| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | Well-Read Students Learn Better |
| Author | |
| Downloads | 91,550+ |
What is bert_uncased_L-4_H-512_A-8?
This is a compact variant of BERT (BERT-Small) designed for environments with limited computational resources. It features 4 layers, 512 hidden dimensions, and 8 attention heads, offering a balanced trade-off between model size and performance. Part of Google's BERT miniatures collection, it achieves impressive results on the GLUE benchmark while maintaining efficiency.
Implementation Details
The model follows the standard BERT architecture but with reduced parameters. It's trained using WordPiece masking on uncased English text, making it particularly suitable for English language tasks. It can be fine-tuned similarly to larger BERT models but performs best when used with knowledge distillation from larger teachers.
- Architecture: 4 transformer layers
- Hidden size: 512 dimensions
- Attention heads: 8
- GLUE Score: 71.2
Core Capabilities
- Text Classification (89.7% on SST-2)
- Paraphrase Detection (83.4/76.2 on MRPC)
- Natural Language Inference (77.6/77.0 on MNLI)
- Question Answering (86.4 on QNLI)
Frequently Asked Questions
Q: What makes this model unique?
This model represents an optimal balance between model size and performance, particularly suitable for production environments where computational resources are constrained but reasonable performance is required.
Q: What are the recommended use cases?
It's ideal for resource-constrained environments and works best in knowledge distillation scenarios where it can learn from larger models. It's particularly effective for basic NLP tasks like classification and inference where state-of-the-art performance isn't critical.