FLAN-T5-Large-SQuAD2
| Property | Value |
|---|---|
| Base Model | FLAN-T5-Large |
| Training Data | SQuAD 2.0 |
| Task | Extractive Question Answering |
| Accuracy (Exact Match) | 86.79% |
| Model Hub | Hugging Face |
What is flan-t5-large-squad2?
FLAN-T5-Large-SQuAD2 is a specialized question-answering model that builds upon the powerful FLAN-T5-Large architecture. Fine-tuned on the SQuAD 2.0 dataset, this model excels at extractive question answering tasks, including the ability to handle unanswerable questions. The model was trained using LoRA (Low-Rank Adaptation) through the PEFT library, optimizing for both performance and efficiency.
Implementation Details
The model requires specific handling for optimal performance. A key requirement is the manual addition of the
- Achieves 85.09% exact match accuracy on answerable questions
- 88.48% accuracy on identifying unanswerable questions
- Overall F1 score of 89.54% on SQuAD 2.0 dataset
- Compatible with latest Transformers library (version 4.31.0+)
Core Capabilities
- Extractive question answering from provided context
- Handling of unanswerable questions
- High-precision answer span identification
- Efficient inference through LoRA optimization
- Integration with both pipeline and custom implementations
Frequently Asked Questions
Q: What makes this model unique?
The model combines the powerful FLAN-T5-Large architecture with SQuAD 2.0 training, utilizing LoRA for efficient fine-tuning. Its ability to handle both answerable and unanswerable questions while maintaining high accuracy makes it particularly valuable for real-world applications.
Q: What are the recommended use cases?
This model is ideal for applications requiring precise extractive question answering, such as document analysis, automated FAQ systems, and information extraction tasks. It's particularly useful when the ability to identify unanswerable questions is crucial.
