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Doc2query T5 Base Msmarco

Developed by macavaney
A document expansion model based on T5-base architecture, trained on the MS MARCO dataset, used to generate potential queries related to document content to enhance retrieval effectiveness
Downloads 341
Release Time : 10/27/2022

Model Overview

This model automatically generates relevant queries by analyzing document content, used to expand original documents to improve the recall rate of information retrieval systems. Mainly applied in document retrieval enhancement scenarios.

Model Features

Document Expansion Capability
Automatically generates relevant queries for input documents, effectively expanding document content
Retrieval Augmentation
Significantly improves the recall rate of retrieval systems by expanding original documents with generated queries
Ready-to-Use Conversion
Provides pre-trained PyTorch format models that can be directly integrated into the PyTerrier retrieval framework

Model Capabilities

Document Content Analysis
Query Generation
Retrieval System Enhancement

Use Cases

Information Retrieval
Document Retrieval System Enhancement
Use the model to expand document content before building document indexes
Improves the recall rate of retrieval systems for relevant queries
Academic Literature Retrieval
Automatically generates relevant search terms for academic papers
Helps users discover more related literature
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