文档 AWS SDK 示例 GitHub 存储库中还有更多 [S AWS DK 示例](https://github.com/awsdocs/aws-doc-sdk-examples)。
本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。
# 使用 Amazon Comprehend 的基本示例 AWS SDKs
以下代码示例展示了如何使用 Amazon Comprehend 的基础知识。 AWS SDKs
**Contents**
+ [操作](comprehend_code_examples_actions.md)
+ [`CreateDocumentClassifier`](comprehend_example_comprehend_CreateDocumentClassifier_section.md)
+ [`DeleteDocumentClassifier`](comprehend_example_comprehend_DeleteDocumentClassifier_section.md)
+ [`DescribeDocumentClassificationJob`](comprehend_example_comprehend_DescribeDocumentClassificationJob_section.md)
+ [`DescribeDocumentClassifier`](comprehend_example_comprehend_DescribeDocumentClassifier_section.md)
+ [`DescribeTopicsDetectionJob`](comprehend_example_comprehend_DescribeTopicsDetectionJob_section.md)
+ [`DetectDominantLanguage`](comprehend_example_comprehend_DetectDominantLanguage_section.md)
+ [`DetectEntities`](comprehend_example_comprehend_DetectEntities_section.md)
+ [`DetectKeyPhrases`](comprehend_example_comprehend_DetectKeyPhrases_section.md)
+ [`DetectPiiEntities`](comprehend_example_comprehend_DetectPiiEntities_section.md)
+ [`DetectSentiment`](comprehend_example_comprehend_DetectSentiment_section.md)
+ [`DetectSyntax`](comprehend_example_comprehend_DetectSyntax_section.md)
+ [`ListDocumentClassificationJobs`](comprehend_example_comprehend_ListDocumentClassificationJobs_section.md)
+ [`ListDocumentClassifiers`](comprehend_example_comprehend_ListDocumentClassifiers_section.md)
+ [`ListTopicsDetectionJobs`](comprehend_example_comprehend_ListTopicsDetectionJobs_section.md)
+ [`StartDocumentClassificationJob`](comprehend_example_comprehend_StartDocumentClassificationJob_section.md)
+ [`StartTopicsDetectionJob`](comprehend_example_comprehend_StartTopicsDetectionJob_section.md)
# 使用 Amazon Comprehend 执行的操作 AWS SDKs
以下代码示例演示了如何使用执行单个 Amazon Comprehend 操作。 AWS SDKs每个示例都包含一个指向的链接 GitHub，您可以在其中找到有关设置和运行代码的说明。
这些代码节选调用了 Amazon Comprehend API，是必须在上下文中运行的大型程序的代码节选。您可以在[Amazon Comprehend 使用场景 AWS SDKs](comprehend_code_examples_scenarios.md)中结合上下文查看操作。
以下示例仅包括最常用的操作。有关完整列表，请参阅 [Amazon Comprehend API 参考](https://docs.aws.amazon.com/comprehend/latest/APIReference/welcome.html)。
**Topics**
+ [`CreateDocumentClassifier`](comprehend_example_comprehend_CreateDocumentClassifier_section.md)
+ [`DeleteDocumentClassifier`](comprehend_example_comprehend_DeleteDocumentClassifier_section.md)
+ [`DescribeDocumentClassificationJob`](comprehend_example_comprehend_DescribeDocumentClassificationJob_section.md)
+ [`DescribeDocumentClassifier`](comprehend_example_comprehend_DescribeDocumentClassifier_section.md)
+ [`DescribeTopicsDetectionJob`](comprehend_example_comprehend_DescribeTopicsDetectionJob_section.md)
+ [`DetectDominantLanguage`](comprehend_example_comprehend_DetectDominantLanguage_section.md)
+ [`DetectEntities`](comprehend_example_comprehend_DetectEntities_section.md)
+ [`DetectKeyPhrases`](comprehend_example_comprehend_DetectKeyPhrases_section.md)
+ [`DetectPiiEntities`](comprehend_example_comprehend_DetectPiiEntities_section.md)
+ [`DetectSentiment`](comprehend_example_comprehend_DetectSentiment_section.md)
+ [`DetectSyntax`](comprehend_example_comprehend_DetectSyntax_section.md)
+ [`ListDocumentClassificationJobs`](comprehend_example_comprehend_ListDocumentClassificationJobs_section.md)
+ [`ListDocumentClassifiers`](comprehend_example_comprehend_ListDocumentClassifiers_section.md)
+ [`ListTopicsDetectionJobs`](comprehend_example_comprehend_ListTopicsDetectionJobs_section.md)
+ [`StartDocumentClassificationJob`](comprehend_example_comprehend_StartDocumentClassificationJob_section.md)
+ [`StartTopicsDetectionJob`](comprehend_example_comprehend_StartTopicsDetectionJob_section.md)
# `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：
+ [训练自定义分类器并对文档进行分类](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ 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://amzn-s3-demo-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](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[CreateDocumentClassifier](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/create-document-classifier.html)*中的。
------
#### [ Java ]
**适用于 Java 的 SDK 2.x**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples)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:
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);
}
}
}
```
+ 有关 API 的详细信息，请参阅 *AWS SDK for Java 2.x API 参考[CreateDocumentClassifier](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/CreateDocumentClassifier)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)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 API 的详细信息，请参阅适用[CreateDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/CreateDocumentClassifier)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->createdocumentclassifier(
iv_documentclassifiername = iv_classifier_name
iv_languagecode = iv_language_code
io_inputdataconfig = NEW /aws1/cl_cpddocclifierinpdat00(
iv_s3uri = iv_training_s3_uri
)
iv_dataaccessrolearn = iv_data_access_role_arn
iv_mode = iv_mode
).
MESSAGE 'Document classifier creation started.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdresrclimitexcdex.
MESSAGE 'Resource limit exceeded.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanytagsex.
MESSAGE 'Too many tags.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[CreateDocumentClassifier](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `DeleteDocumentClassifier`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `DeleteDocumentClassifier`。
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：
+ [训练自定义分类器并对文档进行分类](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**删除自定义文档分类器**
以下 `delete-document-classifier` 示例删除了自定义文档分类器模型。
```
aws comprehend delete-document-classifier \
--document-classifier-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-1
```
此命令不生成任何输出。
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[管理 Amazon Comprehend 端点](https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[DeleteDocumentClassifier](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/delete-document-classifier.html)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)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 delete(self):
"""
Deletes the classifier.
"""
try:
self.comprehend_client.delete_document_classifier(
DocumentClassifierArn=self.classifier_arn
)
logger.info("Deleted classifier %s.", self.classifier_arn)
self.classifier_arn = None
except ClientError:
logger.exception("Couldn't deleted classifier %s.", self.classifier_arn)
raise
```
+ 有关 API 的详细信息，请参阅适用[DeleteDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DeleteDocumentClassifier)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->deletedocumentclassifier(
iv_documentclassifierarn = iv_classifier_arn
).
MESSAGE 'Document classifier deleted.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdresourcenotfoundex.
MESSAGE 'Resource not found.' TYPE 'E'.
CATCH /aws1/cx_cpdresourceinuseex.
MESSAGE 'Resource in use.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[DeleteDocumentClassifier](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `DescribeDocumentClassificationJob`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `DescribeDocumentClassificationJob`。
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：
+ [训练自定义分类器并对文档进行分类](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**描述文档分类作业**
以下 `describe-document-classification-job` 示例将获取异步文档分类作业的属性。
```
aws comprehend describe-document-classification-job \
--job-id 123456abcdeb0e11022f22a11EXAMPLE
```
输出：
```
{
"DocumentClassificationJobProperties": {
"JobId": "123456abcdeb0e11022f22a11EXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:document-classification-job/123456abcdeb0e11022f22a11EXAMPLE",
"JobName": "exampleclassificationjob",
"JobStatus": "COMPLETED",
"SubmitTime": "2023-06-14T17:09:51.788000+00:00",
"EndTime": "2023-06-14T17:15:58.582000+00:00",
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/mymodel/version/1",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket/jobdata/",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-CLN-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
},
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole"
}
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[自定义分类](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[DescribeDocumentClassificationJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-document-classification-job.html)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)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 describe_job(self, job_id):
"""
Gets metadata about a classification job.
