convbert-base-turkish-mc4-cased
| Property | Value |
|---|---|
| Parameter Count | 107M |
| License | MIT |
| Training Data | mC4 Turkish Corpus |
| Developed By | DBMDZ |
What is convbert-base-turkish-mc4-cased?
This is a Turkish language ConvBERT model trained on the massive multilingual C4 (mC4) corpus. The model was trained on 242GB of Turkish text, processing over 31 billion tokens, making it one of the most comprehensive Turkish language models available. It utilizes a 32k vocabulary and maintains case sensitivity for better linguistic precision.
Implementation Details
The model was trained for 1M steps on a v3-32 TPU using a sequence length of 512. It implements the ConvBERT architecture, which combines conventional BERT architecture with convolution operations for improved efficiency and performance.
- Trained on filtered mC4 Turkish corpus (242GB)
- 31,240,963,926 tokens processed
- Original 32k vocabulary preserved
- Full sequence length of 512 tokens
Core Capabilities
- Masked Language Modeling
- Turkish text understanding and generation
- Support for both PyTorch and TensorFlow frameworks
- Optimized for downstream NLP tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its extensive training on clean Turkish text data and its use of the ConvBERT architecture, which provides better efficiency than traditional BERT models while maintaining high performance.
Q: What are the recommended use cases?
The model is ideal for Turkish language processing tasks including text classification, named entity recognition, and masked language modeling. It's particularly suitable for applications requiring deep understanding of Turkish language context.
