Code and pretrained models for Findings of ACL 2022 paper LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval.

In this repo, we provide codes for reproducing experiments on BEIR dataset in the zero-shot setting of the paper. For fine-tuning, please follow the instructions in BEIR repo.

Note that due to copyright restrictions, some datasets are not available.

Installation

git clone https://github.com/benchmarkir/beir.git
cp code/exact_search.py beir/beir/retrieval/search/dense/exact_search.py
cp code/bm25.py beir
cp code/laprador.py beir
cd beir
pip install -e .
pip install tensorflow==2.5.0

Download pre-processed BM25 index

# cd ./beir
wget https://github.com/JetRunner/LaPraDoR/releases/download/v0.1/BM25-result.zip
unzip BM25-result.zip

Retrieve with LaPraDoR

# Please refer to https://github.com/beir-cellar/beir#beers-available-datasets for dataset name and split
dataset=trec-covid
split=test
python laprador.py $dataset $split

Use your own dataset (requires Elasticsearch)

To use your own dataset, please process the data to the same format as BEIR and then index the dataset following bm25.py. You will need Elasticsearch to index the corpus.

Install Elasticsearch

Installation

Following this link to download Elasticsearch. Here, we use Ubuntu 18.04 as an example.

sudo apt update
sudo apt install apt-transport-https
sudo mkdir -p /usr/share/man/man1
sudo apt install openjdk-8-jdk
wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch | sudo apt-key add -
sudo sh -c 'echo "deb https://artifacts.elastic.co/packages/7.x/apt stable main" > /etc/apt/sources.list.d/elastic-7.x.list'
sudo apt update
sudo apt install elasticsearch

Start Elasticsearch

sudo systemctl enable elasticsearch.service # or sudo service elasticsearch enable
sudo systemctl start elasticsearch.service # or sudo service elasticsearch start

# You can verify that Elasticsearch is running by sending an HTTP request to port 9200 on localhost with the following curl command:
curl -X GET "localhost:9200/"

Citation

@inproceedings{xu2022laprador,
    title={{LaPraDoR}: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval},
    author={Canwen Xu and Daya Guo and Nan Duan and Julian McAuley},
    booktitle={{ACL} 2022 (Findings)},
    year={2022}
}