Classificar textos com um modelo de linguagem grande (IA generativa)

Executar tarefas de classificação que atribuam uma classe ou categoria ao texto. Você pode especificar uma lista de categorias para escolher ou deixar que o modelo escolha entre suas próprias categorias.

Exemplo de código

C#

Antes de testar esse exemplo, siga as instruções de configuração para C# no Guia de início rápido da Vertex AI sobre como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Vertex AI para C#.

Para autenticar na Vertex AI, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

using Google.Cloud.AIPlatform.V1;
using System;
using System.Collections.Generic;
using System.Linq;
using Value = Google.Protobuf.WellKnownTypes.Value;

// Text Classification with a Large Language Model
public class PredictTextClassificationSample
{
    public string PredictTextClassification(
        string projectId = "your-project-id",
        string locationId = "us-central1",
        string publisher = "google",
        string model = "text-bison@001")
    {
        // Initialize client that will be used to send requests.
        // This client only needs to be created once,
        // and can be reused for multiple requests.
        var client = new PredictionServiceClientBuilder
        {
            Endpoint = $"{locationId}-aiplatform.googleapis.com"
        }.Build();

        // Configure the parent resource.
        var endpoint = EndpointName.FromProjectLocationPublisherModel(projectId, locationId, publisher, model);

        // Initialize request argument(s).
        var content = @"What is the topic for a given news headline?
- business
- entertainment
- health
- sports
- technology

Text: Pixel 7 Pro Expert Hands On Review, the Most Helpful Google Phones.
The answer is: technology

Text: Quit smoking?
The answer is: health

Text: Roger Federer reveals why he touched Rafael Nadals hand while they were crying
The answer is: sports

Text: Business relief from Arizona minimum-wage hike looking more remote
The answer is: business

Text: #TomCruise has arrived in Bari, Italy for #MissionImpossible.
The answer is: entertainment

Text: CNBC Reports Rising Digital Profit as Print Advertising Falls
The answer is:";

        var instances = new List<Value>
        {
            Value.ForStruct(new()
            {
                Fields =
                {
                    ["content"] = Value.ForString(content),
                }
            })
        };

        var parameters = Value.ForStruct(new()
        {
            Fields =
            {
                { "temperature", new Value { NumberValue = 0 } },
                { "maxDecodeSteps", new Value { NumberValue = 5 } },
                { "topP", new Value { NumberValue = 0 } },
                { "topK", new Value { NumberValue = 1 } }
            }
        });

        // Make the request.
        var response = client.Predict(endpoint, instances, parameters);

        // Parse and return the content.
        var responseContent = response.Predictions.First().StructValue.Fields["content"].StringValue;
        Console.WriteLine($"Content: {responseContent}");
        return responseContent;
    }
}

Java

Antes de testar esse exemplo, siga as instruções de configuração para Java no Guia de início rápido da Vertex AI sobre como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Vertex AI para Java.

Para autenticar na Vertex AI, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.PredictResponse;
import com.google.cloud.aiplatform.v1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1.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 Classification with a Large Language Model
public class PredictTextClassificationSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String instance =
        "{ \"content\": \"What is the topic for a given news headline?\n"
            + "- business\n"
            + "- entertainment\n"
            + "- health\n"
            + "- sports\n"
            + "- technology\n"
            + "\n"
            + "Text: Pixel 7 Pro Expert Hands On Review, the Most Helpful Google Phones.\n"
            + "The answer is: technology\n"
            + "\n"
            + "Text: Quit smoking?\n"
            + "The answer is: health\n"
            + "\n"
            + "Text: Roger Federer reveals why he touched Rafael Nadals hand while they were"
            + " crying\n"
            + "The answer is: sports\n"
            + "\n"
            + "Text: Business relief from Arizona minimum-wage hike looking more remote\n"
            + "The answer is: business\n"
            + "\n"
            + "Text: #TomCruise has arrived in Bari, Italy for #MissionImpossible.\n"
            + "The answer is: entertainment\n"
            + "\n"
            + "Text: CNBC Reports Rising Digital Profit as Print Advertising Falls\n"
            + "The answer is:\"}";
    String parameters =
        "{\n"
            + "  \"temperature\": 0,\n"
            + "  \"maxDecodeSteps\": 5,\n"
            + "  \"topP\": 0,\n"
            + "  \"topK\": 1\n"
            + "}";
    String project = "YOUR_PROJECT_ID";
    String publisher = "google";
    String model = "text-bison@001";

    predictTextClassification(instance, parameters, project, publisher, model);
  }

  static void predictTextClassification(
      String instance, String parameters, String project, String publisher, String model)
      throws IOException {
    PredictionServiceSettings predictionServiceSettings =
        PredictionServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .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)) {
      String location = "us-central1";
      final EndpointName endpointName =
          EndpointName.ofProjectLocationPublisherModelName(project, location, publisher, model);

      Value.Builder instanceValue = Value.newBuilder();
      JsonFormat.parser().merge(instance, instanceValue);
      List<Value> instances = new ArrayList<>();
      instances.add(instanceValue.build());

      Value.Builder parameterValueBuilder = Value.newBuilder();
      JsonFormat.parser().merge(parameters, parameterValueBuilder);
      Value parameterValue = parameterValueBuilder.build();

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

Node.js

Antes de testar esse exemplo, siga as instruções de configuração para Node.js no Guia de início rápido da Vertex AI sobre como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Vertex AI para Node.js.

Para autenticar na Vertex AI, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const aiplatform = require('@google-cloud/aiplatform');

// Imports the Google Cloud Prediction service client
const {PredictionServiceClient} = aiplatform.v1;

// Import the helper module for converting arbitrary protobuf.Value objects.
const {helpers} = aiplatform;

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

const publisher = 'google';
const model = 'text-bison@001';

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

async function callPredict() {
  // Configure the parent resource
  const endpoint = `projects/${project}/locations/${location}/publishers/${publisher}/models/${model}`;

  const instance = {
    content: `What is the topic for a given news headline?
  - business
  - entertainment
  - health
  - sports
  - technology

  Text: Pixel 7 Pro Expert Hands On Review, the Most Helpful Google Phones.
  The answer is: technology

  Text: Quit smoking?
  The answer is: health

  Text: Best soccer game of the season?
  The answer is: sports

  Text: This stock continues to soar.
  The answer is: business

  Text: What movie should I watch this week?
  The answer is: entertainment

  Text: Airlines expect to make $10 billion this year despite economic slowdown
  The answer is:
  `,
  };
  const instanceValue = helpers.toValue(instance);
  const instances = [instanceValue];

  const parameter = {
    temperature: 0.2,
    maxOutputTokens: 5,
    topP: 0,
    topK: 1,
  };
  const parameters = helpers.toValue(parameter);

  const request = {
    endpoint,
    instances,
    parameters,
  };

  // Predict request
  const [response] = await predictionServiceClient.predict(request);
  console.log('Get text classification response');
  const predictions = response.predictions;
  console.log('\tPredictions :');
  for (const prediction of predictions) {
    console.log(`\t\tPrediction : ${JSON.stringify(prediction)}`);
  }
}

callPredict();

A seguir

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