:param job_id: The ID of the job to look up.
:return: Metadata about the job.
"""
try:
response = self.comprehend_client.describe_document_classification_job(
JobId=job_id
)
job = response["DocumentClassificationJobProperties"]
logger.info("Got classification job %s.", job["JobName"])
except ClientError:
logger.exception("Couldn't get classification job %s.", job_id)
raise
else:
return job
```
+ 有关 API 的详细信息，请参阅适用[DescribeDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassificationJob)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->describedocclassificationjob(
iv_jobid = iv_job_id
).
MESSAGE 'Document classification job described.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdjobnotfoundex.
MESSAGE 'Job not found.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[DescribeDocumentClassificationJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `DescribeDocumentClassifier`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `DescribeDocumentClassifier`。
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：
+ [训练自定义分类器并对文档进行分类](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**描述文档分类器**
以下 `describe-document-classifier` 示例将获取自定义文档分类器模型的属性。
```
aws comprehend describe-document-classifier \
--document-classifier-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-1
```
输出：
```
{
"DocumentClassifierProperties": {
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-1",
"LanguageCode": "en",
"Status": "TRAINED",
"SubmitTime": "2023-06-13T19:04:15.735000+00:00",
"EndTime": "2023-06-13T19:42:31.752000+00:00",
"TrainingStartTime": "2023-06-13T19:08:20.114000+00:00",
"TrainingEndTime": "2023-06-13T19:41:35.080000+00:00",
"InputDataConfig": {
"DataFormat": "COMPREHEND_CSV",
"S3Uri": "s3://amzn-s3-demo-bucket/trainingdata"
},
"OutputDataConfig": {},
"ClassifierMetadata": {
"NumberOfLabels": 3,
"NumberOfTrainedDocuments": 5016,
"NumberOfTestDocuments": 557,
"EvaluationMetrics": {
"Accuracy": 0.9856,
"Precision": 0.9919,
"Recall": 0.9459,
"F1Score": 0.9673,
"MicroPrecision": 0.9856,
"MicroRecall": 0.9856,
"MicroF1Score": 0.9856,
"HammingLoss": 0.0144
}
},
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role",
"Mode": "MULTI_CLASS"
}
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[创建和管理自定义模型](https://docs.aws.amazon.com/comprehend/latest/dg/manage-models.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[DescribeDocumentClassifier](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-document-classifier.html)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)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 describe(self, classifier_arn=None):
"""
Gets metadata about a custom classifier, including its current status.
:param classifier_arn: The ARN of the classifier to look up.
:return: Metadata about the classifier.
"""
if classifier_arn is not None:
self.classifier_arn = classifier_arn
try:
response = self.comprehend_client.describe_document_classifier(
DocumentClassifierArn=self.classifier_arn
)
classifier = response["DocumentClassifierProperties"]
logger.info("Got classifier %s.", self.classifier_arn)
except ClientError:
logger.exception("Couldn't get classifier %s.", self.classifier_arn)
raise
else:
return classifier
```
+ 有关 API 的详细信息，请参阅适用[DescribeDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassifier)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->describedocumentclassifier(
iv_documentclassifierarn = iv_classifier_arn
).
MESSAGE 'Document classifier described.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdresourcenotfoundex.
MESSAGE 'Resource not found.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[DescribeDocumentClassifier](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `DescribeTopicsDetectionJob`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `DescribeTopicsDetectionJob`。
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：
+ [对示例数据运行主题建模任务](comprehend_example_comprehend_Usage_TopicModeler_section.md)
------
#### [ CLI ]
**AWS CLI**
**描述主题检测作业**
以下 `describe-topics-detection-job` 示例获取异步主题检测作业的属性。
```
aws comprehend describe-topics-detection-job \
--job-id 123456abcdeb0e11022f22a11EXAMPLE
```
输出：
```
{
"TopicsDetectionJobProperties": {
"JobId": "123456abcdeb0e11022f22a11EXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
"JobName": "example_topics_detection",
"JobStatus": "IN_PROGRESS",
"SubmitTime": "2023-06-09T18:44:43.414000+00:00",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-TOPICS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
},
"NumberOfTopics": 10,
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-examplerole"
}
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的 [Amazon Comprehend 洞察的异步分析](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[DescribeTopicsDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-topics-detection-job.html)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)中查找完整示例，了解如何进行设置和运行。
```
class ComprehendTopicModeler:
"""Encapsulates a Comprehend topic modeler."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def describe_job(self, job_id):
"""
Gets metadata about a topic modeling job.
:param job_id: The ID of the job to look up.
:return: Metadata about the job.
"""
try:
response = self.comprehend_client.describe_topics_detection_job(
JobId=job_id
)
job = response["TopicsDetectionJobProperties"]
logger.info("Got topic detection job %s.", job_id)
except ClientError:
logger.exception("Couldn't get topic detection job %s.", job_id)
raise
else:
return job
```
+ 有关 API 的详细信息，请参阅适用[DescribeTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeTopicsDetectionJob)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->describetopicsdetectionjob(
iv_jobid = iv_job_id
).
MESSAGE 'Topics detection job described.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdjobnotfoundex.
MESSAGE 'Job not found.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[DescribeTopicsDetectionJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `DetectDominantLanguage`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `DetectDominantLanguage`。
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：
+ [检测文档元素](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**适用于 .NET 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples)中查找完整示例，了解如何进行设置和运行。
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example calls the Amazon Comprehend service to determine the
/// dominant language.
///
public static class DetectDominantLanguage
{
///
/// Calls Amazon Comprehend to determine the dominant language used in
/// the sample text.
///
public static async Task Main()
{
string text = "It is raining today in Seattle.";
var comprehendClient = new AmazonComprehendClient(Amazon.RegionEndpoint.USWest2);
Console.WriteLine("Calling DetectDominantLanguage\n");
var detectDominantLanguageRequest = new DetectDominantLanguageRequest()
{
Text = text,
};
var detectDominantLanguageResponse = await comprehendClient.DetectDominantLanguageAsync(detectDominantLanguageRequest);
foreach (var dl in detectDominantLanguageResponse.Languages)
{
Console.WriteLine($"Language Code: {dl.LanguageCode}, Score: {dl.Score}");
}
Console.WriteLine("Done");
}
}
```
+ 有关 API 的详细信息，请参阅 *适用于 .NET 的 AWS SDK API 参考[DetectDominantLanguage](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectDominantLanguage)*中的。
------
#### [ CLI ]
**AWS CLI**
**检测输入文本的主要语言**
以下 `detect-dominant-language` 分析输入文本并识别主要语言。预训练模型的置信度分数也是输出。
```
aws comprehend detect-dominant-language \
--text "It is a beautiful day in Seattle."
```
输出：
```
{
"Languages": [
{
"LanguageCode": "en",
"Score": 0.9877256155014038
}
]
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[主要语言](https://docs.aws.amazon.com/comprehend/latest/dg/how-languages.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[DetectDominantLanguage](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-dominant-language.html)*中的。
------
#### [ Java ]
**适用于 Java 的 SDK 2.x**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples)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.DetectDominantLanguageRequest;
import software.amazon.awssdk.services.comprehend.model.DetectDominantLanguageResponse;
import software.amazon.awssdk.services.comprehend.model.DominantLanguage;
import java.util.List;
/**
* Before running this Java V2 code example, 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 DetectLanguage {
public static void main(String[] args) {
// Specify French text - "It is raining today in Seattle".
