dpr-reader-single-nq-base
| Property | Value |
|---|---|
| Developer | |
| License | CC-BY-NC-4.0 |
| Paper | Dense Passage Retrieval for Open-Domain Question Answering |
| Training Data | Natural Questions Dataset |
What is dpr-reader-single-nq-base?
The dpr-reader-single-nq-base is a specialized question-answering model that forms part of Facebook's Dense Passage Retrieval (DPR) framework. It's designed to extract precise answers from retrieved passages, trained specifically on the Natural Questions dataset. The model leverages BERT's architecture to process and understand both questions and context passages efficiently.
Implementation Details
The model operates as a reader component within the larger DPR system, utilizing two independent BERT networks (base, uncased) for encoding. It achieves impressive performance metrics, including 78.4% accuracy for top-20 retrieval on the Natural Questions dataset and 85.4% for top-100 retrieval.
- Built on BERT base uncased architecture
- Trained using 8 32GB GPUs
- Implements sophisticated passage retrieval mechanisms
- Optimized for real-world search queries
Core Capabilities
- Efficient passage retrieval in low-dimensional continuous space
- High-precision answer extraction from retrieved passages
- Robust performance across multiple QA datasets
- Real-time processing of search queries
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized training on the Natural Questions dataset and its ability to efficiently process real Google search queries. It achieves state-of-the-art performance in open-domain QA tasks while maintaining practical computational requirements.
Q: What are the recommended use cases?
The model is best suited for open-domain question answering applications, particularly when dealing with real-world search queries requiring precise answer extraction from large document collections. However, it should not be used for generating factual content or in applications that could create hostile environments.
