Interpress Turkish News Classification
| Property | Value |
|---|---|
| Author | serdarakyol |
| Model Type | Text Classification |
| Accuracy | 97% |
| Dataset Size | 108K samples |
What is interpress-turkish-news-classification?
This is a specialized Turkish news classification model trained on real-world data from Interpress. The model can categorize Turkish news articles into 10 distinct categories including Culture-Art, Economy, Politics, Education, World, Sport, Technology, Magazine, Health, and Agenda. Built using transformer architecture, it demonstrates impressive accuracy of 97% on both training and validation datasets.
Implementation Details
The model utilizes the transformers library and can be implemented using either PyTorch or TensorFlow frameworks. It processes input text with a maximum length of 512 tokens and includes special attention mask handling for optimal performance. The dataset was carefully curated from an original 273K samples to 108K for training, with an 80-20 train-validation split.
- Supports both PyTorch and TensorFlow implementations
- Includes pre-trained tokenizer and model weights
- Handles GPU acceleration automatically when available
- Provides straightforward prediction interface
Core Capabilities
- Multi-class classification across 10 news categories
- Real-time news article classification
- High accuracy on Turkish language content
- Efficient processing with batch support
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in Turkish news classification with exceptional accuracy and is trained on real-world data from a professional news service, making it particularly reliable for practical applications in Turkish news categorization.
Q: What are the recommended use cases?
The model is ideal for automated news categorization systems, content aggregation platforms, media monitoring services, and research applications focusing on Turkish news content analysis. It can be integrated into both PyTorch and TensorFlow workflows.
