# 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 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
- 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:
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:
pip install transformers
pip install torch
pip install datasets
Model Card
- Model Name: turkish-sentiment-modern-bert
- Hugging Face Repo: Link to Model Repository
- 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.
- 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.
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