DocNexus Text Summarization Model

World record holding Summarization Model on the Samsum dataset

Overview

World record holding Summarization Model on the Samsum dataset. This model does medium length text output. This text summarization model surpassed both Google & Facebook's Algorithms on Hugging Face and has optimized Rouge Score and loss parameters.

Highlights

World record holding Summarization Model on the Samsum dataset

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DocNexus Text Summarization Model

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Dimension	Description	Cost/host/hour
ml.c5.18xlarge Inference (Batch) Recommended	Model inference on the ml.c5.18xlarge instance type, batch mode	$10.00
ml.c5.9xlarge Inference (Real-Time) Recommended	Model inference on the ml.c5.9xlarge instance type, real-time mode	$10.00
ml.c5.4xlarge Inference (Batch)	Model inference on the ml.c5.4xlarge instance type, batch mode	$5.00
ml.c5.4xlarge Inference (Real-Time)	Model inference on the ml.c5.4xlarge instance type, real-time mode	$5.00

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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:

Real-time inference

Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference .

Batch transform

Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI .

Version release notes

This is our first release of our medium length summarization model.

Additional details

Inputs

Summary: The model input is a JSON object with a single key-value pair. The key is "input_text" and the value is a string containing the text you want to summarize. The input text should be in plain English and can be of any length. The model will process the input text and generate a summarized version of it.

Example input format:

{ "input_text": "Your text to be summarized goes here." }

Input MIME type: application/json

Real-time inference sample input data

https://github.com/aws/amazon-sagemaker-examples/tree/main/aws_marketplace/curating_aws_marketplace_listing_and_sample_notebook/ModelPackage/Sample_Notebook_Template

Batch transform sample input data

https://github.com/aws/amazon-sagemaker-examples/tree/main/aws_marketplace/curating_aws_marketplace_listing_and_sample_notebook/ModelPackage/Sample_Notebook_Template

Input data descriptions

The following table describes supported input data fields for real-time inference and batch transform.

Field name	Description	Constraints	Required
input_text	{ "input_text": "Your text to be summarized goes here." }	Type: FreeText	Yes

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