Sold by: Waltsoft Inc.
Deployed on AWS
6.4M monthly downloads. BERT pre-trained on 2.5B financial words. Classifies earnings calls, SEC filings, and news as positive, negative, or neutral.
Overview
FinBERT is the production standard for financial domain sentiment analysis with 6.4 million monthly downloads from quantitative trading firms, fintech platforms, risk management systems, and compliance teams. Pre-trained on a 2.5-billion-word corpus of Reuters TRC2, Financial PhraseBank, and earnings call transcripts, it understands financial-domain vocabulary that generic BERT models misclassify. Terms like 'headwinds', 'dilutive', 'above consensus', and 'regulatory tailwinds' carry domain-specific sentiment that general models systematically mislabel. Deployed on SageMaker, it delivers real-time three-class classification (positive, negative, neutral) with confidence scores for trading signal generation, earnings call analysis, news sentiment monitoring, and SEC filing screening.
Highlights
- 6.4M monthly downloads -- the production standard for financial NLP sentiment in quantitative and fintech
- Pre-trained on 2.5B words of Reuters, Financial PhraseBank, and earnings call transcripts
- Three-class output with confidence scores -- directly usable as trading signal and risk monitoring input
Details
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Latest version
Deployed on AWS
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.10 |
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.10 |
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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.
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Version release notes
Initial release
Additional details
Inputs
- Summary
6.4M monthly downloads. BERT pre-trained on 2.5B financial words. Classifies earnings calls, SEC filings, and news as positive, negative, or neutral.
- Input MIME type
- application/json
https://waltsoft-marketplace-assets.s3.amazonaws.com/ml-validation/text-sample/sample.jsonl
https://waltsoft-marketplace-assets.s3.amazonaws.com/ml-validation/text-sample/sample.jsonl
Support
Vendor support
Contact support@waltsoft.net for deployment assistance.
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