String text = "Il pleut aujourd'hui à Seattle";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectDominantLanguage");
detectTheDominantLanguage(comClient, text);
comClient.close();
}
public static void detectTheDominantLanguage(ComprehendClient comClient, String text) {
try {
DetectDominantLanguageRequest request = DetectDominantLanguageRequest.builder()
.text(text)
.build();
DetectDominantLanguageResponse resp = comClient.detectDominantLanguage(request);
List allLanList = resp.languages();
for (DominantLanguage lang : allLanList) {
System.out.println("Language is " + lang.languageCode());
}
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ 有关 API 的详细信息，请参阅 *AWS SDK for Java 2.x API 参考[DetectDominantLanguage](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectDominantLanguage)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)中查找完整示例，了解如何进行设置和运行。
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_languages(self, text):
"""
Detects languages used in a document.
:param text: The document to inspect.
:return: The list of languages along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_dominant_language(Text=text)
languages = response["Languages"]
logger.info("Detected %s languages.", len(languages))
except ClientError:
logger.exception("Couldn't detect languages.")
raise
else:
return languages
```
+ 有关 API 的详细信息，请参阅适用[DetectDominantLanguage](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectDominantLanguage)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->detectdominantlanguage( iv_text = iv_text ).
MESSAGE 'Languages detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[DetectDominantLanguage](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `DetectEntities`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `DetectEntities`。
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：
+ [检测文档元素](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**适用于 .NET 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples)中查找完整示例，了解如何进行设置和运行。
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to use the AmazonComprehend service detect any
/// entities in submitted text.
///
public static class DetectEntities
{
///
/// The main method calls the DetectEntitiesAsync method to find any
/// entities in the sample code.
///
public static async Task Main()
{
string text = "It is raining today in Seattle";
var comprehendClient = new AmazonComprehendClient();
Console.WriteLine("Calling DetectEntities\n");
var detectEntitiesRequest = new DetectEntitiesRequest()
{
Text = text,
LanguageCode = "en",
};
var detectEntitiesResponse = await comprehendClient.DetectEntitiesAsync(detectEntitiesRequest);
foreach (var e in detectEntitiesResponse.Entities)
{
Console.WriteLine($"Text: {e.Text}, Type: {e.Type}, Score: {e.Score}, BeginOffset: {e.BeginOffset}, EndOffset: {e.EndOffset}");
}
Console.WriteLine("Done");
}
}
```
+ 有关 API 的详细信息，请参阅 *适用于 .NET 的 AWS SDK API 参考[DetectEntities](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectEntities)*中的。
------
#### [ CLI ]
**AWS CLI**
**检测输入文本中的命名实体**
以下 `detect-entities` 示例分析输入文本并返回命名实体。预训练模型的置信度分数也是每个预测的输出。
```
aws comprehend detect-entities \
--language-code en \
--text "Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card \
account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st. Based on your autopay settings, \
we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. \
Customer feedback for Sunshine Spa, 123 Main St, Anywhere. Send comments to Alice at AnySpa@example.com."
```
输出：
```
{
"Entities": [
{
"Score": 0.9994556307792664,
"Type": "PERSON",
"Text": "Zhang Wei",
"BeginOffset": 6,
"EndOffset": 15
},
{
"Score": 0.9981022477149963,
"Type": "PERSON",
"Text": "John",
"BeginOffset": 22,
"EndOffset": 26
},
{
"Score": 0.9986887574195862,
"Type": "ORGANIZATION",
"Text": "AnyCompany Financial Services, LLC",
"BeginOffset": 33,
"EndOffset": 67
},
{
"Score": 0.9959119558334351,
"Type": "OTHER",
"Text": "1111-XXXX-1111-XXXX",
"BeginOffset": 88,
"EndOffset": 107
},
{
"Score": 0.9708039164543152,
"Type": "QUANTITY",
"Text": ".53",
"BeginOffset": 133,
"EndOffset": 136
},
{
"Score": 0.9987268447875977,
"Type": "DATE",
"Text": "July 31st",
"BeginOffset": 152,
"EndOffset": 161
},
{
"Score": 0.9858865737915039,
"Type": "OTHER",
"Text": "XXXXXX1111",
"BeginOffset": 271,
"EndOffset": 281
},
{
"Score": 0.9700471758842468,
"Type": "OTHER",
"Text": "XXXXX0000",
"BeginOffset": 306,
"EndOffset": 315
},
{
"Score": 0.9591118693351746,
"Type": "ORGANIZATION",
"Text": "Sunshine Spa",
"BeginOffset": 340,
"EndOffset": 352
},
{
"Score": 0.9797496795654297,
"Type": "LOCATION",
"Text": "123 Main St",
"BeginOffset": 354,
"EndOffset": 365
},
{
"Score": 0.994929313659668,
"Type": "PERSON",
"Text": "Alice",
"BeginOffset": 394,
"EndOffset": 399
},
{
"Score": 0.9949769377708435,
"Type": "OTHER",
"Text": "AnySpa@example.com",
"BeginOffset": 403,
"EndOffset": 418
}
]
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[实体](https://docs.aws.amazon.com/comprehend/latest/dg/how-entities.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[DetectEntities](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-entities.html)*中的。
------
#### [ Java ]
**适用于 Java 的 SDK 2.x**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples)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.DetectEntitiesRequest;
import software.amazon.awssdk.services.comprehend.model.DetectEntitiesResponse;
import software.amazon.awssdk.services.comprehend.model.Entity;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import java.util.List;
/**
* Before running this Java V2 code example, 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 DetectEntities {
public static void main(String[] args) {
String text = "Amazon.com, Inc. is located in Seattle, WA and was founded July 5th, 1994 by Jeff Bezos, allowing customers to buy everything from books to blenders. Seattle is north of Portland and south of Vancouver, BC. Other notable Seattle - based companies are Starbucks and Boeing.";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectEntities");
detectAllEntities(comClient, text);
comClient.close();
}
public static void detectAllEntities(ComprehendClient comClient, String text) {
try {
DetectEntitiesRequest detectEntitiesRequest = DetectEntitiesRequest.builder()
.text(text)
.languageCode("en")
.build();
DetectEntitiesResponse detectEntitiesResult = comClient.detectEntities(detectEntitiesRequest);
List entList = detectEntitiesResult.entities();
for (Entity entity : entList) {
System.out.println("Entity text is " + entity.text());
}
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ 有关 API 的详细信息，请参阅 *AWS SDK for Java 2.x API 参考[DetectEntities](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectEntities)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)中查找完整示例，了解如何进行设置和运行。
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_entities(self, text, language_code):
"""
Detects entities in a document. Entities can be things like people and places
or other common terms.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect entities.")
raise
else:
return entities
```
+ 有关 API 的详细信息，请参阅适用[DetectEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectEntities)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->detectentities(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'Entities detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[DetectEntities](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `DetectKeyPhrases`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `DetectKeyPhrases`。
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：
+ [检测文档元素](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**适用于 .NET 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples)中查找完整示例，了解如何进行设置和运行。
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to use the Amazon Comprehend service to
/// search text for key phrases.
///
public static class DetectKeyPhrase
{
///
/// This method calls the Amazon Comprehend method DetectKeyPhrasesAsync
/// to detect any key phrases in the sample text.
