AutoML Translation API 教程

本教程演示了如何使用 AutoML Translation 创建自定义翻译模型。该应用使用软件本地化的技术导向型句对数据集(英语到西班牙语)来训练自定义模型。

本教程包括训练自定义模型、评估其性能以及翻译新内容。

前提条件

配置项目环境

  1. 登录您的 Google Cloud 帐号。如果您是 Google Cloud 新手,请创建一个帐号来评估我们的产品在实际场景中的表现。新客户还可获享 $300 赠金,用于运行、测试和部署工作负载。
  2. 在 Google Cloud Console 的项目选择器页面上,选择或创建一个 Google Cloud 项目。

    转到“项目选择器”

  3. 确保您的 Cloud 项目已启用结算功能。 了解如何确认您的项目是否已启用结算功能

  4. 启用 AutoML Translation API。

    启用 API

  5. 在 Google Cloud Console 的项目选择器页面上,选择或创建一个 Google Cloud 项目。

    转到“项目选择器”

  6. 确保您的 Cloud 项目已启用结算功能。 了解如何确认您的项目是否已启用结算功能

  7. 启用 AutoML Translation API。

    启用 API

  8. 安装 gcloud 命令行工具
  9. 按照说明创建服务帐号并下载密钥文件
  10. GOOGLE_APPLICATION_CREDENTIALS 环境变量设置为如下路径:指向您在创建服务帐号时所下载的服务帐号密钥文件的路径。例如:
    export GOOGLE_APPLICATION_CREDENTIALS=key-file
  11. 使用以下命令将您的新服务帐号添加到 IAM 角色 AutoML Editor。将 project-id 替换为 Google Cloud 项目的名称,并将 service-account-name 替换为新服务帐号的名称,例如 service-account1@myproject.iam.gserviceaccount.com
    gcloud auth login
    gcloud config set project project-id
    gcloud projects add-iam-policy-binding project-id \
      --member=serviceAccount:service-account-name \
      --role='roles/automl.editor'
  12. 允许 AutoML Translation 服务帐号访问您的 Google Cloud 项目资源:
    gcloud projects add-iam-policy-binding project-id \
      --member="serviceAccount:custom-vision@appspot.gserviceaccount.com" \
      --role="roles/storage.admin"
  13. 安装客户端库
  14. 设置 PROJECT_ID 和 REGION_NAME 环境变量。

    project-id 替换为 Google Cloud 项目的项目 ID。AutoML Translation 目前要求将位置设为 us-central1
    export PROJECT_ID="project-id"
    export REGION_NAME="us-central1"
  15. 创建一个 Google Cloud Storage 存储分区以存储您将用于训练自定义模型的文档。

    存储分区名称必须采用以下格式: $PROJECT_ID-vcm. 以下命令将在 us-central1 区域中创建一个名为 $PROJECT_ID-vcm 的存储分区。
    gsutil mb -p $PROJECT_ID -c regional -l $REGION_NAME gs://$PROJECT_ID-vcm/
  16. 下载包含用于训练模型的样本数据的归档文件,提取其内容,然后将提取的文件上传到您的 Google Cloud Storage 存储分区。

    如需详细了解格式,请参阅准备训练数据

    本教程中的示例代码使用英语到西班牙语的数据集。您也可以使用目标语言为德语、法语、俄语和中文的数据集。如果使用以上替代数据集之一,请用合适的语言代码替换示例中的语言代码 es

  17. 在上一步的 en-es.csv 文件中,将 {project_id} 替换为您的项目 ID。

源代码文件位置

您可以从下面提供的位置下载源代码。下载后,您可以将源代码复制到 Google Cloud 项目文件夹中。

Python

本教程包含以下 Python 文件

  • translate_create_dataset.py - 包含创建数据集的功能
  • import_dataset.py - 包含导入数据集的功能
  • translate_create_model.py - 包含创建模型的功能
  • list_model_evaluations.py - 包含列出模型评估的功能
  • translate_predict.py - 包含与预测相关的功能
  • delete_model.py - 包含删除模型的功能

