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# Anserini Regressions: MS MARCO Document Ranking

**Models**: bag-of-words approaches with WordPiece tokenization provided by HuggingFace Tokenizer Integration

This page documents regression experiments on the [MS MARCO document ranking task](https://github.com/microsoft/MSMARCO-Document-Ranking), which is integrated into Anserini's regression testing framework.
Here we are using **WordPiece tokenization** (i.e., from BERT) with the following tokenizer from HuggingFace [`bert-base-uncased`](https://huggingface.co/bert-base-uncased): .

In general, effectiveness is lower than with "standard" Lucene tokenization for two reasons: (1) we're losing stemming, and (2) some terms are chopped into less meaningful subwords.

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-passage-wp/` should be a directory containing the corpus in Anserini's jsonl format.

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/).
The regression experiments here evaluate on the 6980 dev set questions; see [this page](experiments-msmarco-passage.md) for more details.

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}