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README.md

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+---
+language: tr
+tags:
+- sentiment-analysis
+- turkish
+- bert
+- text-classification
+- fine-tuned
+license: apache-2.0
+base_model: codealchemist01/turkish-sentiment-analysis
+datasets:
+- winvoker/turkish-sentiment-analysis-dataset
+- WhiteAngelss/Turkce-Duygu-Analizi-Dataset
+- maydogan/Turkish_SentimentAnalysis_TRSAv1
+- turkish-nlp-suite/MusteriYorumlari
+- W4nkel/turkish-sentiment-dataset
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+---
+
+# Turkish Sentiment Analysis Model (Fine-tuned)
+
+A fine-tuned version of the [codealchemist01/turkish-sentiment-analysis](https://huggingface.co/codealchemist01/turkish-sentiment-analysis) model, improved with additional balanced training data to enhance neutral and negative class performance.
+
+## Model Details
+
+- **Base Model:** [codealchemist01/turkish-sentiment-analysis](https://huggingface.co/codealchemist01/turkish-sentiment-analysis)
+- **Task:** Text Classification (Sentiment Analysis)
+- **Language:** Turkish
+- **Labels:** positive, negative, neutral
+- **Fine-tuning Type:** Continued fine-tuning on balanced dataset
+
+## Training Data
+
+This model was fine-tuned on a balanced combination of the original dataset and additional Turkish sentiment datasets:
+
+### Original Dataset (from base model):
+- `winvoker/turkish-sentiment-analysis-dataset` (440,641 samples)
+- `WhiteAngelss/Turkce-Duygu-Analizi-Dataset` (440,641 samples)
+
+### Additional Datasets for Fine-tuning:
+- `maydogan/Turkish_SentimentAnalysis_TRSAv1` (150,000 samples)
+- `turkish-nlp-suite/MusteriYorumlari` (73,920 samples)
+- `W4nkel/turkish-sentiment-dataset` (4,800 samples)
+- `mustfkeskin/turkish-movie-sentiment-analysis-dataset` (Kaggle, 83,227 samples)
+
+### Final Balanced Dataset:
+- **Total:** 556,888 samples
+- **Positive:** 237,966 (42.7%)
+- **Neutral:** 209,668 (37.6%)
+- **Negative:** 109,254 (19.6%)
+
+**Split Distribution:**
+- **Training:** 445,510 samples
+- **Validation:** 55,689 samples
+- **Test:** 55,689 samples
+
+## Training
+
+### Fine-tuning Parameters:
+- **Base Model:** codealchemist01/turkish-sentiment-analysis
+- **Epochs:** 2
+- **Learning Rate:** 1e-5 (lower than initial training for fine-tuning)
+- **Batch Size:** 12 (per device)
+- **Gradient Accumulation:** 2 (effective batch size: 24)
+- **Max Length:** 128 tokens
+- **Optimizer:** AdamW
+- **Mixed Precision (FP16):** Enabled
+
+## Performance
+
+### Test Set Results (55,689 samples):
+
+**Overall Metrics:**
+- **Accuracy:** 91.96%
+- **Weighted F1:** 91.93%
+- **Weighted Precision:** 91.93%
+- **Weighted Recall:** 91.96%
+
+### Per-Class Performance:
+
+| Class    | Precision | Recall | F1-Score | Support |
+|----------|-----------|--------|----------|---------|
+| Negative | 90.65%    | 86.79% | 88.68%   | 10,926  |
+| Neutral  | 90.91%    | 90.24% | 90.57%   | 20,967  |
+| Positive | 93.41%    | 95.84% | 94.61%   | 23,796  |
+
+## Improvements Over Base Model
+
+### Key Improvements:
+1. **Neutral Class Performance:**
+   - Better recognition of neutral expressions
+   - Improved handling of ambiguous texts
+   - Neutral F1-score: **90.57%** (improved from base model's test performance)
+
+2. **Better Class Balance:**
+   - More balanced dataset (reduced class imbalance)
+   - Negative class improved with more training examples
+   - Neutral class significantly enhanced
+
+3. **General Performance:**
+   - Maintained high accuracy (91.96%)
+   - Improved F1-scores across all classes
+   - Better generalization on diverse Turkish texts
+
+### Test Results Comparison (15 sample test):
+- **Base Model Accuracy:** 66.7% (10/15)
+- **Fine-tuned Model Accuracy:** 86.7% (13/15)
+- **Improvement:** +20.0%
+
+### Per-Class Test Results:
+- **Neutral:** 0% → 80% (+80.0% improvement)
+- **Negative:** 100% → 80% (slight decrease, but more balanced)
+- **Positive:** 100% → 100% (maintained)
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "codealchemist01/turkish-sentiment-analysis-finetuned"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example text
+text = "Bu ürün normal, beklediğim gibi. Özel bir şey yok."
+
+# Tokenize
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
+
+# Predict
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_label_id = predictions.argmax().item()
+
+# Map to label
+id2label = {0: "negative", 1: "neutral", 2: "positive"}
+predicted_label = id2label[predicted_label_id]
+confidence = predictions[0][predicted_label_id].item()
+
+print(f"Label: {predicted_label}")
+print(f"Confidence: {confidence:.4f}")
+```
+
+## Limitations
+
+- The model may not perform well on very short texts (< 3 words)
+- Performance may vary across different domains (social media, news, reviews)
+- Some ambiguous neutral expressions may still be misclassified
+- Negative class performance may vary on different text types
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{turkish-sentiment-analysis-finetuned,
+  title={Turkish Sentiment Analysis Model (Fine-tuned)},
+  author={codealchemist01},
+  year={2024},
+  base_model={codealchemist01/turkish-sentiment-analysis},
+  howpublished={\url{https://huggingface.co/codealchemist01/turkish-sentiment-analysis-finetuned}}
+}
+```
+
+## License
+
+Apache 2.0
+