Console
    Contact Us Start free

    Generate text by using a Gemma open model and the ML.GENERATE_TEXT function

    This tutorial shows you how to create a remote model that's based on the Gemma model , and then how to use that model with the ML.GENERATE_TEXT function to extract keywords and perform sentiment analysis on movie reviews from the bigquery-public-data.imdb.reviews public table.

    Required permissions

    To run this tutorial, you need the following Identity and Access Management (IAM) roles:

    • Create and use BigQuery datasets, connections, and models: BigQuery Admin ( roles/bigquery.admin ).
    • Grant permissions to the connection's service account: Project IAM Admin ( roles/resourcemanager.projectIamAdmin ).
    • Deploy and undeploy models in Vertex AI: Vertex AI Administrator ( roles/aiplatform.admin ).

    These predefined roles contain the permissions required to perform the tasks in this document. To see the exact permissions that are required, expand the Required permissionssection:

    Required permissions

    • Create a dataset: bigquery.datasets.create
    • Create, delegate, and use a connection: bigquery.connections.*
    • Set the default connection: bigquery.config.*
    • Set service account permissions: resourcemanager.projects.getIamPolicy and resourcemanager.projects.setIamPolicy
    • Deploy and undeploy a Vertex AI model:
      • aiplatform.endpoints.deploy
      • aiplatform.endpoints.undeploy
    • Create a model and run inference:
      • bigquery.jobs.create
      • bigquery.models.create
      • bigquery.models.getData
      • bigquery.models.updateData
      • bigquery.models.updateMetadata

    You might also be able to get these permissions with custom roles or other predefined roles .

    Costs

    In this document, you use the following billable components of Google Cloud:

    • BigQuery ML : You incur costs for the data that you process in BigQuery.
    • Vertex AI : You incur costs for calls to the Vertex AI model that's represented by the remote model.

    To generate a cost estimate based on your projected usage, use the pricing calculator .

    New Google Cloud users might be eligible for a free trial .

    For more information about BigQuery pricing, see BigQuery pricing in the BigQuery documentation.

    Open models that you deploy to Vertex AI are charged per machine-hour. This means billing starts as soon as the endpoint is fully set up, and continues until you un-deploy it. For more information about Vertex AI pricing, see the Vertex AI pricing page.

    Before you begin

    1. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

      Roles required to select or create a project

      • Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
      • Create a project : To create a project, you need the Project Creator ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

      Go to project selector

    2. Verify that billing is enabled for your Google Cloud project .

    3. Enable the BigQuery, BigQuery Connection, and Vertex AI APIs.

      Roles required to enable APIs

      To enable APIs, you need the Service Usage Admin IAM role ( roles/serviceusage.serviceUsageAdmin ), which contains the serviceusage.services.enable permission. Learn how to grant roles .

      Enable the APIs

    Create a dataset

    Create a BigQuery dataset to store your ML model.

    Console

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to the BigQuery page

    2. In the Explorerpane, click your project name.

    3. Click View actions > Create dataset

    4. On the Create datasetpage, do the following:

      • For Dataset ID, enter bqml_tutorial .

      • For Location type, select Multi-region, and then select US (multiple regions in United States).

      • Leave the remaining default settings as they are, and click Create dataset.

    bq

    To create a new dataset, use the bq mk command with the --location flag. For a full list of possible parameters, see the bq mk --dataset command reference.

    1. Create a dataset named bqml_tutorial with the data location set to US and a description of BigQuery ML tutorial dataset :

      bq --location=US mk -d \
 --description "BigQuery ML tutorial dataset." \
 bqml_tutorial

      Instead of using the --dataset flag, the command uses the -d shortcut. If you omit -d and --dataset , the command defaults to creating a dataset.

    2. Confirm that the dataset was created:

      bq  
ls

    API

    Call the datasets.insert method with a defined dataset resource .

     { 
  
 "datasetReference" 
 : 
  
 { 
  
 "datasetId" 
 : 
  
 "bqml_tutorial" 
  
 } 
 }

    BigQuery DataFrames

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      import 
  
 google.cloud.bigquery 
 bqclient 
 = 
 google 
 . 
 cloud 
 . 
  bigquery 
 
 . 
  Client 
 
 () 
 bqclient 
 . 
  create_dataset 
 
 ( 
 "bqml_tutorial" 
 , 
 exists_ok 
 = 
 True 
 )

    Create the remote model

    Create a remote model that represents a hosted Vertex AI model:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, run the following statement:

     CREATE 
  
 OR 
  
 REPLACE 
  
 MODEL 
  
 ` 
 bqml_tutorial 
 . 
 gemma_model 
 ` 
  
 REMOTE 
  
 WITH 
  
 CONNECTION 
  
 DEFAULT 
  
 OPTIONS 
  
 ( 
  
 MODEL_GARDEN_MODEL_NAME 
  
 = 
  
 'publishers/google/models/gemma3@gemma-3-270m' 
 , 
  
 MACHINE_TYPE 
  
 = 
  
 'g2-standard-12' 
  
 );

    The query takes up to 20 minutes to complete, after which the gemma_model model appears in the bqml_tutorial dataset in the Explorerpane. Because the query uses a CREATE MODEL statement to create a model, there are no query results.

