BERT Uncased L-12 H-768 A-12
| Property | Value |
|---|---|
| Author | |
| License | Apache-2.0 |
| Paper | Well-Read Students Learn Better |
| Architecture | 12 layers, 768 hidden size, 12 attention heads |
What is bert_uncased_L-12_H-768_A-12?
This model is part of Google's BERT miniatures collection, specifically the BERT-Base variant with 12 layers, 768 hidden dimensions, and 12 attention heads. It's trained using the standard BERT recipe on uncased English text with WordPiece masking, designed to provide effective performance while being computationally efficient.
Implementation Details
The model follows the original BERT architecture but is optimized for environments with restricted computational resources. It can be fine-tuned similarly to other BERT models and performs particularly well in knowledge distillation scenarios where it learns from larger teacher models.
- Uncased tokenization for simplified text processing
- WordPiece masking for robust training
- Compatible with standard BERT fine-tuning approaches
- Optimized for resource-constrained environments
Core Capabilities
- General language understanding and representation
- Effective performance on GLUE benchmark tasks
- Suitable for knowledge distillation applications
- Balanced trade-off between model size and performance
Frequently Asked Questions
Q: What makes this model unique?
This model represents the standard BERT-Base configuration but was retrained under the same regime as the BERT miniatures collection, making it particularly suitable for comparative studies and as a teacher model in knowledge distillation.
Q: What are the recommended use cases?
The model is well-suited for institutions with moderate computational resources, research applications requiring a standard BERT architecture, and scenarios where knowledge distillation to smaller models is desired.
