Bert Restore Punctuation Turkish

🚀 Transformer Based Punctuation Restoration Models for Turkish

This project aims to correctly place pre - decided punctuation marks in a given Turkish text. It presents three pre - trained transformer models to predict period(.), comma(,) and question(?) marks.

You can find the BERT model used in the paper Transformer Based Punctuation Restoration for Turkish. The aim of this work is to correctly place pre - decided punctuation marks in a given text. We present three pre - trained transformer models to predict period(.), comma(,) and question(?) marks for the Turkish language.

🚀 Quick Start

💻 Usage Examples

Basic Usage

Recommended usage is via HuggingFace. You can run an inference using the pre - trained BERT model with the following code:

from transformers import pipeline

pipe = pipeline(task="token - classification", model="uygarkurt/bert - restore - punctuation - turkish")

sample_text = "Türkiye toprakları üzerindeki ilk yerleşmeler Yontma Taş Devri'nde başlar Doğu Trakya'da Traklar olmak üzere Hititler Frigler Lidyalılar ve Dor istilası sonucu Yunanistan'dan kaçan Akalar tarafından kurulan İyon medeniyeti gibi çeşitli eski Anadolu medeniyetlerinin ardından Makedonya kralı Büyük İskender'in egemenliğiyle ve fetihleriyle birlikte Helenistik Dönem başladı"

out = pipe(sample_text)

To use a different pre - trained model you can just replace the model argument with one of the other available models we provided.

📚 Documentation

📦 Data

The dataset is provided in the data/ directory as train, validation and test splits.

The dataset can be summarized as below:

✨ Available Models

We experimented with BERT, ELECTRA and ConvBERT. Pre - trained models can be accessed via Huggingface.

• BERT: https://huggingface.co/uygarkurt/bert - restore - punctuation - turkish
• ELECTRA: https://huggingface.co/uygarkurt/electra - restore - punctuation - turkish
• ConvBERT: https://huggingface.co/uygarkurt/convbert - restore - punctuation - turkish

🔧 Results

Precision, Recall and F1 scores for each model and punctuation mark are summarized below.

📄 License

This project is under the MIT license.

🔗 Citation

@INPROCEEDINGS{10286690,
    author={Kurt, Uygar and Çayır, Aykut},
    booktitle={2023 8th International Conference on Computer Science and Engineering (UBMK)}, 
    title={Transformer Based Punctuation Restoration for Turkish}, 
    year={2023},
    volume={},
    number={},
    pages={169-174},
    doi={10.1109/UBMK59864.2023.10286690}
}