Direct Use
This model is a Turkish email subtag classifier. It predicts a fine-grained category (sub_tag) for the text content of emails, such as competition_violation, price_fixing, or spam.
Intended Users:
Data analysts, AI engineers, or companies processing large volumes of Turkish emails.
Teams needing automated tagging or categorization of email content for workflow optimization, reporting, or compliance.
Input:
A dictionary with "text" (email body or subject+body).
Optional preprocessing (removing extra spaces, page numbers) is recommended but the model handles basic cleaning.
Output:
Predicted sub_tag label.
Confidence score (softmax probability).
Use Cases:
Categorizing incoming emails automatically.
Prioritizing emails based on content type (e.g., flagging potential violations).
Feeding downstream analytics or decision-making pipelines.
Limitations:
The model was trained on a finite dataset; it may misclassify rare or ambiguous cases.
False negatives are costlier than false positives; a thresholding mechanism is applied to reduce missed detections.
Not suitable for legal or critical compliance decisions without human review.
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Turkish Email Subtag Classifier
Model
- Base: bert-base-turkish-cased
- Task: Subtag classification (e.g., competition_violation, price_fixing, spam)
- Macro-F1: 0.82
- Accuracy: 0.85
- FN-aware thresholding applied
Dataset
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Train/Val/Test splits: 70/15/15%
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Total samples: 11748
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Preprocessed PDF texts included
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Repository: [More Information Needed]
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Paper [optional]: [More Information Needed]
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Demo [optional]: [More Information Needed]
Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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