이 문서는 AutoML Natural Language용이며 Vertex AI와는 다릅니다. Vertex AI를 사용하는 경우 Vertex AI 문서를 참조하세요.

항목 추출을 위한 모델 평가 가져오기

텍스트 항목 추출을 위한 모델 평가를 가져옵니다.

코드 샘플

Go

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	automlpb "google.golang.org/genproto/googleapis/cloud/automl/v1"
)

// getModelEvaluation gets a model evaluation.
func getModelEvaluation(w io.Writer, projectID string, location string, modelID string, modelEvaluationID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// modelID := "TRL123456789..."
	// modelEvaluationID := "123456789..."

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %v", err)
	}
	defer client.Close()

	req := &automlpb.GetModelEvaluationRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/models/%s/modelEvaluations/%s", projectID, location, modelID, modelEvaluationID),
	}

	evaluation, err := client.GetModelEvaluation(ctx, req)
	if err != nil {
		return fmt.Errorf("GetModelEvaluation: %v", err)
	}

	fmt.Fprintf(w, "Model evaluation name: %v\n", evaluation.GetName())
	fmt.Fprintf(w, "Model annotation spec id: %v\n", evaluation.GetAnnotationSpecId())
	fmt.Fprintf(w, "Create Time:\n")
	fmt.Fprintf(w, "\tseconds: %v\n", evaluation.GetCreateTime().GetSeconds())
	fmt.Fprintf(w, "\tnanos: %v\n", evaluation.GetCreateTime().GetNanos())
	fmt.Fprintf(w, "Evaluation example count: %v\n", evaluation.GetEvaluatedExampleCount())
	fmt.Fprintf(w, "Entity extraction model evaluation metrics: %v\n", evaluation.GetTextExtractionEvaluationMetrics())

	return nil
}

자바


import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.ModelEvaluation;
import com.google.cloud.automl.v1.ModelEvaluationName;
import java.io.IOException;

class GetModelEvaluation {

  static void getModelEvaluation() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    String modelEvaluationId = "YOUR_MODEL_EVALUATION_ID";
    getModelEvaluation(projectId, modelId, modelEvaluationId);
  }

  // Get a model evaluation
  static void getModelEvaluation(String projectId, String modelId, String modelEvaluationId)
      throws IOException {
    // 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()) {
      // Get the full path of the model evaluation.
      ModelEvaluationName modelEvaluationFullId =
          ModelEvaluationName.of(projectId, "us-central1", modelId, modelEvaluationId);

      // Get complete detail of the model evaluation.
      ModelEvaluation modelEvaluation = client.getModelEvaluation(modelEvaluationFullId);

      System.out.format("Model Evaluation Name: %s\n", modelEvaluation.getName());
      System.out.format("Model Annotation Spec Id: %s", modelEvaluation.getAnnotationSpecId());
      System.out.println("Create Time:");
      System.out.format("\tseconds: %s\n", modelEvaluation.getCreateTime().getSeconds());
      System.out.format("\tnanos: %s", modelEvaluation.getCreateTime().getNanos() / 1e9);
      System.out.format(
          "Evalution Example Count: %d\n", modelEvaluation.getEvaluatedExampleCount());
      System.out.format(
          "Entity Extraction Model Evaluation Metrics: %s\n",
          modelEvaluation.getTextExtractionEvaluationMetrics());
    }
  }
}

Node.js

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const modelId = 'YOUR_MODEL_ID';
// const modelEvaluationId = 'YOUR_MODEL_EVALUATION_ID';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function getModelEvaluation() {
  // Construct request
  const request = {
    name: client.modelEvaluationPath(
      projectId,
      location,
      modelId,
      modelEvaluationId
    ),
  };

  const [response] = await client.getModelEvaluation(request);

  console.log(`Model evaluation name: ${response.name}`);
  console.log(`Model annotation spec id: ${response.annotationSpecId}`);
  console.log(`Model display name: ${response.displayName}`);
  console.log('Model create time');
  console.log(`\tseconds ${response.createTime.seconds}`);
  console.log(`\tnanos ${response.createTime.nanos / 1e9}`);
  console.log(`Evaluation example count: ${response.evaluatedExampleCount}`);
  console.log(
    `Entity extraction model evaluation metrics: ${response.textExtractionEvaluationMetrics}`
  );
}

getModelEvaluation();

Python

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"
# model_evaluation_id = "YOUR_MODEL_EVALUATION_ID"

client = automl.AutoMlClient()
# Get the full path of the model evaluation.
model_path = client.model_path(project_id, "us-central1", model_id)
model_evaluation_full_id = f"{model_path}/modelEvaluations/{model_evaluation_id}"

# Get complete detail of the model evaluation.
response = client.get_model_evaluation(name=model_evaluation_full_id)

print("Model evaluation name: {}".format(response.name))
print("Model annotation spec id: {}".format(response.annotation_spec_id))
print("Create Time: {}".format(response.create_time))
print("Evaluation example count: {}".format(response.evaluated_example_count))
print(
    "Entity extraction model evaluation metrics: {}".format(
        response.text_extraction_evaluation_metrics
    )
)

다음 단계

다른 Google Cloud 제품의 코드 샘플을 검색하고 필터링하려면 Google Cloud 샘플 브라우저를 참조하세요.