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msmarco-v2-passage-augmented-d2q-t5.template
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msmarco-v2-passage-augmented-d2q-t5.template
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# Anserini Regressions: MS MARCO (V2) Passage Ranking
**Models**: BM25 with doc2query-T5 expansions on augmented passages
This page describes regression experiments for passage ranking _on the augmented version_ of the MS MARCO V2 Passage Corpus using the dev queries, which is integrated into Anserini's regression testing framework.
Here, we expand the augmented passage corpus with doc2query-T5.
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-v2-passage-augmented-d2q-t5/` should be a directory containing the compressed `jsonl` files that comprise the corpus.
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.
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}