Sold by: Sigmodata
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SageMaker model package for classifying news text into topics
Overview
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Python predict
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POST /invocations
This SageMaker model package provides a REST API to classify news text into four topics: World, Sports, Business, or Sci/Tech, with a confidence score.
Use it to organize content feeds, tag articles at ingestion time, or build topic-aware recommendation and monitoring pipelines.
The API accepts input as JSON or CSV and supports real-time endpoints and batch transform.
We welcome your feedback at aws-support@sigmodata.com
Model and training data The model is a ModernBERT-base (answerdotai/ModernBERT-base) text classifier fine-tuned with PyTorch and Hugging Face Transformers on the public ag_news dataset. It runs in network isolation on SageMaker, so no data leaves your account.
Known limitations
- English only. Other languages are not supported.
- The model was fine-tuned on inputs up to 512 tokens. Longer text is accepted, but accuracy is only characterized to that length; split long documents into passages before sending.
- The label set is fixed to the four AG News topics: World, Sports, Business, and Sci/Tech. Text outside those topics is still forced into one of the four.
- Trained on news articles; accuracy on other genres will be lower.
Measured performance
- Accuracy 94.4 percent, macro-F1 0.944, measured on 20,000 held-out examples from AG News. The split is seeded (70/20/10, seed 42) and was not used in training.
- Real-time endpoint on ml.m5.large: 268 ms median per request (282 ms p95).
- Throughput on the same instance: about 23 texts per second at a batch size of 32.
Highlights
- Tag incoming news as World, Sports, Business, or Sci/Tech as it hits the ingest pipe
- A four-way news taxonomy with confidence - not a generic topic model you have to interpret
- JSON or CSV; real-time or batch; no training job
Details
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Pricing
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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.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $0.10 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $0.10 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.10 |
ml.c5.2xlarge Inference (Real-Time) | Model inference on the ml.c5.2xlarge instance type, real-time mode | $0.10 |
ml.c5.xlarge Inference (Batch) | Model inference on the ml.c5.xlarge instance type, batch mode | $0.10 |
ml.c5.xlarge Inference (Real-Time) | Model inference on the ml.c5.xlarge instance type, real-time mode | $0.10 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.10 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $0.10 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $0.10 |
ml.m5.xlarge Inference (Real-Time) | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.10 |
Dimensions summary
You pay by the hour based on the SageMaker instance you run. The five instance types (ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.c5.xlarge, ml.c5.2xlarge) span general-purpose and compute-optimized families, letting you match capacity to your workload. Each instance comes in two modes: batch, for processing groups of text at once, and real-time, for live endpoint requests. This gives you 10 combinations. Pricing scales with instance size and mode, so cost tracks the hours each endpoint or batch job runs. The underlying AWS infrastructure is billed separately.
Top-of-mind questions for buyers
One host hour is one hour that a chosen SageMaker instance runs your model. Real-time endpoints accrue hours while the endpoint stays live and waiting for requests. Batch jobs accrue hours only while the job runs. Each running instance is metered separately, so multiple instances multiply the hours.
A real-time endpoint accrues host hours whenever it stays deployed, even with no incoming text. To stop software charges, delete the endpoint. A batch job charges only for the hours it actively runs, then stops. Underlying AWS infrastructure is billed separately by AWS.
Both modes meter the same instance by the hour, but the trigger differs. Real-time bills for continuous endpoint uptime, so it suits steady live traffic. Batch bills only during a processing job, so it suits scoring large groups of text on demand. You pick per instance type.
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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
Important: security update. The inference container is rebuilt on Amazon Linux 2023, replacing a Debian base that carried HIGH and CRITICAL CVEs with no fixed version available upstream.
- The model itself is unchanged. This version is exported from the same fine-tuned checkpoint as version 1.
- Request and response formats are unchanged: JSON or CSV in, label plus confidence out.
Additional details
Inputs
- Summary
A JSON object with a "texts" array of strings to classify (or a CSV file, one text per line, for batch transform).
- Input MIME type
- application/json, text/csv
{"texts": ["The UN security council voted on the resolution"]}
The UN security council voted on the resolution
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
texts | List of text strings to classify | - | Yes |
Resources
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Support
Vendor support
Support contact: Email: support@sigmodata.com
Support description: contact: support@sigmodata.com
AWS infrastructure support
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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