Java

本教程包含以下 Java 文件

  • TranslateCreateDataset.java - 包含创建数据集的功能
  • ImportDataset.java - 包含导入数据集的功能
  • TranslateCreateModel.java - 包含创建模型的功能
  • ListModelEvaluations.java - 包含列出模型评估的功能
  • TranslatePredict.java - 包含与预测相关的功能
  • DeleteModel.java - 包含删除模型的功能

Node.js

本教程包含以下 Node.js 程序

  • translate_create_dataset.js - 包含创建数据集的功能
  • import_dataset.js - 包含导入数据集的功能
  • translate_create_model.js - 包含创建模型的功能
  • list_model_evaluations.js - 包含列出模型评估的功能
  • translate_predict.js - 包含与预测相关的功能
  • delete_model.js - 包含删除模型的功能

运行应用

第 1 步:创建数据集

如需创建自定义模型,首先需要创建一个空数据集,该数据集最终将保存模型的训练数据。创建数据集时,需要为翻译指定源语言和目标语言。

复制代码

Python

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# display_name = "YOUR_DATASET_NAME"

client = automl.AutoMlClient()

# A resource that represents Google Cloud Platform location.
project_location = f"projects/{project_id}/locations/us-central1"
# For a list of supported languages, see:
# https://cloud.google.com/translate/automl/docs/languages
dataset_metadata = automl.TranslationDatasetMetadata(
    source_language_code="en", target_language_code="ja"
)
dataset = automl.Dataset(
    display_name=display_name,
    translation_dataset_metadata=dataset_metadata,
)

# Create a dataset with the dataset metadata in the region.
response = client.create_dataset(parent=project_location, dataset=dataset)

created_dataset = response.result()

# Display the dataset information
print("Dataset name: {}".format(created_dataset.name))
print("Dataset id: {}".format(created_dataset.name.split("/")[-1]))

Java

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.Dataset;
import com.google.cloud.automl.v1.LocationName;
import com.google.cloud.automl.v1.OperationMetadata;
import com.google.cloud.automl.v1.TranslationDatasetMetadata;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class TranslateCreateDataset {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String displayName = "YOUR_DATASET_NAME";
    createDataset(projectId, displayName);
  }

  // Create a dataset
  static void createDataset(String projectId, String displayName)
      throws IOException, ExecutionException, InterruptedException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // A resource that represents Google Cloud Platform location.
      LocationName projectLocation = LocationName.of(projectId, "us-central1");

      // Specify the source and target language.
      TranslationDatasetMetadata translationDatasetMetadata =
          TranslationDatasetMetadata.newBuilder()
              .setSourceLanguageCode("en")
              .setTargetLanguageCode("ja")
              .build();
      Dataset dataset =
          Dataset.newBuilder()
              .setDisplayName(displayName)
              .setTranslationDatasetMetadata(translationDatasetMetadata)
              .build();
      OperationFuture<Dataset, OperationMetadata> future =
          client.createDatasetAsync(projectLocation, dataset);

      Dataset createdDataset = future.get();

      // Display the dataset information.
      System.out.format("Dataset name: %s\n", createdDataset.getName());
      // To get the dataset id, you have to parse it out of the `name` field. As dataset Ids are
      // required for other methods.
      // Name Form: `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`
      String[] names = createdDataset.getName().split("/");
      String datasetId = names[names.length - 1];
      System.out.format("Dataset id: %s\n", datasetId);
    }
  }
}

Node.js

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const displayName = 'YOUR_DISPLAY_NAME';

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

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

async function createDataset() {
  // Construct request
  const request = {
    parent: client.locationPath(projectId, location),
    dataset: {
      displayName: displayName,
      translationDatasetMetadata: {
        sourceLanguageCode: 'en',
        targetLanguageCode: 'ja',
      },
    },
  };

  // Create dataset
  const [operation] = await client.createDataset(request);

  // Wait for operation to complete.
  const [response] = await operation.promise();

  console.log(`Dataset name: ${response.name}`);
  console.log(`
    Dataset id: ${
      response.name
        .split('/')
        [response.name.split('/').length - 1].split('\n')[0]
    }`);
}

createDataset();

请求

运行 create_dataset 函数以创建空数据集。您必须修改以下代码行:

  • project_id 设置为您的 PROJECT_ID
  • 为数据集 (en_es_dataset) 设置 display_name
  • target_language_code 字段从 ja 修改为 es

