Models: various bag-of-words approaches
This page documents regression experiments on the MS MARCO passage ranking task, which is integrated into Anserini's regression testing framework. For more complete instructions on how to run end-to-end experiments, refer to this page.
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-v1-passage
Typical indexing command:
bin/run.sh io.anserini.index.IndexCollection \
-threads 9 \
-collection JsonCollection \
-input /path/to/msmarco-passage \
-generator DefaultLuceneDocumentGenerator \
-index indexes/lucene-inverted.msmarco-v1-passage/ \
-storePositions -storeDocvectors -storeRaw \
>& logs/log.msmarco-passage &
The directory /path/to/msmarco-passage/ should be a directory containing jsonl files converted from the official passage collection, which is in tsv format.
This page explains how to perform this conversion.
For additional details, see explanation of common indexing options.
Topics and qrels are stored here, which is linked to the Anserini repo as a submodule. The regression experiments here evaluate on the 6980 dev set questions; see this page for more details.
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-v1-passage/ \
-topics tools/topics-and-qrels/topics.msmarco-passage.dev-subset.txt \
-topicReader TsvInt \
-output runs/run.msmarco-passage.bm25-default.topics.msmarco-passage.dev-subset.txt \
-bm25 &
bin/run.sh io.anserini.search.SearchCollection \
-index indexes/lucene-inverted.msmarco-v1-passage/ \
-topics tools/topics-and-qrels/topics.msmarco-passage.dev-subset.txt \
-topicReader TsvInt \
-output runs/run.msmarco-passage.bm25-tuned.topics.msmarco-passage.dev-subset.txt \
-bm25 -bm25.k1 0.82 -bm25.b 0.68 &
Evaluation can be performed using trec_eval:
bin/trec_eval -c -m map tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage.bm25-default.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -M 10 -m recip_rank tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage.bm25-default.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage.bm25-default.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage.bm25-default.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -m map tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage.bm25-tuned.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -M 10 -m recip_rank tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage.bm25-tuned.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage.bm25-tuned.topics.msmarco-passage.dev-subset.txt
bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage.bm25-tuned.topics.msmarco-passage.dev-subset.txt
With the above commands, you should be able to reproduce the following results:
| AP@1000 | BM25 (default) | BM25 (tuned) |
|---|---|---|
| MS MARCO Passage: Dev | 0.1926 | 0.1958 |
| RR@10 | BM25 (default) | BM25 (tuned) |
| MS MARCO Passage: Dev | 0.1840 | 0.1875 |
| R@100 | BM25 (default) | BM25 (tuned) |
| MS MARCO Passage: Dev | 0.6578 | 0.6701 |
| R@1000 | BM25 (default) | BM25 (tuned) |
| MS MARCO Passage: Dev | 0.8526 | 0.8573 |
Explanation of settings:
- The setting "default" refers the default BM25 settings of
k1=0.9,b=0.4. - The setting "tuned" refers to
k1=0.82,b=0.68, as described in this page.
To generate runs corresponding to the submissions on the MS MARCO Passage Ranking Leaderboard, follow the instructions below:
Note that prior to December 2021, runs generated with SearchCollection in the TREC format and then converted into the MS MARCO format give slightly different results from runs generated by SearchMsmarco directly in the MS MARCO format, due to tie-breaking effects.
This was fixed with #1458, which also introduced (intra-configuration) multi-threading.
As a result, SearchMsmarco has been deprecated and replaced by SearchCollection; both have been verified to generate identical output.
The commands below have been retained for historical reasons only, since in some cases they correspond to official MS MARCO leaderboard submissions.
The following command generates with SearchMsmarco the run denoted "BM25 (default)" above (k1=0.9, b=0.4), which roughly corresponds to the entry "BM25 (Anserini)" dated 2019/04/10 on the leaderboard (but Anserini was using Lucene 7.6 at the time):
$ sh target/appassembler/bin/SearchMsmarco -hits 1000 -threads 8 \
-index indexes/lucene-index.msmarco-passage/ \
-queries tools/topics-and-qrels/topics.msmarco-passage.dev-subset.txt \
-k1 0.9 -b 0.4 \
-output runs/run.msmarco-passage.bm25.default.tsv
$ python tools/scripts/msmarco/msmarco_passage_eval.py \
tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage.bm25.default.tsv
#####################
MRR @10: 0.18398616227770961
QueriesRanked: 6980
#####################The following command generates with SearchMsmarco the run denoted "BM25 (tuned)" above (k1=0.82, b=0.68), which corresponds to the entry "BM25 (Lucene8, tuned)" dated 2019/06/26 on the leaderboard:
$ sh target/appassembler/bin/SearchMsmarco -hits 1000 -threads 8 \
-index indexes/lucene-index.msmarco-passage/ \
-queries tools/topics-and-qrels/topics.msmarco-passage.dev-subset.txt \
-k1 0.82 -b 0.68 \
-output runs/run.msmarco-passage.bm25.tuned.tsv
$ python tools/scripts/msmarco/msmarco_passage_eval.py \
tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage.bm25.tuned.tsv
#####################
MRR @10: 0.18741227770955546
QueriesRanked: 6980
#####################As of February 2022, following resolution of #1730, BM25 runs for the MS MARCO leaderboard can be generated with the commands below.
For default parameters (k1=0.9, b=0.4):
$ sh target/appassembler/bin/SearchCollection \
-index indexes/lucene-index.msmarco-passage/ \
-topics tools/topics-and-qrels/topics.msmarco-passage.dev-subset.txt \
-topicreader TsvInt \
-output runs/run.msmarco-passage.bm25.default.tsv \
-format msmarco \
-bm25
$ python tools/scripts/msmarco/msmarco_passage_eval.py \
tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage.bm25.default.tsv
#####################
MRR @10: 0.18398616227770961
QueriesRanked: 6980
#####################
For tuned parameters (k1=0.82, b=0.68):
$ sh target/appassembler/bin/SearchCollection \
-index indexes/lucene-index.msmarco-passage/ \
-topics tools/topics-and-qrels/topics.msmarco-passage.dev-subset.txt \
-topicreader TsvInt \
-output runs/run.msmarco-passage.bm25.tuned.tsv \
-format msmarco \
-bm25 -bm25.k1 0.82 -bm25.b 0.68
$ python tools/scripts/msmarco/msmarco_passage_eval.py \
tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage.bm25.tuned.tsv
#####################
MRR @10: 0.18741227770955546
QueriesRanked: 6980
#####################
Note that the resolution of #1730 did not change the results.