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Anserini Regressions: MS MARCO V2.1 Document Ranking

Models: various bag-of-words approaches on complete documents

This page describes regression experiments for document ranking on the MS MARCO V2.1 document corpus using the dev queries, which is integrated into Anserini's regression testing framework. This corpus was derived from the MS MARCO V2 document corpus and prepared for the TREC 2024 RAG Track.

Here, we cover bag-of-words baselines where each document in the MS MARCO V2.1 document corpus is treated as a unit of indexing.

The exact configurations for these regressions are stored in this YAML file. Note that this page is automatically generated from this template as part of Anserini's regression pipeline, so do not modify this page directly; modify the template instead.

From one of our Waterloo servers (e.g., orca), the following command will perform the complete regression, end to end:

python src/main/python/run_regression.py --index --verify --search --regression msmarco-v2.1-doc

Indexing

Typical indexing command:

bin/run.sh io.anserini.index.IndexCollection \
  -collection MsMarcoV2DocCollection \
  -input /path/to/msmarco-v2.1-doc \
  -generator DefaultLuceneDocumentGenerator \
  -index indexes/lucene-inverted.msmarco-v2.1-doc/ \
  -threads 24 -storeRaw \
  >& logs/log.msmarco-v2.1-doc &

The setting of -input should be a directory containing the compressed jsonl files that comprise the corpus.

For additional details, see explanation of common indexing options.

Retrieval

Topics and qrels are stored here, which is linked to the Anserini repo as a submodule. These evaluation resources are from the original V2 corpus, but have been "projected" over to the V2.1 corpus.

After indexing has completed, you should be able to perform retrieval as follows:

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-inverted.msmarco-v2.1-doc/ \
  -topics tools/topics-and-qrels/topics.msmarco-v2-doc.dev.txt \
  -topicReader TsvInt \
  -output runs/run.msmarco-v2.1-doc.bm25-default.topics.msmarco-v2-doc.dev.txt \
  -bm25 &
bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-inverted.msmarco-v2.1-doc/ \
  -topics tools/topics-and-qrels/topics.msmarco-v2-doc.dev2.txt \
  -topicReader TsvInt \
  -output runs/run.msmarco-v2.1-doc.bm25-default.topics.msmarco-v2-doc.dev2.txt \
  -bm25 &

Evaluation can be performed using trec_eval:

bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.msmarco-v2.1-doc.dev.txt runs/run.msmarco-v2.1-doc.bm25-default.topics.msmarco-v2-doc.dev.txt
bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.msmarco-v2.1-doc.dev.txt runs/run.msmarco-v2.1-doc.bm25-default.topics.msmarco-v2-doc.dev.txt
bin/trec_eval -c -M 100 -m map -c -M 100 -m recip_rank tools/topics-and-qrels/qrels.msmarco-v2.1-doc.dev.txt runs/run.msmarco-v2.1-doc.bm25-default.topics.msmarco-v2-doc.dev.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.msmarco-v2.1-doc.dev2.txt runs/run.msmarco-v2.1-doc.bm25-default.topics.msmarco-v2-doc.dev2.txt
bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.msmarco-v2.1-doc.dev2.txt runs/run.msmarco-v2.1-doc.bm25-default.topics.msmarco-v2-doc.dev2.txt
bin/trec_eval -c -M 100 -m map -c -M 100 -m recip_rank tools/topics-and-qrels/qrels.msmarco-v2.1-doc.dev2.txt runs/run.msmarco-v2.1-doc.bm25-default.topics.msmarco-v2-doc.dev2.txt

Effectiveness

With the above commands, you should be able to reproduce the following results:

MAP@100 BM25 (default)
MS MARCO V2 Doc: Dev 0.1634
MS MARCO V2 Doc: Dev2 0.1711
MRR@100 BM25 (default)
MS MARCO V2 Doc: Dev 0.1654
MS MARCO V2 Doc: Dev2 0.1732
R@100 BM25 (default)
MS MARCO V2 Doc: Dev 0.6104
MS MARCO V2 Doc: Dev2 0.6087
R@1000 BM25 (default)
MS MARCO V2 Doc: Dev 0.8114
MS MARCO V2 Doc: Dev2 0.8069