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。

# `StartDocumentClassificationJob`与 AWS SDK 或 CLI 配合使用
<a name="comprehend_example_comprehend_StartDocumentClassificationJob_section"></a>

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*。

------