---
library_name: transformers
tags:
- named-entity-recognition
- ner
- turkish
- ottoman
- historical-nlp
- token-classification
- pytorch
language:
- tr
license: mit
datasets:
- custom
metrics:
- seqeval
base_model:
- dbmdz/bert-base-turkish-cased
pipeline_tag: text-classification
---

# Ottoman-NER Latin (`fatihburakkaragoz/ottoman-ner-latin`)

This model performs Named Entity Recognition (NER) on Ottoman Turkish texts **transliterated into Latin script**. It identifies named entities such as persons (PER), locations (LOC), and organizations (ORG) in historical Ottoman Turkish texts. The model is fine-tuned from `dbmdz/bert-base-turkish-cased` using a custom CONLL-formatted dataset annotated specifically for Ottoman Latin script.

---

## Part of the Ottoman-NLP Project

While this specific model (`ottoman-ner-latin`) was developed independently, it builds upon the broader **Ottoman-NLP** research direction initiated within **Boğaziçi University - BUCOLIN Lab** under the guidance of **Prof. Dr. Şaziye Betül Özateş**.

Explore more from the lab at: [huggingface.co/BUCOLIN](https://huggingface.co/BUCOLIN)  
Main project repository: [github.com/Ottoman-NLP](https://github.com/Ottoman-NLP)


---

## Model Details

- **Model Type:** Token Classification (NER)
- **Base Model:** `dbmdz/bert-base-turkish-cased`
- **Language:** Ottoman Turkish (Latin transliteration)
- **Training Dataset:** Custom manually annotated dataset
- **Fine-tuned by:** [Fatih Burak Karagöz](https://github.com/fbkaragoz)
- **License:** MIT
- **Usage:** Historical Turkish NLP, research, digital humanities

---

## Usage

### Transformers pipeline

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model = AutoModelForTokenClassification.from_pretrained("fatihburakkaragoz/ottoman-ner-latin")
tokenizer = AutoTokenizer.from_pretrained("fatihburakkaragoz/ottoman-ner-latin")

ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

text = "Emin Bey’in kuklaları bir haftadır Tepebaşı’nda oynuyor."
entities = ner_pipeline(text)
print(entities)
```

---

## Training & Evaluation

- Training Script: scripts/train_latin_ner.py

  - Labels: PER (Person), LOC (Location), ORG (Organization), O

  - Metrics (seqeval):

    - Precision: e.g., 89.4%

    - Recall: e.g., 87.1%

    - F1 Score: e.g., 88.2%

    - Epochs: 5

    - Batch Size: 8

    - Hardware: RTX 4090, CUDA 12.8

---

## Example Output

```
[
  {
    "entity_group": "PER",
    "word": "Emin Bey",
    "start": 0,
    "end": 8,
    "score": 0.998
  },
  {
    "entity_group": "LOC",
    "word": "Tepebaşı",
    "start": 34,
    "end": 42,
    "score": 0.997
  }
]
```
---

## Citation

Please cite the model as:

```bibtex
@software{karagoz_ottoman_ner_latin_2025,
  author = {Karagöz, Fatih Burak},
  title = {Ottoman-NER Latin: A Named Entity Recognition Model for Transliterated Ottoman Turkish},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/fatihburakkaragoz/ottoman-ner-latin},
  note = {Model version 0.2.0. Developed as part of the Ottoman-NLP initiative.}
}
```
---

## Citation for research

```
@article{ozates2025building,
  title={Building Foundations for Natural Language Processing of Historical Turkish: Resources and Models},
  author={Özateş, Şaziye Betül and Tıraş, Tuğba Eser and Adak, Elif Esra and Doğan, Berkay and Karagöz, Fatih Burak and Genç, Elif Esma and others},
  journal={arXiv preprint arXiv:2501.04828},
  year={2025}
}

@inproceedings{karagoz2024towards,
  title={Towards a Clean Text Corpus for Ottoman Turkish},
  author={Karagöz, Fatih Burak and Doğan, Berkay and Özateş, Şaziye Betül},
  booktitle={Proceedings of the First Workshop on Natural Language Processing for Turkic Languages},
  year={2024}
}
```

---

## Contact 
- Author: Fatih Burak Karagöz
- Email: fatihburak@pm.me
- Github: fbkaragoz
- web: https://www.karagoz.io