beir-v1.0.0-cqadupstack-tex-flat-wp.template

# Anserini Regressions: BEIR (v1.0.0) — CQADupStack-tex

This page documents BM25 regression experiments for [BEIR (v1.0.0) — CQADupStack-tex](http://beir.ai/).
These experiments index the corpus in a "flat" manner, by concatenating the "title" and "text" into the "contents" field.
All the documents and queries are pre-tokenized with `bert-base-uncased` tokenizer.

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, pre-tokenized with the `bert-base-uncased` tokenizer, are available for download:

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

The tarball is 13 GB and has MD5 checksum `3cf8f3dcdcadd49362965dd4466e6ff2`.
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}