Sold by: Sigmodata
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SageMaker model package for sentiment analysis of financial text
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 financial text such as news headlines, analyst notes, and market commentary.
Each input text is classified as negative, neutral, or positive with a confidence score. The model is fine-tuned on financial news and financial phrase datasets, so it reads market language such as "beat estimates" or "missed guidance" in context rather than as generic positive or negative wording.
Use it to tag news and analyst commentary in a research pipeline, monitor tone around a ticker or sector, or enrich market data feeds before downstream analysis.
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 nickmuchi/financial-classification and zeroshot/twitter-financial-news-sentiment datasets. 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.
- Tuned for short financial text such as headlines and analyst notes. Long filings should be split into sentences first.
- Sentiment is about the language used, not a prediction of price movement. Do not use it as investment advice.
Measured performance
- Accuracy 84.9 percent, macro-F1 0.831, measured on 3,397 held-out examples from the financial news and phrase datasets. 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 (273 ms p95).
- Throughput on the same instance: about 24 texts per second at a batch size of 32.
Highlights
- Classify financial news and market commentary as negative, neutral, or positive - not a movie-review model
- Fine-tuned for headlines and analyst language; ModernBERT + ONNX for cheap, fast SageMaker inference
- JSON or CSV; real-time endpoint or batch transform, 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 for model inference, billed per host-hour on the instance you run. Pricing splits along two lines. First, you pick an instance type: ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.c5.xlarge, or ml.c5.2xlarge. These differ in compute and memory size. Second, you pick a mode: real-time for live endpoints or batch for bulk jobs. Each instance-and-mode pairing is a separate rate. Your cost scales with how long each endpoint or batch job runs, plus standard AWS infrastructure charges. There is no upfront commitment.
Top-of-mind questions for buyers
Real-time mode runs a live endpoint that stays up to answer requests, billing per host-hour while the endpoint is active. Batch mode runs a job over bulk data, billing per host-hour while the job runs, then stops. Real-time suits continuous request flows; batch suits large one-off processing.
One host-hour is one hour that your chosen instance runs the model, whether serving requests or waiting. A real-time endpoint accrues charges the whole time it stays up, even with no traffic. To stop software charges, delete the endpoint. Batch jobs meter only while running.
Both combine: your per-host-hour rate depends on the instance type and mode you pick, then multiplies by hours run. Larger instances carry a higher hourly rate. Runtime hours usually drive total cost for steady real-time endpoints. AWS infrastructure charges apply on top of the software rate.
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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 company reported strong revenue growth exceeding analyst expectations"]}
The company reported strong revenue growth exceeding analyst expectations
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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