Sold by: MphasisÂ
Deployed on AWS
This solution helps in extracting legal names of organizations and their aliases from text documents.
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.
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
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
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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
This is version 3.1
Additional details
Inputs
- 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/plain
https://github.com/Mphasis-ML-Marketplace/Legal-Entity-Name-Extraction/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Legal-Entity-Name-Extraction/tree/main/input
Resources
Vendor resources
Support
Vendor support
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