Skip to content

regressions-msmarco-passage-distill-splade-max.md

Latest commit

 

History

History
144 lines (98 loc) · 8.15 KB

regressions-msmarco-passage-distill-splade-max.md

File metadata and controls

144 lines (98 loc) · 8.15 KB
Raw

Anserini Regressions: MS MARCO Passage Ranking

Model: DistilSPLADE-max

This page describes regression experiments, integrated into Anserini's regression testing framework, using the DistilSPLADE-max model from SPLADEv2 on the MS MARCO passage ranking task. The DistilSPLADE-max model is described in the following paper:

Thibault Formal, Carlos Lassance, Benjamin Piwowarski, Stéphane Clinchant. SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval. arXiv:2109.10086.

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 msmarco-passage-distill-splade-max

Corpus

We make available a version of the MS MARCO passage corpus that has already been processed with the model (i.e., with inference applied to generate the lexical representations). Thus, no neural inference is involved.

Download the corpus and unpack into collections/:

wget https://rgw.cs.uwaterloo.ca/JIMMYLIN-bucket0/data/msmarco-passage-distill-splade-max.tar -P collections/

# Alternate mirror:
# wget https://vault.cs.uwaterloo.ca/s/poCLbJDMm7JxwPk/download -O collections/msmarco-passage-distill-splade-max.tar

tar xvf collections/msmarco-passage-distill-splade-max.tar -C collections/

To confirm, msmarco-passage-distill-splade-max.tar is 9.9 GB and has MD5 checksum 95b89a7dfd88f3685edcc2d1ffb120d1.

With the corpus downloaded, the following command will perform the complete regression, end to end, on any machine:

python src/main/python/run_regression.py --index --verify --search --regression msmarco-passage-distill-splade-max \
  --corpus-path collections/msmarco-passage-distill-splade-max

Alternatively, you can simply copy/paste from the commands below and obtain the same results.

Indexing

Sample indexing command:

target/appassembler/bin/IndexCollection \
  -collection JsonVectorCollection \
  -input /path/to/msmarco-passage-distill-splade-max \
  -index indexes/lucene-index.msmarco-passage-distill-splade-max/ \
  -generator DefaultLuceneDocumentGenerator \
  -threads 16 -impact -pretokenized \
  >& logs/log.msmarco-passage-distill-splade-max &

The path /path/to/msmarco-passage-distill-splade-max/ should point to the corpus downloaded above.

The important indexing options to note here are -impact -pretokenized: the first tells Anserini not to encode BM25 doc lengths into Lucene's norms (which is the default) and the second option says not to apply any additional tokenization on the SPLADEv2 tokens. Upon completion, we should have an index with 8,841,823 documents.

For additional details, see explanation of common indexing options.

Retrieval

Topics and qrels are stored in src/main/resources/topics-and-qrels/. The regression experiments here evaluate on the 6980 dev set questions; see this page for more details.

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

target/appassembler/bin/SearchCollection \
  -index indexes/lucene-index.msmarco-passage-distill-splade-max/ \
  -topics src/main/resources/topics-and-qrels/topics.msmarco-passage.dev-subset.distill-splade-max.tsv.gz \
  -topicreader TsvInt \
  -output runs/run.msmarco-passage-distill-splade-max.distill-splade-max.topics.msmarco-passage.dev-subset.distill-splade-max.txt \
  -impact -pretokenized &

Evaluation can be performed using trec_eval:

tools/eval/trec_eval.9.0.4/trec_eval -c -m map src/main/resources/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage-distill-splade-max.distill-splade-max.topics.msmarco-passage.dev-subset.distill-splade-max.txt
tools/eval/trec_eval.9.0.4/trec_eval -c -M 10 -m recip_rank src/main/resources/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage-distill-splade-max.distill-splade-max.topics.msmarco-passage.dev-subset.distill-splade-max.txt
tools/eval/trec_eval.9.0.4/trec_eval -c -m recall.100 src/main/resources/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage-distill-splade-max.distill-splade-max.topics.msmarco-passage.dev-subset.distill-splade-max.txt
tools/eval/trec_eval.9.0.4/trec_eval -c -m recall.1000 src/main/resources/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt runs/run.msmarco-passage-distill-splade-max.distill-splade-max.topics.msmarco-passage.dev-subset.distill-splade-max.txt

Effectiveness

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

AP@1000 DistilSPLADE-max
MS MARCO Passage: Dev 0.3746
RR@10 DistilSPLADE-max
MS MARCO Passage: Dev 0.3686
R@100 DistilSPLADE-max
MS MARCO Passage: Dev 0.8984
R@1000 DistilSPLADE-max
MS MARCO Passage: Dev 0.9787

The above runs are in TREC output format and evaluated with trec_eval. In order to reproduce results reported in the paper, we need to convert to MS MARCO output format and then evaluate:

python tools/scripts/msmarco/convert_trec_to_msmarco_run.py \
   --input runs/run.msmarco-passage-distill-splade-max.distill-splade-max.topics.msmarco-passage.dev-subset.distill-splade-max.txt \
   --output runs/run.msmarco-passage-distill-splade-max.distill-splade-max.topics.msmarco-passage.dev-subset.distill-splade-max.tsv --quiet

python tools/scripts/msmarco/msmarco_passage_eval.py \
   tools/topics-and-qrels/qrels.msmarco-passage.dev-subset.txt \
   runs/run.msmarco-passage-distill-splade-max.distill-splade-max.topics.msmarco-passage.dev-subset.distill-splade-max.tsv

The results should be as follows:

#####################
MRR @10: 0.36852691363078205
QueriesRanked: 6980
#####################

This corresponds to the effectiveness reported in the paper.

Reproduction Log*

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