    Perform keyword extraction

    Perform keyword extraction on IMDB movie reviews by using the remote model and the ML.GENERATE_TEXT function:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, enter the following statement to perform keyword extraction on five movie reviews:

       SELECT 
  
 * 
 FROM 
  
 ML 
 . 
 GENERATE_TEXT 
 ( 
  
 MODEL 
  
 ` 
 bqml_tutorial 
 . 
 gemma_model 
 ` 
 , 
  
 ( 
  
 SELECT 
  
 CONCAT 
 ( 
 'Extract the key words from the movie review below: ' 
 , 
  
 review 
 ) 
  
 AS 
  
 prompt 
 , 
  
 * 
  
 FROM 
  
 ` 
 bigquery 
 - 
 public 
 - 
 data 
 . 
 imdb 
 . 
 reviews 
 ` 
  
 LIMIT 
  
 10 
  
 ), 
  
 STRUCT 
 ( 
  
 0 
 . 
 2 
  
 AS 
  
 temperature 
 , 
  
 100 
  
 AS 
  
 max_output_tokens 
 , 
  
 TRUE 
  
 AS 
  
 flatten_json_output 
 ));

      The output is similar to the following, with non-generated columns omitted for clarity:

      +----------------------------------------------+-------------------------+-----------------------------+-----+
| generated_text                               | ml_generate_text_status | prompt                      | ... |
+----------------------------------------------+-------------------------------------------------------+-----+
| Here are some key words from the             |                         | Extract the key words from  |     |
| movie review: * **Romance:**                 |                         | the movie review below:     |     |
| "romantic tryst," "elope" * **Comedy:**      |                         | Linda Arvidson (as Jennie)  |     |
| "Contrived Comedy" * **Burglary:**           |                         | and Harry Solter (as Frank) |     |
| "burglar," "rob," "booty" * **Chase:**       |                         | are enjoying a romantic     |     |
| "chases," "escape" * **Director:** "D.W.     |                         | tryst, when in walks her    |     |
| Griffith" * **Actors:** "Linda Arvidson,"... |                         | father Charles Inslee;...   |     |
+----------------------------------------------+-------------------------+-----------------------------+-----+
| Here are some key words from the             |                         | Extract the key words from  |     |
| movie review: * **Elderbush Gilch:** The     |                         | the movie review below:     |     |
| name of the movie being reviewed. *          |                         | This is the second addition |     |
| **Disappointment:** The reviewer's           |                         | to Frank Baum's personally  |     |
| overall feeling about the film. *            |                         | produced trilogy of Oz      |     |
| **Dim-witted:** Describes the story          |                         | films. It's essentially the |     |
| line negatively. * **Moronic, sadistic,...   |                         | same childishness as the... |     |
+----------------------------------------------+-------------------------+-----------------------------+-----+

      The results include the following columns:

      • generated_text : the generated text.
      • ml_generate_text_status : the API response status for the corresponding row. If the operation was successful, this value is empty.
      • prompt : the prompt that is used for the sentiment analysis.
      • All of the columns from the bigquery-public-data.imdb.reviews table.

    Perform sentiment analysis

    Perform sentiment analysis on IMDB movie reviews by using the remote model and the ML.GENERATE_TEXT function:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

    2. In the query editor, run the following statement to perform sentiment analysis on five movie reviews:

       SELECT 
  
 * 
 FROM 
  
 ML 
 . 
 GENERATE_TEXT 
 ( 
  
 MODEL 
  
 ` 
 bqml_tutorial 
 . 
 gemma_model 
 ` 
 , 
  
 ( 
  
 SELECT 
  
 CONCAT 
 ( 
 'Analyze the sentiment of the following movie review and classify it as either POSITIVE or NEGATIVE. \nMovie Review: ' 
 , 
  
 review 
 ) 
  
 AS 
  
 prompt 
 , 
  
 * 
  
 FROM 
  
 ` 
 bigquery 
 - 
 public 
 - 
 data 
 . 
 imdb 
 . 
 reviews 
 ` 
  
 LIMIT 
  
 10 
  
 ), 
  
 STRUCT 
 ( 
  
 0 
 . 
 2 
  
 AS 
  
 temperature 
 , 
  
 128 
  
 AS 
  
 max_output_tokens 
 , 
  
 TRUE 
  
 AS 
  
 flatten_json_output 
 ));

