ALBERT Base v1
| Property | Value |
|---|---|
| Parameter Count | 11M parameters |
| License | Apache 2.0 |
| Paper | arXiv:1909.11942 |
| Training Data | BookCorpus & Wikipedia |
| Architecture | 12 repeating layers, 128 embedding dim, 768 hidden dim, 12 attention heads |
What is albert-base-v1?
ALBERT Base v1 is a lightweight variant of BERT that introduces parameter reduction techniques to lower memory consumption while maintaining good performance. It's particularly notable for its architecture that shares parameters across layers, resulting in a significantly smaller model size of just 11M parameters.
Implementation Details
The model utilizes an innovative approach to transformer architecture, featuring 12 repeating layers with shared parameters, 128-dimensional embeddings that are projected to a 768-dimensional space, and 12 attention heads. It was trained on BookCorpus and English Wikipedia using two primary objectives: Masked Language Modeling (MLM) and Sentence Ordering Prediction (SOP).
- Parameter sharing across layers for reduced memory footprint
- Cross-layer parameter sharing
- Factorized embedding parameterization
- SOP loss instead of traditional Next Sentence Prediction
Core Capabilities
- Masked language modeling with 15% token masking
- Sentence ordering prediction
- Feature extraction for downstream tasks
- Bidirectional context understanding
- Support for 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, which dramatically reduces model size while maintaining performance. This version 1 model represents the first iteration of this architecture, making it particularly suitable for resource-constrained environments.
Q: What are the recommended use cases?
The model is best suited for sequence classification, token classification, and question answering tasks. It's designed for tasks that benefit from bidirectional context understanding, though it's not recommended for text generation tasks where models like GPT-2 would be more appropriate.
