# RankT5

> Open-source reranking model that uses an encoder-decoder (T5) architecture, fine-tuned to generate classification tokens indicating whether query-document pairs are relevant or irrelevant. Formulates document ranking as a generation task.

- **URL:** https://github.com/castorini/RankT5
- **Category:** Vector Database Engines
- **Tags:** Open Source, encoder-decoder, LLM-reranking
- **Updated:** 2026-04-21 10:14
- **Canonical page:** https://vectordb.ever.works/items/rankt5

## Details

## Overview

RankT5 is a supervised LLM reranker that employs a T5 encoder-decoder architecture for document reranking. It formulates the ranking problem as a text generation task, outputting classification tokens to indicate relevance.

## Architecture

- **Model Type**: Encoder-decoder (T5) backbone
- **Approach**: Pointwise or pairwise ranking formulation as a generation task
- **Output**: Generates classification tokens (e.g., "relevant" or "irrelevant") for query-document pairs

## Training

- Supervised fine-tuning on passage ranking datasets such as MS MARCO
- Adjusts pre-trained T5 parameters for improved relevance measurement
- Addresses the lack of ranking awareness inherent in pre-trained language models

## Use Cases

- Search result reranking
- RAG pipeline document prioritization
- Semantic relevance scoring for query-passage pairs

## Performance

- Competitive with other LLM-based rerankers on standard benchmarks
- More efficient than listwise LLM reranking methods that require sliding window strategies
- Open-source and can be self-hosted, avoiding API costs

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