DistilBART-MNLI-12-9
| Property | Value |
|---|---|
| Author | valhalla |
| Model Type | Distilled Language Model |
| Architecture | 12 encoder layers, 9 decoder layers |
| Base Model | BART-large-MNLI |
| Model Link | Hugging Face |
What is distilbart-mnli-12-9?
DistilBART-MNLI-12-9 is a carefully optimized distilled version of the BART-large-MNLI model, created using the No Teacher Distillation technique. This model maintains impressive performance while reducing computational requirements through strategic layer selection. It achieves 89.56% matched accuracy and 89.52% mismatched accuracy, coming remarkably close to its larger parent model's performance.
Implementation Details
The model employs a unique distillation approach where alternating layers from the original BART-large-MNLI are selectively copied and then fine-tuned. This implementation retains 12 encoder layers and 9 decoder layers, striking an optimal balance between model size and performance.
- Utilizes No Teacher Distillation technique
- Maintains high performance with minimal accuracy drop (less than 0.4% from original)
- Efficiently preserves model capabilities through strategic layer selection
- Supports fine-tuning on MNLI tasks
Core Capabilities
- Natural Language Inference tasks
- High accuracy on both matched (89.56%) and mismatched (89.52%) datasets
- Efficient processing with reduced parameter count
- Compatible with standard fine-tuning procedures
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its ability to maintain near-original performance while reducing complexity through strategic layer selection and the No Teacher Distillation technique. It achieves this with minimal performance degradation compared to the original BART-large-MNLI model.
Q: What are the recommended use cases?
This model is particularly well-suited for Natural Language Inference tasks where computational efficiency is important. It's ideal for applications requiring high-quality inference capabilities while maintaining reasonable resource requirements.
