bert-tiny
| Property | Value |
|---|---|
| Architecture | BERT (L=2, H=128) |
| License | MIT |
| Downloads | 1,043,787 |
| Primary Paper | Well-Read Students Learn Better |
What is bert-tiny?
bert-tiny is a highly compressed variant of BERT, designed as part of a family of compact models for efficient natural language understanding. Developed by converting the original Google BERT TensorFlow checkpoint to PyTorch, this model features just 2 layers and a hidden size of 128, making it significantly smaller than standard BERT implementations.
Implementation Details
The model represents a minimal BERT architecture that maintains core transformer capabilities while drastically reducing the parameter count. It was introduced in the study "Well-Read Students Learn Better" and further validated in "Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics".
- Lightweight architecture with 2 transformer layers
- Hidden size of 128 dimensions
- PyTorch implementation
- Optimized for downstream task fine-tuning
Core Capabilities
- Natural Language Inference (NLI) tasks
- Efficient pre-training and transfer learning
- Resource-conscious deployment scenarios
- Quick inference with minimal computational overhead
Frequently Asked Questions
Q: What makes this model unique?
bert-tiny stands out for being one of the smallest pre-trained BERT variants available, offering a balance between model size and performance. Its minimal architecture makes it particularly suitable for resource-constrained environments and rapid prototyping.
Q: What are the recommended use cases?
The model is best suited for scenarios requiring quick inference on NLI tasks, educational applications where computational resources are limited, and as a starting point for fine-tuning on specific downstream tasks where a full-sized BERT model might be overkill.
