HLATR

Introduction

Hybrid List Aware Transformer Reranking, is a lightweight reranking framework for text retrieval. This framework is premised on combing the retrieval featuring and ranking feature with a list-wise encoder as the reranking model. More details can be found in our paper.

2022-03-17: HLATR got first place on MS MARCO Passage Ranking Leaderboard.

Code

The retrieval, rerank, hltar folders contain how to train a dense passage retrieval, reranking model amd the hltar model. This code is based on the previous work tevatron and reranker produced by luyug. Many thanks to luyug.

Requirements

python=3.8
transformers>=4.18.0
tqdm==4.49.0
datasets>=1.11.0
torch==1.11.0
faiss==1.7.0
scikit-learn== 0.22 

Model Checkpoint

Pre-trained anguage models for MS MARCO PassageRanking LearderBoard (including the retrieval and reranking model) has been gradually open-sourced through ModelScope platform, welcome to download and experience.

Model Type	Model Name	Url
Retrieval	CoROM	nlp_corom_sentence-embedding_english-base
Reranking	CoROM-Reranking	nlp_corom_passage-ranking_english-base

Citing us

If you feel this paper helpful, please cite us:

@article{Zhang2022HLATREM,
  title={HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking},
  author={Yanzhao Zhang and Dingkun Long and Guangwei Xu and Pengjun Xie},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.10569}
}