Sign in
Sign in
or
Create a new account
Categories
Your Saved List Partners Sell in AWS Marketplace Amazon Web Services Home Help
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.

Mphasis DeepInsights Document Grouping

By: Mphasis Latest Version: 3.4

An ML based solution to group a corpus of documents into clusters based on topics

Product Overview

Document grouping is a solution based on unsupervised machine learning that takes textual information and identifies topics across the given text corpus. Documents are grouped based on similarity of syntactic and contextual information present in them. This model takes a maximum of 30 documents (with each under 10Kb) as input and groups them into optimal number of clusters.

Key Data

Version
By Mphasis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Input

1) The input file should be a zip of text files (.txt) in utf-8 encoding

2) The zipped file can have a maximum of 30 documents

3) The maximum size of the each file should be <= 10KB (1000 lines)

4) Supported Content type: application/zip

Output

1) The output from the model is a json file, supported content type: application/json

2) The processed output is a json file which has lists of documents clusters, each representing the documents grouped on the basis of similar information

3) Sample output file:

{

“Cluster –1” : [“Doc-1”, “Doc-6”, “Doc-3”, “Doc-7”, “Doc-8”],

“Cluster –2” : [“Doc-2”, “Doc-4”]

}

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 "endpoint-name" --body fileb://input.zip --content-type application/zip --accept application/json result.json

Substitute the following parameters:

  • endpoint-name - name of the inference endpoint where the model is deployed

  • input.zip - input file

  • application/zip - MIME type of the given input file (above)

  • result.json - filename where the inference results are written to.

Resources

Sample Notebook

Sample Input

Sample Output

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

Mphasis DeepInsights Document Grouping

For any assistance reach out to us at:

https://www2.mphasis.com/AWS-Marketplace-Support-LP.html

AWS Infrastructure

AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services. Learn more

Refund Policy

Currently we do not support refunds, but you can cancel your subscription to the service at any time.

Customer Reviews