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Adds baselines for rag24.test with umbrela qrel
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UShivani3 committed Nov 21, 2024
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# Anserini Regressions: TREC 2024 RAG Track Test Topics

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

This page describes experiments, integrated into Anserini's regression testing framework, on the "RAGgy dev topics" on the MS MARCO V2.1 _segmented_ document corpus.
This corpus was derived from the MS MARCO V2 _segmented_ document corpus and prepared for the TREC 2024 RAG Track.

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.
These evaluation resources are from the original V2 corpus, but have been "projected" over to the V2.1 corpus.

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}
101 changes: 101 additions & 0 deletions src/main/resources/regression/rag24-doc-segmented-test.yaml
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---
corpus: msmarco-v2.1-doc-segmented
corpus_path: collections/msmarco/msmarco_v2.1_doc_segmented/

index_path: indexes/lucene-inverted.msmarco-v2.1-doc-segmented/
collection_class: MsMarcoV2DocCollection
generator_class: DefaultLuceneDocumentGenerator
index_threads: 24
index_options: -storeRaw
index_stats:
documents: 113520750
documents (non-empty): 113520750
total terms: 22707699649

metrics:
- metric: MAP@100
command: bin/trec_eval
params: -c -M 100 -m map
separator: "\t"
parse_index: 2
metric_precision: 4
can_combine: no
- metric: MRR@100
command: bin/trec_eval
params: -c -M 100 -m recip_rank
separator: "\t"
parse_index: 2
metric_precision: 4
can_combine: true
- metric: nDCG@10
command: bin/trec_eval
params: -c -m ndcg_cut.10
separator: "\t"
parse_index: 2
metric_precision: 4
can_combine: true
- metric: R@100
command: bin/trec_eval
params: -c -m recall.100
separator: "\t"
parse_index: 2
metric_precision: 4
can_combine: false
- metric: R@1000
command: bin/trec_eval
params: -c -m recall.1000
separator: "\t"
parse_index: 2
metric_precision: 4
can_combine: false

topic_reader: TsvInt
topics:
- name: "RAG 24: Test queries"
id: rag24.test
path: topics.rag24.test.txt
qrel: qrels.rag24.test-umbrela-all.txt

models:
- name: bm25-default
display: BM25 (default)
params: -bm25
results:
MAP@100:
- 0.0861
MRR@100:
- 0.7010
nDCG@10:
- 0.3290
R@100:
- 0.1395
R@1000:
- 0.3467
- name: bm25-default+rm3
display: +RM3
params: -bm25 -rm3 -collection MsMarcoV2DocCollection
results:
MAP@100:
- 0.0873
MRR@100:
- 0.6687
nDCG@10:
- 0.3256
R@100:
- 0.1318
R@1000:
- 0.3521
- name: bm25-default+rocchio
display: +Rocchio
params: -bm25 -rocchio -collection MsMarcoV2DocCollection
results:
MAP@100:
- 0.0929
MRR@100:
- 0.6791
nDCG@10:
- 0.3307
R@100:
- 0.1384
R@1000:
- 0.3667

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