File size: 4,235 Bytes
f199601 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 |
---
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.
|