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.
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Natural Language Question Generator
By:
Latest Version:
2.5
This solution uses Natural Language Understanding and Processing to generate relevant questions from paragraphs.
Product Overview
Natural Language Question Generator can be used to generate questions from free text content in scenarios such as educational content, conversational systems like chatbots, virtual assistants, FAQ creation etc. This solution leverages attention based models to generate appropriate questions from given paragraphs. Deep Neural Network based transformer model have been trained to create this question generator. This solution can generate coherent and intelligent questions based on the most important aspects of the paragraph.
Key Data
Version
By
Type
Model Package
Highlights
This solution is an open domain question generator. It uses of state of the art transformer based models that capture context and frame relevant questions from a given text content.
The solution can be leveraged in industries such as EdTech, health care, banking, insurance, retail, e-commerce to power systems like intelligent chatbots, virtual assistants, FAQ generation, knowledge games etc.
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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.
Contact us to request contract pricing for this product.
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$10.00/hr
running on ml.m5.12xlarge
Model Batch Transform$20.00/hr
running on ml.m5.12xlarge
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$2.765/host/hr
running on ml.m5.12xlarge
SageMaker Batch Transform$2.765/host/hr
running on ml.m5.12xlarge
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 | $10.00 | |
ml.m5.4xlarge | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge | $10.00 | |
ml.p3.16xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.p3.2xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.p2.xlarge | $10.00 | |
ml.m5.12xlarge Vendor Recommended | $10.00 | |
ml.p2.16xlarge | $10.00 | |
ml.c4.4xlarge | $10.00 | |
ml.c5.large | $10.00 | |
ml.m5.xlarge | $10.00 | |
ml.c5.9xlarge | $10.00 | |
ml.m4.xlarge | $10.00 | |
ml.c5.4xlarge | $10.00 | |
ml.p3.8xlarge | $10.00 | |
ml.c4.large | $10.00 | |
ml.m5.large | $10.00 | |
ml.c4.8xlarge | $10.00 | |
ml.p2.8xlarge | $10.00 | |
ml.c5.18xlarge | $10.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Input
• Supported content types: text/plain
• Sample input file: (https://tinyurl.com/yyv7uapx )
• Input file should be of .txt type and with 'ascii' encoding
• Input file should contain paragraph for which question needs to be generated
• Input file size should be less than 2 kb
• It is recomended to use high configuration systems for bigger paragraphs
Output
• Content type: text/plain
• Sample output file:(https://tinyurl.com/y3szyyps )
• Output file will be of .txt type
• Output file will contain questions generated from the input paragraph
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 $model_name --body fileb://$file_name --content-type 'text/plain' --region us-east-2 result.txt
Substitute the following parameters:
"model-name"
- name of the inference endpoint where the model is deployedfile_name
- input file nametext/plain
- type of the given inputresult.txt
- filename where the inference results are written to.
Resources
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
Natural Language Question Generator
For any assistance, please reach out at:
AWS Infrastructure
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