Model Registry page

2. For Region, select us-central1 (Iowa).

3. Select Create to open the Train new model window.

4. In the Train new model window, complete the following steps:

   1. Click Continue.

   2. Click Start training.

Training takes several hours. You will receive an email notification when model training finishes.

What's next

Follow the next page of this tutorial to deploy your trained AutoML model to an endpoint and send documents to the model for prediction.