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Anserini Regressions: TREC 2024 RAG Track Test Topics

Model: Snowflake's Arctic-embed-l with flat indexes (using ONNX for on-the-fly query encoding)

This page describes regression experiments for ranking on the segmented version of the MS MARCO V2.1 document corpus using the test topics (= queries in TREC parlance), which is integrated into Anserini's regression testing framework. This corpus was derived from the MS MARCO V2 segmented document corpus and prepared for the TREC 2024 RAG Track.

We build on embeddings made available by Snowflake on Hugging Face Datasets, which contains vectors already encoded by the Arctic-embed-l model. The complete dataset comprises 60 parquet files (from 00 to 59). Due to its large size (472 GB), we have divided the vectors into ten shards, each comprised of six files: for example shard00 spans 00.parquet to 05.parquet; shard01 spans the next six parquet files, etc.

This page documents experiments for shard03; we expect the corpus to be in msmarco_v2.1_doc_segmented.arctic-embed-l/shard00 (relative to the base collection path). In these experiments, we are performing query inference "on-the-fly" with ONNX, using flat vector indexes.

The exact configurations for these regressions are stored in this YAML file. Note that this page is automatically generated from this 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 rag24-doc-segmented-test.arctic-embed-l.parquet.shard03.flat.onnx

Indexing

Typical indexing command:

bin/run.sh io.anserini.index.IndexFlatDenseVectors \
  -threads 6 \
  -collection ParquetDenseVectorCollection \
  -input /path/to/msmarco-v2.1-doc-segmented-shard03.arctic-embed-l \
  -generator DenseVectorDocumentGenerator \
  -index indexes/lucene-flat.msmarco-v2.1-doc-segmented-shard03.arctic-embed-l \
  -docidField doc_id -vectorField embedding -normalizeVectors \
  >& logs/log.msmarco-v2.1-doc-segmented-shard03.arctic-embed-l &

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.

Retrieval

Topics and qrels are stored here, which is linked to the Anserini repo as a submodule.

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

bin/run.sh io.anserini.search.SearchFlatDenseVectors \
  -index indexes/lucene-flat.msmarco-v2.1-doc-segmented-shard03.arctic-embed-l \
  -topics tools/topics-and-qrels/topics.rag24.test.txt \
  -topicReader TsvString \
  -output runs/run.msmarco-v2.1-doc-segmented-shard03.arctic-embed-l.arctic-embed-l-flat-onnx.topics.rag24.test.txt \
  -topics rag24.test -topicReader TsvString -topicField title -encoder ArcticEmbedLEncoder &

Evaluation can be performed using trec_eval:

bin/trec_eval -c -m ndcg_cut.20 tools/topics-and-qrels/qrels.rag24.test-umbrela-all.txt runs/run.msmarco-v2.1-doc-segmented-shard03.arctic-embed-l.arctic-embed-l-flat-onnx.topics.rag24.test.txt
bin/trec_eval -c -m ndcg_cut.100 tools/topics-and-qrels/qrels.rag24.test-umbrela-all.txt runs/run.msmarco-v2.1-doc-segmented-shard03.arctic-embed-l.arctic-embed-l-flat-onnx.topics.rag24.test.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.rag24.test-umbrela-all.txt runs/run.msmarco-v2.1-doc-segmented-shard03.arctic-embed-l.arctic-embed-l-flat-onnx.topics.rag24.test.txt

Effectiveness

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

nDCG@20 ArcticEmbedL
RAG 24: Test queries 0.2695
nDCG@100 ArcticEmbedL
RAG 24: Test queries 0.1552
R@100 ArcticEmbedL
RAG 24: Test queries 0.0631