Sold by: Sigmodata
Deployed on AWS
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Transformer NER for English with 18 default entity types and custom types at inference. Runs on CPU; uses a GPU automatically when you deploy on a GPU instance.
Overview
Image
Python predict
Image
POST /invocations
This SageMaker model package provides a REST API to detect named entities in US English text. Send a JSON array of sentences and receive labeled spans with character offsets and confidence scores.
Eighteen entity types are detected by default (person, organization, location, date, money, and more). Pass an optional "labels" array to extract any entity types you need (drugs, statutes, tickers) without retraining.
GPU is optional. Deploy on a GPU instance (for example ml.g4dn.xlarge) for higher throughput; the same image uses CUDA automatically when a GPU is present, and falls back to CPU otherwise. The API accepts JSON or CSV and supports real-time endpoints and SageMaker batch transform.
We welcome your feedback at aws-support@sigmodata.com . Usage notebook: https://colab.research.google.com/drive/1iL1Q0FiYfKoUqYzglpeBFDxzDjV8IF9s
Highlights
- Extract people, orgs, dates, money, and 14 more English types - or pass your own labels at inference
- Custom types (drug, statute, SKU) with no retraining - most Marketplace NER packages are frozen to one schema
- Invoke with JSON or CSV; ONNX uses a GPU automatically when present
Details
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Try this product free for 5 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g4dn.xlarge instance type, real-time mode | $0.10 |
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $0.10 |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $0.10 |
ml.c4.2xlarge Inference (Real-Time) | Model inference on the ml.c4.2xlarge instance type, real-time mode | $0.10 |
ml.c4.4xlarge Inference (Batch) | Model inference on the ml.c4.4xlarge instance type, batch mode | $0.10 |
ml.c4.4xlarge Inference (Real-Time) | Model inference on the ml.c4.4xlarge instance type, real-time mode | $0.10 |
ml.c4.8xlarge Inference (Batch) | Model inference on the ml.c4.8xlarge instance type, batch mode | $0.10 |
ml.c4.8xlarge Inference (Real-Time) | Model inference on the ml.c4.8xlarge instance type, real-time mode | $0.10 |
ml.c4.xlarge Inference (Batch) | Model inference on the ml.c4.xlarge instance type, batch mode | $0.10 |
ml.c4.xlarge Inference (Real-Time) | Model inference on the ml.c4.xlarge instance type, real-time 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
Listing and documentation updates. Same serving image as version 3.
- Default entity types listed in full (PERSON, ORG, DATE, MONEY, ...)
- Product page URL for documentation (no public git repository)
- Hosted sample notebook
Additional details
Inputs
- Summary
A JSON object with an "input" array of strings to tag (or a CSV file, one text per line, for batch transform). Optional "labels" array of entity type names (omit to use the 18 defaults listed at https://www.sigmodata.com/products?product=ner-model ) and optional "threshold" (0-1).
- Input MIME type
- application/json, text/csv
{"input": ["Apple reported $5 million in revenue yesterday in Cupertino"]}
Apple reported $5 million in revenue yesterday in Cupertino
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 | - | Yes |
labels | Optional types to extract (for example ["drug", "ticker"]). Omit to use the 18 defaults: PERSON, NORP, FAC, ORG, GPE, LOC, PRODUCT, EVENT, WORK_OF_ART, LAW, LANGUAGE, DATE, TIME, PERCENT, MONEY, QUANTITY, ORDINAL, CARDINAL. Definitions: https://www.sigmodata.com/products?product=ner-model | - | No |
threshold | Optional confidence cutoff between 0 and 1 (default 0.5) | - | No |
Resources
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Support
Vendor support
contact: aws-support@sigmodata.com
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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.
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