CreateDocumentClassifier与 AWS SDK 或 CLI 配合使用 - Amazon Comprehend

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CreateDocumentClassifier与 AWS SDK 或 CLI 配合使用

以下代码示例演示如何使用 CreateDocumentClassifier

操作示例是大型程序的代码摘录,必须在上下文中运行。在以下代码示例中,您可以查看此操作的上下文:

CLI
AWS CLI

创建文档分类器对文档进行分类

以下 create-document-classifier 示例启动文档分类器模型的训练过程。训练数据文件 training.csv 位于 --input-data-config 标签处。training.csv 是一个两列文档,其中第一列提供标签或分类,第二列提供文档。

aws comprehend create-document-classifier \ --document-classifier-name example-classifier \ --data-access-arn arn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE \ --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET/" \ --language-code en

输出:

{ "DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier" }

有关更多信息,请参阅《Amazon Comprehend 开发人员指南》中的自定义分类

Java
适用于 Java 2.x 的 SDK
注意

还有更多相关信息 GitHub。在 AWS 代码示例存储库中查找完整示例,了解如何进行设置和运行。

import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.comprehend.ComprehendClient; import software.amazon.awssdk.services.comprehend.model.ComprehendException; import software.amazon.awssdk.services.comprehend.model.CreateDocumentClassifierRequest; import software.amazon.awssdk.services.comprehend.model.CreateDocumentClassifierResponse; import software.amazon.awssdk.services.comprehend.model.DocumentClassifierInputDataConfig; /** * Before running this code example, you can setup the necessary resources, such * as the CSV file and IAM Roles, by following this document: * https://aws.amazon.com/blogs/machine-learning/building-a-custom-classifier-using-amazon-comprehend/ * * Also, set up your development environment, including your credentials. * * For more information, see the following documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html */ public class DocumentClassifierDemo { public static void main(String[] args) { final String usage = """ Usage: <dataAccessRoleArn> <s3Uri> <documentClassifierName> Where: dataAccessRoleArn - The ARN value of the role used for this operation. s3Uri - The Amazon S3 bucket that contains the CSV file. documentClassifierName - The name of the document classifier. """; if (args.length != 3) { System.out.println(usage); System.exit(1); } String dataAccessRoleArn = args[0]; String s3Uri = args[1]; String documentClassifierName = args[2]; Region region = Region.US_EAST_1; ComprehendClient comClient = ComprehendClient.builder() .region(region) .build(); createDocumentClassifier(comClient, dataAccessRoleArn, s3Uri, documentClassifierName); comClient.close(); } public static void createDocumentClassifier(ComprehendClient comClient, String dataAccessRoleArn, String s3Uri, String documentClassifierName) { try { DocumentClassifierInputDataConfig config = DocumentClassifierInputDataConfig.builder() .s3Uri(s3Uri) .build(); CreateDocumentClassifierRequest createDocumentClassifierRequest = CreateDocumentClassifierRequest.builder() .documentClassifierName(documentClassifierName) .dataAccessRoleArn(dataAccessRoleArn) .languageCode("en") .inputDataConfig(config) .build(); CreateDocumentClassifierResponse createDocumentClassifierResult = comClient .createDocumentClassifier(createDocumentClassifierRequest); String documentClassifierArn = createDocumentClassifierResult.documentClassifierArn(); System.out.println("Document Classifier ARN: " + documentClassifierArn); } catch (ComprehendException e) { System.err.println(e.awsErrorDetails().errorMessage()); System.exit(1); } } }
Python
SDK for Python (Boto3)
注意

还有更多相关信息 GitHub。在 AWS 代码示例存储库中查找完整示例,了解如何进行设置和运行。

class ComprehendClassifier: """Encapsulates an Amazon Comprehend custom classifier.""" def __init__(self, comprehend_client): """ :param comprehend_client: A Boto3 Comprehend client. """ self.comprehend_client = comprehend_client self.classifier_arn = None def create( self, name, language_code, training_bucket, training_key, data_access_role_arn, mode, ): """ Creates a custom classifier. After the classifier is created, it immediately starts training on the data found in the specified Amazon S3 bucket. Training can take 30 minutes or longer. The `describe_document_classifier` function can be used to get training status and returns a status of TRAINED when the classifier is ready to use. :param name: The name of the classifier. :param language_code: The language the classifier can operate on. :param training_bucket: The Amazon S3 bucket that contains the training data. :param training_key: The prefix used to find training data in the training bucket. If multiple objects have the same prefix, all of them are used. :param data_access_role_arn: The Amazon Resource Name (ARN) of a role that grants Comprehend permission to read from the training bucket. :return: The ARN of the newly created classifier. """ try: response = self.comprehend_client.create_document_classifier( DocumentClassifierName=name, LanguageCode=language_code, InputDataConfig={"S3Uri": f"s3://{training_bucket}/{training_key}"}, DataAccessRoleArn=data_access_role_arn, Mode=mode.value, ) self.classifier_arn = response["DocumentClassifierArn"] logger.info("Started classifier creation. Arn is: %s.", self.classifier_arn) except ClientError: logger.exception("Couldn't create classifier %s.", name) raise else: return self.classifier_arn

有关 S AWS DK 开发者指南和代码示例的完整列表,请参阅将 Amazon Comprehend 与 SDK 配合 AWS 使用。本主题还包括有关入门的信息以及有关先前的 SDK 版本的详细信息。