///
public static async Task Main()
{
string text = "It is raining today in Seattle";
var comprehendClient = new AmazonComprehendClient(Amazon.RegionEndpoint.USWest2);
// Call DetectKeyPhrases API
Console.WriteLine("Calling DetectKeyPhrases");
var detectKeyPhrasesRequest = new DetectKeyPhrasesRequest()
{
Text = text,
LanguageCode = "en",
};
var detectKeyPhrasesResponse = await comprehendClient.DetectKeyPhrasesAsync(detectKeyPhrasesRequest);
foreach (var kp in detectKeyPhrasesResponse.KeyPhrases)
{
Console.WriteLine($"Text: {kp.Text}, Score: {kp.Score}, BeginOffset: {kp.BeginOffset}, EndOffset: {kp.EndOffset}");
}
Console.WriteLine("Done");
}
}
```
+ 有关 API 的详细信息，请参阅 *适用于 .NET 的 AWS SDK API 参考[DetectKeyPhrases](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectKeyPhrases)*中的。
------
#### [ CLI ]
**AWS CLI**
**检测输入文本中的关键词**
以下 `detect-key-phrases` 示例分析输入文本并识别关键名词短语。预训练模型的置信度分数也是每个预测的输出。
```
aws comprehend detect-key-phrases \
--language-code en \
--text "Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card \
account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st. Based on your autopay settings, \
we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. \
Customer feedback for Sunshine Spa, 123 Main St, Anywhere. Send comments to Alice at AnySpa@example.com."
```
输出：
```
{
"KeyPhrases": [
{
"Score": 0.8996376395225525,
"Text": "Zhang Wei",
"BeginOffset": 6,
"EndOffset": 15
},
{
"Score": 0.9992469549179077,
"Text": "John",
"BeginOffset": 22,
"EndOffset": 26
},
{
"Score": 0.988385021686554,
"Text": "Your AnyCompany Financial Services",
"BeginOffset": 28,
"EndOffset": 62
},
{
"Score": 0.8740853071212769,
"Text": "LLC credit card account 1111-XXXX-1111-XXXX",
"BeginOffset": 64,
"EndOffset": 107
},
{
"Score": 0.9999437928199768,
"Text": "a minimum payment",
"BeginOffset": 112,
"EndOffset": 129
},
{
"Score": 0.9998900890350342,
"Text": ".53",
"BeginOffset": 133,
"EndOffset": 136
},
{
"Score": 0.9979453086853027,
"Text": "July 31st",
"BeginOffset": 152,
"EndOffset": 161
},
{
"Score": 0.9983011484146118,
"Text": "your autopay settings",
"BeginOffset": 172,
"EndOffset": 193
},
{
"Score": 0.9996572136878967,
"Text": "your payment",
"BeginOffset": 211,
"EndOffset": 223
},
{
"Score": 0.9995037317276001,
"Text": "the due date",
"BeginOffset": 227,
"EndOffset": 239
},
{
"Score": 0.9702621698379517,
"Text": "your bank account number XXXXXX1111",
"BeginOffset": 245,
"EndOffset": 280
},
{
"Score": 0.9179925918579102,
"Text": "the routing number XXXXX0000.Customer feedback",
"BeginOffset": 286,
"EndOffset": 332
},
{
"Score": 0.9978160858154297,
"Text": "Sunshine Spa",
"BeginOffset": 337,
"EndOffset": 349
},
{
"Score": 0.9706913232803345,
"Text": "123 Main St",
"BeginOffset": 351,
"EndOffset": 362
},
{
"Score": 0.9941995143890381,
"Text": "comments",
"BeginOffset": 379,
"EndOffset": 387
},
{
"Score": 0.9759287238121033,
"Text": "Alice",
"BeginOffset": 391,
"EndOffset": 396
},
{
"Score": 0.8376792669296265,
"Text": "AnySpa@example.com",
"BeginOffset": 400,
"EndOffset": 415
}
]
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[关键词](https://docs.aws.amazon.com/comprehend/latest/dg/how-key-phrases.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[DetectKeyPhrases](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-key-phrases.html)*中的。
------
#### [ Java ]
**适用于 Java 的 SDK 2.x**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples)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.DetectKeyPhrasesRequest;
import software.amazon.awssdk.services.comprehend.model.DetectKeyPhrasesResponse;
import software.amazon.awssdk.services.comprehend.model.KeyPhrase;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import java.util.List;
/**
* Before running this Java V2 code example, 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 DetectKeyPhrases {
public static void main(String[] args) {
String text = "Amazon.com, Inc. is located in Seattle, WA and was founded July 5th, 1994 by Jeff Bezos, allowing customers to buy everything from books to blenders. Seattle is north of Portland and south of Vancouver, BC. Other notable Seattle - based companies are Starbucks and Boeing.";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectKeyPhrases");
detectAllKeyPhrases(comClient, text);
comClient.close();
}
public static void detectAllKeyPhrases(ComprehendClient comClient, String text) {
try {
DetectKeyPhrasesRequest detectKeyPhrasesRequest = DetectKeyPhrasesRequest.builder()
.text(text)
.languageCode("en")
.build();
DetectKeyPhrasesResponse detectKeyPhrasesResult = comClient.detectKeyPhrases(detectKeyPhrasesRequest);
List phraseList = detectKeyPhrasesResult.keyPhrases();
for (KeyPhrase keyPhrase : phraseList) {
System.out.println("Key phrase text is " + keyPhrase.text());
}
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ 有关 API 的详细信息，请参阅 *AWS SDK for Java 2.x API 参考[DetectKeyPhrases](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectKeyPhrases)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)中查找完整示例，了解如何进行设置和运行。
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_key_phrases(self, text, language_code):
"""
Detects key phrases in a document. A key phrase is typically a noun and its
modifiers.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of key phrases along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_key_phrases(
Text=text, LanguageCode=language_code
)
phrases = response["KeyPhrases"]
logger.info("Detected %s phrases.", len(phrases))
except ClientError:
logger.exception("Couldn't detect phrases.")
raise
else:
return phrases
```
+ 有关 API 的详细信息，请参阅适用[DetectKeyPhrases](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectKeyPhrases)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->detectkeyphrases(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'Key phrases detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[DetectKeyPhrases](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `DetectPiiEntities`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `DetectPiiEntities`。
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：
+ [检测文档元素](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**适用于 .NET 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples)中查找完整示例，了解如何进行设置和运行。
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to use the Amazon Comprehend service to find
/// personally identifiable information (PII) within text submitted to the
/// DetectPiiEntitiesAsync method.
///
public class DetectingPII
{
///
/// This method calls the DetectPiiEntitiesAsync method to locate any
/// personally dientifiable information within the supplied text.
///
public static async Task Main()
{
var comprehendClient = new AmazonComprehendClient();
var text = @"Hello Paul Santos. The latest statement for your
credit card account 1111-0000-1111-0000 was
mailed to 123 Any Street, Seattle, WA 98109.";
var request = new DetectPiiEntitiesRequest
{
Text = text,
LanguageCode = "EN",
};
var response = await comprehendClient.DetectPiiEntitiesAsync(request);
if (response.Entities.Count > 0)
{
foreach (var entity in response.Entities)
{
var entityValue = text.Substring(entity.BeginOffset, entity.EndOffset - entity.BeginOffset);
Console.WriteLine($"{entity.Type}: {entityValue}");
}
}
}
}
```
+ 有关 API 的详细信息，请参阅 *适用于 .NET 的 AWS SDK API 参考[DetectPiiEntities](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectPiiEntities)*中的。
------
#### [ CLI ]
**AWS CLI**
**检测输入文本中的 PII 实体**
以下 `detect-pii-entities` 示例分析输入文本，并识别包含个人身份信息（PII）的实体。预训练模型的置信度分数也是每个预测的输出。
```
aws comprehend detect-pii-entities \
--language-code en \
--text "Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card \
account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st. Based on your autopay settings, \
we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. \
Customer feedback for Sunshine Spa, 123 Main St, Anywhere. Send comments to Alice at AnySpa@example.com."
