T5-v1.1-XL
| Property | Value |
|---|---|
| Author | |
| License | Apache 2.0 |
| Training Data | C4 (Colossal Clean Crawled Corpus) |
| Primary Paper | Link |
What is t5-v1_1-xl?
T5-v1.1-XL is an advanced version of Google's Text-to-Text Transfer Transformer (T5) model, representing a significant evolution in transfer learning for NLP tasks. This version introduces several improvements over the original T5, including GEGLU activation in feed-forward layers and optimized architecture parameters.
Implementation Details
The model features a unique architecture with a larger d_model and optimized num_heads and d_ff parameters compared to its predecessors. Notable technical specifications include: disabled dropout during pre-training (to be re-enabled during fine-tuning), exclusive C4 corpus training, and eliminated parameter sharing between embedding and classifier layers.
- Implements GEGLU activation function instead of ReLU
- Pre-trained exclusively on the C4 dataset
- Optimized architecture with larger d_model
- No parameter sharing between embedding and classifier layer
Core Capabilities
- Text-to-text generation tasks
- Transfer learning applications
- Summarization
- Question answering
- Text classification
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its improved architecture (v1.1) featuring GEGLU activation and optimized parameters, making it more efficient than the original T5. It's specifically designed for fine-tuning on downstream tasks, having been pre-trained solely on the C4 corpus.
Q: What are the recommended use cases?
The model requires fine-tuning before use in specific applications. It's particularly well-suited for tasks like summarization, question answering, and text classification. However, users should note that dropout should be re-enabled during fine-tuning for optimal performance.
