turkish-sentiment

This model is a fine-tuned version of dbmdz/bert-base-turkish-cased on winvoker/turkish-sentiment-analysis-dataset 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

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.

• Model type: BertForSequenceClassification
• Base model: dbmdz/bert-base-turkish-cased
• Language(s): Turkish

Intended uses & limitations

• Turkish text classification tasks involving sentiment analysis.
• Suitable for social media data, product reviews, or general-purpose sentiment detection in Turkish.

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}]

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}},
}