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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DeepInsights Named Entity Recognizer
By:
Latest Version:
3.0
This solution helps in processing textual data to identify named entities present in the corpus of text.
Product Overview
Mphasis DeepInsights Named Entity Recognizer is an efficient way of identifying named entities present in the corpus of text. This solution applies NLP techniques to extract the named entities which can be used for further text analytics and for providing useful insights about the text. The model takes a corpus of text as input file, processes it and provides a CSV file containing all the named entities present in the text as output.
Key Data
Version
By
Type
Model Package
Highlights
Mphasis DeepInsights Named Entity Recognizer is a Natural Language Processing based solution for extracting named entities such as person, organization, geopolitical entities and various others present in the text data. This solution enables business users to analyse large volume of unstructured text and identifying named entities to build better search solutions, provide content recommendations, provide better customer support, SEO, virtual assistant etc.
The model can take a maximum of 350kb ( ~ 2500 rows of text) as input and extract named entities present in the text.
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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$5.00/hr
running on ml.m5.large
Model Batch Transform$10.00/hr
running on ml.m5.large
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$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
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 | $5.00 | |
ml.m5.4xlarge | $5.00 | |
ml.m5.12xlarge | $5.00 | |
ml.m5d.24xlarge | $5.00 | |
ml.m5d.4xlarge | $5.00 | |
ml.m4.16xlarge | $5.00 | |
ml.m5.2xlarge | $5.00 | |
ml.c5.large | $5.00 | |
ml.m5.xlarge | $5.00 | |
ml.m4.xlarge | $5.00 | |
ml.m4.2xlarge | $5.00 | |
ml.m5d.2xlarge | $5.00 | |
ml.c5.2xlarge | $5.00 | |
ml.m5d.12xlarge | $5.00 | |
ml.m5.large Vendor Recommended | $5.00 | |
ml.t2.xlarge | $5.00 | |
ml.m4.10xlarge | $5.00 | |
ml.m5.24xlarge | $5.00 | |
ml.m5d.xlarge | $5.00 | |
ml.m5d.large | $5.00 | |
ml.c5.xlarge | $5.00 | |
ml.t2.large | $5.00 | |
ml.t2.medium | $5.00 | |
ml.t2.2xlarge | $5.00 |
Usage Information
Model input and output details
Input
Summary
- Supported content types: text/csv
- The input has to be a '.txt' file with 'utf-8' encoding.
Limitations for input type
Maximum input file size: 350kb ( ~ 2500 rows of text)
Input MIME type
text/csv, text/plainSample input data
Output
Summary
The output of this algorithm will be a csv file cotaining the named entities.
Output MIME type
text/csvSample output data
Sample notebook
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
DeepInsights Named Entity Recognizer
For any assistance reach out to us at:
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