Sold by: Sigmodata
Deployed on AWS
Free Trial
SageMaker model package for detecting spam in messages and emails
Overview
Image
Python predict
Image
POST /invocations
This SageMaker model package provides a REST API to classify messages as spam or ham (legitimate).
The model is fine-tuned on SMS and email spam corpora and returns a label with a confidence score. Use it to filter user-generated content, protect messaging platforms, or pre-screen inbound email.
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 sms_spam and SetFit/enron_spam corpora. 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.
- Trained on SMS and email text. Spam styles drift over time, so evaluate against your own recent traffic before relying on it.
Measured performance
- Accuracy 99.1 percent, macro-F1 0.991, measured on 7,847 held-out examples from the SMS and Enron spam corpora. The split is seeded (70/20/10, seed 42) and was not used in training.
- Real-time endpoint on ml.m5.large: 255 ms median per request (267 ms p95).
- Throughput on the same instance: about 24 texts per second at a batch size of 32.
Highlights
- Flag spam in SMS-style messages and email before it hits users or agents
- Fine-tuned on SMS and email corpora - not a generic toxicity or sentiment model
- JSON or CSV with a spam/ham score; SageMaker real-time or batch
Details
Sold by
Categories
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Free trial
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 for the SageMaker instance that runs the spam detection model. Pricing splits along three lines. First, you pick an instance family and size: ml.c5.xlarge, ml.c5.2xlarge, ml.m5.large, ml.m5.xlarge, or ml.m5.2xlarge. Larger sizes carry more compute. Second, you choose a mode: batch for scheduled bulk jobs, or real-time for a live endpoint. Third, each size-and-mode pairing bills separately per host hour. You are charged for the time the instance runs, so cost scales with instance choice and hours used.
Top-of-mind questions for buyers
One host hour is one hour that a SageMaker instance runs the spam detection model in your account. Charges begin when the instance starts and stop when it shuts down. A running real-time endpoint accrues hours continuously. Batch charges apply only while the job runs.
Real-time mode runs a live endpoint that stays on to answer requests, so it bills for continuous uptime. Batch mode runs scheduled bulk jobs and bills only during the job. Real-time suits steady request traffic; batch suits large one-time or periodic message sets.
No. You are billed per host hour, not per message or request. Throughput does not add separate charges. Cost depends only on which instance size you pick, whether you run batch or real-time, and how many hours the instance runs.
www.sigmodata.com
Helpful?
Vendor refund policy
No refunds offered but you may cancel at any time
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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": ["CONGRATULATIONS you have won a FREE iPhone click here NOW"]}
CONGRATULATIONS you have won a FREE iPhone click here NOW
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
Vendor resources
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.
Similar products
This is a NLP based solution that can be leveraged for any text classification problem to get the best model through AutoML training.
This solution categorizes different documents types like contract documents, broker submissions, invoices, etc.
This algorithm trains a supervised text classification model and uses Active Learning to provide the most relevant samples for tagging
Which categories are relevant? Suggest related categories for each blog, article, or post.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.