T5-v1.1-small
| Property | Value |
|---|---|
| Developer | |
| License | Apache 2.0 |
| Training Data | C4 Dataset |
| Paper | View Paper |
| Downloads | 61,663 |
What is t5-v1_1-small?
T5-v1.1-small is an improved version of Google's Text-to-Text Transfer Transformer (T5) model. This smaller variant maintains the innovative text-to-text approach while incorporating several architectural improvements over the original T5. The model represents a significant step forward in transfer learning for NLP tasks, trained exclusively on the Colossal Clean Crawled Corpus (C4).
Implementation Details
The v1.1 architecture introduces several key improvements over its predecessor, including the implementation of GEGLU activation in the feed-forward hidden layer, replacing the traditional ReLU activation. The model features disabled dropout during pre-training (though it should be re-enabled during fine-tuning) and eliminates parameter sharing between embedding and classifier layers.
- Exclusive pre-training on C4 dataset without task mixing
- GEGLU activation function implementation
- Optimized architecture with modified d_model and head configurations
- No parameter sharing between embedding and classifier layers
Core Capabilities
- Text-to-text generation tasks
- Transfer learning for various NLP applications
- Adaptable for fine-tuning on specific downstream tasks
- Efficient processing with smaller parameter count
Frequently Asked Questions
Q: What makes this model unique?
T5-v1.1-small's uniqueness lies in its improved architecture with GEGLU activation and its focused training approach on the C4 dataset, making it more efficient and adaptable for various NLP tasks.
Q: What are the recommended use cases?
This model needs to be fine-tuned before use in downstream tasks. It's particularly suitable for text generation, summarization, question answering, and text classification applications where computational efficiency is important.
