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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Mphasis DeepInsights Text Summarizer
By:
Latest Version:
3.2
Mphasis DeepInsights Text Summarizer helps in summarizing text documents.
Product Overview
Text Summarizer solution is an optimal way to tackle the problem of information overload by reducing the size of long documents into a few sentences . Neural-network-based models have the ability to automatically learn the distributed representation for sentences and documents. This summarizer is built using Transfer Learning and Transformer based models which use self attention. The input can have a maximum of 512 words and gives output of 3 sentences (approximately 30 words).
Key Data
Version
By
Type
Model Package
Highlights
Use of State of the Art Transformer based models that capture context and helps in decision making for classification.
Extractive summarization model that automatically determines and subsequently concatenates relevant sentences from a document to create its summary preserving its original information content. Underlying model understands the document and distills the important information in approximately 3 lines or 30 words. It can have varied applications in the areas of marketing, content generation, Search Engine Optimization and document management.
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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.m5.2xlarge
Model Batch Transform$20.00/hr
running on ml.m5.2xlarge
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$0.461/host/hr
running on ml.m5.2xlarge
SageMaker Batch Transform$0.461/host/hr
running on ml.m5.2xlarge
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.m5d.24xlarge | $10.00 | |
ml.c5d.large | $10.00 | |
ml.m4.16xlarge | $10.00 | |
ml.m5.2xlarge Vendor Recommended | $10.00 | |
ml.r5d.large | $10.00 | |
ml.c5d.4xlarge | $10.00 | |
ml.m4.2xlarge | $10.00 | |
ml.c5.2xlarge | $10.00 | |
ml.c5d.9xlarge | $10.00 | |
ml.c4.2xlarge | $10.00 | |
ml.m4.10xlarge | $10.00 | |
ml.c4.xlarge | $10.00 | |
ml.m5.24xlarge | $10.00 | |
ml.m5d.xlarge | $10.00 | |
ml.m5d.large | $10.00 | |
ml.c5.xlarge | $10.00 | |
ml.m5.12xlarge | $10.00 | |
ml.m5d.4xlarge | $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.m5d.2xlarge | $10.00 | |
ml.c5d.xlarge | $10.00 | |
ml.m5d.12xlarge | $10.00 | |
ml.c4.large | $10.00 | |
ml.m5.large | $10.00 | |
ml.c5d.18xlarge | $10.00 | |
ml.r5.2xlarge | $10.00 | |
ml.c5d.2xlarge | $10.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Input
Usage Methodology for the algorithm:
- The input has to be a '.txt' file with 'utf-8' encoding. PLEASE NOTE: If your input .txt file is not 'utf-8' encoded, model will not perform as expected
- To make sure that your input file is 'UTF-8' encoded please 'Save As' using Encoding as 'UTF-8'
- The input can have a maximum of 512 words (Sagemaker restriction)
- Input should have atleast 3 sentences (Model limitation)
- Supported content types:
text/plain
Output
Content type: text/plain
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.txt --content-type text/plain --accept text/plain result.txt
Substitute the following parameters:
"endpoint-name"
- name of the inference endpoint where the model is deployedinput.txt
- input filetext/plain
- MIME type of the given input file (above)result.txt
- filename where the inference results are written to.
Python
Real-time inference snippet (more detailed example can be found in sample notebook): sample_txt = 'location of input text file' transformer = model.transformer(1, 'ml.m5.xlarge') transformer.transform(sample_txt, content_type="text/plain") transformer.wait() print("Batch Transform output saved to " + transformer.output_path)
Sample Notebook :https://tinyurl.com/yyu32g32 Sample Input : https://tinyurl.com/tx94grp Sample Output: https://tinyurl.com/wnzfy9c
Additional Resources
End User License Agreement
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Support Information
Mphasis DeepInsights Text Summarizer
For any assistance, please reach out to:
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 MoreRefund Policy
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