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Anserini: BM25 Baselines for MS MARCO Passage Ranking

This page contains instructions for running BM25 baselines on the MS MARCO passage ranking task. Note that there is a separate MS MARCO document ranking task. We also have a separate page describing document expansion experiments (Doc2query) for this task.

Data Prep

We're going to use the repository's root directory as the working directory. First, we need to download and extract the MS MARCO passage dataset:

mkdir collections/msmarco-passage

wget https://msmarco.blob.core.windows.net/msmarcoranking/collectionandqueries.tar.gz -P collections/msmarco-passage

# Alternative mirror:
# wget https://www.dropbox.com/s/9f54jg2f71ray3b/collectionandqueries.tar.gz -P collections/msmarco-passage

tar xvfz collections/msmarco-passage/collectionandqueries.tar.gz -C collections/msmarco-passage

To confirm, collectionandqueries.tar.gz should have MD5 checksum of 31644046b18952c1386cd4564ba2ae69.

Next, we need to convert the MS MARCO tsv collection into Anserini's jsonl files (which have one json object per line):

python tools/scripts/msmarco/convert_collection_to_jsonl.py \
 --collection-path collections/msmarco-passage/collection.tsv \
 --output-folder collections/msmarco-passage/collection_jsonl

The above script should generate 9 jsonl files in collections/msmarco-passage/collection_jsonl, each with 1M lines (except for the last one, which should have 841,823 lines).

We can now index these docs as a JsonCollection using Anserini:

sh target/appassembler/bin/IndexCollection -threads 9 -collection JsonCollection \
 -generator DefaultLuceneDocumentGenerator -input collections/msmarco-passage/collection_jsonl \
 -index indexes/msmarco-passage/lucene-index-msmarco -storePositions -storeDocvectors -storeRaw

Upon completion, we should have an index with 8,841,823 documents. The indexing speed may vary; on a modern desktop with an SSD, indexing takes a couple of minutes.

Performing Retrieval on the Dev Queries

Since queries of the set are too many (+100k), it would take a long time to retrieve all of them. To speed this up, we use only the queries that are in the qrels file:

python tools/scripts/msmarco/filter_queries.py \
 --qrels collections/msmarco-passage/qrels.dev.small.tsv \
 --queries collections/msmarco-passage/queries.dev.tsv \
 --output collections/msmarco-passage/queries.dev.small.tsv

The output queries file should contain 6980 lines. We can now perform a retrieval run using this smaller set of queries:

sh target/appassembler/bin/SearchMsmarco -hits 1000 -threads 1 \
 -index indexes/msmarco-passage/lucene-index-msmarco \
 -queries collections/msmarco-passage/queries.dev.small.tsv \
 -output runs/run.msmarco-passage.dev.small.tsv

Note that by default, the above script uses BM25 with tuned parameters k1=0.82, b=0.68. The option -hits specifies the number of documents per query to be retrieved. Thus, the output file should have approximately 6980 × 1000 = 6.9M lines.

Retrieval speed will vary by machine: On a modern desktop with an SSD, we can get ~0.07 s/query, so the run should finish in under ten minutes. We can perform multi-threaded retrieval by changing the -threads argument.

Finally, we can evaluate the retrieved documents using this the official MS MARCO evaluation script:

python tools/scripts/msmarco/msmarco_passage_eval.py \
 collections/msmarco-passage/qrels.dev.small.tsv runs/run.msmarco-passage.dev.small.tsv

And the output should be like this:

#####################
MRR @10: 0.18741227770955546
QueriesRanked: 6980
#####################

You can find this entry on the MS MARCO Passage Ranking Leaderboard as entry "BM25 (Lucene8, tuned)", so you've just replicated (part of) a leaderboard submission!

We can also use the official TREC evaluation tool, trec_eval, to compute other metrics than MRR@10. For that we first need to convert runs and qrels files to the TREC format:

python tools/scripts/msmarco/convert_msmarco_to_trec_run.py \
 --input runs/run.msmarco-passage.dev.small.tsv \
 --output runs/run.msmarco-passage.dev.small.trec

python tools/scripts/msmarco/convert_msmarco_to_trec_qrels.py \
 --input collections/msmarco-passage/qrels.dev.small.tsv \
 --output collections/msmarco-passage/qrels.dev.small.trec

And run the trec_eval tool:

tools/eval/trec_eval.9.0.4/trec_eval -c -mrecall.1000 -mmap \
 collections/msmarco-passage/qrels.dev.small.trec runs/run.msmarco-passage.dev.small.trec

The output should be:

map                   	all	0.1957
recall_1000           	all	0.8573

Average precision and recall@1000 are the two metrics we care about the most.

BM25 Tuning

Note that this figure differs slightly from the value reported in Document Expansion by Query Prediction, which uses the Anserini (system-wide) default of k1=0.9, b=0.4.

Tuning was accomplished with tools/scripts/msmarco/tune_bm25.py, using the queries found here; the basic approach is grid search of parameter values in tenth increments. There are five different sets of 10k samples (using the shuf command). We tuned on each individual set and then averaged parameter values across all five sets (this has the effect of regularization). In separate trials, we optimized for:

  • recall@1000, since Anserini output serves as input to downstream rerankers (e.g., based on BERT), and we want to maximize the number of relevant documents the rerankers have to work with;
  • MRR@10, for the case where Anserini output is directly presented to users (i.e., no downstream reranking).

It turns out that optimizing for MRR@10 and MAP yields the same settings.

Here's the comparison between the Anserini default and optimized parameters:

Setting MRR@10 MAP Recall@1000
Default (k1=0.9, b=0.4) 0.1840 0.1926 0.8526
Optimized for recall@1000 (k1=0.82, b=0.68) 0.1874 0.1957 0.8573
Optimized for MRR@10/MAP (k1=0.60, b=0.62) 0.1892 0.1972 0.8555

To replicate these results, the SearchMsmarco class above takes k1 and b parameters as command-line arguments, e.g., -k1 0.60 -b 0.62 (note that the default setting is k1=0.82 and b=0.68).

Replication Log