Mengklasifikasikan teks dengan model bahasa besar (AI Generatif)

Melakukan tugas klasifikasi yang menetapkan class atau kategori ke teks. Anda dapat menentukan daftar kategori yang dapat dipilih atau membiarkan model memilih dari kategorinya sendiri.

Contoh kode

C#

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan C# di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API C# Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Java di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Java Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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 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

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Node.js di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Node.js Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

/**
 * 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();

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

from vertexai.language_models import TextGenerationModel

def classify_news_items(temperature: float = 0.2) -> None:
    """Text Classification Example with a Large Language Model"""

    # TODO developer - override these parameters as needed:
    parameters = {
        "temperature": temperature,  # Temperature controls the degree of randomness in token selection.
        "max_output_tokens": 5,  # Token limit determines the maximum amount of text output.
        "top_p": 0,  # Tokens are selected from most probable to least until the sum of their probabilities equals the top_p value.
        "top_k": 1,  # A top_k of 1 means the selected token is the most probable among all tokens.
    }

    model = TextGenerationModel.from_pretrained("text-bison@002")
    response = model.predict(
        """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:
""",
        **parameters,
    )

    print(f"Response from Model: {response.text}")

    return response

Langkah selanjutnya

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