---
license: mit
language:
- tr
datasets:
- winvoker/turkish-sentiment-analysis-dataset
metrics:
- accuracy
base_model:
- answerdotai/ModernBERT-base
---

```markdown
# Turkish Sentiment Modern BERT
```
This model is a fine-tuned **ModernBERT** for **Turkish Sentiment Analysis**. It was trained on the [winvoker/turkish-sentiment-analysis-dataset](https://huggingface.co/datasets/winvoker/turkish-sentiment-analysis-dataset) and is designed to classify Turkish text into sentiment categories, such as **Positive**, **Negative**, and **Neutral**.

## Model Overview

- **Model Type**: ModernBERT (BERT variant)
- **Task**: Sentiment Analysis
- **Languages**: Turkish
- **Dataset**: [winvoker/turkish-sentiment-analysis-dataset](https://huggingface.co/datasets/winvoker/turkish-sentiment-analysis-dataset)
- **Labels**: Positive, Negative, Neutral
- **Fine-Tuning**: Fine-tuned for sentiment classification.

## Performance Metrics

The model was trained for **2 epochs** with the following results:

| Epoch | Training Loss | Validation Loss | Accuracy | F1 Score |
|-------|---------------|-----------------|-----------|-----------|
| 1 | 0.2182 | 0.1920 | 92.16% | 84.57% |
| 2 | 0.1839 | 0.1826 | 92.58% | 86.05% |

- **Training Loss**: Measures the model's fit to the training data.
- **Validation Loss**: Measures the model's generalization to unseen data.
- **Accuracy**: The percentage of correct predictions over all examples.
- **F1 Score**: A balanced metric between precision and recall.

## Model Inference Example

Here’s an example of how to use the model for sentiment analysis of Turkish text:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load the pre-trained model and tokenizer
model_name = "bayrameker/turkish-sentiment-modern-bert"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example texts for prediction
texts = ["bu ürün çok iyi", "bu ürün berbat"]

# Tokenize the inputs
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")

# Make predictions
with torch.no_grad():
logits = model(**inputs).logits

# Get the predicted sentiment labels
predictions = torch.argmax(logits, dim=-1)
labels = ["Negative", "Neutral", "Positive"] # Adjust based on your label mapping
for text, pred in zip(texts, predictions):
print(f"Text: {text} -> Sentiment: {labels[pred.item()]}")
```

### Example Output:

```
Text: bu ürün çok iyi -> Sentiment: Positive
Text: bu ürün berbat -> Sentiment: Negative
```

## Installation

To use this model, first install the required dependencies:

```bash
pip install transformers
pip install torch
pip install datasets
```

## Model Card

- **Model Name**: turkish-sentiment-modern-bert
- **Hugging Face Repo**: [Link to Model Repository](https://huggingface.co/bayrameker/turkish-sentiment-modern-bert)
- **License**: MIT (or another applicable license)
- **Author**: Bayram Eker
- **Date**: 2024-12-21

## Training Details

- **Model**: ModernBERT (Base variant)
- **Framework**: PyTorch
- **Training Time**: 34 minutes (2 epochs)
- **Batch Size**: 32
- **Learning Rate**: 8e-5
- **Optimizer**: AdamW

## Acknowledgments

- The model was trained on the [winvoker/turkish-sentiment-analysis-dataset](https://huggingface.co/datasets/winvoker/turkish-sentiment-analysis-dataset).
- Special thanks to the Hugging Face community and all contributors to the transformers library.

## Future Work

- Expand the model with more complex sentiment labels (e.g., multi-class sentiment, aspect-based sentiment analysis).
- Fine-tune the model on a larger, more diverse dataset for better generalization across various domains.

## License

This model is licensed under the MIT License. See the LICENSE file for more details.