Sold by: MphasisÂ
Deployed on AWS
This is a NLP based solution to identify key topics present in credit reporting specific customer complaints.
Overview
Topic Modelling based solution for customer complaints about Credit Reporting. The solution clusters customer complaints into topics based on the narratives provided. Given a set of customer complaints narratives, this solution identifies for each complaint the top three issues mentioned in the narrative. The task of identifying what credit reporting specific issue is being mentioned in a customer complaint requires human involvement and the same is being automated with this solution.
Highlights
- The solution takes customer complaints narratives as input and predicts the top three categories of complaints thereby automating the task which previously required manual classification. This solution has been trained on customer complaint databases.
- The solution is specifically trained on customers complaints narratives pertaining to credit reporting issues faced by customers.
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP 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 | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
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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
It is 1.2 version of the solution
Additional details
Inputs
- Summary
- This solution takes input as csv file
- This file contains "Text" column which contains textual data
- Input MIME type
- text/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/Credit-Report-Complaint-Topic-Modelling/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/Credit-Report-Complaint-Topic-Modelling/tree/main/Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Text | This column contains the textual data | Type: FreeText | Yes |
Resources
Vendor resources
Support
Vendor support
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