内容分类

内容分类分析文档并返回文档中找到的文本所属的内容类别的列表。如需对文档中的内容进行分类，请调用 classifyText 方法。

如需查看 classifyText 方法返回的内容分类的完整列表，请点击此处。

您可以通过设置可选的 classificationModelOptions 字段，选择要为 classifyText 方法使用的模型：

本部分演示如何对文档中的内容进行分类。 您必须针对每个文档分别提交请求。

内容分类

以下是以字符串形式提供的内容分类示例：

协议

如需对文档中的内容进行分类，请按照下面示例中所示，向 documents:classifyText REST 方法发出 POST 请求，并提供相应的请求正文。

该示例使用 gcloud auth application-default print-access-token 命令获取通过 Google Cloud Platform gcloud CLI 为项目设置的服务账号的访问令牌。如需了解有关安装 gcloud CLI 以及使用服务账号设置项目的说明，请参阅快速入门。

curl -X POST \
     -H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
     -H "Content-Type: application/json; charset=utf-8" \
     --data "{
  'document':{
    'type':'PLAIN_TEXT',
    'content':'Google, headquartered in Mountain View, unveiled the new Android
    phone at the Consumer Electronic Show.  Sundar Pichai said in his keynote
    that users love their new Android phones.'
  },
  'classificationModelOptions': {
    'v2Model': {
      'contentCategoriesVersion': 'V2',
    }
  }
}" "https://language.googleapis.com/v1/documents:classifyText"

Go

如需了解如何安装和使用 Natural Language 的客户端库，请参阅 Natural Language 客户端库。 如需了解详情，请参阅 Natural Language Go API 参考文档。

如需向 Natural Language 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 


func classifyText(ctx context.Context, client *language.Client, text string) (*languagepb.ClassifyTextResponse, error) {
	return client.ClassifyText(ctx, &languagepb.ClassifyTextRequest{
		Document: &languagepb.Document{
			Source: &languagepb.Document_Content{
				Content: text,
			},
			Type: languagepb.Document_PLAIN_TEXT,
		},
		ClassificationModelOptions: &languagepb.ClassificationModelOptions{
			ModelType: &languagepb.ClassificationModelOptions_V2Model_{
				V2Model: &languagepb.ClassificationModelOptions_V2Model{
					ContentCategoriesVersion: languagepb.ClassificationModelOptions_V2Model_V2,
				},
			},
		},
	})
}

Java

如需了解如何安装和使用 Natural Language 的客户端库，请参阅 Natural Language 客户端库。 如需了解详情，请参阅 Natural Language Java API 参考文档。

如需向 Natural Language 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

// Instantiate the Language client com.google.cloud.language.v2.LanguageServiceClient
try (LanguageServiceClient language = LanguageServiceClient.create()) {
  // Set content to the text string
  Document doc = Document.newBuilder().setContent(text).setType(Type.PLAIN_TEXT).build();
  ClassifyTextRequest request = ClassifyTextRequest.newBuilder().setDocument(doc).build();
  // Detect categories in the given text
  ClassifyTextResponse response = language.classifyText(request);

  for (ClassificationCategory category : response.getCategoriesList()) {
    System.out.printf(
        "Category name : %s, Confidence : %.3f\n",
        category.getName(), category.getConfidence());
  }
}

Node.js

如需了解如何安装和使用 Natural Language 的客户端库，请参阅 Natural Language 客户端库。 如需了解详情，请参阅 Natural Language Node.js API 参考文档。

如需向 Natural Language 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

// Imports the Google Cloud client library
const language = require('@google-cloud/language');

// Creates a client
const client = new language.LanguageServiceClient();

/**
 * TODO(developer): Uncomment the following line to run this code.
 */
// const text = 'Your text to analyze, e.g. Hello, world!';

// Prepares a document, representing the provided text
const document = {
  content: text,
  type: 'PLAIN_TEXT',
};

const classificationModelOptions = {
  v2Model: {
    contentCategoriesVersion: 'V2',
  },
};

// Classifies text in the document
const [classification] = await client.classifyText({
  document,
  classificationModelOptions,
});
console.log('Categories:');
classification.categories.forEach(category => {
  console.log(`Name: ${category.name}, Confidence: ${category.confidence}`);
});

Python

如需了解如何安装和使用 Natural Language 的客户端库，请参阅 Natural Language 客户端库。 如需了解详情，请参阅 Natural Language Python API 参考文档。

如需向 Natural Language 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

from google.cloud import language_v1


def sample_classify_text(text_content):
    """
    Classifying Content in a String

    Args:
      text_content The text content to analyze.
    """

    client = language_v1.LanguageServiceClient()

    # text_content = "That actor on TV makes movies in Hollywood and also stars in a variety of popular new TV shows."

    # Available types: PLAIN_TEXT, HTML
    type_ = language_v1.Document.Type.PLAIN_TEXT

    # Optional. If not specified, the language is automatically detected.
    # For list of supported languages:
    # https://cloud.google.com/natural-language/docs/languages
    language = "en"
    document = {"content": text_content, "type_": type_, "language": language}

    content_categories_version = (
        language_v1.ClassificationModelOptions.V2Model.ContentCategoriesVersion.V2
    )
    response = client.classify_text(
        request={
            "document": document,
            "classification_model_options": {
                "v2_model": {"content_categories_version": content_categories_version}
            },
        }
    )
    # Loop through classified categories returned from the API
    for category in response.categories:
        # Get the name of the category representing the document.
        # See the predefined taxonomy of categories:
        # https://cloud.google.com/natural-language/docs/categories
        print(f"Category name: {category.name}")
        # Get the confidence. Number representing how certain the classifier
        # is that this category represents the provided text.
        print(f"Confidence: {category.confidence}")

