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    Wipro HOLMES™ E-KYC Controller Extractor

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    Deployed on AWS
    The Wipro HOLMES™ Controller Extractor helps to identify controllers (CEO, CTO, Chairman etc.) of an organization using its annual report

    Overview

    A part of Wipro HOLMES™ E-KYC product, IEF Controller Extractor helps you to identify controllers (CEO, CTO, Chairman etc.) of an organization using the annual report of the organization. Currently annual reports of only '.pdf' form are supported. It is recommended that the model be used in batch transform mode wherein you can keep a base64 encoded file as part of the input schema in s3 bucket. Sample code on converting a file to Base64 string can be found in additional resources. Currently the model supports documents from Australia (AUS) & Canada (CAN) geographies.

    Highlights

    • Now you can use Wipro HOLMES™ IEF Controller Extractor module to extract controllers of an organization. This model can be used directly inside your own products to extract and provide information about an organization.

    Details

    Delivery method

    Latest version

    Deployed on AWS

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    Pricing

    Wipro HOLMES™ E-KYC Controller Extractor

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    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (3)

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    Dimension
    Description
    Cost/host/hour
    ml.m5.2xlarge Inference (Batch)
    Recommended
    Model inference on the ml.m5.2xlarge instance type, batch mode
    $0.00
    ml.m5.2xlarge Inference (Real-Time)
    Recommended
    Model inference on the ml.m5.2xlarge instance type, real-time mode
    $0.00
    ml.m4.2xlarge Inference (Batch)
    Model inference on the ml.m4.2xlarge instance type, batch mode
    $0.00

    Vendor refund policy

    Since you are not being charged currently for the use of this software there will be no refund of any charges.

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    Usage information

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    Delivery details

    Amazon SageMaker model

    An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.

    Deploy the model on Amazon SageMaker AI using the following options:
    Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference  .
    Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI  .
    Version release notes

    This is the first version of the model

    Additional details

    Inputs

    Summary

    Requirements for consuming the service: a. Subscription access to Amazon Sage maker and the model product. b. S3 bucket for specifying input and output locations. c. Necessary permissions to S3 locations and model packages. d. Code scripts for encoding/decoding files to/from base64 format for use in consumer scripts.

    Input schema: To use the model create a batch transform job and place your input data in the following schema in a json file in your input s3 bucket: { "geo" : "AUS", "org" : "ABC Limited", "threshold" : "0.8", "file" : "sbfuf()&%#=dhbsdb++fb*++" } geo: The geographical location. Currently Australia (AUS) & Canada (CAN) are supported geographies. org: Name of the organization. threshold: Threshold of the confidence score file: Base64 encoded pdf file

    Output schema: Batch transform job will create an output file of extension ‘.out’ in the output s3 bucket specified by user in the request. The schema of output is described below. { "status":"success", "output":[ { "text":{ "File":"ABC Limited", "Sentence":"Yours sincerely Mr Xyz Chairman." }, "classes":[ "BOARD_MEMBER_DESG" ], "confidence":[ "0.96" ], "entity1":{ "entity_name":"Mr Xyz", "entity_nerclass":"PERSON" }, "entity2":{ "entity_name":"Chairman", "entity_nerclass":"DESG" } }, { "text":{ "File":"ABC Limited", "Sentence":"In April 2014, Mr ABC was appointed Chief Executive Officer and has continued to play an instrumental role in driving and delivering key innovation and successes for the Group." }, "classes":[ "BOARD_MEMBER_DESG" ], "confidence":[ "0.99" ], "entity1":{ "entity_name":"Mr ABC", "entity_nerclass":"PERSON" }, "entity2":{ "entity_name":"Chief Executive Officer", "entity_nerclass":"DESG" } } ] }

    status: Whether the request completed successfully. text: The details of inference i.e. which text in the file was used for designation extraction. a. File: Name of the organization b. Sentence: The sentence identified for information extraction.

    class: Class of extracted relation. This is internal to IEF and may not be useful for the user. confidence: The confidence score of the extracted controller of organization. entity1 & entity2: a. entity_name: Name of the entity extracted from text. b. entity_nerclass: NER tag of the entity.

    Input MIME type
    application/json
    See Input Summary
    See Input Summary

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