turkish-privacy-filter-pii

Turkish Privacy Filter PII

Repository: BTX24/turkish-privacy-filter-pii

This repository contains a Turkish-oriented fine-tuned checkpoint of openai/privacy-filter for privacy-focused PII span detection.

The model is designed to detect Turkish privacy-sensitive spans such as Turkish-style person names, e-mail addresses, phone numbers, dates, addresses, account identifiers, IBAN-like values, TCKN-like values, VKN-like values, and secret-like tokens.

Important: This model is a privacy redaction aid. It is not a legal anonymization guarantee.

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Türkçe Açıklama

Bu model, openai/privacy-filter temel alınarak Türkçe gizlilik odaklı PII / kişisel veri tespiti için fine-tune edilmiş bir checkpoint’tir.

Modelin amacı; Türkçe metinlerde geçen kişi adı, e-posta, telefon, tarih, adres, hesap numarası, IBAN benzeri değer, T.C. kimlik numarası benzeri değer, vergi kimlik numarası benzeri değer ve secret/token/parola benzeri hassas alanları span düzeyinde tespit etmektir.

Önemli: Bu model gizlilik odaklı veri maskeleme/redaction için yardımcı bir araçtır. Tek başına hukuki anonimleştirme veya KVKK uyumluluk garantisi sağlamaz.

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Base Model

Base model:

openai/privacy-filter

OpenAI Privacy Filter is a bidirectional token-classification model for detecting and redacting personally identifiable information (PII) in text. This checkpoint adapts the base model to a Turkish-oriented privacy label space.

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Training Dataset

This model was fine-tuned on:

BTX24/turkish-privacy-pii-ner

Dataset summary:

The training data is fully synthetic and contains no intentionally collected real personal information.

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Label Space

The fine-tuned checkpoint uses the following Turkish privacy label space:

{
  "category_version": "tr_privacy_v1",
  "span_class_names": [
    "O",
    "tckn",
    "secret",
    "iban",
    "vkn",
    "account_number",
    "private_address",
    "private_date",
    "private_phone",
    "private_email",
    "private_person"
  ]
}

Label Descriptions

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Training Summary

The model was fine-tuned for Turkish privacy-oriented span detection.

Epoch Metrics

The best checkpoint was selected at epoch 3 based on validation loss.

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Test Evaluation

Evaluation was performed on the synthetic Turkish test split.

Detection Metrics

Per-Class Span Metrics

These scores are measured on a synthetic test split. Real-world performance may differ, especially on noisy user text, OCR output, mixed-language documents, informal spelling, or domain-specific records.

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Intended Use

This checkpoint is intended for research and development in Turkish privacy-preserving NLP.

Recommended use cases:

• Turkish PII detection
• Turkish privacy-oriented NER
• Character-level privacy span detection
• Data redaction and masking
• Privacy-aware preprocessing pipelines
• Fine-tuning / evaluating OpenAI Privacy Filter-style models on Turkish data
• Academic NLP projects
• Controlled experiments with synthetic Turkish privacy data

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Out-of-Scope Use

This model should not be used as:

• A legal compliance guarantee
• A complete anonymization system by itself
• A guarantee that all sensitive information will be detected
• A replacement for human privacy review
• A production privacy safeguard without further evaluation
• A detector for real-world high-risk documents without additional domain testing

For production or high-risk use cases, evaluate the model on in-domain Turkish data and combine it with rule-based checks, human review, logging safeguards, and privacy-by-design processes.

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Local Usage

1. Install OpenAI Privacy Filter

git clone https://github.com/openai/privacy-filter.git
cd privacy-filter
pip install -e .

2. Download this checkpoint

python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='BTX24/turkish-privacy-filter-pii', local_dir='tr_privacy_filter_pii')"

3. Run inference with OPF CLI

For CUDA:

opf --checkpoint ./tr_privacy_filter_pii --device cuda --format json "Mehmet Kaya TCKN 12345678901"

For CPU:

opf --checkpoint ./tr_privacy_filter_pii --device cpu --format json "Mehmet Kaya TCKN 12345678901"

Example Turkish inputs:

opf --checkpoint ./tr_privacy_filter_pii --device cuda --format json "Ahmet Yılmaz için telefon numarası 0532 000 00 00 olarak kaydedildi."

opf --checkpoint ./tr_privacy_filter_pii --device cuda --format json "İade için IBAN TR00 0000 0000 0000 0000 0000 00 bilgisi girildi."

opf --checkpoint ./tr_privacy_filter_pii --device cuda --format json "Doğrulama kodu OTP-482193 destek kaydına yazılmış."

opf --checkpoint ./tr_privacy_filter_pii --device cuda --format json "Fatura için VKN 1234567890 ve referans kodu REF-TR-000918 girildi."