```
输出：
```
{
"Entities": [
{
"Score": 0.9998322129249573,
"Type": "NAME",
"BeginOffset": 6,
"EndOffset": 15
},
{
"Score": 0.9998878240585327,
"Type": "NAME",
"BeginOffset": 22,
"EndOffset": 26
},
{
"Score": 0.9994089603424072,
"Type": "CREDIT_DEBIT_NUMBER",
"BeginOffset": 88,
"EndOffset": 107
},
{
"Score": 0.9999760985374451,
"Type": "DATE_TIME",
"BeginOffset": 152,
"EndOffset": 161
},
{
"Score": 0.9999449253082275,
"Type": "BANK_ACCOUNT_NUMBER",
"BeginOffset": 271,
"EndOffset": 281
},
{
"Score": 0.9999847412109375,
"Type": "BANK_ROUTING",
"BeginOffset": 306,
"EndOffset": 315
},
{
"Score": 0.999925434589386,
"Type": "ADDRESS",
"BeginOffset": 354,
"EndOffset": 365
},
{
"Score": 0.9989161491394043,
"Type": "NAME",
"BeginOffset": 394,
"EndOffset": 399
},
{
"Score": 0.9994171857833862,
"Type": "EMAIL",
"BeginOffset": 403,
"EndOffset": 418
}
]
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[个人身份信息（PII）](https://docs.aws.amazon.com/comprehend/latest/dg/pii.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[DetectPiiEntities](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-pii-entities.html)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)中查找完整示例，了解如何进行设置和运行。
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_pii(self, text, language_code):
"""
Detects personally identifiable information (PII) in a document. PII can be
things like names, account numbers, or addresses.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of PII entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_pii_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s PII entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect PII entities.")
raise
else:
return entities
```
+ 有关 API 的详细信息，请参阅适用[DetectPiiEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectPiiEntities)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->detectpiientities(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'PII entities detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[DetectPiiEntities](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `DetectSentiment`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `DetectSentiment`。
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：
+ [检测文档元素](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**适用于 .NET 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples)中查找完整示例，了解如何进行设置和运行。
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to detect the overall sentiment of the supplied
/// text using the Amazon Comprehend service.
///
public static class DetectSentiment
{
///
/// This method calls the DetetectSentimentAsync method to analyze the
/// supplied text and determine the overal sentiment.
///
public static async Task Main()
{
string text = "It is raining today in Seattle";
var comprehendClient = new AmazonComprehendClient(Amazon.RegionEndpoint.USWest2);
// Call DetectKeyPhrases API
Console.WriteLine("Calling DetectSentiment");
var detectSentimentRequest = new DetectSentimentRequest()
{
Text = text,
LanguageCode = "en",
};
var detectSentimentResponse = await comprehendClient.DetectSentimentAsync(detectSentimentRequest);
Console.WriteLine($"Sentiment: {detectSentimentResponse.Sentiment}");
Console.WriteLine("Done");
}
}
```
+ 有关 API 的详细信息，请参阅 *适用于 .NET 的 AWS SDK API 参考[DetectSentiment](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectSentiment)*中的。
------
#### [ CLI ]
**AWS CLI**
**检测输入文本的情绪**
以下 `detect-sentiment` 示例分析输入文本，并返回占主导地位的情绪（`POSITIVE`、`NEUTRAL`、`MIXED` 或`NEGATIVE`）的推断。
```
aws comprehend detect-sentiment \
--language-code en \
--text "It is a beautiful day in Seattle"
```
输出：
```
{
"Sentiment": "POSITIVE",
"SentimentScore": {
"Positive": 0.9976957440376282,
"Negative": 9.653854067437351e-05,
"Neutral": 0.002169104292988777,
"Mixed": 3.857641786453314e-05
}
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[情绪](https://docs.aws.amazon.com/comprehend/latest/dg/how-sentiment.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[DetectSentiment](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-sentiment.html)*中的。
------
#### [ Java ]
**适用于 Java 的 SDK 2.x**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples)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.DetectSentimentRequest;
import software.amazon.awssdk.services.comprehend.model.DetectSentimentResponse;
/**
* Before running this Java V2 code example, 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 DetectSentiment {
public static void main(String[] args) {
String text = "Amazon.com, Inc. is located in Seattle, WA and was founded July 5th, 1994 by Jeff Bezos, allowing customers to buy everything from books to blenders. Seattle is north of Portland and south of Vancouver, BC. Other notable Seattle - based companies are Starbucks and Boeing.";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectSentiment");
detectSentiments(comClient, text);
comClient.close();
}
public static void detectSentiments(ComprehendClient comClient, String text) {
try {
DetectSentimentRequest detectSentimentRequest = DetectSentimentRequest.builder()
.text(text)
.languageCode("en")
.build();
DetectSentimentResponse detectSentimentResult = comClient.detectSentiment(detectSentimentRequest);
System.out.println("The Neutral value is " + detectSentimentResult.sentimentScore().neutral());
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ 有关 API 的详细信息，请参阅 *AWS SDK for Java 2.x API 参考[DetectSentiment](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectSentiment)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)中查找完整示例，了解如何进行设置和运行。
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_sentiment(self, text, language_code):
"""
Detects the overall sentiment expressed in a document. Sentiment can
be positive, negative, neutral, or a mixture.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The sentiments along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_sentiment(
Text=text, LanguageCode=language_code
)
logger.info("Detected primary sentiment %s.", response["Sentiment"])
except ClientError:
logger.exception("Couldn't detect sentiment.")
raise
else:
return response
```
+ 有关 API 的详细信息，请参阅适用[DetectSentiment](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSentiment)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->detectsentiment(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'Sentiment detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[DetectSentiment](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `DetectSyntax`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `DetectSyntax`。
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：
+ [检测文档元素](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**适用于 .NET 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples)中查找完整示例，了解如何进行设置和运行。
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to use Amazon Comprehend to detect syntax
/// elements by calling the DetectSyntaxAsync method.
///
public class DetectingSyntax
{
///
/// This method calls DetectSynaxAsync to identify the syntax elements
/// in the sample text.
///
public static async Task Main()
{
string text = "It is raining today in Seattle";
var comprehendClient = new AmazonComprehendClient();
// Call DetectSyntax API
Console.WriteLine("Calling DetectSyntaxAsync\n");
var detectSyntaxRequest = new DetectSyntaxRequest()
{
Text = text,
LanguageCode = "en",
};
DetectSyntaxResponse detectSyntaxResponse = await comprehendClient.DetectSyntaxAsync(detectSyntaxRequest);
foreach (SyntaxToken s in detectSyntaxResponse.SyntaxTokens)
{
Console.WriteLine($"Text: {s.Text}, PartOfSpeech: {s.PartOfSpeech.Tag}, BeginOffset: {s.BeginOffset}, EndOffset: {s.EndOffset}");
}
Console.WriteLine("Done");
}
}
```
+ 有关 API 的详细信息，请参阅 *适用于 .NET 的 AWS SDK API 参考[DetectSyntax](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectSyntax)*中的。
------
#### [ CLI ]
**AWS CLI**
**检测输入文本中的语音部分**
以下 `detect-syntax` 示例分析输入文本的语法并返回语音的不同部分。预训练模型的置信度分数也是每个预测的输出。
```
aws comprehend detect-syntax \
--language-code en \
--text "It is a beautiful day in Seattle."
