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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 in [`src/main/resources/topics-and-qrels/`](../src/main/resources/topics-and-qrels/).
These regression experiments use the [dev queries](../src/main/resources/topics-and-qrels/topics.msmarco-v2-passage.dev.txt) and the [dev2 queries](../src/main/resources/topics-and-qrels/topics.msmarco-v2-passage.dev2.txt).

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}