BERT-Mini (bert_uncased_L-4_H-256_A-4)
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Author | |
| Paper | Well-Read Students Learn Better |
| GLUE Score | 65.8 |
What is bert_uncased_L-4_H-256_A-4?
BERT-Mini is a compact variant of BERT designed for environments with limited computational resources. It features 4 layers, 256 hidden dimensions, and 4 attention heads, offering a balanced trade-off between model size and performance. This model is part of Google's BERT miniatures collection, specifically optimized for efficient deployment while maintaining reasonable performance.
Implementation Details
The model follows the standard BERT architecture but with reduced parameters. It's trained using WordPiece masking on English uncased text and can be fine-tuned similar to larger BERT models. Key technical specifications include:
- 4 transformer layers (L=4)
- 256 hidden dimensions (H=256)
- 4 attention heads (A=4)
- Trained with standard BERT pre-training objectives
- Uncased tokenization
Core Capabilities
- Achieves 85.9% accuracy on SST-2
- 81.1/71.8 performance on MRPC
- 74.8/74.3 accuracy on MNLI
- Excellent for knowledge distillation applications
- Suitable for resource-constrained environments
Frequently Asked Questions
Q: What makes this model unique?
BERT-Mini provides an excellent balance between model size and performance, making it ideal for deployment in environments with limited computational resources while still maintaining reasonable accuracy across various NLP tasks.
Q: What are the recommended use cases?
This model is particularly well-suited for: 1) Edge device deployment, 2) Knowledge distillation applications where it can learn from larger teachers, 3) Rapid prototyping of NLP applications, and 4) Production environments with resource constraints.
