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CreateTrainandRunCustomEntityDetector.py

CreateTrainandRunCustomEntityDetector.py demonstrates how to create and identify the custom entities in a document.

# Copyright 2010-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # This file is licensed under the Apache License, Version 2.0 (the "License"). # You may not use this file except in compliance with the License. A copy of the # License is located at # # http://aws.amazon.com/apache2.0/ # # This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS # OF ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. import boto3 import time import uuid comprehend = boto3.client('comprehend') response = comprehend.create_entity_recognizer( RecognizerName="Recognizer-Name-Goes-Here-{}".format(str(uuid.uuid4())), LanguageCode="en", DataAccessRoleArn="Role ARN", InputDataConfig={ "EntityTypes": [ { "Type": "ENTITY_TYPE" } ], "Documents": { "S3Uri": "s3://Bucket Name/Bucket Path/documents" }, "Annotations": { "S3Uri": "s3://Bucket Name/Bucket Path/annotations" } } ) recognizer_arn = response['EntityRecognizerArn'] response = comprehend.list_entity_recognizers() trained = False while not trained: response = comprehend.describe_entity_recognizer( EntityRecognizerArn=recognizer_arn ) status = response['EntityRecognizerProperties']['Status'] if status == 'IN_ERROR': exit(1) elif status == 'TRAINED': trained = True continue time.sleep(10) response = comprehend.start_entities_detection_job( EntityRecognizerArn=recognizer_arn, JobName='Detection-Job-Name-{}'.format(str(uuid.uuid4())), LanguageCode='en', DataAccessRoleArn='ROLE_ARN', InputDataConfig={ 'InputFormat': 'ONE_DOC_PER_LINE', 'S3Uri': 's3://BUCKET_NAME/BUCKET_PATH/documents' }, OutputDataConfig={ 'S3Uri': 's3://BUCKET_NAME/BUCKET_PATH/output' })

Sample Details

Service: comprehend

Last tested: 2019-03-13

Author: AWS

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