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Monot5 Large Msmarco
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Monot5 Large Msmarco

Developed by castorini
This model is a reranker based on the T5-large architecture, fine-tuned for 100,000 steps (i.e., 10 epochs) on the MS MARCO passage dataset, primarily used for document and passage reranking tasks.
Large Language Model#MSMARCO Passage Reranking#T5-large Fine-tuning#Document Relevance Optimization
Downloads 254
Release Time : 3/2/2022

Model Overview

This model is a document reranking model based on the T5-large architecture, fine-tuned on the MS MARCO passage dataset, capable of reranking retrieval results to improve relevance.

Model Features

Based on T5-large Architecture
Leverages the powerful T5-large pre-trained model as its foundation, with excellent text understanding and generation capabilities.
MS MARCO Fine-tuning
Fine-tuned for 100,000 steps on the MS MARCO passage dataset, specifically optimized for reranking performance.
Efficient Reranking
Capable of quickly and effectively reranking retrieval results to improve the ranking of relevant documents.

Model Capabilities

Document Reranking
Passage Relevance Scoring
Retrieval Result Optimization

Use Cases

Information Retrieval
Search Engine Result Optimization
Reranks search engine results to improve the ranking of the most relevant results.
Significantly improves the relevance of retrieval results and user satisfaction.
Question Answering Systems
Reranks candidate answers in QA systems to select the most relevant answer.
Improves the accuracy and user experience of QA systems.
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