Overview

In this lab, you implement text models to recognize the probable source (Github, Tech-Crunch, or The New-York Times) of titles present in the title dataset, which are created in the respective labs.

Learning objectives

In this lab, you learn how to:

• Use pre-trained TF Hub text modules to generate sentence vectors.
• Incorporate a pre-trained TF-Hub module into a Keras model.

Setup and requirements

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Google Skills using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time.
   There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.
   If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

Enable the AI Platform Training & Prediction API

1. On the Navigation menu, navigate to APIs & services > Library and search for AI Platform Training & Prediction API in the search box.
2. Click on AI Platform Training & Prediction API, then click Enable.

Task 1. Create a Cloud Storage bucket

1. Navigate to Navigation menu > Cloud Storage in the Cloud console for your project, then click CREATE BUCKET.

2. Set a unique name (use your project ID because it is unique) and then choose region as . Then, click Create.

3. Confirm Enforce public access prevention on this bucket on "Public access will be prevented" pop-up.

Task 2. Open the notebook in Vertex AI Workbench

1. In the Google Cloud console, from the Navigation menu (), select Vertex AI > Dashboard.

2. In the Navigation menu, click Workbench.

   At the top of the Workbench page, ensure you are in the Instances view.

3. Find the cloudlearningservices instance and click on the Open JupyterLab button.

The JupyterLab interface for your Workbench instance opens in a new browser tab.

4. Click the Python 3 icon to launch a new Python notebook.

5. Right-click on the Untitled.ipynb file in the menu bar and select Rename Notebook to give it a meaningful name.

Your environment is set up. You are now ready to start working with your Vertex AI Workbench notebook.

Task 3. Clone a course repo within your JupyterLab interface

The GitHub repo contains both the lab file and solutions files for the course.

1. Copy and run the following code in the first cell of your notebook to clone the training-data-analyst repository.

2. Confirm that you have cloned the repository. Double-click on the training-data-analyst directory and ensure that you can see its contents.

Click Check my progress to verify the objective. Clone a course repo within your JupyterLab interface

Task 4. Classify text using reusable embeddings

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > text_classification > labs > reusable_embeddings.ipynb.

In the Select Kernel dialog, choose TensorFlow 2-11 (Local) from the list of available kernels.

2. In the notebook interface, click on Edit > Clear All Outputs (click on Edit, then in the drop-down menu, select Clear All Outputs).

3. Carefully read through the notebook instructions and fill in lines marked with #TODO where you need to complete the code as needed

Tip: To run the current cell, click the cell and press SHIFT+ENTER. Other cell commands are listed in the notebook UI under Run.

• Hints may also be provided for the tasks to guide you along. Highlight the text to read the hints (they are in white text).
• If you need more help, look at the complete solution by navigating to training-data-analyst > courses > machine_learning > deepdive2 > text_classification > solutions and opening reusable_embeddings.ipynb.

Click Check my progress to verify the objective. Classify text using reusable embeddings

End your lab

When you have completed your lab, click End Lab. Google Skills removes the resources you’ve used and cleans the account for you.

You will be given an opportunity to rate the lab experience. Select the applicable number of stars, type a comment, and then click Submit.

The number of stars indicates the following:

• 1 star = Very dissatisfied
• 2 stars = Dissatisfied
• 3 stars = Neutral
• 4 stars = Satisfied
• 5 stars = Very satisfied

You can close the dialog box if you don't want to provide feedback.

For feedback, suggestions, or corrections, please use the Support tab.

Copyright 2026 Google LLC All rights reserved. Google and the Google logo are trademarks of Google LLC. All other company and product names may be trademarks of the respective companies with which they are associated.