Sold by: Sigmodata
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SageMaker model package for classifying customer banking queries into 77 intents
Overview
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Python predict
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POST /invocations
This SageMaker model package provides a REST API to classify customer messages into 77 fine-grained banking intents (card issues, transfers, top-ups, account access, fees, and more).
Use it to route support tickets, power chatbots and IVR systems, or analyze contact-center transcripts. Each prediction returns the intent label and a confidence score.
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 banking77 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 77 banking77 intents. Messages outside retail banking are still mapped to one of them, so use the confidence score to route low-confidence traffic to a human.
- Single-intent: a message covering two intents returns only the strongest.
Measured performance
- Accuracy 92.2 percent, macro-F1 0.923, measured on 2,616 held-out examples from banking77. The split is seeded (70/20/10, seed 42) and was not used in training.
- Real-time endpoint on ml.m5.large: 252 ms median per request (267 ms p95).
- Throughput on the same instance: about 24 texts per second at a batch size of 32.
Highlights
- Route banking chat and IVR turns into 77 intents covering cards, payments, transfers, access, and fees
- Fine-grained banking taxonomy out of the box - no intent-training project to stand up
- 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 compute instance you choose and the inference mode you run. Pricing covers two instance families: compute-optimized c5 sizes (xlarge, 2xlarge) and general-purpose m5 sizes (large, xlarge, 2xlarge). Each instance offers two modes. Batch mode processes a group of records in a scheduled job. Real-time mode serves an always-on endpoint for live requests. Larger instances carry higher hourly rates than smaller ones in the same family. You select the size and mode that fit your workload, and charges accrue for each host hour used.
Top-of-mind questions for buyers
One host hour is one hour that a chosen instance runs the model. A real-time endpoint meters every hour it stays active, even when idle between requests. A batch job meters only while the job runs. Charges stop when you delete the endpoint or the batch job finishes.
Real-time mode runs an always-on endpoint for live requests, so hours accrue continuously until you delete it. Batch mode processes a group of records in a scheduled job, so hours accrue only during that job. Real-time suits steady live traffic; batch suits periodic bulk classification.
You pay only for instance host hours. The number of records classified or labels returned does not change the rate. Cost depends on which instance size runs and how long it runs, not on how much text you send through it.
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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": ["How do I change my PIN?"]}
How do I change my PIN?
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.
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