Classifique o sentimento no texto (IA generativa)

Classifique o sentimento transmitido no texto como positivo ou negativo.

Exemplo de código

Java

Antes de experimentar este exemplo, siga as Javainstruções de configuração no início rápido do Vertex AI com bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Java Vertex AI.

Para se autenticar no Vertex AI, configure as Credenciais padrão da aplicação. Para mais informações, consulte o artigo Configure a autenticação para um ambiente de desenvolvimento local.

import com.google.cloud.aiplatform.v1beta1.EndpointName;
import com.google.cloud.aiplatform.v1beta1.PredictResponse;
import com.google.cloud.aiplatform.v1beta1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1beta1.PredictionServiceSettings;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

// Text sentiment analysis with a Large Language Model
public class PredictTextSentimentSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    // The details of designing text prompts for supported large language models:
    // https://cloud.google.com/vertex-ai/docs/generative-ai/text/text-overview
    String instance =
        "{ \"content\": \"I had to compare two versions of Hamlet for my Shakespeare \n"
            + "class and unfortunately I picked this version. Everything from the acting \n"
            + "(the actors deliver most of their lines directly to the camera) to the camera \n"
            + "shots (all medium or close up shots...no scenery shots and very little back \n"
            + "ground in the shots) were absolutely terrible. I watched this over my spring \n"
            + "break and it is very safe to say that I feel that I was gypped out of 114 \n"
            + "minutes of my vacation. Not recommended by any stretch of the imagination.\n"
            + "Classify the sentiment of the message: negative\n"
            + "\n"
            + "Something surprised me about this movie - it was actually original. It was \n"
            + "not the same old recycled crap that comes out of Hollywood every month. I saw \n"
            + "this movie on video because I did not even know about it before I saw it at my \n"
            + "local video store. If you see this movie available - rent it - you will not \n"
            + "regret it.\n"
            + "Classify the sentiment of the message: positive\n"
            + "\n"
            + "My family has watched Arthur Bach stumble and stammer since the movie first \n"
            + "came out. We have most lines memorized. I watched it two weeks ago and still \n"
            + "get tickled at the simple humor and view-at-life that Dudley Moore portrays. \n"
            + "Liza Minelli did a wonderful job as the side kick - though I'm not her \n"
            + "biggest fan. This movie makes me just enjoy watching movies. My favorite scene \n"
            + "is when Arthur is visiting his fiancée's house. His conversation with the \n"
            + "butler and Susan's father is side-spitting. The line from the butler, \n"
            + "\\\"Would you care to wait in the Library\\\" followed by Arthur's reply, \n"
            + "\\\"Yes I would, the bathroom is out of the question\\\", is my NEWMAIL \n"
            + "notification on my computer.\n"
            + "Classify the sentiment of the message: positive\n"
            + "\n"
            + "This Charles outing is decent but this is a pretty low-key performance. Marlon \n"
            + "Brando stands out. There's a subplot with Mira Sorvino and Donald Sutherland \n"
            + "that forgets to develop and it hurts the film a little. I'm still trying to \n"
            + "figure out why Charlie want to change his name.\n"
            + "Classify the sentiment of the message: negative\n"
            + "\n"
            + "Tweet: The Pixel 7 Pro, is too big to fit in my jeans pocket, so I bought new \n"
            + "jeans.\n"
            + "Classify the sentiment of the message: \"}";
    String parameters =
        "{\n"
            + "  \"temperature\": 0,\n"
            + "  \"maxDecodeSteps\": 5,\n"
            + "  \"topP\": 0,\n"
            + "  \"topK\": 1\n"
            + "}";
    String project = "YOUR_PROJECT_ID";
    String location = "us-central1";
    String publisher = "google";
    String model = "text-bison@001";

    predictTextSentiment(instance, parameters, project, location, publisher, model);
  }

  static void predictTextSentiment(
      String instance,
      String parameters,
      String project,
      String location,
      String publisher,
      String model)
      throws IOException {
    String endpoint = String.format("%s-aiplatform.googleapis.com:443", location);
    PredictionServiceSettings predictionServiceSettings =
        PredictionServiceSettings.newBuilder().setEndpoint(endpoint).build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests.
    try (PredictionServiceClient predictionServiceClient =
        PredictionServiceClient.create(predictionServiceSettings)) {
      final EndpointName endpointName =
          EndpointName.ofProjectLocationPublisherModelName(project, location, publisher, model);

      // Use Value.Builder to convert instance to a dynamically typed value that can be
      // processed by the service.
      VValueBuilder instanceValue = VValuenewBuilder();
      JJsonFormatparser().merge(instance, instanceValue);
      LLis<tValu>einstances = new ArrayList<>();
      instances.add(instanceValue.build());

      // Use Value.Builder to convert parameter to a dynamically typed value that can be
      // processed by the service.
      VValueBuilder parameterValueBuilder = VValuenewBuilder();
      JJsonFormatparser().merge(parameters, parameterValueBuilder);
      VValueparameterValue = parameterValueBuilder.build();

      PredictResponse predictResponse =
          predictionServiceClient.predict(endpointName, instances, parameterValue);
      System.out.println("Predict Response");
      System.out.println(predictResponse);
    }
  }
}

O que se segue?

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