ALBERT Large v2
| Property | Value |
|---|---|
| Parameter Count | 17M |
| License | Apache 2.0 |
| Paper | View Paper |
| Architecture | 24 repeating layers, 16 attention heads |
| Training Data | BookCorpus + Wikipedia |
What is albert-large-v2?
ALBERT Large v2 is an efficient transformer-based language model that introduces parameter reduction techniques while maintaining strong performance. This second version features improved dropout rates, additional training data, and longer training periods compared to its predecessor.
Implementation Details
The model implements a unique architecture with 24 repeating layers, 128 embedding dimension, 1024 hidden dimension, and 16 attention heads. It uses parameter sharing across layers to achieve a compact 17M parameter footprint while maintaining computational capabilities similar to larger BERT models.
- Employs masked language modeling (MLM) and sentence ordering prediction (SOP)
- Uses SentencePiece tokenization with 30,000 vocabulary size
- Supports both PyTorch and TensorFlow implementations
Core Capabilities
- Fill-mask prediction for contextual understanding
- Sentence pair classification tasks
- Token classification capabilities
- Question answering applications
- Feature extraction for downstream tasks
Frequently Asked Questions
Q: What makes this model unique?
ALBERT Large v2 stands out through its parameter-sharing mechanism across layers, significantly reducing model size while maintaining performance. It achieves state-of-the-art results on various benchmarks despite having only 17M parameters.
Q: What are the recommended use cases?
The model is best suited for tasks that require whole-sentence understanding, including sequence classification, token classification, and question answering. It's not recommended for text generation tasks, where models like GPT-2 would be more appropriate.
