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SageMaker model package for Sentiment Analysis
Overview
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Python predict
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POST /invocations
This SageMaker model package provides a REST api to analyze the sentiment of English sentences.
The API accepts input as JSON, CSV or plain text, and identifies the sentiment (positive or negative) and provides a confidence level (float number from 0 to 1).
Use it to score product reviews and app store feedback, triage support tickets by tone, moderate community content, or track sentiment trends across a review backlog.
We welcome your feedback at aws-support@sigmodata.com
Model and training data Version 3 is a ModernBERT-base (answerdotai/ModernBERT-base) text classifier fine-tuned with PyTorch and Hugging Face Transformers on the public IMDB movie review, SST-2 (GLUE), and dair-ai/emotion datasets, and served as ONNX. The model package runs in network isolation on SageMaker, so no data leaves your account. It supports real-time endpoints and batch transform, and takes JSON, CSV, or plain text.
Known limitations
- English only. Other languages are not supported.
- Binary output (positive or negative). There is no neutral class, so neutral text is forced into the closer of the two labels.
- Tuned for short review-style text. Split long documents into sentences or passages before sending.
Measured performance
- Accuracy 95.4 percent, macro-F1 0.954, measured on 20,000 held-out examples from the public IMDB, SST-2 (GLUE), and dair-ai/emotion datasets. The split is seeded (70/20/10, seed 42) and was not used in training.
- Real-time endpoint on ml.m5.large: 263 ms median per request (275 ms p95).
- Throughput on the same instance: about 23 texts per second at a batch size of 32.
Highlights
- Score English reviews and comments as positive or negative, with a confidence on every sentence
- Batch hundreds of sentences per request with sub-second responses - built for review and moderation queues
- SageMaker real-time or batch; JSON, CSV, or plain text - 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.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.10 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.10 |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $0.10 |
ml.c4.4xlarge Inference (Batch) | Model inference on the ml.c4.4xlarge instance type, batch mode | $0.10 |
ml.c4.8xlarge Inference (Batch) | Model inference on the ml.c4.8xlarge instance type, batch mode | $0.10 |
ml.c4.xlarge Inference (Batch) | Model inference on the ml.c4.xlarge instance type, batch mode | $0.10 |
ml.c5.18xlarge Inference (Batch) | Model inference on the ml.c5.18xlarge instance type, batch mode | $0.10 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.10 |
ml.c5.4xlarge Inference (Batch) | Model inference on the ml.c5.4xlarge instance type, batch mode | $0.10 |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $0.10 |
Dimensions summary
You pay by the hour for the SageMaker instance you run, billed per host hour. Pricing splits into two modes: Batch inference for processing text in bulk jobs, and Real-Time inference for live endpoint requests. Within each mode, you choose an instance type across four families: c4 and c5 compute instances, m4 and m5 general-purpose instances, and p2 and p3 GPU instances. Larger sizes within a family (from xlarge up to 24xlarge) carry higher hourly rates, so cost scales with the compute you select. Model deployment runs in your own AWS account.
Top-of-mind questions for buyers
One host hour is one hour that a chosen SageMaker instance runs your model. Charges begin when the instance starts and stop when you stop it. A batch job meters only while the job runs. A real-time endpoint meters for as long as it stays active, even when idle.
Batch inference meters host hours only while a bulk processing job runs, then stops. Real-Time inference runs a live endpoint that meters continuously until you shut it down, whether or not requests arrive. Batch suits scheduled bulk text; real-time suits on-demand requests.
You pay the hourly rate tied to whichever single instance type you run. When you move to a larger size, the new hourly rate applies to all hours on that instance. There is no blended or partial rate. You choose one instance per endpoint or batch job.
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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
Fixed security vulnerabilities
Additional details
Inputs
- Summary
The model can extract sentiment out of input text. Provide a list of sentences to classify.
- Limitations for input type
- Maximum number of words per sentence is 200
- Input MIME type
- text/csv, application/json, application/x-www-form-urlencoded
["His behavior was unnaceptable", "I feel pretty good about this"]
https://raw.githubusercontent.com/sigmodata/public/main/products/Sentiment-Model/sentiment-batch-transform-input.json
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 strings | Type: FreeText
Limitations: max 200 words per string | 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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