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# Anserini Regressions: TREC 2024 RAG Track Test Topics

**Models**: various bag-of-words approaches on segmented documents

This page describes regression experiments for ranking _on the segmented version_ of the MS MARCO V2.1 document corpus using the test topics (= queries in TREC parlance), which is integrated into Anserini's regression testing framework.
This corpus was derived from the MS MARCO V2 _segmented_ document corpus and prepared for the TREC 2024 RAG Track.
Instructions for downloading the corpus can be found [here](https://trec-rag.github.io/annoucements/2024-corpus-finalization/).

Here, we cover bag-of-words baselines where each _segment_ in the MS MARCO V2.1 segmented document corpus is treated as a unit of indexing.

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 setting of `-input` should be a directory containing the compressed `jsonl` files that comprise the corpus.

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}