deberta-v3-xsmall-zeroshot-v1.1-all-33
| Property | Value |
|---|---|
| Parameter Count | 70.8M |
| License | MIT |
| Paper | Research Paper |
| Base Model | microsoft/deberta-v3-xsmall |
| Model Size | 142 MB |
What is deberta-v3-xsmall-zeroshot-v1.1-all-33?
This is a highly efficient zero-shot classification model based on DeBERTa-v3-xsmall architecture. It features 22 million backbone parameters and 128 million vocabulary parameters, specifically optimized for edge devices and browser-based applications using transformers.js. The model represents a careful balance between performance and efficiency, making it particularly suitable for resource-constrained environments.
Implementation Details
The model utilizes the DeBERTa-v3-xsmall architecture as its foundation and has been fine-tuned using an advanced pipeline detailed in the associated research paper. It employs FP16 tensor precision to optimize memory usage and processing speed. During inference, primarily the backbone parameters are active, which significantly reduces computational overhead compared to larger models.
- Compact size of 142 MB for easy deployment
- FP16 precision for optimal performance
- Efficient architecture with separate backbone and vocabulary parameters
- Optimized for transformers.js implementation
Core Capabilities
- Zero-shot classification across multiple domains
- High-speed inference (1500+ texts/sec on A10G)
- Strong performance on sentiment analysis (94%+ accuracy on Amazon Polarity)
- Effective handling of hate speech and toxicity detection
- Robust performance on news classification and topic categorization
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional balance between size and performance, specifically designed for edge computing and browser-based applications. Its small footprint (142 MB) and efficient architecture make it ideal for deployment in resource-constrained environments while maintaining reasonable accuracy across various tasks.
Q: What are the recommended use cases?
The model is particularly well-suited for: 1) Edge device deployments requiring zero-shot classification, 2) Browser-based applications using transformers.js, 3) Real-time classification tasks with resource constraints, 4) Applications requiring quick inference without compromising too much on accuracy.
