Add model card with pipeline tag, license, library name, and usage instructions

README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-ranking
+---
+
+# MVP: Multi-view-guided Passage Reranking with Large Language Models
+
+This repository contains the official implementation for the EMNLP 2025 paper:
+[**Multi-view-guided Passage Reranking with Large Language Models**](https://huggingface.co/papers/2509.07485)
+by Jeongwoo Na*, Jun Kwon*, Eunseong Choi, Jongwuk Lee (* : equal contribution)
+
+## Overview
+MVP (Multi-View-guided Passage Reranking) is a non-generative LLM-based reranking method designed to overcome the efficiency and bias sensitivity challenges of existing LLM-based rerankers. It encodes query-passage information into diverse view embeddings, ensuring accurate representation without external biases. The model then combines query-aware passage embeddings to produce distinct anchor vectors, which are used to directly compute relevance scores in a single decoding step. An orthogonal loss encourages diversity across these views.
+
+With just 220M parameters, MVP matches the performance of much larger 7B-scale fine-tuned models while achieving a 100x reduction in inference latency. The 3B-parameter variant of MVP achieves state-of-the-art performance on both in-domain and out-of-domain benchmarks.
+
+## How to Use
+
+### Setup Environment
+```
+conda env create -f mvp.yaml
+conda activate mvp
+```
+
+### Run MVP
+```
+cd inference
+bash run_evaluation.sh
+```
+
+## Model Checkpoints
+- [`MVP-base`](https://huggingface.co/Jun421/MVP-base)
+- [`MVP-3b`](https://huggingface.co/Jun421/MVP-3b)
+
+## Datasets
+
+### Evaluation Datasets
+- [BM25-Top100](https://huggingface.co/datasets/Soyoung97/beir-eval-bm25-top100) (`Soyoung97/beir-eval-bm25-top100`)
+
+### Training Datasets
+- [Train/Valid](https://huggingface.co/datasets/Jun421/MVP-train) (`Jun421/MVP-train`)
+This dataset is derived from the BEIR/MSMARCO license, and its usage is restricted to **academic purposes** only.
+
+## Acknowledgments
+The implementation of this model is based on the [ListT5](https://github.com/soyoung97/ListT5) repository.