Python

python translate_create_dataset.py

Java

mvn compile exec:java -Dexec.mainClass="com.example.automl.TranslateCreateDataset"

Node.js

node translate_create_dataset.js

响应

响应中包含新创建的数据集的详细信息,其中包括数据集 ID(用于在以后的请求中引用该数据集)。我们建议您设置环境变量 DATASET_ID,并将其值设置为返回的数据集 ID。

Dataset name: projects/216065747626/locations/us-central1/datasets/TRL7372141011130533778
Dataset id: TRL7372141011130533778
Dataset display name: en_es_dataset
Translation dataset Metadata:
        source_language_code: en
        target_language_code: es
Dataset example count: 0
Dataset create time:
       seconds: 1530251987
       nanos: 216586000

第 2 步:将训练句对导入数据集

下一步是使用训练句对列表填充数据集。

import_dataset 函数接口接受 .csv 文件作为输入,该文件列出了所有训练文档的位置以及每个训练文档的正确标签。(如需详细了解所需格式,请参阅准备数据。)在本教程中,我们将使用上一步中您上传到 Google Cloud Storage 的 en-es.csv

复制代码

Python

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# dataset_id = "YOUR_DATASET_ID"
# path = "gs://YOUR_BUCKET_ID/path/to/data.csv"

client = automl.AutoMlClient()
# Get the full path of the dataset.
dataset_full_id = client.dataset_path(project_id, "us-central1", dataset_id)
# Get the multiple Google Cloud Storage URIs
input_uris = path.split(",")
gcs_source = automl.GcsSource(input_uris=input_uris)
input_config = automl.InputConfig(gcs_source=gcs_source)
# Import data from the input URI
response = client.import_data(name=dataset_full_id, input_config=input_config)

print("Processing import...")
print("Data imported. {}".format(response.result()))

Java

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.DatasetName;
import com.google.cloud.automl.v1.GcsSource;
import com.google.cloud.automl.v1.InputConfig;
import com.google.cloud.automl.v1.OperationMetadata;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.Arrays;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

class ImportDataset {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String datasetId = "YOUR_DATASET_ID";
    String path = "gs://BUCKET_ID/path_to_training_data.csv";
    importDataset(projectId, datasetId, path);
  }

  // Import a dataset
  static void importDataset(String projectId, String datasetId, String path)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // Get the complete path of the dataset.
      DatasetName datasetFullId = DatasetName.of(projectId, "us-central1", datasetId);

      // Get multiple Google Cloud Storage URIs to import data from
      GcsSource gcsSource =
          GcsSource.newBuilder().addAllInputUris(Arrays.asList(path.split(","))).build();

      // Import data from the input URI
      InputConfig inputConfig = InputConfig.newBuilder().setGcsSource(gcsSource).build();
      System.out.println("Processing import...");

      // Start the import job
      OperationFuture<Empty, OperationMetadata> operation =
          client.importDataAsync(datasetFullId, inputConfig);

      System.out.format("Operation name: %s%n", operation.getName());

      // If you want to wait for the operation to finish, adjust the timeout appropriately. The
      // operation will still run if you choose not to wait for it to complete. You can check the
      // status of your operation using the operation's name.
      Empty response = operation.get(45, TimeUnit.MINUTES);
      System.out.format("Dataset imported. %s%n", response);
    } catch (TimeoutException e) {
      System.out.println("The operation's polling period was not long enough.");
      System.out.println("You can use the Operation's name to get the current status.");
      System.out.println("The import job is still running and will complete as expected.");
      throw e;
    }
  }
}

Node.js

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const datasetId = 'YOUR_DISPLAY_ID';
// const path = 'gs://BUCKET_ID/path_to_training_data.csv';

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

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

async function importDataset() {
  // Construct request
  const request = {
    name: client.datasetPath(projectId, location, datasetId),
    inputConfig: {
      gcsSource: {
        inputUris: path.split(','),
      },
    },
  };

  // Import dataset
  console.log('Proccessing import');
  const [operation] = await client.importData(request);

  // Wait for operation to complete.
  const [response] = await operation.promise();
  console.log(`Dataset imported: ${response}`);
}

importDataset();

请求

运行 import_data 函数以导入训练内容。您必须修改以下代码行:

  • project_id 设置为您的 PROJECT_ID
  • 为数据集设置 dataset_id(来自上一步的输出)
  • 设置 path,这是 gs://YOUR_PROJECT_ID-vcm/en-es.csv 的 URI

Python

python import_dataset.py

Java

mvn compile exec:java -Dexec.mainClass="com.example.automl.ImportDataset"

Node.js

node import_dataset.js

响应

Processing import...
Dataset imported.

