Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Legal Entity Name Extraction
By:
Latest Version:
3.1
This solution helps in extracting legal names of organizations and their aliases from text documents.
Product Overview
Legal entity name extraction is an optimal way to identify and classify legal organization name and their aliases in an unstructured text. It can consume the texts such as legal documents and process it to identify all the legal entities/aliases in the document.
Key Data
Version
By
Type
Model Package
Highlights
This solution can be leveraged to solve the problem of legal named entity extraction from noisy text in legal documents. This solution leverages pretrained models to extract organization tags from a given input text. The input can have a maximum of 50000 characters and gives output as a list of dictionaries containing legal as well as generally pronounced names of any organization.
The solution uses English text as input and uses names entity recognition techniques to extract organization tags from a given input text. The extracted organization tags are then compared with the list of available legal entity types across several countries to identify whether the extracted tags are the legal names or just a general abbreviation. Presently, our solution can identify legal organization names from countries such as Australia,Ethiopia,Ghana, Hong Kong, India, New Zealand, Philippines, Nigeria, Singapore, Ukraine, United Arab Emirates, United Kingdom, United States
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
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Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$8.00/hr
running on ml.m5.large
Model Batch Transform$16.00/hr
running on ml.m5.large
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.m4.4xlarge | $8.00 | |
ml.m5.4xlarge | $8.00 | |
ml.m4.16xlarge | $8.00 | |
ml.m5.2xlarge | $8.00 | |
ml.p3.16xlarge | $8.00 | |
ml.m4.2xlarge | $8.00 | |
ml.c5.2xlarge | $8.00 | |
ml.p3.2xlarge | $8.00 | |
ml.c4.2xlarge | $8.00 | |
ml.m4.10xlarge | $8.00 | |
ml.c4.xlarge | $8.00 | |
ml.m5.24xlarge | $8.00 | |
ml.c5.xlarge | $8.00 | |
ml.p2.xlarge | $8.00 | |
ml.m5.12xlarge | $8.00 | |
ml.p2.16xlarge | $8.00 | |
ml.c4.4xlarge | $8.00 | |
ml.m5.xlarge | $8.00 | |
ml.c5.9xlarge | $8.00 | |
ml.m4.xlarge | $8.00 | |
ml.c5.4xlarge | $8.00 | |
ml.p3.8xlarge | $8.00 | |
ml.m5.large Vendor Recommended | $8.00 | |
ml.c4.8xlarge | $8.00 | |
ml.p2.8xlarge | $8.00 | |
ml.c5.18xlarge | $8.00 |
Usage Information
Model input and output details
Input
Summary
sample_input.txt contains the input data.
Limitations for input type
1) The input has to be a '.txt' file with 'utf-8' encoding.
2) Input file should not contain more than 50000 characters
Input MIME type
application/zip, text/plainSample input data
Output
Summary
output.json contains list of dictionaries containing extracted legal organization name and their aliases. Each dictionary conatins following keys:
- legal_entity : legal organization name
- aka : Aliases
Output MIME type
application/zip, application/jsonSample output data
Sample notebook
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
Legal Entity Name Extraction
For any assistance reach out to us at: https://www2.mphasis.com/AWS-Marketplace-Support-LP.html
AWS Infrastructure
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