| Property | Value |
|---|---|
| Parameter Count | 111M parameters |
| Tensor Type | F32 |
| Research Paper | arXiv:2401.17396 |
| Author | savasy |
What is bert-base-turkish-squad?
bert-base-turkish-squad is a specialized question-answering model fine-tuned on the Turkish version of SQuAD (TQuAD). Built upon the dbmdz/bert-base-turkish-uncased architecture, this model is specifically optimized for Turkish language understanding and question-answering tasks.
Implementation Details
The model was trained using a fine-tuning approach with specific hyperparameters including a learning rate of 3e-5, batch size of 12, and 5 training epochs. It utilizes a maximum sequence length of 384 tokens and a document stride of 128, making it efficient for processing longer Turkish texts.
- Based on BERT architecture fine-tuned for Turkish language
- Trained on TQuAD dataset for question-answering capabilities
- Supports both PyTorch and JAX frameworks
- Implements Safetensors for improved security and performance
Core Capabilities
- Turkish language question-answering
- Context-aware answer extraction
- Support for long-form text analysis
- Integration with Hugging Face's transformers library
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Turkish language question-answering tasks, making it one of the few specialized models in this domain. It combines the power of BERT architecture with Turkish language understanding capabilities.
Q: What are the recommended use cases?
The model is ideal for applications requiring Turkish language question-answering capabilities, such as chatbots, information extraction systems, and automated customer service solutions. It's particularly effective for extracting specific information from longer Turkish texts.