Official scorer checkpoints for Expand, Rerank, and Retrieve: Query
Reranking for Open-Domain Question Answering (Findings of ACL 2023).
EAR is a query Expansion And Reranking method. It samples diverse query
expansions and trains a reranker to select expansions that improve passage
retrieval.
Contents
The repository preserves the original experiment directory names:
Path
Dataset
Variant
Files
best_nq/vanilla
Natural Questions
EAR-RI
3
best_nq/wtop1
Natural Questions
EAR-RD
3
best_trivia/vanilla
TriviaQA
EAR-RI
3
best_trivia/wtop1
TriviaQA
EAR-RD
4
Each directory contains answer, sentence, and title scorer checkpoints.
best_trivia/wtop1 additionally preserves the historical
scorer-answer-best.bin file from the original release.
These are raw PyTorch checkpoint/state-dict files used by the EAR codebase.
They are not standalone AutoModel.from_pretrained repositories.
The original release used Python 3.7.13, PyTorch 1.10.1,
Transformers 4.24.0, Tokenizers 0.11.1, Pyserini, and Weights & Biases.
The rerankers were trained from microsoft/deberta-v3-base.
SOURCE_MANIFEST.sha256 contains a SHA-256 checksum for every checkpoint.
The source archive was models_ear.tar.gz with SHA-256
d756d111404fe8859fd094e313f1e2b95c489691228dfb05044c6471fe31c819.
No training or evaluation dataset is included in this model repository.
@inproceedings{chuang-etal-2023-expand,
title = {Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering},
author = {Chuang, Yung-Sung and Fang, Wei and Li, Shang-Wen and Yih, Wen-tau and Glass, James},
booktitle = {Findings of the Association for Computational Linguistics: ACL 2023},
year = {2023},
pages = {12131--12147},
doi = {10.18653/v1/2023.findings-acl.768},
url = {https://aclanthology.org/2023.findings-acl.768/}
}
