テキストにクラスまたはカテゴリを割り当てる分類タスクを実行します。カテゴリのリストを指定して選択することも、モデルに独自のカテゴリから選択させることもできます。
コードサンプル
C#
このサンプルを試す前に、Vertex AI クイックスタート: クライアント ライブラリの使用にある C# の設定手順を完了してください。詳細については、Vertex AI C# API のリファレンス ドキュメントをご覧ください。
Vertex AI に対する認証を行うには、アプリケーションのデフォルト認証情報を設定します。詳細については、ローカル開発環境の認証を設定するをご覧ください。
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
このサンプルを試す前に、Vertex AI クイックスタート: クライアント ライブラリの使用にある Java の設定手順を完了してください。詳細については、Vertex AI Java API のリファレンス ドキュメントをご覧ください。
Vertex AI に対する認証を行うには、アプリケーションのデフォルト認証情報を設定します。詳細については、ローカル開発環境の認証を設定するをご覧ください。
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
このサンプルを試す前に、Vertex AI クイックスタート: クライアント ライブラリの使用にある Node.js の設定手順を完了してください。詳細については、Vertex AI Node.js API のリファレンス ドキュメントをご覧ください。
Vertex AI に対する認証を行うには、アプリケーションのデフォルト認証情報を設定します。詳細については、ローカル開発環境の認証を設定するをご覧ください。
/**
* 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
このサンプルを試す前に、Vertex AI クイックスタート: クライアント ライブラリの使用にある Python の設定手順を完了してください。詳細については、Vertex AI Python API のリファレンス ドキュメントをご覧ください。
Vertex AI に対する認証を行うには、アプリケーションのデフォルト認証情報を設定します。詳細については、ローカル開発環境の認証を設定するをご覧ください。
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
次のステップ
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