import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.LocationName;
import com.google.cloud.automl.v1.Model;
import com.google.cloud.automl.v1.OperationMetadata;
import com.google.cloud.automl.v1.TextSentimentModelMetadata;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
class LanguageSentimentAnalysisCreateModel {
static void createModel() throws IOException, ExecutionException, InterruptedException {
// TODO(developer): Replace these variables before running the sample.
String projectId = "YOUR_PROJECT_ID";
String datasetId = "YOUR_DATASET_ID";
String displayName = "YOUR_DATASET_NAME";
createModel(projectId, datasetId, displayName);
}
// Create a model
static void createModel(String projectId, String datasetId, String displayName)
throws IOException, ExecutionException, InterruptedException {
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
try (AutoMlClient client = AutoMlClient.create()) {
// A resource that represents Google Cloud Platform location.
LocationName projectLocation = LocationName.of(projectId, "us-central1");
// Set model metadata.
System.out.println(datasetId);
TextSentimentModelMetadata metadata = TextSentimentModelMetadata.newBuilder().build();
Model model =
Model.newBuilder()
.setDisplayName(displayName)
.setDatasetId(datasetId)
.setTextSentimentModelMetadata(metadata)
.build();
// Create a model with the model metadata in the region.
OperationFuture<Model, OperationMetadata> future =
client.createModelAsync(projectLocation, model);
// OperationFuture.get() will block until the model is created, which may take several hours.
// You can use OperationFuture.getInitialFuture to get a future representing the initial
// response to the request, which contains information while the operation is in progress.
System.out.format("Training operation name: %s\n", future.getInitialFuture().get().getName());
System.out.println("Training started...");
}
}
}
from google.cloud import automl
# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# dataset_id = "YOUR_DATASET_ID"
# display_name = "YOUR_MODEL_NAME"
client = automl.AutoMlClient()
# A resource that represents Google Cloud Platform location.
project_location = f"projects/{project_id}/locations/us-central1"
# Leave model unset to use the default base model provided by Google
metadata = automl.TextSentimentModelMetadata()
model = automl.Model(
display_name=display_name,
dataset_id=dataset_id,
text_sentiment_model_metadata=metadata,
)
# Create a model with the model metadata in the region.
response = client.create_model(parent=project_location, model=model)
print("Training operation name: {}".format(response.operation.name))
print("Training started...")