```
输出：
```
{
"SyntaxTokens": [
{
"TokenId": 1,
"Text": "It",
"BeginOffset": 0,
"EndOffset": 2,
"PartOfSpeech": {
"Tag": "PRON",
"Score": 0.9999740719795227
}
},
{
"TokenId": 2,
"Text": "is",
"BeginOffset": 3,
"EndOffset": 5,
"PartOfSpeech": {
"Tag": "VERB",
"Score": 0.999901294708252
}
},
{
"TokenId": 3,
"Text": "a",
"BeginOffset": 6,
"EndOffset": 7,
"PartOfSpeech": {
"Tag": "DET",
"Score": 0.9999938607215881
}
},
{
"TokenId": 4,
"Text": "beautiful",
"BeginOffset": 8,
"EndOffset": 17,
"PartOfSpeech": {
"Tag": "ADJ",
"Score": 0.9987351894378662
}
},
{
"TokenId": 5,
"Text": "day",
"BeginOffset": 18,
"EndOffset": 21,
"PartOfSpeech": {
"Tag": "NOUN",
"Score": 0.9999796748161316
}
},
{
"TokenId": 6,
"Text": "in",
"BeginOffset": 22,
"EndOffset": 24,
"PartOfSpeech": {
"Tag": "ADP",
"Score": 0.9998047947883606
}
},
{
"TokenId": 7,
"Text": "Seattle",
"BeginOffset": 25,
"EndOffset": 32,
"PartOfSpeech": {
"Tag": "PROPN",
"Score": 0.9940530061721802
}
}
]
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[语法分析](https://docs.aws.amazon.com/comprehend/latest/dg/how-syntax.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[DetectSyntax](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-syntax.html)*中的。
------
#### [ Java ]
**适用于 Java 的 SDK 2.x**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples)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.DetectSyntaxRequest;
import software.amazon.awssdk.services.comprehend.model.DetectSyntaxResponse;
import software.amazon.awssdk.services.comprehend.model.SyntaxToken;
import java.util.List;
/**
* Before running this Java V2 code example, 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 DetectSyntax {
public static void main(String[] args) {
String text = "Amazon.com, Inc. is located in Seattle, WA and was founded July 5th, 1994 by Jeff Bezos, allowing customers to buy everything from books to blenders. Seattle is north of Portland and south of Vancouver, BC. Other notable Seattle - based companies are Starbucks and Boeing.";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectSyntax");
detectAllSyntax(comClient, text);
comClient.close();
}
public static void detectAllSyntax(ComprehendClient comClient, String text) {
try {
DetectSyntaxRequest detectSyntaxRequest = DetectSyntaxRequest.builder()
.text(text)
.languageCode("en")
.build();
DetectSyntaxResponse detectSyntaxResult = comClient.detectSyntax(detectSyntaxRequest);
List syntaxTokens = detectSyntaxResult.syntaxTokens();
for (SyntaxToken token : syntaxTokens) {
System.out.println("Language is " + token.text());
System.out.println("Part of speech is " + token.partOfSpeech().tagAsString());
}
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ 有关 API 的详细信息，请参阅 *AWS SDK for Java 2.x API 参考[DetectSyntax](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectSyntax)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)中查找完整示例，了解如何进行设置和运行。
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_syntax(self, text, language_code):
"""
Detects syntactical elements of a document. Syntax tokens are portions of
text along with their use as parts of speech, such as nouns, verbs, and
interjections.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of syntax tokens along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_syntax(
Text=text, LanguageCode=language_code
)
tokens = response["SyntaxTokens"]
logger.info("Detected %s syntax tokens.", len(tokens))
except ClientError:
logger.exception("Couldn't detect syntax.")
raise
else:
return tokens
```
+ 有关 API 的详细信息，请参阅适用[DetectSyntax](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSyntax)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->detectsyntax(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'Syntax tokens detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[DetectSyntax](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `ListDocumentClassificationJobs`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `ListDocumentClassificationJobs`。
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：
+ [训练自定义分类器并对文档进行分类](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**列出所有文档分类作业**
以下 `list-document-classification-jobs` 示例列出所有文档分类作业。
```
aws comprehend list-document-classification-jobs
```
输出：
```
{
"DocumentClassificationJobPropertiesList": [
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classification-job/123456abcdeb0e11022f22a11EXAMPLE",
"JobName": "exampleclassificationjob",
"JobStatus": "COMPLETED",
"SubmitTime": "2023-06-14T17:09:51.788000+00:00",
"EndTime": "2023-06-14T17:15:58.582000+00:00",
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classifier/mymodel/version/12",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket/jobdata/",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/1234567890101-CLN-e758dd56b824aa717ceab551f11749fb/output/output.tar.gz"
},
"DataAccessRoleArn": "arn:aws:iam::1234567890101:role/service-role/AmazonComprehendServiceRole-example-role"
},
{
"JobId": "123456abcdeb0e11022f22a1EXAMPLE2",
"JobArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classification-job/123456abcdeb0e11022f22a1EXAMPLE2",
"JobName": "exampleclassificationjob2",
"JobStatus": "COMPLETED",
"SubmitTime": "2023-06-14T17:22:39.829000+00:00",
"EndTime": "2023-06-14T17:28:46.107000+00:00",
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classifier/mymodel/version/12",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket/jobdata/",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/1234567890101-CLN-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz"
},
"DataAccessRoleArn": "arn:aws:iam::1234567890101:role/service-role/AmazonComprehendServiceRole-example-role"
}
]
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[自定义分类](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[ListDocumentClassificationJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-document-classification-jobs.html)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)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 list_jobs(self):
"""
Lists the classification jobs for the current account.
:return: The list of jobs.
"""
try:
response = self.comprehend_client.list_document_classification_jobs()
jobs = response["DocumentClassificationJobPropertiesList"]
logger.info("Got %s document classification jobs.", len(jobs))
except ClientError:
logger.exception(
"Couldn't get document classification jobs.",
)
raise
else:
return jobs
```
+ 有关 API 的详细信息，请参阅适用[ListDocumentClassificationJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassificationJobs)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->listdocclassificationjobs( ).
MESSAGE 'Document classification jobs listed.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidfilterex.
MESSAGE 'Invalid filter.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[ListDocumentClassificationJobs](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `ListDocumentClassifiers`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `ListDocumentClassifiers`。
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：
+ [训练自定义分类器并对文档进行分类](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**列出所有文档分类器**
以下 `list-document-classifiers` 示例列出所有经过训练和正在训练的文档分类器模型。
```
aws comprehend list-document-classifiers
```
输出：
```
{
"DocumentClassifierPropertiesList": [
{
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier1",
"LanguageCode": "en",
"Status": "TRAINED",
"SubmitTime": "2023-06-13T19:04:15.735000+00:00",
"EndTime": "2023-06-13T19:42:31.752000+00:00",
"TrainingStartTime": "2023-06-13T19:08:20.114000+00:00",
"TrainingEndTime": "2023-06-13T19:41:35.080000+00:00",
"InputDataConfig": {
"DataFormat": "COMPREHEND_CSV",
"S3Uri": "s3://amzn-s3-demo-bucket/trainingdata"
},
"OutputDataConfig": {},
"ClassifierMetadata": {
"NumberOfLabels": 3,
"NumberOfTrainedDocuments": 5016,
"NumberOfTestDocuments": 557,
"EvaluationMetrics": {
"Accuracy": 0.9856,
"Precision": 0.9919,
"Recall": 0.9459,
"F1Score": 0.9673,
"MicroPrecision": 0.9856,
"MicroRecall": 0.9856,
"MicroF1Score": 0.9856,
"HammingLoss": 0.0144
}
},
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-testorle",
"Mode": "MULTI_CLASS"
},
{
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier2",
"LanguageCode": "en",
"Status": "TRAINING",
"SubmitTime": "2023-06-13T21:20:28.690000+00:00",
"InputDataConfig": {
"DataFormat": "COMPREHEND_CSV",
"S3Uri": "s3://amzn-s3-demo-bucket/trainingdata"
},
"OutputDataConfig": {},
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-testorle",
"Mode": "MULTI_CLASS"
}
]
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[创建和管理自定义模型](https://docs.aws.amazon.com/comprehend/latest/dg/manage-models.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[ListDocumentClassifiers](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-document-classifiers.html)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)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 list(self):
"""
Lists custom classifiers for the current account.
:return: The list of classifiers.
"""
try:
response = self.comprehend_client.list_document_classifiers()
classifiers = response["DocumentClassifierPropertiesList"]
logger.info("Got %s classifiers.", len(classifiers))
except ClientError:
logger.exception(
"Couldn't get classifiers.",
)
raise
else:
return classifiers
```
+ 有关 API 的详细信息，请参阅适用[ListDocumentClassifiers](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassifiers)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->listdocumentclassifiers( ).
MESSAGE 'Document classifiers listed.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidfilterex.
