BERT-Tiny_L-2_H-128_A-2
| Property | Value |
|---|---|
| Model Type | BERT |
| Architecture | 2 layers, 128 hidden units, 2 attention heads |
| Author | nreimers |
| Source | Google Research |
What is BERT-Tiny_L-2_H-128_A-2?
BERT-Tiny is a highly compressed version of the original BERT model, designed for scenarios where computational resources are limited. This implementation features just 2 transformer layers, 128 hidden units, and 2 attention heads, making it significantly lighter than its larger counterparts while maintaining the core BERT architecture.
Implementation Details
The model is based on Google's BERT architecture but dramatically reduced in size. It maintains the fundamental transformer-based architecture while scaling down all key parameters for efficiency.
- 2 transformer layers (compared to 12/24 in larger versions)
- 128 hidden unit size for dense representations
- 2 attention heads for processing contextual relationships
- Efficient parameter footprint for resource-constrained environments
Core Capabilities
- Basic language understanding tasks
- Lightweight text processing
- Efficient inference on edge devices
- Foundation for transfer learning in resource-limited scenarios
Frequently Asked Questions
Q: What makes this model unique?
BERT-Tiny stands out for its extremely compact architecture while retaining the core BERT methodology. It's specifically designed for scenarios where model size and computational efficiency are crucial considerations.
Q: What are the recommended use cases?
This model is ideal for edge devices, mobile applications, or scenarios where quick inference is required with limited computational resources. It's suitable for basic NLP tasks where a full-sized BERT model would be overkill.
