Turkish Sentiment

🚀 Turkish Sentiment Analysis Model

This model is a fine - tuned version of the dbmdz Turkish BERT, designed to classify Turkish text into sentiment categories. It offers high - accuracy results on Turkish sentiment analysis tasks, making it suitable for various applications such as social media data analysis and product reviews.

🚀 Quick Start

This model is a fine - tuned version of [dbmdz/bert - base - turkish - cased](https://huggingface.co/dbmdz/bert - base - turkish - cased) on the winvoker/turkish - sentiment - analysis - dataset. It achieves the following results on the evaluation set:

• Loss: 0.0880
• Accuracy: 0.9688
• F1 Macro: 0.9454
• F1 Weighted: 0.9685
• Precision: 0.9683
• Recall: 0.9688

✨ Features

• High - Performance Sentiment Classification: Capable of accurately classifying Turkish text into three sentiment classes: Negative, Notr (Neutral), and Positive.
• Large - Scale Training: Fine - tuned on a large - scale Turkish sentiment analysis dataset, ensuring broad applicability.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

# Use a pipeline as a high - level helper
from transformers import pipeline

pipe = pipeline("text - classification", model="kaixkhazaki/turkish - sentiment")


pipe("Kargo geç geldi ve ürün beklentimi pek karşılamadı.")
>> [{'label': 'Negative', 'score': 0.984860897064209}]

pipe("Yemek lezzetliydi ancak servis yavaş ve çalışanlar ilgisizdi, pek anlayamadım nasıl hissettiğimi.")
>> [{'label': 'Notr', 'score': 0.9881975054740906}]

pipe("Gerçekten müthiş bir deneyimdi, keşke hep burda kalabilsem.")
>> [{'label': 'Positive', 'score': 0.9942901134490967}]

📚 Documentation

Model description

A BERT - based (dbmdz Turkish BERT) model fine - tuned on a large - scale Turkish sentiment analysis dataset. This model classifies Turkish text into three sentiment classes: Negative, Notr (Neutral), and Positive.

Intended uses & limitations

• Intended uses: Turkish text classification tasks involving sentiment analysis. Suitable for social media data, product reviews, or general - purpose sentiment detection in Turkish.
• Limitations: Not specified in the original document.

Training and evaluation data

Fine - tuned on a combined dataset with 440,679 training samples and 48,965 validation samples.

Training procedure

Trained on using the entire dataset on a single GPU for apx. 25 mins (1600 steps).

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e - 05
• train_batch_size: 64
• eval_batch_size: 128
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon = 1e - 08 and optimizer_args = No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 400
• training_steps: 1600

Training results

Framework versions

• Transformers 4.48.0.dev0
• Pytorch 2.4.1+cu121
• Datasets 3.1.0
• Tokenizers 0.21.0

Citation

@misc{turkish - sentiment,
  title={Turkish Sentiment Analysis using Turkish BERT},
  author={Fatih Demrici},
  year={2025},
  howpublished={\url{https://huggingface.co/kaixkhazaki/turkish - sentiment}},
}

📄 License

This model is released under the MIT license.