MESSAGE 'Invalid filter.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[ListDocumentClassifiers](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `ListTopicsDetectionJobs`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `ListTopicsDetectionJobs`。
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：
+ [对示例数据运行主题建模任务](comprehend_example_comprehend_Usage_TopicModeler_section.md)
------
#### [ CLI ]
**AWS CLI**
**列出所有主题检测作业**
以下 `list-topics-detection-jobs` 示例列出所有正在进行和已完成的异步主题检测作业。
```
aws comprehend list-topics-detection-jobs
```
输出：
```
{
"TopicsDetectionJobPropertiesList": [
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
"JobName" "topic-analysis-1"
"JobStatus": "IN_PROGRESS",
"SubmitTime": "2023-06-09T18:40:35.384000+00:00",
"EndTime": "2023-06-09T18:46:41.936000+00:00",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
},
"NumberOfTopics": 10,
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
},
{
"JobId": "123456abcdeb0e11022f22a1EXAMPLE2",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a1EXAMPLE2",
"JobName": "topic-analysis-2",
"JobStatus": "COMPLETED",
"SubmitTime": "2023-06-09T18:44:43.414000+00:00",
"EndTime": "2023-06-09T18:50:50.872000+00:00",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz"
},
"NumberOfTopics": 10,
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
},
{
"JobId": "123456abcdeb0e11022f22a1EXAMPLE3",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a1EXAMPLE3",
"JobName": "topic-analysis-2",
"JobStatus": "IN_PROGRESS",
"SubmitTime": "2023-06-09T18:50:56.737000+00:00",
"InputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-bucket",
"InputFormat": "ONE_DOC_PER_LINE"
},
"OutputDataConfig": {
"S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a1EXAMPLE3/output/output.tar.gz"
},
"NumberOfTopics": 10,
"DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
}
]
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的 [Amazon Comprehend 洞察的异步分析](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[ListTopicsDetectionJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-topics-detection-jobs.html)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)中查找完整示例，了解如何进行设置和运行。
```
class ComprehendTopicModeler:
"""Encapsulates a Comprehend topic modeler."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def list_jobs(self):
"""
Lists topic modeling jobs for the current account.
:return: The list of jobs.
"""
try:
response = self.comprehend_client.list_topics_detection_jobs()
jobs = response["TopicsDetectionJobPropertiesList"]
logger.info("Got %s topic detection jobs.", len(jobs))
except ClientError:
logger.exception("Couldn't get topic detection jobs.")
raise
else:
return jobs
```
+ 有关 API 的详细信息，请参阅适用[ListTopicsDetectionJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListTopicsDetectionJobs)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->listtopicsdetectionjobs( ).
MESSAGE 'Topics detection jobs listed.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidfilterex.
MESSAGE 'Invalid filter.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[ListTopicsDetectionJobs](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `StartDocumentClassificationJob`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `StartDocumentClassificationJob`。
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：
+ [训练自定义分类器并对文档进行分类](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**列出文档分类作业**
以下 `start-document-classification-job` 示例以自定义模型启动文档分类作业，该作业对 `--input-data-config` 标签所指定地址处的所有文件都使用自定义模型。在此示例中，输入 S3 存储桶包含 `SampleSMStext1.txt`、`SampleSMStext2.txt`、和 `SampleSMStext3.txt`。该模型之前曾接受过关于垃圾邮件和非垃圾邮件，或“ham”、短信的文档分类训练。作业完成后，`output.tar.gz` 将放置在 `--output-data-config` 标签指定的位置。`output.tar.gz` 包含 `predictions.jsonl`，其中列出了每个文档的分类。Json 输出在每个文件的一行上打印，但是为了便于阅读，此处设置了格式。
```
aws comprehend start-document-classification-job \
--job-name exampleclassificationjob \
--input-data-config "S3Uri=s3://amzn-s3-demo-bucket-INPUT/jobdata/" \
--output-data-config "S3Uri=s3://amzn-s3-demo-destination-bucket/testfolder/" \
--data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
--document-classifier-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/mymodel/version/12
```
`SampleSMStext1.txt` 的内容：
```
"CONGRATULATIONS! TXT 2155550100 to win $5000"
```
`SampleSMStext2.txt` 的内容：
```
"Hi, when do you want me to pick you up from practice?"
```
`SampleSMStext3.txt` 的内容：
```
"Plz send bank account # to 2155550100 to claim prize!!"
```
输出：
```
{
"JobId": "e758dd56b824aa717ceab551fEXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:document-classification-job/e758dd56b824aa717ceab551fEXAMPLE",
"JobStatus": "SUBMITTED"
}
```
`predictions.jsonl` 的内容：
```
{"File": "SampleSMSText1.txt", "Line": "0", "Classes": [{"Name": "spam", "Score": 0.9999}, {"Name": "ham", "Score": 0.0001}]}
{"File": "SampleSMStext2.txt", "Line": "0", "Classes": [{"Name": "ham", "Score": 0.9994}, {"Name": "spam", "Score": 0.0006}]}
{"File": "SampleSMSText3.txt", "Line": "0", "Classes": [{"Name": "spam", "Score": 0.9999}, {"Name": "ham", "Score": 0.0001}]}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[自定义分类](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[StartDocumentClassificationJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-document-classification-job.html)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)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 start_job(
self,
job_name,
input_bucket,
input_key,
input_format,
output_bucket,
output_key,
data_access_role_arn,
):
"""
Starts a classification job. The classifier must be trained or the job
will fail. Input is read from the specified Amazon S3 input bucket and
written to the specified output bucket. Output data is stored in a tar
archive compressed in gzip format. The job runs asynchronously, so you can
call `describe_document_classification_job` to get job status until it
returns a status of SUCCEEDED.
:param job_name: The name of the job.
:param input_bucket: The Amazon S3 bucket that contains input data.
:param input_key: The prefix used to find input data in the input
bucket. If multiple objects have the same prefix, all
of them are used.
:param input_format: The format of the input data, either one document per
file or one document per line.
:param output_bucket: The Amazon S3 bucket where output data is written.
:param output_key: The prefix prepended to the output data.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
input bucket and write to the output bucket.
:return: Information about the job, including the job ID.
"""
try:
response = self.comprehend_client.start_document_classification_job(
DocumentClassifierArn=self.classifier_arn,
JobName=job_name,
InputDataConfig={
"S3Uri": f"s3://{input_bucket}/{input_key}",
"InputFormat": input_format.value,
},
OutputDataConfig={"S3Uri": f"s3://{output_bucket}/{output_key}"},
DataAccessRoleArn=data_access_role_arn,
)
logger.info(
"Document classification job %s is %s.", job_name, response["JobStatus"]
)
except ClientError:
logger.exception("Couldn't start classification job %s.", job_name)
raise
else:
return response
```
+ 有关 API 的详细信息，请参阅适用[StartDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartDocumentClassificationJob)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->startdocclassificationjob(
iv_jobname = iv_job_name
iv_documentclassifierarn = iv_classifier_arn
io_inputdataconfig = NEW /aws1/cl_cpdinputdataconfig(
iv_s3uri = iv_input_s3_uri
iv_inputformat = iv_input_format
)
io_outputdataconfig = NEW /aws1/cl_cpdoutputdataconfig(
iv_s3uri = iv_output_s3_uri
)
iv_dataaccessrolearn = iv_data_access_role_arn
).
MESSAGE 'Document classification job started.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdresourcenotfoundex.
MESSAGE 'Resource not found.' TYPE 'E'.
CATCH /aws1/cx_cpdresourceunavailex.
MESSAGE 'Resource unavailable.' TYPE 'E'.
CATCH /aws1/cx_cpdkmskeyvalidationex.
MESSAGE 'KMS key validation error.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanytagsex.
MESSAGE 'Too many tags.' TYPE 'E'.
CATCH /aws1/cx_cpdresrclimitexcdex.
