osirrc / birch-docker

OSIRRC Docker Image for Birch (BERT-based retrieval)

OSIRRC Docker Image for Birch

Zeynep Akkalyoncu Yilmaz, Wei Yang, Haotian Zhang, and Jimmy Lin

This is the docker image for Birch, a BERT-based experimental IR system, conforming to the OSIRRC jig for the Open-Source IR Replicability Challenge (OSIRRC) at SIGIR 2019. The image is available on Docker Hub.

• Supported test collections: robust04
• Supported hooks: init, index, search

Quick Start

The following jig command can be used to index TREC disks 4/5 for robust04:

python run.py prepare \
  --repo osirrc2019/birch \
  --tag v0.1.0 \
  --collections robust04=/path/to/disk45=trectext

The following jig command can be used to perform a retrieval run as described in Simple Applications of BERT for Document Retrieval:

python run.py search \
    --repo osirrc2019/birch \
    --tag v0.1.0 \
    --collection robust04 \
    --topic topics/topics.robust04.txt \
    --qrels qrels/qrels.robust04.txt \
    --output out/birch \
    --measures map P.20 \
    --opts experiment=[experiment_name] num_folds=2 anserini_path=anserini tune_params=False

The parameter experiment can take on the values qa_2cv, mb_2cv, qa_5cv or mb_5cv, and denotes the pretraining data and cross-validation setting. Likewise,num_folds should be set accordingly. Use tune_params=False to directly evaluate on the collection, and tune_params=True to tune the hyperparameters yourself first.

The expected output for experiment=mb_2cv is as follows:

Evaluating results using trec_eval...
###
# out/birch/run.mb_2cv.cv.a
###
map                   	all	0.3241
P_20                  	all	0.4217

###
# out/birch/run.mb_2cv.cv.ab
###
map                   	all	0.3240
P_20                  	all	0.4209

# out/birch/run.mb_2cv.cv.abc
###
map                   	all	0.3244
P_20                  	all	0.4219

*.a refer to runs where only the top sentence is considered, *.ab top 2, and *.abc top 3 sentences.

Reviews

• Documentation reviewed at commit 4fcfe3c (2019-06-14) by Ryan Clancy.