DistilBART-MNLI-12-3
| Property | Value |
|---|---|
| Author | valhalla |
| Downloads | 22,963 |
| Task | Zero-Shot Classification |
| Framework | PyTorch, JAX |
What is distilbart-mnli-12-3?
DistilBART-MNLI-12-3 is a distilled version of the BART-large-MNLI model, created using the No Teacher Distillation technique. It maintains 12 encoder layers but reduces the decoder layers to 3, achieving an impressive 88.1% matched accuracy and 88.19% mismatched accuracy on the MNLI dataset, while being more efficient than its parent model.
Implementation Details
The model is implemented using a novel distillation approach where alternating layers from BART-large-MNLI are copied and then fine-tuned on the same data. This technique provides an excellent balance between model performance and efficiency.
- Uses 12 encoder layers and 3 decoder layers
- Achieves near-parent model performance with reduced parameters
- Implemented in both PyTorch and JAX frameworks
Core Capabilities
- Zero-shot text classification
- Natural language inference tasks
- Efficient processing with reduced parameter count
- Maintains 88.1% matched accuracy on MNLI dataset
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient distillation technique that maintains high performance while reducing model size. It achieves only a 1.8% drop in accuracy compared to the original BART-large-MNLI while being significantly more efficient.
Q: What are the recommended use cases?
The model is ideal for zero-shot classification tasks, especially when computational efficiency is important. It's particularly well-suited for natural language inference and text classification scenarios where full BART-large-MNLI might be computationally expensive.
