license: mit
language:
- tr
tags:
- punctuation restoration
- punctuation prediction
widget:
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
Transformer Based Punctuation Restoration Models for Turkish
Liked our work? give us a ⭐ on GitHub!
You can find the BERT model used in the paper Transformer Based Punctuation Restoration for Turkish. Aim of this work is 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.
Usage
Inference
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.
Data
Dataset is provided in data/ directory as train, validation and test splits.
Dataset can be summarized as below:
| Split | Total | Period (.) | Comma (,) | Question (?) |
|---|---|---|---|---|
| Train | 1471806 | 124817 | 98194 | 9816 |
| Validation | 180326 | 15306 | 11980 | 1199 |
| Test | 182487 | 15524 | 12242 | 1255 |
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 and Recall and F1 scores for each model and punctuation mark are summarized below.
| Model | PERIOD | COMMA | QUESTION | OVERALL | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Score Type | P | R | F1 | P | R | F1 | P | R | F1 | P | R | F1 |
| BERT | 0.972602 | 0.947504 | 0.959952 | 0.576145 | 0.700010 | 0.632066 | 0.927642 | 0.911342 | 0.919420 | 0.825506 | 0.852952 | 0.837146 |
| ELECTRA | 0.972602 | 0.948689 | 0.960497 | 0.576800 | 0.710208 | 0.636590 | 0.920325 | 0.921074 | 0.920699 | 0.823242 | 0.859990 | 0.839262 |
| ConvBERT | 0.972731 | 0.946791 | 0.959585 | 0.576964 | 0.708124 | 0.635851 | 0.922764 | 0.913849 | 0.918285 | 0.824153 | 0.856254 | 0.837907 |