msmarco-v1-passage.cos-dpr-distil.hnsw-int8.cached.template

# Anserini Regressions: MS MARCO Passage Ranking

**Model**: cosDPR-distil with quantized HNSW indexes (using cached queries)

This page describes regression experiments, integrated into Anserini's regression testing framework, using the cosDPR-distil model on the [MS MARCO passage ranking task](https://github.com/microsoft/MSMARCO-Passage-Ranking), as described in the following paper:

> Xueguang Ma, Tommaso Teofili, and Jimmy Lin. [Anserini Gets Dense Retrieval: Integration of Lucene's HNSW Indexes.](https://dl.acm.org/doi/10.1145/3583780.3615112) _Proceedings of the 32nd International Conference on Information and Knowledge Management (CIKM 2023)_, October 2023, pages 5366–5370, Birmingham, the United Kingdom.

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 and then run `bin/build.sh` to rebuild the documentation.

From one of our Waterloo servers (e.g., `orca`), the following command will perform the complete regression, end to end:

```bash
python src/main/python/run_regression.py --index --verify --search --regression ${test_name}
```

We make available a version of the MS MARCO Passage Corpus that has already been encoded with cosDPR-distil.

From any machine, the following command will download the corpus and perform the complete regression, end to end:

```bash
python src/main/python/run_regression.py --download --index --verify --search --regression ${test_name}
```

The `run_regression.py` script automates the following steps, but if you want to perform each step manually, simply copy/paste from the commands below and you'll obtain the same regression results.

## Corpus Download

Download the corpus and unpack into `collections/`:

```bash
wget ${download_url} -P collections/
tar xvf collections/${corpus}.tar -C collections/
```

To confirm, `${corpus}.tar` is 57 GB and has MD5 checksum `${download_checksum}`.
With the corpus downloaded, the following command will perform the remaining steps below:

```bash
python src/main/python/run_regression.py --index --verify --search --regression ${test_name} \
  --corpus-path collections/${corpus}
```

## Indexing

Sample indexing command, building quantized HNSW indexes:

```bash
${index_cmds}
```

The path `/path/to/${corpus}/` should point to the corpus downloaded above.
Upon completion, we should have an index with 8,841,823 documents.

Note that here we are explicitly using Lucene's `NoMergePolicy` merge policy, which suppresses any merging of index segments.
This is because merging index segments is a costly operation and not worthwhile given our query set.
Furthermore, we are using Lucene's [Automatic Byte Quantization](https://www.elastic.co/search-labs/blog/articles/scalar-quantization-in-lucene) feature, which increase the on-disk footprint of the indexes since we're storing both the int8 quantized vectors and the float32 vectors, but only the int8 quantized vectors need to be loaded into memory.
See [issue #2292](https://github.com/castorini/anserini/issues/2292) for some experiments reporting the performance impact.

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

After indexing has completed, you should be able to perform retrieval as follows using HNSW indexes:

```bash
${ranking_cmds}
```

Evaluation can be performed using `trec_eval`:

```bash
${eval_cmds}
```

## Effectiveness

With the above commands, you should be able to reproduce the following results:

${effectiveness}

The above figures are from running brute-force search with cached queries on non-quantized **flat** indexes.
With cached queries on quantized HNSW indexes, observed results are likely to differ; scores may be lower by up to 0.01, sometimes more.
Note that both HNSW indexing and quantization are non-deterministic (i.e., results may differ slightly between trials).

## Reproduction Log[*](${root_path}/docs/reproducibility.md)

To add to this reproduction log, modify [this template](${template}) and run `bin/build.sh` to rebuild the documentation.

+ Results reproduced by [@yilinjz](https://github.com/yilinjz) on 2023-09-01 (commit [`4ae518b`](https://github.com/castorini/anserini/commit/4ae518bb284ebcba0b273a473bc8774735cb7d19))