ALBERT Base v2
| Property | Value |
|---|---|
| Parameter Count | 11.8M |
| License | Apache 2.0 |
| Paper | View Paper |
| Training Data | BookCorpus + Wikipedia |
What is albert-base-v2?
ALBERT Base v2 is a lightweight variant of BERT that introduces parameter reduction techniques while maintaining strong performance. It's the second iteration of the base model, featuring improved dropout rates and extended training time. The model employs a unique architecture with 12 repeating layers, making it memory-efficient while preserving computational capabilities.
Implementation Details
The model utilizes a distinctive architecture with parameter sharing across layers:
- 128 embedding dimension
- 768 hidden dimension
- 12 attention heads
- 12 repeating layers with shared parameters
- Trained using Masked Language Modeling (MLM) and Sentence Ordering Prediction (SOP)
Core Capabilities
- Masked language modeling for bidirectional context understanding
- Sentence ordering prediction for improved text coherence
- Efficient parameter usage through layer sharing
- Suitable for fine-tuning on downstream tasks
- Supports both PyTorch and TensorFlow implementations
Frequently Asked Questions
Q: What makes this model unique?
ALBERT's key innovation is its parameter-sharing mechanism across layers, resulting in a significantly smaller model (11.8M parameters) while maintaining performance comparable to larger models. Version 2 specifically improves upon the original with better dropout rates and extended training.
Q: What are the recommended use cases?
The model excels in sequence classification, token classification, and question answering tasks. It's particularly effective for tasks requiring whole-sentence understanding. However, for text generation tasks, models like GPT-2 would be more appropriate.
