Skip to content

Latest commit

 

History

History
74 lines (55 loc) · 4.44 KB

regressions-msmarco-v1-doc.wp-ca.md

File metadata and controls

74 lines (55 loc) · 4.44 KB
Raw

Anserini Regressions: MS MARCO Document Ranking

Models: various bag-of-words approaches on complete documents using CompositeAnalyzer.

This page documents regression experiments on the MS MARCO document ranking task, which is integrated into Anserini's regression testing framework. Here we are using CompositeAnalyzer which combines Lucene tokenization with WordPiece tokenization (i.e., from BERT) using the following tokenizer from HuggingFace bert-base-uncased.

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-v1-doc.wp-ca

Indexing

Typical indexing command:

bin/run.sh io.anserini.index.IndexCollection \
  -threads 7 \
  -collection JsonCollection \
  -input /path/to/msmarco-doc \
  -generator DefaultLuceneDocumentGenerator \
  -index indexes/lucene-inverted.msmarco-v1-doc.wp-ca/ \
  -storePositions -storeDocvectors -storeRaw -analyzeWithHuggingFaceTokenizer bert-base-uncased -useCompositeAnalyzer \
  >& logs/log.msmarco-doc &

The directory /path/to/msmarco-doc/ should be a directory containing the document corpus in Anserini's jsonl format. See this page for how to prepare 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. The regression experiments here evaluate on the 5193 dev set questions.

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

bin/run.sh io.anserini.search.SearchCollection \
  -index indexes/lucene-inverted.msmarco-v1-doc.wp-ca/ \
  -topics tools/topics-and-qrels/topics.msmarco-doc.dev.txt \
  -topicReader TsvInt \
  -output runs/run.msmarco-doc.bm25-default.topics.msmarco-doc.dev.txt \
  -bm25 -analyzeWithHuggingFaceTokenizer bert-base-uncased -useCompositeAnalyzer &

Evaluation can be performed using trec_eval:

bin/trec_eval -c -m map tools/topics-and-qrels/qrels.msmarco-doc.dev.txt runs/run.msmarco-doc.bm25-default.topics.msmarco-doc.dev.txt
bin/trec_eval -c -M 100 -m recip_rank tools/topics-and-qrels/qrels.msmarco-doc.dev.txt runs/run.msmarco-doc.bm25-default.topics.msmarco-doc.dev.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.msmarco-doc.dev.txt runs/run.msmarco-doc.bm25-default.topics.msmarco-doc.dev.txt
bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.msmarco-doc.dev.txt runs/run.msmarco-doc.bm25-default.topics.msmarco-doc.dev.txt

Effectiveness

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

AP@1000 BM25 (default)
MS MARCO Doc: Dev 0.2410
RR@100 BM25 (default)
MS MARCO Doc: Dev 0.2403
R@100 BM25 (default)
MS MARCO Doc: Dev 0.7441
R@1000 BM25 (default)
MS MARCO Doc: Dev 0.9004