gpt2-turkish-cased
| Property | Value |
|---|---|
| Framework Support | PyTorch, TensorFlow |
| Training Data | OSCAR Corpus (Turkish) |
| Vocabulary Size | 52K byte-level BPE |
| Downloads | 486 |
What is gpt2-turkish-cased?
gpt2-turkish-cased is a specialized GPT-2 language model trained specifically for Turkish text generation. Developed by redrussianarmy, this model represents a significant step forward in Turkish natural language processing, offering a foundation model that can be fine-tuned for various Turkish language tasks.
Implementation Details
The model was trained on Turkish texts from the OSCAR corpus using two 2080TI GPUs over five epochs. It implements a byte-level BPE tokenization strategy with a 52K vocabulary size, created using Huggingface's Tokenizers library. The training process was thoroughly monitored, with logs available on TensorBoard.
- Dual framework support (PyTorch and TensorFlow)
- Custom byte-level BPE tokenization
- Comprehensive training over five epochs
- Optimized for Turkish language understanding
Core Capabilities
- Turkish text generation
- Foundation for fine-tuning on specific Turkish NLP tasks
- Efficient tokenization of Turkish text
- Integration with Huggingface's transformers library
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Turkish language processing, using a custom byte-level BPE vocabulary that's tailored to Turkish language characteristics. It provides a rare specialized resource for Turkish NLP tasks.
Q: What are the recommended use cases?
The model is particularly suited for Turkish text generation tasks and can serve as a foundation for fine-tuning on specific applications such as content generation, text completion, or specialized Turkish language tasks. It's designed to be an entry point for further fine-tuning on domain-specific texts.