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Python Download Example

from huggingface_hub import snapshot_download

checkpoint_dir = snapshot_download(
    repo_id="BTX24/turkish-privacy-filter-pii",
    local_dir="tr_privacy_filter_pii",
)

print(checkpoint_dir)

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Training / Fine-tuning Notebook

This repository includes the training notebook:

privacy_filter_tr_pii_colab.ipynb

The notebook documents the Turkish fine-tuning workflow.

It may include steps such as:

• Loading the Turkish privacy PII dataset
• Preparing the Turkish privacy label space
• Validating character-level spans
• Preparing train/validation/test files
• Running OpenAI Privacy Filter fine-tuning
• Saving model artifacts
• Testing inference examples
• Exporting checkpoint files for Hugging Face upload
• Evaluating the fine-tuned checkpoint on the synthetic test split

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Artifacts

Expected repository artifacts:

.
├── README.md
├── config.json
├── model.safetensors
├── finetune_summary.json
├── USAGE.txt
├── label_space.json
└── privacy_filter_tr_pii_colab.ipynb

Artifact Descriptions

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Suggested Evaluation

Recommended metrics:

• Entity-level precision
• Entity-level recall
• Entity-level F1
• Typed span F1
• Untyped span F1
• Per-label F1
• Boundary error analysis
• False positive analysis
• False negative analysis

Recommended comparison baselines:

• Original openai/privacy-filter without Turkish fine-tuning
• Regex-based detector for structured fields such as phone, e-mail, IBAN, TCKN-like values and VKN-like values
• Turkish BERT-style NER model
• This fine-tuned Turkish Privacy Filter PII checkpoint

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Limitations

This checkpoint was fine-tuned on synthetic Turkish privacy data. Therefore:

• It may not fully capture noisy real-world Turkish text.
• It may overfit to synthetic templates.
• It may miss rare PII formats not represented in the dataset.
• It may produce false positives for numeric or code-like strings.
• It may struggle with long documents, OCR errors, mixed-language text, informal spelling, or domain-specific formats.
• It should be evaluated on in-domain data before production use.
• It should not be treated as a legal anonymization guarantee.

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Ethical Considerations

The training dataset is synthetic and was designed to avoid intentionally collecting or distributing real personal information.

However, a model trained on synthetic data can still make mistakes. It may fail to detect sensitive information or incorrectly classify non-sensitive text as PII. For sensitive applications, this model should be used as one layer in a broader privacy-preserving pipeline, not as the only safeguard.

Recommended safeguards:

• Human review for high-risk workflows
• Domain-specific evaluation
• Regex/rule-based checks for structured identifiers
• Logging and monitoring of redaction failures
• Conservative handling of uncertain predictions
• Regular evaluation on newly observed data distributions

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License

This fine-tuned checkpoint is released under the Apache License 2.0.

The base model is openai/privacy-filter.

The training dataset BTX24/turkish-privacy-pii-ner is released under Creative Commons Attribution 4.0 International (CC BY 4.0).

Please review the licenses of the base model, dataset, and this fine-tuned checkpoint before use.

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Citation

If you use this model, please cite:

@model{toktay_2026_turkish_privacy_filter_pii,
  title        = {Turkish Privacy Filter PII},
  author       = {Boran Toktay},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/BTX24/turkish-privacy-filter-pii}},
  note         = {Fine-tuned OpenAI Privacy Filter checkpoint for Turkish PII span detection}
}

If you use the training dataset, please also cite:

@dataset{toktay_2026_turkish_privacy_pii_ner,
  title        = {Turkish Privacy PII NER Dataset},
  author       = {Boran Toktay},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/BTX24/turkish-privacy-pii-ner}},
  note         = {Synthetic Turkish privacy-oriented named entity recognition dataset for PII detection}
}

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Contact

For questions, issues, or suggestions, please open an issue or discussion on the model repository:

BTX24/turkish-privacy-filter-pii