ZEBRA KB PIQA Train
| Property | Value |
|---|---|
| Base Model | intfloat/e5-base-v2 |
| License | Creative Commons |
| Paper | Research Paper |
| Language | English |
What is zebra-kb-piqa-train?
ZEBRA KB PIQA Train is a specialized knowledge base component of the ZEBRA (Zero-Shot Example-Based Retrieval Augmentation) framework, specifically trained on the Physical Interaction Question Answering (PIQA) dataset. It's designed to enhance commonsense question answering through a sophisticated retrieval augmentation approach.
Implementation Details
The model implements a three-stage pipeline architecture: example retrieval, knowledge generation, and informed reasoning. Built on the E5-base-v2 foundation, it specializes in retrieving relevant question-knowledge pairs to support commonsense reasoning tasks.
- Zero-shot capability requiring no task-specific fine-tuning
- Retrieval-augmented architecture for enhanced accuracy
- Integration with the broader ZEBRA framework
Core Capabilities
- Example-based retrieval for physical commonsense questions
- Knowledge generation from retrieved examples
- Support for multi-choice question answering
- Performance improvement of up to 4% over base models on PIQA dataset
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines zero-shot learning with retrieval augmentation, specifically optimized for physical interaction questions. It's part of a framework that has demonstrated significant improvements over traditional approaches, showing up to 80.2% accuracy on PIQA tasks.
Q: What are the recommended use cases?
The model is best suited for commonsense question answering tasks involving physical interactions and practical scenarios. It's particularly effective when integrated with the full ZEBRA pipeline for tasks requiring physical common sense reasoning.
