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# Anserini Regressions: BEIR (v1.0.0) — DBPedia

**Model**: uniCOIL without any expansions (using cached queries)

This page describes regression experiments, integrated into Anserini's regression testing framework, using uniCOIL (without any expansions) on [BEIR (v1.0.0) — DBPedia](http://beir.ai/).
The uniCOIL model is described in the following paper:

> Jimmy Lin and Xueguang Ma. [A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniques.](https://arxiv.org/abs/2106.14807) _arXiv:2106.14807_.

In these experiments, we are using cached queries (i.e., cached results of query encoding).

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}
```

All the BEIR corpora, encoded by the uniCOIL-noexp model, are available for download:

```bash
wget https://rgw.cs.uwaterloo.ca/pyserini/data/beir-v1.0.0-unicoil-noexp.tar -P collections/
tar xvf collections/beir-v1.0.0-unicoil-noexp.tar -C collections/
```

The tarball is 30 GB and has MD5 checksum `4fd04d2af816a6637fc12922cccc8a83`.
After download and unpacking the corpora, the `run_regression.py` command above should work without any issue.

## Indexing

Typical indexing command:

```
${index_cmds}
```

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}