Summary of Scaling Bert Models For Turkish Automatic Punctuation and Capitalization Correction, by Abdulkader Saoud et al.
Scaling BERT Models for Turkish Automatic Punctuation and Capitalization Correction
by Abdulkader Saoud, Mahmut Alomeyr, Himmet Toprak Kesgin, Mehmet Fatih Amasyali
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the performance of BERT-based models for automated punctuation and capitalization corrections in Turkish texts. The models are categorized into five sizes: Tiny, Mini, Small, Medium, and Base. Each model is designed to tackle the unique challenges of the Turkish language while minimizing computational overhead. The study compares the precision, recall, and F1 score of each model, providing insights into their suitability for various operational contexts. As model size increases, text readability and accuracy improve, with the Base model achieving the highest correction precision. This research provides a comprehensive guide for selecting an appropriate model size based on specific user needs and computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well BERT-based models can correct punctuation and capitalization mistakes in Turkish texts. The researchers create five different-sized models that are designed to work with the unique features of the Turkish language. They test each model and compare its performance using metrics like precision, recall, and F1 score. They find that as the models get bigger, they get better at correcting mistakes and making text more readable. This research helps people decide which model size is best for their needs and computer power. |
Keywords
» Artificial intelligence » Bert » F1 score » Precision » Recall