第 3 步:创建(训练)模型

拥有已导入带标签训练文档的数据集后,您就可以训练新模型了。

复制代码

Python

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# dataset_id = "YOUR_DATASET_ID"
# display_name = "YOUR_MODEL_NAME"

client = automl.AutoMlClient()

# A resource that represents Google Cloud Platform location.
project_location = f"projects/{project_id}/locations/us-central1"
# Leave model unset to use the default base model provided by Google
translation_model_metadata = automl.TranslationModelMetadata()
model = automl.Model(
    display_name=display_name,
    dataset_id=dataset_id,
    translation_model_metadata=translation_model_metadata,
)

# Create a model with the model metadata in the region.
response = client.create_model(parent=project_location, model=model)

print("Training operation name: {}".format(response.operation.name))
print("Training started...")

Java

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.LocationName;
import com.google.cloud.automl.v1.Model;
import com.google.cloud.automl.v1.OperationMetadata;
import com.google.cloud.automl.v1.TranslationModelMetadata;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class TranslateCreateModel {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String datasetId = "YOUR_DATASET_ID";
    String displayName = "YOUR_DATASET_NAME";
    createModel(projectId, datasetId, displayName);
  }

  // Create a model
  static void createModel(String projectId, String datasetId, String displayName)
      throws IOException, ExecutionException, InterruptedException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // A resource that represents Google Cloud Platform location.
      LocationName projectLocation = LocationName.of(projectId, "us-central1");
      // Leave model unset to use the default base model provided by Google
      TranslationModelMetadata translationModelMetadata =
          TranslationModelMetadata.newBuilder().build();
      Model model =
          Model.newBuilder()
              .setDisplayName(displayName)
              .setDatasetId(datasetId)
              .setTranslationModelMetadata(translationModelMetadata)
              .build();

      // Create a model with the model metadata in the region.
      OperationFuture<Model, OperationMetadata> future =
          client.createModelAsync(projectLocation, model);
      // OperationFuture.get() will block until the model is created, which may take several hours.
      // You can use OperationFuture.getInitialFuture to get a future representing the initial
      // response to the request, which contains information while the operation is in progress.
      System.out.format("Training operation name: %s\n", future.getInitialFuture().get().getName());
      System.out.println("Training started...");
    }
  }
}

Node.js

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const dataset_id = 'YOUR_DATASET_ID';
// const displayName = 'YOUR_DISPLAY_NAME';

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

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

async function createModel() {
  // Construct request
  const request = {
    parent: client.locationPath(projectId, location),
    model: {
      displayName: displayName,
      datasetId: datasetId,
      translationModelMetadata: {}, // Leave unset, to use the default base model
    },
  };

  // Don't wait for the LRO
  const [operation] = await client.createModel(request);
  console.log('Training started...');
  console.log(`Training operation name: ${operation.name}`);
}

createModel();

请求

如需运行 create_model,您必须修改以下代码行:

  • project_id 设置为您的 PROJECT_ID
  • 为数据集(来自上一步的输出)设置 dataset_id
  • 为新模型 (en_es_test_model) 设置 display_name

Python

python translate_create_model.py

Java

mvn compile exec:java -Dexec.mainClass="com.example.automl.TranlateCreateModel"

Node.js

node translate_create_model.js

响应

create_model 函数开始训练操作并输出操作名称。训练是异步进行的,可能需要一段时间才能完成,因此您可以使用操作 ID 来检查训练状态。训练完成后,create_model 会返回模型 ID。与数据集 ID 一样,我们建议您将环境变量 MODEL_ID 设置为返回的模型 ID 值。

