Sold by: SigmoDataÂ
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/1iL1Q0FiYfKoUqYzglpeBFDxzDjV8IF9sÂ
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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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.10 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.10 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.10 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.10 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.10 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.10 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $0.10 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $0.10 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.10 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $0.10 |
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Delivery details
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:
Version release notes
Updated python to 3.8 Updated packages to latest version
Additional details
Inputs
- 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/json
["I went to Paris last summer. It was 95 degrees"]
https://raw.githubusercontent.com/sigmodata/public/main/products/NER-Model/batch-transform-input.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
input | List of text strings to extract entities from | Type: FreeText | Yes |
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