其他语言

C#：请按照客户端库页面上的 C# 设置说明操作，然后访问 .NET 版 Natural Language 参考文档。

PHP：请按照客户端库页面上的 PHP 设置说明操作，然后访问 PHP 版 Natural Language 参考文档。

Ruby：请按照客户端库页面上的 Ruby 设置说明操作，然后访问 Ruby 版 Natural Language 参考文档。

对 Cloud Storage 中内容进行分类

以下示例介绍如何对 Cloud Storage 上文本文件中存储的内容进行分类：

协议

如需对 Cloud Storage 中存储的文档的内容进行分类，请向 documents:classifyText REST 方法发出 POST 请求，并提供带有文档路径的相应请求正文，如以下示例所示。

curl -X POST \
     -H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
     -H "Content-Type: application/json; charset=utf-8" \
     --data "{
  'document':{
    'type':'PLAIN_TEXT',
    'gcsContentUri':'gs://<bucket-name>/<object-name>'
  }
  'classificationModelOptions': {
    'v1Model': {
    }
  }
}" "https://language.googleapis.com/v1/documents:classifyText"

Go

如需了解如何安装和使用 Natural Language 的客户端库，请参阅 Natural Language 客户端库。 如需了解详情，请参阅 Natural Language Go API 参考文档。

如需向 Natural Language 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 


func classifyTextFromGCS(ctx context.Context, gcsURI string) (*languagepb.ClassifyTextResponse, error) {
	return client.ClassifyText(ctx, &languagepb.ClassifyTextRequest{
		Document: &languagepb.Document{
			Source: &languagepb.Document_GcsContentUri{
				GcsContentUri: gcsURI,
			},
			Type: languagepb.Document_PLAIN_TEXT,
		},
	})
}

Java

如需了解如何安装和使用 Natural Language 的客户端库，请参阅 Natural Language 客户端库。 如需了解详情，请参阅 Natural Language Java API 参考文档。

如需向 Natural Language 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

// Instantiate the Language client com.google.cloud.language.v2.LanguageServiceClient
try (LanguageServiceClient language = LanguageServiceClient.create()) {
  // Set the GCS content URI path
  Document doc =
      Document.newBuilder().setGcsContentUri(gcsUri).setType(Type.PLAIN_TEXT).build();
  ClassifyTextRequest request = ClassifyTextRequest.newBuilder().setDocument(doc).build();
  // Detect categories in the given file
  ClassifyTextResponse response = language.classifyText(request);

  for (ClassificationCategory category : response.getCategoriesList()) {
    System.out.printf(
        "Category name : %s, Confidence : %.3f\n",
        category.getName(), category.getConfidence());
  }
}

Node.js

如需了解如何安装和使用 Natural Language 的客户端库，请参阅 Natural Language 客户端库。 如需了解详情，请参阅 Natural Language Node.js API 参考文档。

如需向 Natural Language 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

// Imports the Google Cloud client library.
const language = require('@google-cloud/language');

// Creates a client.
const client = new language.LanguageServiceClient();

/**
 * TODO(developer): Uncomment the following lines to run this code
 */
// const bucketName = 'Your bucket name, e.g. my-bucket';
// const fileName = 'Your file name, e.g. my-file.txt';

// Prepares a document, representing a text file in Cloud Storage
const document = {
  gcsContentUri: `gs://${bucketName}/${fileName}`,
  type: 'PLAIN_TEXT',
};

// Classifies text in the document
const [classification] = await client.classifyText({document});

console.log('Categories:');
classification.categories.forEach(category => {
  console.log(`Name: ${category.name}, Confidence: ${category.confidence}`);
});

Python

如需了解如何安装和使用 Natural Language 的客户端库，请参阅 Natural Language 客户端库。 如需了解详情，请参阅 Natural Language Python API 参考文档。

如需向 Natural Language 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。 

from google.cloud import language_v1


def sample_classify_text(gcs_content_uri):
    """
    Classifying Content in text file stored in Cloud Storage

    Args:
      gcs_content_uri Google Cloud Storage URI where the file content is located.
      e.g. gs://[Your Bucket]/[Path to File]
      The text file must include at least 20 words.
    """

    client = language_v1.LanguageServiceClient()

    # gcs_content_uri = 'gs://cloud-samples-data/language/classify-entertainment.txt'

    # Available types: PLAIN_TEXT, HTML
    type_ = language_v1.Document.Type.PLAIN_TEXT

    # Optional. If not specified, the language is automatically detected.
    # For list of supported languages:
    # https://cloud.google.com/natural-language/docs/languages
    language = "en"
    document = {
        "gcs_content_uri": gcs_content_uri,
        "type_": type_,
        "language": language,
    }

    response = client.classify_text(request={"document": document})
    # Loop through classified categories returned from the API
    for category in response.categories:
        # Get the name of the category representing the document.
        # See the predefined taxonomy of categories:
        # https://cloud.google.com/natural-language/docs/categories
        print(f"Category name: {category.name}")
        # Get the confidence. Number representing how certain the classifier
        # is that this category represents the provided text.
        print(f"Confidence: {category.confidence}")

其他语言

C#：请按照客户端库页面上的 C# 设置说明操作，然后访问 .NET 版 Natural Language 参考文档。

PHP：请按照客户端库页面上的 PHP 设置说明操作，然后访问 PHP 版 Natural Language 参考文档。

Ruby：请按照客户端库页面上的 Ruby 设置说明操作，然后访问 Ruby 版 Natural Language 参考文档。