bert-turkish-subtag-classifier

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.

• 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

• Train/Val/Test splits: 70/15/15%

• Total samples: 11748

• Preprocessed PDF texts included

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Uses

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Direct Use

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Out-of-Scope Use

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Recommendations

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How to Get Started with the Model

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Training Details

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Preprocessing [optional]

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Training Hyperparameters

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Testing Data, Factors & Metrics

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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