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Monot5 Base Msmarco
M
Monot5 Base Msmarco
Developed by castorini
A re-ranking model based on the T5-base architecture, fine-tuned for 100,000 steps on the MS MARCO passage dataset, suitable for document re-ranking tasks in information retrieval.
Downloads 7,405
Release Time : 3/2/2022
Model Overview
This model is a document re-ranker primarily used in information retrieval systems to optimize the ranking of initial search results, improving the position of relevant documents.
Model Features
Based on T5 Architecture
Utilizes the powerful T5 sequence-to-sequence transformation architecture with strong text comprehension capabilities.
MS MARCO Fine-tuning
Fine-tuned for 100,000 steps on the large-scale information retrieval dataset MS MARCO.
Zero-shot Transfer Capability
Recommended for achieving better zero-shot performance on other datasets.
Model Capabilities
Document Relevance Scoring
Search Result Re-ranking
Zero-shot Transfer Learning
Use Cases
Information Retrieval
Search Engine Result Optimization
Re-ranks initial search engine results to improve the ranking of the most relevant results.
Validated as effective on the MS MARCO dataset.
Academic Literature Retrieval
Re-ranks retrieved academic literature for relevance in scholarly databases.
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