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---
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language: pl
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license: apache-2.0
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tags:
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- ner
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- anonymization
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- rodo
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- gdpr
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- polish
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- herbert
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- medical
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- privacy
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---
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# HerBERT NER – RODO / GDPR Anonymization (PL)
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Fine-tuned **HerBERT** model for **Named Entity Recognition (NER)** focused on
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automatic anonymization of **RODO / GDPR-sensitive data** in **Polish-language texts**,
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with emphasis on medical and administrative documents.
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The model is designed to detect personal data, sensitive attributes,
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identifiers, and contact information to support downstream anonymization pipelines.
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---
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## ✨ Use case
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- Anonymization of medical documentation
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- GDPR / RODO compliance
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- Text preprocessing before analytics or ML
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- Research on privacy-preserving NLP for Polish
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---
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## 🧠 Model details
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- Base model: **HerBERT (Polish BERT)**
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- Task: Token classification (NER)
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- Labeling scheme: **BIO**
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- Training: Supervised fine-tuning
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- Framework: 🤗 Transformers + PyTorch
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---
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## 🏷️ Supported entity classes
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### Personal identifiers
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- `NAME` – first names
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- `SURNAME` – last names
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- `AGE` – age
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- `DATE_OF_BIRTH` – date of birth
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- `DATE` – other dates identifying events
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- `SEX` – sex / gender (explicit)
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- `RELIGION` – religion
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- `POLITICAL_VIEW` – political views
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- `ETHNICITY` – ethnicity / nationality
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- `SEXUAL_ORIENTATION` – sexual orientation
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- `HEALTH` – health-related information
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- `RELATIVE` – family relations revealing identity
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### Contact & location
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- `CITY` – city (general context)
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- `ADDRESS` – full address
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- `EMAIL` – email addresses
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- `PHONE` – phone numbers
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### Identifiers
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- `PESEL` – Polish national ID number
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- `DOCUMENT_NUMBER` – ID / passport / license numbers
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### Professional & education
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- `COMPANY` – employer
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- `SCHOOL_NAME` – school name
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- `JOB_TITLE` – job position
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### Financial
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- `BANK_ACCOUNT` – bank account numbers
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- `CREDIT_CARD_NUMBER` – payment cards
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### Digital identifiers
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- `USERNAME` – usernames / logins
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- `SECRET` – passwords, API keys
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---
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## 📊 Evaluation
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- Validation F1-score: **~0.80**
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- Precision-oriented configuration (privacy-first)
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- Evaluation performed on held-out validation set
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> Note: For anonymization tasks, recall is prioritized to minimize data leakage.
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---
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