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Anserini Regressions: TREC 2019 Deep Learning Track (Passage)

Model: BGE-base-en-v1.5 with flat indexes (using cached queries)

This page describes regression experiments, integrated into Anserini's regression testing framework, using the BGE-base-en-v1.5 model on the TREC 2019 Deep Learning Track passage ranking task, as described in the following paper:

Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff. C-Pack: Packaged Resources To Advance General Chinese Embedding. arXiv:2309.07597, 2023.

In these experiments, we are using cached queries (i.e., cached results of query encoding).

Note that the NIST relevance judgments provide far more relevant passages per topic, unlike the "sparse" judgments provided by Microsoft (these are sometimes called "dense" judgments to emphasize this contrast). For additional instructions on working with MS MARCO passage collection, refer to this page.

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 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:

python src/main/python/run_regression.py --index --verify --search --regression dl19-passage.bge-base-en-v1.5.parquet.flat.cached

We make available a version of the MS MARCO Passage Corpus that has already been encoded by the BGE-base-en-v1.5 model.

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

python src/main/python/run_regression.py --download --index --verify --search --regression dl19-passage.bge-base-en-v1.5.parquet.flat.cached

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/:

wget https://rgw.cs.uwaterloo.ca/pyserini/data/msmarco-passage-bge-base-en-v1.5.parquet.tar -P collections/
tar xvf collections/msmarco-passage-bge-base-en-v1.5.parquet.tar -C collections/

To confirm, msmarco-passage-bge-base-en-v1.5.parquet.tar is 39 GB and has MD5 checksum b235e19ec492c18a18057b30b8b23fd4. With the corpus downloaded, the following command will perform the remaining steps below:

python src/main/python/run_regression.py --index --verify --search --regression dl19-passage.bge-base-en-v1.5.parquet.flat.cached \
  --corpus-path collections/msmarco-passage-bge-base-en-v1.5.parquet

Indexing

Sample indexing command, building flat indexes:

bin/run.sh io.anserini.index.IndexFlatDenseVectors \
  -threads 16 \
  -collection ParquetDenseVectorCollection \
  -input /path/to/msmarco-passage-bge-base-en-v1.5.parquet \
  -generator ParquetDenseVectorDocumentGenerator \
  -index indexes/lucene-flat.msmarco-v1-passage.bge-base-en-v1.5/ \
  >& logs/log.msmarco-passage-bge-base-en-v1.5.parquet &

The path /path/to/msmarco-passage-bge-base-en-v1.5.parquet/ should point to the corpus downloaded above. Upon completion, we should have an index with 8,841,823 documents.

Retrieval

Topics and qrels are stored here, which is linked to the Anserini repo as a submodule. The regression experiments here evaluate on the 43 topics for which NIST has provided judgments as part of the TREC 2019 Deep Learning Track. The original data can be found here.

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-v1-passage.bge-base-en-v1.5/ \
  -topics tools/topics-and-qrels/topics.dl19-passage.bge-base-en-v1.5.jsonl.gz \
  -topicReader JsonIntVector \
  -output runs/run.msmarco-passage-bge-base-en-v1.5.parquet.bge-flat-cached.topics.dl19-passage.bge-base-en-v1.5.jsonl.txt \
  -hits 1000 -threads 16 &

Evaluation can be performed using trec_eval:

bin/trec_eval -m map -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-bge-base-en-v1.5.parquet.bge-flat-cached.topics.dl19-passage.bge-base-en-v1.5.jsonl.txt
bin/trec_eval -m ndcg_cut.10 -c tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-bge-base-en-v1.5.parquet.bge-flat-cached.topics.dl19-passage.bge-base-en-v1.5.jsonl.txt
bin/trec_eval -m recall.100 -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-bge-base-en-v1.5.parquet.bge-flat-cached.topics.dl19-passage.bge-base-en-v1.5.jsonl.txt
bin/trec_eval -m recall.1000 -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-bge-base-en-v1.5.parquet.bge-flat-cached.topics.dl19-passage.bge-base-en-v1.5.jsonl.txt

Effectiveness

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

AP@1000 BGE-base-en-v1.5
DL19 (Passage) 0.4435
nDCG@10 BGE-base-en-v1.5
DL19 (Passage) 0.7065
R@100 BGE-base-en-v1.5
DL19 (Passage) 0.6171
R@1000 BGE-base-en-v1.5
DL19 (Passage) 0.8472

Note that since we're running brute-force search with cached queries on non-quantized indexes, the results should be reproducible exactly.

❗ Retrieval metrics here are computed to depth 1000 hits per query (as opposed to 100 hits per query for document ranking). For computing nDCG, remember that we keep qrels of all relevance grades, whereas for other metrics (e.g., AP), relevance grade 1 is considered not relevant (i.e., use the -l 2 option in trec_eval). The experimental results reported here are directly comparable to the results reported in the track overview paper.

Reproduction Log*

To add to this reproduction log, modify this template and run bin/build.sh to rebuild the documentation.