MESSAGE 'Resource limit exceeded.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[StartDocumentClassificationJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------
# `StartTopicsDetectionJob`与 AWS SDK 或 CLI 配合使用
以下代码示例演示如何使用 `StartTopicsDetectionJob`。
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：
+ [对示例数据运行主题建模任务](comprehend_example_comprehend_Usage_TopicModeler_section.md)
------
#### [ .NET ]
**适用于 .NET 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples)中查找完整示例，了解如何进行设置和运行。
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example scans the documents in an Amazon Simple Storage Service
/// (Amazon S3) bucket and analyzes it for topics. The results are stored
/// in another bucket and then the resulting job properties are displayed
/// on the screen. This example was created using the AWS SDK for .NEt
/// version 3.7 and .NET Core version 5.0.
///
public static class TopicModeling
{
///
/// This methos calls a topic detection job by calling the Amazon
/// Comprehend StartTopicsDetectionJobRequest.
///
public static async Task Main()
{
var comprehendClient = new AmazonComprehendClient();
string inputS3Uri = "s3://input bucket/input path";
InputFormat inputDocFormat = InputFormat.ONE_DOC_PER_FILE;
string outputS3Uri = "s3://output bucket/output path";
string dataAccessRoleArn = "arn:aws:iam::account ID:role/data access role";
int numberOfTopics = 10;
var startTopicsDetectionJobRequest = new StartTopicsDetectionJobRequest()
{
InputDataConfig = new InputDataConfig()
{
S3Uri = inputS3Uri,
InputFormat = inputDocFormat,
},
OutputDataConfig = new OutputDataConfig()
{
S3Uri = outputS3Uri,
},
DataAccessRoleArn = dataAccessRoleArn,
NumberOfTopics = numberOfTopics,
};
var startTopicsDetectionJobResponse = await comprehendClient.StartTopicsDetectionJobAsync(startTopicsDetectionJobRequest);
var jobId = startTopicsDetectionJobResponse.JobId;
Console.WriteLine("JobId: " + jobId);
var describeTopicsDetectionJobRequest = new DescribeTopicsDetectionJobRequest()
{
JobId = jobId,
};
var describeTopicsDetectionJobResponse = await comprehendClient.DescribeTopicsDetectionJobAsync(describeTopicsDetectionJobRequest);
PrintJobProperties(describeTopicsDetectionJobResponse.TopicsDetectionJobProperties);
var listTopicsDetectionJobsResponse = await comprehendClient.ListTopicsDetectionJobsAsync(new ListTopicsDetectionJobsRequest());
foreach (var props in listTopicsDetectionJobsResponse.TopicsDetectionJobPropertiesList)
{
PrintJobProperties(props);
}
}
///
/// This method is a helper method that displays the job properties
/// from the call to StartTopicsDetectionJobRequest.
///
/// A list of properties from the call to
/// StartTopicsDetectionJobRequest.
private static void PrintJobProperties(TopicsDetectionJobProperties props)
{
Console.WriteLine($"JobId: {props.JobId}, JobName: {props.JobName}, JobStatus: {props.JobStatus}");
Console.WriteLine($"NumberOfTopics: {props.NumberOfTopics}\nInputS3Uri: {props.InputDataConfig.S3Uri}");
Console.WriteLine($"InputFormat: {props.InputDataConfig.InputFormat}, OutputS3Uri: {props.OutputDataConfig.S3Uri}");
}
}
```
+ 有关 API 的详细信息，请参阅 *适用于 .NET 的 AWS SDK API 参考[StartTopicsDetectionJob](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/StartTopicsDetectionJob)*中的。
------
#### [ CLI ]
**AWS CLI**
**启动主题检测分析作业**
以下 `start-topics-detection-job` 示例为位于 `--input-data-config` 标签指定地址的所有文件启动异步主题检测作业。作业完成后，文件夹 `output` 将放置在 `--ouput-data-config` 标签指定的位置。`output` 包含 topic-terms.csv 和 doc-topics.csv。第一个输出文件 topic-terms.csv 是集合中的主题列表。对于每个主题，默认情况下，该列表按权重排列主题列出根据其的热门术语。第二个文件 `doc-topics.csv` 列出了与主题相关的文档以及与该主题相关的文档比例。
```
aws comprehend start-topics-detection-job \
--job-name example_topics_detection_job \
--language-code en \
--input-data-config "S3Uri=s3://amzn-s3-demo-bucket/" \
--output-data-config "S3Uri=s3://amzn-s3-demo-destination-bucket/testfolder/" \
--data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
--language-code en
```
输出：
```
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
"JobStatus": "SUBMITTED"
}
```
有关更多信息，请参阅《Amazon Comprehend 开发人员指南》**中的[主题建模](https://docs.aws.amazon.com/comprehend/latest/dg/topic-modeling.html)。
+ 有关 API 的详细信息，请参阅*AWS CLI 命令参考[StartTopicsDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-topics-detection-job.html)*中的。
------
#### [ Python ]
**适用于 Python 的 SDK（Boto3）**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples)中查找完整示例，了解如何进行设置和运行。
```
class ComprehendTopicModeler:
"""Encapsulates a Comprehend topic modeler."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def start_job(
self,
job_name,
input_bucket,
input_key,
input_format,
output_bucket,
output_key,
data_access_role_arn,
):
"""
Starts a topic modeling job. Input is read from the specified Amazon S3
input bucket and written to the specified output bucket. Output data is stored
in a tar archive compressed in gzip format. The job runs asynchronously, so you
can call `describe_topics_detection_job` to get job status until it
returns a status of SUCCEEDED.
:param job_name: The name of the job.
:param input_bucket: An Amazon S3 bucket that contains job input.
:param input_key: The prefix used to find input data in the input
bucket. If multiple objects have the same prefix, all
of them are used.
:param input_format: The format of the input data, either one document per
file or one document per line.
:param output_bucket: The Amazon S3 bucket where output data is written.
:param output_key: The prefix prepended to the output data.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
input bucket and write to the output bucket.
:return: Information about the job, including the job ID.
"""
try:
response = self.comprehend_client.start_topics_detection_job(
JobName=job_name,
DataAccessRoleArn=data_access_role_arn,
InputDataConfig={
"S3Uri": f"s3://{input_bucket}/{input_key}",
"InputFormat": input_format.value,
},
OutputDataConfig={"S3Uri": f"s3://{output_bucket}/{output_key}"},
)
logger.info("Started topic modeling job %s.", response["JobId"])
except ClientError:
logger.exception("Couldn't start topic modeling job.")
raise
else:
return response
```
+ 有关 API 的详细信息，请参阅适用[StartTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartTopicsDetectionJob)于 *Python 的AWS SDK (Boto3) API 参考*。
------
#### [ SAP ABAP ]
**适用于 SAP ABAP 的 SDK**
还有更多相关信息 GitHub。在 [AWS 代码示例存储库](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples)中查找完整示例，了解如何进行设置和运行。
```
TRY.
oo_result = lo_cpd->starttopicsdetectionjob(
iv_jobname = iv_job_name
io_inputdataconfig = NEW /aws1/cl_cpdinputdataconfig(
iv_s3uri = iv_input_s3_uri
iv_inputformat = iv_input_format
)
io_outputdataconfig = NEW /aws1/cl_cpdoutputdataconfig(
iv_s3uri = iv_output_s3_uri
)
iv_dataaccessrolearn = iv_data_access_role_arn
).
MESSAGE 'Topics detection job started.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdkmskeyvalidationex.
MESSAGE 'KMS key validation error.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanytagsex.
MESSAGE 'Too many tags.' TYPE 'E'.
CATCH /aws1/cx_cpdresrclimitexcdex.
MESSAGE 'Resource limit exceeded.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ 有关 API 的详细信息，请参阅适用[StartTopicsDetectionJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)于 S *AP 的AWS SDK ABAP API 参考*。
------