MoritzLaurer-roberta-base-zeroshot-v2.0-c-onnx
| Property | Value |
|---|---|
| Original Author | MoritzLaurer |
| ONNX Conversion | ProtectAI |
| Model Hub | Hugging Face |
What is MoritzLaurer-roberta-base-zeroshot-v2.0-c-onnx?
This is an ONNX-optimized version of the RoBERTa-based zero-shot classification model, specifically converted from the original Hugging Face implementation. The conversion was performed using the Optimum library, making it more efficient for production deployment and inference.
Implementation Details
The model represents a specialized conversion of the base RoBERTa architecture, optimized for zero-shot classification tasks. The ONNX format enables faster inference and broader deployment options across different platforms and hardware.
- ONNX-optimized architecture for improved performance
- Built on RoBERTa base model architecture
- Converted using Hugging Face Optimum library
- Designed for zero-shot classification tasks
Core Capabilities
- Zero-shot text classification
- Efficient inference through ONNX optimization
- Cross-platform compatibility
- Production-ready deployment support
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its ONNX optimization of the original RoBERTa zero-shot classifier, offering improved inference performance while maintaining the original model's capabilities.
Q: What are the recommended use cases?
The model is ideal for production environments requiring efficient zero-shot text classification, especially where cross-platform compatibility and optimized inference are priorities.
