ottoman-ner 2.0.0

Ottoman Turkish Named Entity Recognition toolkit

Ottoman NER

A focused toolkit for Ottoman Turkish Named Entity Recognition

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About

Ottoman NER is a specialized Python package for Named Entity Recognition (NER) in Ottoman Turkish texts. This package provides a clean, modern interface for training, evaluating, and using NER models specifically designed for historical Ottoman Turkish documents.

Key Features

• 🎯 Focused NER Solution: Dedicated solely to Ottoman Turkish named entity recognition
• 🚀 Simple API: Single class interface for all NER operations
• ⚙️ Easy Training: Train custom models with JSON configuration
• 📊 Built-in Evaluation: Comprehensive evaluation metrics with seqeval
• 🔮 Fast Prediction: Real-time entity recognition
• 🛠️ CLI Interface: Command-line tools for all operations
• 📦 PyPI Ready: Easy installation via pip

Supported Entity Types

• PER: Person names (Sultan Abdülhamid, Ahmet Paşa)
• LOC: Locations (İstanbul, Rumeli, Anadolu)
• ORG: Organizations (Divan-ı Hümayun, Meclis-i Mebusan)
• MISC: Miscellaneous entities (dates, events, titles)

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Installation

From PyPI (Recommended)

pip install ottoman-ner

From Source

git clone https://github.com/fatihburakkarag/ottoman-ner.git
cd ottoman-ner
pip install -e .

# Install with development dependencies
pip install -e .[dev]

# Install with full features (visualization, experiment tracking)
pip install -e .[full]

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Quick Start

1. Using Pre-trained Models

from ottoman_ner import OttomanNER

# Initialize the NER system
ner = OttomanNER()

# Load a pre-trained model
ner.load_model("models_hub/ner/ottoman-ner-standard")

# Make predictions
text = "Sultan Abdülhamid İstanbul'da yaşıyordu."
entities = ner.predict(text)

for entity in entities:
    print(f"{entity['text']} -> {entity['label']} ({entity['confidence']:.2f})")

2. Training Custom Models

from ottoman_ner import OttomanNER

# Initialize
ner = OttomanNER()

# Train from configuration file
results = ner.train_from_config("configs/training.json")
print(f"Training completed! F1 Score: {results['eval_f1']:.4f}")

3. Model Evaluation

from ottoman_ner import OttomanNER

# Initialize and evaluate
ner = OttomanNER()
results = ner.evaluate(
    model_path="models_hub/ner/ottoman-ner-standard",
    test_file="data/test.txt"
)

print(f"F1 Score: {results['overall_f1']:.4f}")
print(f"Precision: {results['overall_precision']:.4f}")
print(f"Recall: {results['overall_recall']:.4f}")

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Command Line Interface

Ottoman NER provides a comprehensive CLI for all operations:

Training

# Train a new model
ottoman-ner train --config configs/training.json

# Train with verbose output
ottoman-ner --verbose train --config configs/training.json

Evaluation

# Evaluate a trained model
ottoman-ner eval --model-path models_hub/ner/ottoman-ner-standard --test-file data/test.txt

# Save evaluation results
ottoman-ner eval --model-path models_hub/ner/ottoman-ner-standard --test-file data/test.txt --output-dir results/

Prediction

# Predict on single text
ottoman-ner predict --model-path models_hub/ner/ottoman-ner-standard --text "Sultan Abdülhamid İstanbul'da yaşıyordu"

# Predict on file
ottoman-ner predict --model-path models_hub/ner/ottoman-ner-standard --input-file input.txt --output-file predictions.json

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Configuration

Create a training configuration file in JSON format:

{
  "experiment": {
    "experiment_name": "my-ottoman-ner"
  },
  "model": {
    "model_name_or_path": "dbmdz/bert-base-turkish-cased",
    "num_labels": 9
  },
  "data": {
    "train_file": "data/train.txt",
    "dev_file": "data/dev.txt",
    "test_file": "data/test.txt",
    "max_length": 512
  },
  "training": {
    "output_dir": "models/my-model",
    "num_train_epochs": 3,
    "per_device_train_batch_size": 4,
    "learning_rate": 2e-5,
    "eval_strategy": "steps",
    "eval_steps": 100,
    "save_steps": 100,
    "load_best_model_at_end": true,
    "metric_for_best_model": "eval_f1"
  }
}

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Data Format

Ottoman NER expects CoNLL format data with BIO tagging:

Sultan B-PER
Abdülhamid I-PER
İstanbul B-LOC
'da O
yaşıyordu O
. O

Osmanlı B-ORG
Devleti I-ORG
'nin O
başkenti O
İstanbul B-LOC
'dur O
. O

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Project Background & Acknowledgments

This project builds upon foundational work in Ottoman Turkish NLP and represents a focused effort to provide a clean, maintainable NER solution for historical Turkish texts.

References

• Karagöz et al. (2024) — "Towards a Clean Text Corpus for Ottoman Turkish" ACL Anthology
• Özateş et al. (2025) — "Building Foundations for Natural Language Processing of Historical Turkish: Resources and Models" arXiv:2501.04828

Special Thanks

Sincere gratitude to Assoc. Prof. Şaziye Betül Özateş and the Boğaziçi University Computational Linguistics Lab (BUColin) for their foundational contributions to historical Turkish NLP.

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Requirements

• Python 3.8+
• PyTorch 1.9+
• Transformers 4.20+
• See requirements.txt for complete dependencies

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Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Citation

If you use Ottoman NER in your research, please cite:

@software{ottoman_ner_2024,
  title={Ottoman NER: A Toolkit for Ottoman Turkish Named Entity Recognition},
  author={Karagöz, Fatih Burak},
  year={2024},
  url={https://github.com/fatihburakkarag/ottoman-ner},
  version={2.0.0}
}

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Related Projects

For broader Ottoman Turkish NLP research and experimental tools, see the upcoming ottominer repository (coming soon).