T5-v1_1-XXL Model
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Primary Paper | Link |
| Training Data | C4 (Colossal Clean Crawled Corpus) |
| Developer |
What is t5-v1_1-xxl?
T5-v1_1-XXL is Google's advanced version of the original T5 (Text-to-Text Transfer Transformer) model, representing a significant evolution in transfer learning for NLP tasks. This version introduces several architectural improvements over its predecessor, including the implementation of GEGLU activation in feed-forward layers and optimized model dimensions.
Implementation Details
The model features several key technical improvements over the original T5:
- Utilizes GEGLU activation instead of ReLU in feed-forward hidden layers
- Disabled dropout during pre-training (should be re-enabled for fine-tuning)
- Independent parameters between embedding and classifier layers
- Optimized architecture with larger d_model and refined num_heads and d_ff parameters
- Exclusive pre-training on the C4 dataset without task mixing
Core Capabilities
- Text-to-text generation and transformation
- Adaptable to various NLP tasks through fine-tuning
- Enhanced performance on summarization, question answering, and text classification
- Supports transfer learning applications
Frequently Asked Questions
Q: What makes this model unique?
This model represents a significant architectural improvement over the original T5, with GEGLU activation, optimized training procedures, and a focused pre-training approach on C4 data only. It's designed for superior transfer learning capabilities in NLP tasks.
Q: What are the recommended use cases?
The model requires fine-tuning before use in specific tasks. It's particularly well-suited for summarization, question answering, text classification, and other NLP tasks that can benefit from transfer learning.