Training operation name: projects/216065747626/locations/us-central1/operations/TRL3007727620979824033
Training started...
Model name: projects/216065747626/locations/us-central1/models/TRL3007727620979824033
Model id: TRL3007727620979824033
Model display name: en_es_test_model
Model create time:
        seconds: 1529649600
        nanos: 966000000
Model deployment state: deployed

第 4 步:评估模型

训练结束后,您可以通过查看模型的 BLEU 得分来评估其就绪情况。

list_model_evaluations 函数将模型 ID 作为参数。

复制代码

Python

from google.cloud import automl

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

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

print("List of model evaluations:")
for evaluation in client.list_model_evaluations(parent=model_full_id, filter=""):
    print("Model evaluation name: {}".format(evaluation.name))
    print("Model annotation spec id: {}".format(evaluation.annotation_spec_id))
    print("Create Time: {}".format(evaluation.create_time))
    print("Evaluation example count: {}".format(evaluation.evaluated_example_count))
    print(
        "Translation model evaluation metrics: {}".format(
            evaluation.translation_evaluation_metrics
        )
    )

Java


import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.ListModelEvaluationsRequest;
import com.google.cloud.automl.v1.ModelEvaluation;
import com.google.cloud.automl.v1.ModelName;
import java.io.IOException;

class ListModelEvaluations {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    listModelEvaluations(projectId, modelId);
  }

  // List model evaluations
  static void listModelEvaluations(String projectId, String modelId) 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 (AutoMlClient client = AutoMlClient.create()) {
      // Get the full path of the model.
      ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
      ListModelEvaluationsRequest modelEvaluationsrequest =
          ListModelEvaluationsRequest.newBuilder().setParent(modelFullId.toString()).build();

      // List all the model evaluations in the model by applying filter.
      System.out.println("List of model evaluations:");
      for (ModelEvaluation modelEvaluation :
          client.listModelEvaluations(modelEvaluationsrequest).iterateAll()) {

        System.out.format("Model Evaluation Name: %s\n", modelEvaluation.getName());
        System.out.format("Model Annotation Spec Id: %s", modelEvaluation.getAnnotationSpecId());
        System.out.println("Create Time:");
        System.out.format("\tseconds: %s\n", modelEvaluation.getCreateTime().getSeconds());
        System.out.format("\tnanos: %s", modelEvaluation.getCreateTime().getNanos() / 1e9);
        System.out.format(
            "Evalution Example Count: %d\n", modelEvaluation.getEvaluatedExampleCount());
        System.out.format(
            "Translate Model Evaluation Metrics: %s\n",
            modelEvaluation.getTranslationEvaluationMetrics());
      }
    }
  }
}

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';

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

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

async function listModelEvaluations() {
  // Construct request
  const request = {
    parent: client.modelPath(projectId, location, modelId),
    filter: '',
  };

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

  console.log('List of model evaluations:');
  for (const evaluation of response) {
    console.log(`Model evaluation name: ${evaluation.name}`);
    console.log(`Model annotation spec id: ${evaluation.annotationSpecId}`);
    console.log(`Model display name: ${evaluation.displayName}`);
    console.log('Model create time');
    console.log(`\tseconds ${evaluation.createTime.seconds}`);
    console.log(`\tnanos ${evaluation.createTime.nanos / 1e9}`);
    console.log(
      `Evaluation example count: ${evaluation.evaluatedExampleCount}`
    );
    console.log(
      `Translation model evaluation metrics: ${evaluation.translationEvaluationMetrics}`
    );
  }
}

listModelEvaluations();

请求

通过执行以下请求来发出显示模型整体评估性能的请求。您必须修改以下代码行:

  • project_id 设置为您的 PROJECT_ID
  • model_id 设置为您的模型 ID

Python

python list_model_evaluations.py

Java

mvn compile exec:java -Dexec.mainClass="com.example.automl.ListModelEvaluations"

Node.js

node list_model_evaluations.js

响应

如果 BLEU 得分过低,您可以强化训练数据集并重新训练模型。如需了解详情,请参阅评估模型

List of model evaluations:
name: "projects/216065747626/locations/us-central1/models/5419131644870929143/modelEvaluations/TRL7683346839371803263"
create_time {
  seconds: 1530196488
  nanos: 509247000
}
evaluated_example_count: 3
translation_evaluation_metrics {
  bleu_score: 19.23076957464218
  base_bleu_score: 11.428571492433548
}

