bert_turkish_sentiment
| Property | Value |
|---|---|
| Parameter Count | 163M |
| License | MIT |
| Base Model | VRLLab/TurkishBERTweet |
| Accuracy | 99.72% |
| Tensor Type | F32 |
What is bert_turkish_sentiment?
bert_turkish_sentiment is a sophisticated Turkish language sentiment analysis model based on the BERT architecture. Fine-tuned on the TurkishBERTweet foundation, this model demonstrates exceptional performance with a remarkable 99.72% accuracy in sentiment classification tasks. With 163M parameters, it represents a substantial yet efficient implementation for Turkish natural language processing.
Implementation Details
The model was trained using carefully selected hyperparameters, including a learning rate of 1e-05 and Adam optimizer with betas=(0.9,0.999). The training process spanned 3 epochs with batch sizes of 8 for both training and evaluation, showing consistent improvement in performance from 99.26% to the final 99.72% accuracy.
- Linear learning rate scheduler implementation
- Optimized with Adam optimizer
- Trained over 1320 steps
- Validation loss improved from 0.0516 to 0.0155
Core Capabilities
- High-accuracy Turkish sentiment analysis
- Efficient F32 tensor operations
- Support for TensorBoard integration
- Compatible with Transformers framework
- Inference endpoint availability
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional accuracy in Turkish sentiment analysis, achieving 99.72% accuracy through careful fine-tuning of the TurkishBERTweet base model. Its optimization for Turkish language processing makes it particularly valuable for local applications.
Q: What are the recommended use cases?
The model is ideal for Turkish text sentiment analysis tasks, including social media monitoring, customer feedback analysis, and automated content moderation for Turkish language content. Its high accuracy makes it suitable for production environments requiring reliable sentiment classification.
