msmarco-v2-doc.d2q-t5.template

# Anserini Regressions: MS MARCO (V2) Document Ranking

**Models**: BM25 on complete documents with doc2query-T5 expansions

This page describes regression experiments for document ranking on the MS MARCO (V2) document corpus using the dev queries, which is integrated into Anserini's regression testing framework.
Here, we expand the document corpus with doc2query-T5.
For additional instructions on working with the MS MARCO V2 document corpus, refer to [this page](${root_path}/docs/experiments-msmarco-v2.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-v2-doc-d2q-t5/` should be a directory containing the compressed `jsonl` files that comprise the corpus.
See [this page](${root_path}/docs/experiments-msmarco-v2.md) for additional details.

For additional details, see explanation of [common indexing options](${root_path}/docs/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}