다음과 StartDocumentClassificationJob 함께 사용하십시오. AWS SDK또는 CLI

문서 분류 작업 나열

다음 start-document-classification-job 예제에서는 --input-data-config 태그로 지정된 주소의 모든 파일에서 사용자 지정 모델을 사용하여 문서 분류 작업을 시작합니다. 이 예제에서 입력 S3 버킷에는 SampleSMStext1.txt, SampleSMStext2.txt 및 SampleSMStext3.txt가 포함되어 있습니다. 이 모델은 이전에 스팸 및 비스팸 또는 “햄” 메시지의 문서 분류에 대해 학습되었습니다. SMS 작업이 완료되면 --output-data-config 태그에 지정된 위치에 output.tar.gz가 배치됩니다. output.tar.gz에는 각 문서의 분류가 나열되는 predictions.jsonl이 들어 있습니다. Json 출력은 파일당 한 줄로 인쇄되지만 여기서는 가독성을 위해 형식이 지정됩니다.

aws comprehend start-document-classification-job \
    --job-name exampleclassificationjob \
    --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET-INPUT/jobdata/" \
    --output-data-config "S3Uri=s3://DOC-EXAMPLE-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.txt의 콘텐츠:

"CONGRATULATIONS! TXT 2155550100 to win $5000"

SampleSMStext2.txt의 콘텐츠:

"Hi, when do you want me to pick you up from practice?"

SampleSMStext3.txt의 콘텐츠:

"Plz send bank account # to 2155550100 to claim prize!!"

출력:

{
    "JobId": "e758dd56b824aa717ceab551fEXAMPLE",
    "JobArn": "arn:aws:comprehend:us-west-2:111122223333:document-classification-job/e758dd56b824aa717ceab551fEXAMPLE",
    "JobStatus": "SUBMITTED"
}

predictions.jsonl의 콘텐츠:

{"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}]}

자세한 내용은 Amazon Comprehend 개발자 가이드의 사용자 지정 분류를 참조하세요.

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