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Anserini Regressions: BEIR (v1.0.0) — Webis-Touche2020

Model: SPLADE++ (CoCondenser-EnsembleDistil)

This page describes regression experiments, integrated into Anserini's regression testing framework, using SPLADE++ (CoCondenser-EnsembleDistil) on BEIR (v1.0.0) — Webis-Touche2020. See the official SPLADE repo for more details; the model itself can be download here.

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 beir-v1.0.0-webis-touche2020-splade-pp-ed

Indexing

Sample indexing command:

target/appassembler/bin/IndexCollection \
  -collection JsonVectorCollection \
  -input /path/to/beir-v1.0.0-webis-touche2020-splade-pp-ed \
  -generator DefaultLuceneDocumentGenerator \
  -index indexes/lucene-index.beir-v1.0.0-webis-touche2020-splade-pp-ed/ \
  -threads 16 -impact -pretokenized -optimize \
  >& logs/log.beir-v1.0.0-webis-touche2020-splade-pp-ed &

The important indexing options to note here are -impact -pretokenized: the first tells Anserini not to encode BM25 doclengths into Lucene's norms (which is the default) and the second option says not to apply any additional tokenization on the pre-encoded tokens. 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:

target/appassembler/bin/SearchCollection \
  -index indexes/lucene-index.beir-v1.0.0-webis-touche2020-splade-pp-ed/ \
  -topics tools/topics-and-qrels/topics.beir-v1.0.0-webis-touche2020.test.splade-pp-ed.tsv.gz \
  -topicReader TsvString \
  -output runs/run.beir-v1.0.0-webis-touche2020-splade-pp-ed.splade-pp-ed.topics.beir-v1.0.0-webis-touche2020.test.splade-pp-ed.txt \
  -impact -pretokenized -removeQuery -hits 1000 &

Evaluation can be performed using trec_eval:

tools/eval/trec_eval.9.0.4/trec_eval -c -m ndcg_cut.10 tools/topics-and-qrels/qrels.beir-v1.0.0-webis-touche2020.test.txt runs/run.beir-v1.0.0-webis-touche2020-splade-pp-ed.splade-pp-ed.topics.beir-v1.0.0-webis-touche2020.test.splade-pp-ed.txt
tools/eval/trec_eval.9.0.4/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.beir-v1.0.0-webis-touche2020.test.txt runs/run.beir-v1.0.0-webis-touche2020-splade-pp-ed.splade-pp-ed.topics.beir-v1.0.0-webis-touche2020.test.splade-pp-ed.txt
tools/eval/trec_eval.9.0.4/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.beir-v1.0.0-webis-touche2020.test.txt runs/run.beir-v1.0.0-webis-touche2020-splade-pp-ed.splade-pp-ed.topics.beir-v1.0.0-webis-touche2020.test.splade-pp-ed.txt

Effectiveness

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

nDCG@10 SPLADE++ (CoCondenser-EnsembleDistil)
BEIR (v1.0.0): Webis-Touche2020 0.2468
R@100 SPLADE++ (CoCondenser-EnsembleDistil)
BEIR (v1.0.0): Webis-Touche2020 0.4715
R@1000 SPLADE++ (CoCondenser-EnsembleDistil)
BEIR (v1.0.0): Webis-Touche2020 0.8191

Reproduction Log*

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