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Ance Dpr Question Multi
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Ance Dpr Question Multi

Developed by castorini
ANCE is a dense text retrieval model that optimizes retrieval effectiveness through approximate nearest neighbor negative contrastive learning.
Text Embedding
Transformers
#Dense Text Retrieval#Negative Contrastive Learning#Pyserini Adaptation
Downloads 334
Release Time : 3/2/2022

Model Overview

This model enhances the effectiveness and efficiency of text retrieval tasks through dense vector representations and approximate nearest neighbor search techniques.

Model Features

Approximate Nearest Neighbor Negative Contrastive Learning
Uses approximate nearest neighbor techniques for negative sampling to optimize contrastive learning effectiveness.
Dense Vector Retrieval
Employs dense vector representations for documents and queries to improve retrieval accuracy.
Efficient Retrieval
Combines approximate nearest neighbor search techniques to enable efficient retrieval of large-scale documents.

Model Capabilities

Text Retrieval
Document Similarity Calculation
Information Retrieval

Use Cases

Information Retrieval
Document Retrieval System
Builds an efficient document retrieval system to quickly find relevant documents.
Offers better precision and recall compared to traditional retrieval methods.
Question Answering System
Serves as the retrieval component in a question answering system to quickly locate documents related to questions.
Improves the response speed and accuracy of the question answering system.
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