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msmarco-passage.template
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# Anserini Regressions: MS MARCO Passage Ranking
**Models**: various bag-of-words approaches
This page documents regression experiments on the [MS MARCO passage ranking task](https://github.com/microsoft/MSMARCO-Passage-Ranking), 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](experiments-msmarco-passage.md).
The exact configurations for these regressions are stored in [this YAML file](${yaml}).
Note that this page is automatically generated from [this template](${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 ${test_name}
```
## Indexing
Typical indexing command:
```
${index_cmds}
```
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](experiments-msmarco-passage.md) explains how to perform this conversion.
For additional details, see explanation of [common indexing options](common-indexing-options.md).
## Retrieval
Topics and qrels are stored [here](https://github.com/castorini/anserini-tools/tree/master/topics-and-qrels), which is linked to the Anserini repo as a submodule.
The regression experiments here evaluate on the 6980 dev set questions; see [this page](experiments-msmarco-passage.md) for more details.
After indexing has completed, you should be able to perform retrieval as follows:
```
${ranking_cmds}
```
Evaluation can be performed using `trec_eval`:
```
${eval_cmds}
```
## Effectiveness
With the above commands, you should be able to reproduce the following results:
${effectiveness}
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](experiments-msmarco-passage.md).
To generate runs corresponding to the submissions on the [MS MARCO Passage Ranking Leaderboard](https://microsoft.github.io/msmarco/), follow the instructions below:
## Additional Implementation Details
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](https://github.com/castorini/anserini/issues/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):
```bash
$ 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:
```bash
$ 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](https://github.com/castorini/anserini/issues/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](https://github.com/castorini/anserini/issues/1730) did not change the results.