Create README.md

README.md

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+---
+datasets:
+- maydogan/Turkish_SentimentAnalysis_TRSAv1
+language:
+- tr
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model:
+- dbmdz/bert-base-turkish-128k-cased
+pipeline_tag: text-classification
+tags:
+- Turkish Sentiment Analysis
+---
+# 🇹🇷 BERTurk for Turkish Sentiment Analysis
+This model is a fine-tuned version of [BERTurk 32k](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the [TRSAv1 dataset](https://huggingface.co/maydogan/Turkish_SentimentAnalysis_TRSAv1), a labeled collection of Turkish e-commerce reviews categorized into positive, neutral, and negative sentiments. For more details about the dataset, methodology, and experiments, you can refer to the corresponding [research paper](https://dergipark.org.tr/en/pub/ejt/issue/92270/1592448).
+
+---
+## How to Use
+
+You can use the model directly with 🤗 Transformers:
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="incidelen/bert-base-turkish-sentiment-analysis-cased")
+result = classifier("Ürün çok kaliteli, paketleme harikaydı. Kesinlikle tavsiye ederim!")
+print(result)
+```
+---
+## Citation
+If you use this model in your research or application, please cite the following paper:
+
+```
+@article{incidelen15sentiment,
+  title={Sentiment Analysis in Turkish Using Language Models: A Comparative Study},
+  author={{\.I}ncidelen, Mert and Aydo{\u{g}}an, Murat},
+  journal={European Journal of Technique (EJT)},
+  volume={15},
+  number={1},
+  pages={68--74},
+  publisher={Hibetullah KILI{\c{C}}}
+}
+```
+---
+## Dataset Overview
+The [TRSAv1 dataset](https://huggingface.co/maydogan/Turkish_SentimentAnalysis_TRSAv1) includes 150,000 Turkish product reviews from e-commerce platforms. It is balanced across three sentiment classes:
+
+| Sentiment    | Count                 |
+|--------------|-----------------------|
+| Negative     | 50,000                |
+| Neutral      | 50,000                |
+| Positive     | 50,000                |
+| TOTAL        | 150,000               |
+---
+## Evaluation Results
+### Overall Performance
+| Accuracy (%)    | Precision (%)    | Recall (%)      | F1 Score (%)      |
+|-----------------|------------------|-----------------|-------------------|
+| 83.68           | 83.69            | 83.68           | 83.66             |
+### Class-wise Performance
+| Sentiment       | Precision (%)    | Recall (%)      | F1 Score (%)      |
+|-----------------|------------------|-----------------|-------------------|
+| Negative        | 88.35            | 85.20           | 86.74             |
+| Neutral         | 77.01            | 76.45           | 76.73             |
+| Positive        | 85.70            | 89.38           | 87.50             |
+---
+## Acknowledgments
+Special thanks to [maydogan](https://huggingface.co/maydogan) for their contributions and support.
+---