第 5 步:使用模型进行预测

当您的自定义模型达到您的质量标准时,就可以用它来翻译新内容了。

复制代码

Python

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"
# file_path = "path_to_local_file.txt"

prediction_client = automl.PredictionServiceClient()

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

# Read the file content for translation.
with open(file_path, "rb") as content_file:
    content = content_file.read()
content.decode("utf-8")

text_snippet = automl.TextSnippet(content=content)
payload = automl.ExamplePayload(text_snippet=text_snippet)

response = prediction_client.predict(name=model_full_id, payload=payload)
translated_content = response.payload[0].translation.translated_content

print(u"Translated content: {}".format(translated_content.content))

Java

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.TextSnippet;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;

class TranslatePredict {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    String filePath = "path_to_local_file.txt";
    predict(projectId, modelId, filePath);
  }

  static void predict(String projectId, String modelId, String filePath) 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);

      String content = new String(Files.readAllBytes(Paths.get(filePath)));

      TextSnippet textSnippet = TextSnippet.newBuilder().setContent(content).build();
      ExamplePayload payload = ExamplePayload.newBuilder().setTextSnippet(textSnippet).build();
      PredictRequest predictRequest =
          PredictRequest.newBuilder().setName(name.toString()).setPayload(payload).build();

      PredictResponse response = client.predict(predictRequest);
      TextSnippet translatedContent =
          response.getPayload(0).getTranslation().getTranslatedContent();
      System.out.format("Translated Content: %s\n", translatedContent.getContent());
    }
  }
}

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 filePath = 'path_to_local_file.txt';

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

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

// Read the file content for translation.
const content = fs.readFileSync(filePath, 'utf8');

async function predict() {
  // Construct request
  const request = {
    name: client.modelPath(projectId, location, modelId),
    payload: {
      textSnippet: {
        content: content,
      },
    },
  };

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

  console.log(
    'Translated content: ',
    response.payload[0].translation.translatedContent.content
  );
}

predict();

请求

对于 predict 函数,您必须修改以下代码行:

  • project_id 设置为您的 PROJECT_ID
  • model_id 设置为您的模型 ID
  • file_path 设置为下载的文件(“resources/input.txt”)

Python

python tranlsate_predict.py

Java

mvn compile exec:java -Dexec.mainClass="com.example.automl.TranslatePredict"

Node.js

node translate_predict.js predict

响应

该函数返回译文的内容。

Translated content: Ver y administrar tus cuentas de Google Tag Manager.

以上是英语句子“View and manage your Google Tag Manager accounts.”的西班牙语译文。将此自定义译文与基本 Google 模型的译文进行对比:

Ver y administrar sus cuentas de Administrador de etiquetas de Google

第 6 步:删除模型

使用完此示例模型后,可以永久删除该模型。但之后您将无法再使用该模型进行预测。

复制代码

Python

from google.cloud import automl

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

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

print("Model deleted. {}".format(response.result()))

Java

import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.ModelName;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class DeleteModel {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    deleteModel(projectId, modelId);
  }

  // Delete a model
  static void deleteModel(String projectId, String modelId)
      throws IOException, ExecutionException, InterruptedException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // Get the full path of the model.
      ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);

      // Delete a model.
      Empty response = client.deleteModelAsync(modelFullId).get();

      System.out.println("Model deletion started...");
      System.out.println(String.format("Model deleted. %s", response));
    }
  }
}

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';

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

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

async function deleteModel() {
  // Construct request
  const request = {
    name: client.modelPath(projectId, location, modelId),
  };

  const [response] = await client.deleteModel(request);
  console.log(`Model deleted: ${response}`);
}

deleteModel();

请求

使用操作类型 delete_model 发出请求以删除您创建的模型。您必须修改以下代码行:

  • project_id 设置为您的 PROJECT_ID
  • model_id 设置为您的模型 ID

Python

python delete_model.py

Java

mvn compile exec:java -Dexec.mainClass="com.example.automl.DeleteModel"

Node.js

node delete_model.js

响应

Model deleted.