OpenNIR

This project is maintained by Georgetown-IR-Lab

OpenNIR

A Complete Neural Ad-Hoc Ranking Pipeline

From Georgetown Information Retrieval Lab

Key Features

An end-to-end neural ranking pipeline

Quick Start

Ready? Clone and resolve dependencies (python 3.6)

$ git clone https://github.com/Georgetown-IR-Lab/OpenNIR.git
$ cd OpenNIR
$ pip install -r requirements.txt

Set? Let’s first try a traditional ranker (config/trivial) on the antique QA dataset (config/antique). Or try other built-in datasets, such as TREC CAR (config/car), MS MARCO (config/msmarco), or TREC Robust 2004 (config/robust). Necessary files are downloaded (when avaialble), records are preprocessed, and initial ranking sets are retrieved — all automatically.

$ bash scripts/pipeline.sh config/antique config/trivial
...
[XXXX-XX-XX XX:XX:XX,XXX][pipeline:default][INFO] valid epoch=-1 judged@10=0.1530 map_rel-3=0.1472 [mrr_rel-3=0.4422] ndcg_gain-1=0:2=1:3=2:4=3@1=0.3688 ndcg_gain-1=0:2=1:3=2:4=3@10=0.2348 ndcg_gain-1=0:2=1:3=2:4=3@3=0.2821 p_rel-3@1=0.3700 p_rel-3@10=0.1310 p_rel-3@3=0.2450

Go! Train your model. We’ll start with Vanilla BERT. But go ahead and try other rankers too!

$ bash scripts/pipeline.sh config/antique config/vanilla_bert

Dig deeper. See documentation.

Citing OpenNIR

If you use OpenNIR, please cite the following WSDM demonstration paper:

@InProceedings{macavaney:wsdm2020-onir,
  author = {MacAvaney, Sean},
  title = {{OpenNIR}: A Complete Neural Ad-Hoc Ranking Pipeline},
  booktitle = {{WSDM} 2020},
  year = {2020}
}

Acknowledgements

I gratefully acknowledge support for this work from the ARCS Endowment Fellowship. I thank Andrew Yates, Arman Cohan, Luca Soldaini, Nazli Goharian, and Ophir Frieder for valuable feedback on the manuscript and/or code contributions to OpenNIR.