      The output is similar to the following, with non-generated columns omitted for clarity:

        +----------------------------------------------+-------------------------+-----------------------------+-----+ 
 | 
  
 generated_text 
  
 | 
  
 ml_generate_text_status 
  
 | 
  
 prompt 
  
 | 
  
 ... 
  
 | 
 +----------------------------------------------+-------------------------------------------------------+-----+ 
 | 
  
 ** 
 Sentiment 
 : 
 ** 
  
 NEGATIVE 
  
 ** 
 Justification 
 : 
 ** 
  
 | 
  
 | 
  
 Analyze 
  
 the 
  
 sentiment 
  
 of 
  
 | 
  
 | 
 | 
  
 * 
  
 ** 
 Negative 
  
 Language 
 : 
 ** 
  
 The 
  
 reviewer 
  
 uses 
  
 | 
  
 | 
  
 movie 
  
 review 
  
 and 
  
 classify 
  
 | 
  
 | 
 | 
  
 phrases 
  
 like 
  
 "don't quite make it," 
  
 "come to |                         | it as either POSITIVE or    |     | 
 | mind," 
  
 "quite disappointing," 
  
 and 
  
 "not many  |                         | NEGATIVE. Movie Review:     |     | 
 | laughs." 
  
 * 
  
 ** 
 Specific 
  
 Criticisms 
 : 
 ** 
  
 The 
  
 | 
  
 | 
  
 Although 
  
 Charlie 
  
 Chaplin 
  
 | 
  
 | 
 | 
  
 reviewer 
  
 points 
  
 out 
  
 specific 
  
 flaws 
  
 in 
  
 the 
  
 | 
  
 | 
  
 made 
  
 some 
  
 great 
  
 short 
  
 | 
  
 | 
 | 
  
 plot 
  
 and 
  
 humor 
 , 
  
 stating 
  
 that 
  
 the 
  
 manager 
 ... 
  
 | 
  
 | 
  
 comedies 
  
 in 
  
 the 
  
 late 
 ... 
  
 | 
  
 | 
 +----------------------------------------------+-------------------------+-----------------------------+-----+ 
 | 
  
 ** 
 Sentiment 
 : 
 ** 
  
 NEGATIVE 
  
 ** 
 Reasoning 
 : 
 ** 
  
 | 
  
 | 
  
 Analyze 
  
 the 
  
 sentiment 
  
 of 
  
 | 
  
 | 
 | 
  
 * 
  
 ** 
 Negative 
  
 Language 
 : 
 ** 
  
 The 
  
 reviewer 
  
 uses 
  
 | 
  
 | 
  
 movie 
  
 review 
  
 and 
  
 classify 
  
 | 
  
 | 
 | 
  
 phrases 
  
 like 
  
 "poor writing," 
  
 "static camera- |                         | it as either POSITIVE or    |     | 
 | work," 
  
 "chews the scenery," 
  
 "all surface and |                         | NEGATIVE. Movie Review:     |     | 
 | no depth," 
  
 "sterile spectacles," 
  
 which 
  
 all 
  
 | 
  
 | 
  
 Opulent 
  
 sets 
  
 and 
  
 sumptuous 
  
 | 
  
 | 
 | 
  
 carry 
  
 negative 
  
 connotations 
 . 
  
 * 
  
 ** 
 Comparison 
  
 | 
  
 | 
  
 costumes 
  
 well 
  
 photographed 
  
 | 
  
 | 
 | 
  
 to 
  
 a 
  
 More 
  
 Successful 
  
 Film 
 : 
 **... 
  
 | 
  
 | 
  
 by 
  
 Theodor 
  
 Sparkuhl 
 , 
  
 and 
 ... 
  
 | 
  
 | 
 +----------------------------------------------+-------------------------+-----------------------------+-----+

      The results include the same columns documented for Perform keyword extraction .

    Undeploy model

    If you choose not to delete your project as recommended , you must undeploy the Gemma model in Vertex AI to avoid continued billing for it. BigQuery automatically undeploys the model after a specified period of idleness (6.5 hours by default). Alternatively, you can immediately undeploy the model by using the ALTER MODEL statement , as shown in the following example:

     ALTER 
  
 MODEL 
  
 ` 
 bqml_tutorial 
 . 
 gemma_model 
 ` 
 SET 
  
 OPTIONS 
  
 ( 
 deploy_model 
  
 = 
  
 false 
 );

    For more information, see Automatic or immediate open model undeployment .

    Clean up

    1. In the Google Cloud console, go to the Manage resources page.

      Go to Manage resources

    2. In the project list, select the project that you want to delete, and then click Delete .
    3. In the dialog, type the project ID, and then click Shut down to delete the project.
    Design a Mobile Site
    View Site in Mobile | Classic
    Share by: