DistilBERT ONNX
| Property | Value |
|---|---|
| Author | philschmid |
| Model Type | Question Answering |
| Base Architecture | DistilBERT-base-cased |
| Performance | 87.1 F1 Score on SQuAD v1.1 |
| Model URL | Hugging Face |
What is distilbert-onnx?
DistilBERT ONNX is an optimized version of the DistilBERT base cased model that has been specifically fine-tuned for question answering tasks using the SQuAD v1.1 dataset. This model represents a significant achievement in model optimization, utilizing knowledge distillation to maintain high performance while reducing computational requirements.
Implementation Details
The model is implemented as an ONNX conversion of the original DistilBERT architecture, allowing for efficient deployment across different platforms and hardware. It achieves an impressive F1 score of 87.1 on the SQuAD dev set, coming close to its larger BERT counterpart (which achieves 88.7), while being more efficient.
- ONNX-optimized architecture for improved deployment efficiency
- Based on DistilBERT-base-cased architecture
- Fine-tuned using knowledge distillation on SQuAD v1.1
- Maintains high performance with reduced computational overhead
Core Capabilities
- Efficient question answering on general domain text
- High-performance text comprehension and information extraction
- Cross-platform compatibility through ONNX format
- Reduced model size while maintaining near-BERT level performance
Frequently Asked Questions
Q: What makes this model unique?
This model combines the efficiency of DistilBERT with ONNX optimization, making it particularly suitable for production deployments while maintaining strong performance (87.1 F1 score) on question answering tasks.
Q: What are the recommended use cases?
The model is ideal for applications requiring efficient question answering capabilities, particularly in production environments where deployment efficiency and cross-platform compatibility are important considerations.
