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SageMaker model package for detecting emotions in English text
Overview
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Python predict
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POST /invocations
This SageMaker model package provides a REST API to detect the dominant emotion in English text.
Each input text is classified into one of six emotions: sadness, joy, love, anger, fear, or surprise, with a confidence score. Use it for customer feedback analysis, social listening, or conversational analytics.
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 dair-ai/emotion 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.
- Returns one dominant emotion from a fixed set of six. Mixed or absent emotion is still mapped to the closest label.
- Trained on short social media text; longer formal prose is out of domain.
Measured performance
- Accuracy 93.2 percent, macro-F1 0.903, measured on 4,000 held-out examples from the public dair-ai/emotion dataset. The split is seeded (70/20/10, seed 42) and was not used in training.
- Real-time endpoint on ml.m5.large: 262 ms median per request (271 ms p95).
- Throughput on the same instance: about 24 texts per second at a batch size of 32.
Highlights
- Label user text as sadness, joy, love, anger, fear, or surprise, with a confidence score
- Six emotions, not just positive/negative - built for social, support, and UGC triage
- JSON or CSV; real-time or batch; no training job
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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 compute instance running the model, not by text volume. Pricing splits along two lines. First, choose an instance size across two families: general-purpose ml.m5 (large, xlarge, 2xlarge) and compute-optimized ml.c5 (xlarge, 2xlarge). Larger instances carry higher hourly rates. Second, choose a mode: batch, which processes text in scheduled jobs, or real-time, which serves a live endpoint. Each size and mode pairing is billed separately per host hour. You run these instances in your own AWS account.
Top-of-mind questions for buyers
One host hour is one hour that your chosen instance runs the model in your AWS account. Charges accrue by running time, not by how much text you send. Sending more text within a running hour does not raise the host-hour charge. You send JSON, CSV, or plain text and get a label plus a confidence score.
Real-time endpoints bill per host hour while the endpoint stays running, even with no requests. Stop the endpoint to end software charges. Batch mode bills only while a job runs, then stops when the job finishes. Underlying AWS storage or other resources may still incur separate AWS fees.
Real-time mode keeps a live endpoint running and bills every host hour it stays up, suiting steady request traffic. Batch mode runs a scheduled job over your data, then stops, so you pay only for job runtime. Batch suits large one-off datasets; real-time suits continuous, on-demand classification.
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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": ["I am so happy today everything is going great"]}
I am so happy today everything is going great
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
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