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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Named Entity Detector - US English Free trial
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Latest Version:
2
English language model package for detecting entities in text
Product Overview
This SageMaker model package provides a REST api to detect named entities in US English sentences. The API accepts input as JSON and identifies entities in an input text array. We welcome your feedback at aws-support@sigmodata.com. To see a notebook with usage instructions go to https://colab.research.google.com/drive/1iL1Q0FiYfKoUqYzglpeBFDxzDjV8IF9s
Key Data
Version
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Type
Model Package
Highlights
US English language model detects 18 entity types
Detects the following entities: Person, Nationality/Religious/Political group, Buildings/airports/highways/bridges, Companies, Countries/cities/states, Locations, Objects/vehicles/foods, Famous Events, Works of art, Laws, Languages, Dates, Times, Percentages, Money, Quantity, Ordinals, Cardinals
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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.
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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$0.10/hr
running on ml.m5.xlarge
Model Batch Transform$0.10/hr
running on ml.m5.xlarge
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$0.23/host/hr
running on ml.m5.xlarge
SageMaker Batch Transform$0.23/host/hr
running on ml.m5.xlarge
About Free trial
Try this product for 1 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
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 | $0.10 | |
ml.m5.4xlarge | $0.10 | |
ml.m4.16xlarge | $0.10 | |
ml.m5.2xlarge | $0.10 | |
ml.p3.16xlarge | $0.10 | |
ml.m4.2xlarge | $0.10 | |
ml.c5.2xlarge | $0.10 | |
ml.p3.2xlarge | $0.10 | |
ml.c4.2xlarge | $0.10 | |
ml.m4.10xlarge | $0.10 | |
ml.c4.xlarge | $0.10 | |
ml.m5.24xlarge | $0.10 | |
ml.c5.xlarge | $0.10 | |
ml.p2.xlarge | $0.10 | |
ml.m5.12xlarge | $0.10 | |
ml.p2.16xlarge | $0.10 | |
ml.c4.4xlarge | $0.10 | |
ml.m5.xlarge Vendor Recommended | $0.10 | |
ml.c5.9xlarge | $0.10 | |
ml.m4.xlarge | $0.10 | |
ml.c5.4xlarge | $0.10 | |
ml.p3.8xlarge | $0.10 | |
ml.m5.large | $0.10 | |
ml.c4.8xlarge | $0.10 | |
ml.p2.8xlarge | $0.10 | |
ml.c5.18xlarge | $0.10 |
Usage Information
Model input and output details
Input
Summary
This SageMaker model package provides a REST api to detect named entities in US english sentences. The model accepts a list of strings. You can provide that input as csv or json.
Input MIME type
text/csv, application/jsonSample input data
[
"I went to Paris last summer. It was 95 degrees"
]
Output
Summary
The model detects the following:
PERSON People NORP Nationalities FAC Buildings, airports ... ORG Companies ... GPE Countries/cities/states. LOC Non-GPE locations PRODUCT Vehicles/foods ... EVENT World Events WORK_OF_ART Titles of books, songs, etc. LAW Laws LANGUAGE DATE Absolute/relative/periods TIME Times PERCENT MONEY Monetary values, including unit. QUANTITY Quantities ORDINAL “first”, “second”, etc. CARDINAL Numerals
Output MIME type
text/csv, application/jsonSample output data
{
"results": [
[
{
"end": 15,
"label": "GPE",
"start": 10,
"text": "Paris"
},
{
"end": 27,
"label": "DATE",
"start": 16,
"text": "last summer"
},
{
"end": 46,
"label": "QUANTITY",
"start": 36,
"text": "95 degrees"
}
]
]
}
Sample notebook
Additional Resources
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Support Information
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