Diese Legacy-Version von AutoML Natural Language wurde verworfen und ist nach dem 15. März 2024 nicht mehr in Google Cloud verfügbar. Alle Funktionen der Legacy-Version von AutoML Natural Language sowie neue Features sind auf der Vertex AI-Plattform verfügbar. Informationen zum Migrieren Ihrer Ressourcen finden Sie unter Zu Vertex AI migrieren.

Entitätsextraktion vorhersagen

Sagt die Extraktion von Textentitäten vorher.

Weitere Informationen

Eine ausführliche Dokumentation, die dieses Codebeispiel enthält, finden Sie hier:

Dokumente analysieren

Codebeispiel

Go

Informationen zum Installieren und Verwenden der Clientbibliothek für AutoML Natural Language finden Sie unter AutoML Natural Language-Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur AutoML Natural Language Go API.

Richten Sie zur Authentifizierung bei AutoML Natural Language Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten.

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
)

// languageEntityExtractionPredict does a prediction for text entity extraction.
func languageEntityExtractionPredict(w io.Writer, projectID string, location string, modelID string, content string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// modelID := "TEN123456789..."
	// content := "text to extract entities"

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

	req := &automlpb.PredictRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/models/%s", projectID, location, modelID),
		Payload: &automlpb.ExamplePayload{
			Payload: &automlpb.ExamplePayload_TextSnippet{
				TextSnippet: &automlpb.TextSnippet{
					Content:  content,
					MimeType: "text/plain", // Types: "text/plain", "text/html"
				},
			},
		},
	}

	resp, err := client.Predict(ctx, req)
	if err != nil {
		return fmt.Errorf("Predict: %w", err)
	}

	for _, payload := range resp.GetPayload() {
		fmt.Fprintf(w, "Text extract entity types: %v\n", payload.GetDisplayName())
		fmt.Fprintf(w, "Text score: %v\n", payload.GetTextExtraction().GetScore())
		textSegment := payload.GetTextExtraction().GetTextSegment()
		fmt.Fprintf(w, "Text extract entity content: %v\n", textSegment.GetContent())
		fmt.Fprintf(w, "Text start offset: %v\n", textSegment.GetStartOffset())
		fmt.Fprintf(w, "Text end offset: %v\n", textSegment.GetEndOffset())
	}

	return nil
}

Java

import com.google.cloud.automl.v1.AnnotationPayload;
import com.google.cloud.automl.v1.ExamplePayload;
import com.google.cloud.automl.v1.ModelName;
import com.google.cloud.automl.v1.PredictRequest;
import com.google.cloud.automl.v1.PredictResponse;
import com.google.cloud.automl.v1.PredictionServiceClient;
import com.google.cloud.automl.v1.TextSegment;
import com.google.cloud.automl.v1.TextSnippet;
import java.io.IOException;

class LanguageEntityExtractionPredict {

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

  static void predict(String projectId, String modelId, String content) 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 (PredictionServiceClient client = PredictionServiceClient.create()) {
      // Get the full path of the model.
      ModelName name = ModelName.of(projectId, "us-central1", modelId);

      // For available mime types, see:
      // https://cloud.google.com/automl/docs/reference/rest/v1/projects.locations.models/predict#textsnippet
      TextSnippet textSnippet =
          TextSnippet.newBuilder()
              .setContent(content)
              .setMimeType("text/plain") // Types: text/plain, text/html
              .build();
      ExamplePayload payload = ExamplePayload.newBuilder().setTextSnippet(textSnippet).build();
      PredictRequest predictRequest =
          PredictRequest.newBuilder().setName(name.toString()).setPayload(payload).build();

      PredictResponse response = client.predict(predictRequest);

      for (AnnotationPayload annotationPayload : response.getPayloadList()) {
        System.out.format("Text Extract Entity Type: %s\n", annotationPayload.getDisplayName());
        System.out.format("Text score: %.2f\n", annotationPayload.getTextExtraction().getScore());
        TextSegment textSegment = annotationPayload.getTextExtraction().getTextSegment();
        System.out.format("Text Extract Entity Content: %s\n", textSegment.getContent());
        System.out.format("Text Start Offset: %s\n", textSegment.getStartOffset());
        System.out.format("Text End Offset: %s\n\n", textSegment.getEndOffset());
      }
    }
  }
}

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 content = 'text to predict'

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

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

async function predict() {
  // Construct request
  const request = {
    name: client.modelPath(projectId, location, modelId),
    payload: {
      textSnippet: {
        content: content,
        mimeType: 'text/plain', // Types: 'test/plain', 'text/html'
      },
    },
  };

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

  for (const annotationPayload of response.payload) {
    console.log(
      `Text Extract Entity Types: ${annotationPayload.displayName}`
    );
    console.log(`Text Score: ${annotationPayload.textExtraction.score}`);
    const textSegment = annotationPayload.textExtraction.textSegment;
    console.log(`Text Extract Entity Content: ${textSegment.content}`);
    console.log(`Text Start Offset: ${textSegment.startOffset}`);
    console.log(`Text End Offset: ${textSegment.endOffset}`);
  }
}

predict();

Python

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"
# content = "text to predict"

prediction_client = automl.PredictionServiceClient()

# Get the full path of the model.
model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1", model_id)

# Supported mime_types: 'text/plain', 'text/html'
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#textsnippet
text_snippet = automl.TextSnippet(content=content, mime_type="text/plain")
payload = automl.ExamplePayload(text_snippet=text_snippet)

response = prediction_client.predict(name=model_full_id, payload=payload)

for annotation_payload in response.payload:
    print(f"Text Extract Entity Types: {annotation_payload.display_name}")
    print(f"Text Score: {annotation_payload.text_extraction.score}")
    text_segment = annotation_payload.text_extraction.text_segment
    print(f"Text Extract Entity Content: {text_segment.content}")
    print(f"Text Start Offset: {text_segment.start_offset}")
    print(f"Text End Offset: {text_segment.end_offset}")

Nächste Schritte

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