Update README.md

README.md

@@ -1,3 +1,121 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- winvoker/turkish-sentiment-analysis-dataset
+language:
+- tr
+base_model:
+- answerdotai/ModernBERT-large
+---
+
+Here's an updated **Model Card** in a **README format** based on the training results and the model you've used (ModernBERT-large for Turkish sentiment analysis):
+
+```markdown
+# Turkish Sentiment ModernBERT-large
+```
+This is a fine-tuned **ModernBERT-large** model for **Turkish Sentiment Analysis**. The model was trained on the `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 **4 epochs** with the following results:
+
+| Epoch | Training Loss | Validation Loss | Accuracy | F1 Score |
+|-------|---------------|-----------------|----------|----------|
+| 1     | 0.2884        | 0.1133          | 95.72%   | 92.18%   |
+| 2     | 0.1759        | 0.1050          | 96.24%   | 93.33%   |
+| 3     | 0.0633        | 0.1233          | 96.14%   | 93.19%   |
+| 4     | 0.0623        | 0.1213          | 96.14%   | 93.19%   |
+
+- **Training Loss**: Measures how well the model fits the training data.
+- **Validation Loss**: Measures how well the model generalizes to unseen data.
+- **Accuracy**: Percentage of correct predictions over all examples.
+- **F1 Score**: A balanced metric between precision and recall, accounting for both false positives and false negatives.
+
+## Model Inference Example
+
+You can use this model for sentiment analysis of Turkish text. Here’s an example of how to use it:
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load the pre-trained model and tokenizer
+model_name = "bayrameker/Turkish-sentiment-ModernBERT-large"
+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, install the following dependencies:
+
+```bash
+pip install transformers
+pip install torch
+pip install datasets
+```
+
+## Model Card
+
+- **Model Name**: Turkish-sentiment-ModernBERT-large
+- **Hugging Face Repo**: [Link to Model Repository](https://huggingface.co/bayrameker/Turkish-sentiment-ModernBERT-large)
+- **License**: MIT (or any applicable license you choose)
+- **Author**: Bayram Eker
+- **Date**: 2024-12-21
+
+## Training Details
+
+- **Model**: ModernBERT-large
+- **Framework**: PyTorch
+- **Training Time**: Approximately 50 minutes (4 epochs)
+- **Batch Size**: 64
+- **Learning Rate**: 8e-5
+- **Optimizer**: AdamW
+- **Mixed Precision**: bf16 for A100 GPU
+
+## Acknowledgments
+
+- The model was trained on the `winvoker/turkish-sentiment-analysis-dataset` dataset.
+- Special thanks to the Hugging Face community and the contributors to the transformers library.
+- Thanks to all contributors of the dataset and pretrained models.
+
+## Future Work
+
+- Expand the model with more complex sentiment labels (e.g., multi-class sentiments, 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.