Omar1010/bert-turkish-sentiment

BERTurk Turkish Sentiment Analysis

A fine-tuned version of dbmdz/bert-base-turkish-cased (BERTurk) for sentiment classification of Turkish text. Given a Turkish sentence, it predicts whether the sentiment is Positive, Negative, or Notr (neutral).

Results

Evaluated on the held-out test split of the winvoker/turkish-sentiment-analysis-dataset:

Metric	Score
Accuracy	0.9669
Macro F1	0.9414

Usage

from transformers import pipeline

clf = pipeline("text-classification", model="Omar1010/bert-turkish-sentiment")
print(clf("Bu ürün gerçekten harika, çok memnun kaldım."))

Labels

• Positive — olumlu
• Negative — olumsuz
• Notr — nötr / neutral

Training

• Base model: BERTurk (dbmdz/bert-base-turkish-cased)
• Dataset: winvoker/turkish-sentiment-analysis-dataset (subsampled to 60,000 train / 8,000 test)
• Epochs: 2, learning rate: 2e-5, max length: 128, fp16

Limitations

Trained mainly on product-review-style Turkish text, so it performs best on similar language. It may be less reliable on sarcasm, very informal slang, or highly domain-specific text.