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Dpr Question Encoder Multiset Base

Developed by facebook
BERT-based Dense Passage Retrieval (DPR) question encoder for open-domain QA research, trained on multiple QA datasets
Downloads 17.51k
Release Time : 3/2/2022

Model Overview

This model is the question encoder in the DPR toolkit, used to encode natural language questions into low-dimensional vector representations for retrieving relevant passages in open-domain QA tasks.

Model Features

Multi-dataset training
Jointly trained on four QA datasets: Natural Questions (NQ), TriviaQA, WebQuestions (WQ), and Curated TREC (TREC), with stronger generalization capabilities
Dense vector representation
Encodes questions and passages into dense vectors in low-dimensional continuous space, supporting efficient similarity computation and retrieval
FAISS compatible
Generated vector representations can be directly used with efficient similarity search libraries like FAISS for large-scale passage retrieval

Model Capabilities

Question vectorization
Semantic similarity computation
Open-domain QA support

Use Cases

QA systems
Open-domain QA
Building systems capable of answering questions from any domain by first retrieving relevant passages and then generating answers
Achieves 86% top-100 accuracy on NQ dataset
Knowledge retrieval
Retrieving the most relevant passages from large document collections for given questions
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