T5-Efficient-Base
| Property | Value |
|---|---|
| Parameter Count | 222.93M parameters |
| Memory Usage | 891.73 MB (FP32) / 445.86 MB (FP16) |
| Architecture | Deep-Narrow T5 Variant |
| Pre-training Data | C4 (Colossal Clean Common Crawl) |
| Training Steps | 524,288 |
| Paper | Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers |
What is t5-efficient-base?
T5-efficient-base is a specialized variant of Google's T5 model that implements a Deep-Narrow architecture strategy. This model represents a significant advancement in transformer efficiency, featuring 12 transformer blocks in both encoder and decoder, with 768-dimensional embeddings and 12 attention heads.
Implementation Details
The model employs a base architecture with specific dimensions: 3072 for feed-forward projections, 768 for embedding vectors, and 64 for key/value projections. It was pretrained using span-based masked language modeling on the C4 dataset.
- Deep-Narrow architecture prioritizing model depth
- 222.93M parameters optimized for efficiency
- Balanced encoder-decoder structure with 12 layers each
- Specialized for English NLP tasks
Core Capabilities
- Text summarization
- Question answering
- Text classification (with adaptation)
- General language understanding tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's Deep-Narrow architecture prioritizes depth over width, which research has shown to be more efficient for downstream performance. This approach provides better Pareto-efficiency compared to wider, shallower models of similar parameter count.
Q: What are the recommended use cases?
While this is a pretrained-only checkpoint requiring fine-tuning, it's particularly well-suited for English language tasks including summarization, question answering, and classification tasks. The model can be fine-tuned using PyTorch, TensorFlow, or JAX/Flax frameworks.
