Anserini is an open-source information retrieval toolkit built on Lucene that aims to bridge the gap between academic information retrieval research and the practice of building real-world search applications. Among other goals, our effort aims to be the opposite of this. Anserini grew out of a reproducibility study of various open-source retrieval engines in 2016 (Lin et al., ECIR 2016). See Yang et al. (SIGIR 2017) and Yang et al. (JDIQ 2018) for overviews.
A low-effort way to try out Anserini is to look at our online notebooks, which will allow you to get started with just a few clicks. For convenience, we've pre-built a few common indexes, available to download here.
If you want to build Anserini itself, then start by verifying the main dependencies:
- Anserini was upgraded to Java 11 at commit
17b702d(7/11/2019) from Java 8. Maven 3.3+ is also required. - Anserini was upgraded to Lucene 8.0 as of commit
75e36f9(6/12/2019); prior to that, the toolkit uses Lucene 7.6. Based on preliminary experiments, query evaluation latency has been much improved in Lucene 8. As a result of this upgrade, results of all regressions have changed slightly. To replicate old results from Lucene 7.6, use v0.5.1.
After cloning our repo (use --recurse-submodules option to make sure the eval/ submodule also gets cloned), build using Maven:
mvn clean package appassembler:assemble
The tools/ directory, which contains evaluation tools and other scripts, is actually this repo, integrated as a Git submodule (so that it can be shared across related projects).
Build as follows (you might get warnings, but okay to ignore):
cd tools/eval && tar xvfz trec_eval.9.0.4.tar.gz && cd trec_eval.9.0.4 && make && cd ../../..
cd tools/eval/ndeval && make && cd ../../..With that, you should be ready to go!
Anserini is designed to support experiments on various standard IR test collections out of the box.
The following experiments are backed by rigorous end-to-end regression tests with run_regression.py and the Anserini replicability promise.
For the most part, these runs are based on default parameter settings.
- Regressions for Disks 1 & 2
- Regressions for Disks 4 & 5 (Robust04)
- Regressions for AQUAINT (Robust05)
- Regressions for the New York Times (Core17)
- Regressions for the Washington Post (Core18)
- Regressions for Wt10g
- Regressions for Gov2
- Regressions for ClueWeb09 (Category B)
- Regressions for ClueWeb12-B13
- Regressions for ClueWeb12
- Regressions for Tweets2011 (MB11 & MB12)
- Regressions for Tweets2013 (MB13 & MB14)
- Regressions for Complex Answer Retrieval v1.5 (CAR17)
- Regressions for Complex Answer Retrieval v2.0 (CAR17)
- Regressions for Complex Answer Retrieval v2.0 (CAR17) with doc2query expansion
- Regressions for MS MARCO Passage Ranking
- Regressions for MS MARCO Passage Ranking with doc2query expansion
- Regressions for MS MARCO Passage Ranking with docTTTTTquery expansion
- Regressions for MS MARCO Document Ranking
- Regressions for MS MARCO Document Ranking with per-doc docTTTTTquery expansion
- Regressions for the TREC 2019 Deep Learning Track (Passage Ranking Task)
- Regressions for the TREC 2019 Deep Learning Track (Document Ranking Task)
- Regressions for the TREC 2018 News Track (Background Linking Task)
- Regressions for the TREC 2019 News Track (Background Linking Task)
- Regressions for NTCIR-8 ACLIA (IR4QA subtask, Monolingual Chinese)
- Regressions for CLEF 2006 Monolingual French
- Regressions for TREC 2002 Monolingual Arabic
- Regressions for FIRE 2012 Monolingual Bengali
- Regressions for FIRE 2012 Monolingual Hindi
- Regressions for FIRE 2012 Monolingual English
The experiments described below are not associated with rigorous end-to-end regression testing and thus provide a lower standard of replicability. For the most part, manual copying and pasting of commands into a shell is required to replicate our results:
- Working with AI2's COVID-19 Open Research Dataset
- Ingesting AI2's COVID-19 Open Research Dataset into Solr and Elasticsearch
- Baselines for the TREC-COVID Challenge
- Baselines for the TREC-COVID Challenge using doc2query
- Working with the 20 Newsgroups Dataset
- Replicating "Neural Hype" Experiments
- Guide to BM25 baselines for the MS MARCO Passage Ranking Task
- Guide to BM25 baselines for the MS MARCO Document Ranking Task
- Guide to BM25 baselines for the FEVER Fact Verification Task
- Guide to replicating doc2query results (MS MARCO passage ranking and TREC-CAR)
- Guide to replicating docTTTTTquery results (MS MARCO passage and document ranking)
- Guide to running experiments on the AI2 Open Research Corpus
- Experiments from Yang et al. (JDIQ 2018)
- Runbooks for TREC 2018: [Anserini group] [h2oloo group]
- Runbook for ECIR 2019 paper on axiomatic semantic term matching
- Runbook for ECIR 2019 paper on cross-collection relevance feedback
See this page for additional documentation.
- Use Anserini in Python via Pyserini
- Anserini integrates with SolrCloud via Solrini
- Anserini integrates with Elasticsearch via Elasterini
- Anserini supports approximate nearest-neighbor search on arbitrary dense vectors with Lucene
If you've found Anserini to be helpful, we have a simple request for you to contribute back. In the course of replicating baseline results on standard test collections, please let us know if you're successful by sending us a pull request with a simple note, like what appears at the bottom of the Robust04 page. Replicability is important to us, and we'd like to know about successes as well as failures. Since the regression documentation is auto-generated, pull requests should be sent against the raw templates. In turn, you'll be recognized as a contributor.
Beyond that, there are always open issues we would appreciate help on!
- v0.9.4: June 25, 2020 [Release Notes]
- v0.9.3: May 26, 2020 [Release Notes]
- v0.9.2: May 14, 2020 [Release Notes]
- v0.9.1: May 6, 2020 [Release Notes]
- v0.9.0: April 18, 2020 [Release Notes]
- v0.8.1: March 22, 2020 [Release Notes]
- v0.8.0: March 11, 2020 [Release Notes]
- v0.7.2: January 25, 2020 [Release Notes]
- v0.7.1: January 9, 2020 [Release Notes]
- v0.7.0: December 13, 2019 [Release Notes]
- v0.6.0: September 6, 2019 [Release Notes][Known Issues]
- v0.5.1: June 11, 2019 [Release Notes]
- v0.5.0: June 5, 2019 [Release Notes]
- v0.4.0: March 4, 2019 [Release Notes]
- v0.3.0: December 16, 2018 [Release Notes]
- v0.2.0: September 10, 2018 [Release Notes]
- v0.1.0: July 4, 2018 [Release Notes]
- Jimmy Lin, Matt Crane, Andrew Trotman, Jamie Callan, Ishan Chattopadhyaya, John Foley, Grant Ingersoll, Craig Macdonald, Sebastiano Vigna. Toward Reproducible Baselines: The Open-Source IR Reproducibility Challenge. ECIR 2016.
- Peilin Yang, Hui Fang, and Jimmy Lin. Anserini: Enabling the Use of Lucene for Information Retrieval Research. SIGIR 2017.
- Peilin Yang, Hui Fang, and Jimmy Lin. Anserini: Reproducible Ranking Baselines Using Lucene. Journal of Data and Information Quality, 10(4), Article 16, 2018.
This research is supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada. Previous support came from the U.S. National Science Foundation under IIS-1423002 and CNS-1405688. Any opinions, findings, and conclusions or recommendations expressed do not necessarily reflect the views of the sponsors.