T5-Efficient-Small
| Property | Value |
|---|---|
| Parameter Count | 60.52M |
| Memory Usage | 242.08 MB (FP32) / 121.04 MB (FP16) |
| Architecture Type | Deep-Narrow T5 Variant |
| Training Data | C4 (Colossal Clean Common Crawl) |
| Developer |
What is t5-efficient-small?
T5-efficient-small 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, focusing on increased depth rather than width to achieve better performance per parameter. With 60.52 million parameters, it strikes a balance between computational efficiency and model capability.
Implementation Details
The model features a sophisticated architecture with 6 encoder and decoder layers, 2048-dimensional feed-forward networks, and 512-dimensional embedding vectors. It utilizes 8 attention heads with a key/value dimension of 32, optimized for efficient processing while maintaining strong performance characteristics.
- Pre-trained on C4 dataset for 524,288 steps
- Implements span-based masked language modeling objective
- Optimized for English NLP tasks
- Requires fine-tuning for specific applications
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 represents a novel approach to transformer scaling, prioritizing depth over width. This design choice has been shown to be more parameter-efficient than traditional scaling methods, offering better performance per parameter.
Q: What are the recommended use cases?
While the model requires fine-tuning, it's particularly well-suited for English language processing tasks including summarization, question answering, and classification. The model's efficient architecture makes it particularly valuable for applications where computational resources are constrained but good performance is still required.
