Sold by: Mphasis
Overview
Auditor name extraction from financial statements is an NLP based data extraction model for information retrieval. The solution uses NLP parsers combined with clustering and classification methodology to extract Auditor’s name. The solution applies NLP to extract word agnostic patterns from financial statements. The patterns are then analyzed, clustered and classified to extract the relevant Auditor’s name.
Highlights
- This solution takes HTML based financial statements as input and it uses NLP to generate word agnostic patterns. These patterns are then analyzed to extract different name entities present in the financial statements and these name entities are further analyzed to extract Auditor’s name.
- Auditor name extraction algorithm can be used across various domains to retrieve the Auditor name from financial statements.
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Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
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.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $16.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $8.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $16.00 |
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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:
Version release notes
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Input
- Supported content types: text/html
- Sample input file: (https://tinyurl.com/y8ytk2aq ) Input instruction
- The input must be provided as .html file
- Document must be from financial domain
- Financial documents must be from USA region
Output
- Content type: text/plain
- Sample output file:(https://tinyurl.com/y7hqtbu8 )
Invoking endpoint
AWS CLI Command
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
!aws sagemaker-runtime invoke-endpoint --endpoint-name $model_name --body fileb://$file_name --content-type 'text/html' --region us-east-2 result.txtSubstitute the following parameters:
- "model-name" - name of the inference endpoint where the model is deployed
- file_name - input html name
- text/html - content type of the given input
- result.txt - filename where the inference results are written to.
Resources
- Input MIME type
- application/json, text/html
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out at:
AWS infrastructure support
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