metadata

tags:
  - pyterrier
  - pyterrier-artifact
  - pyterrier-artifact.sparse_index
  - pyterrier-artifact.sparse_index.pisa
task_categories:
  - text-retrieval
viewer: false

MS MARCO PISA Index

Description

This is an index of the MS MARCO passage (v1) dataset with PISA. It can be used for passage retrieval using lexical methods.

Usage

>>> from pyterrier_pisa import PisaIndex
>>> index = PisaIndex.from_hf('macavaney/msmarco-passage.pisa')
>>> bm25 = index.bm25()
>>> bm25.search('terrier breeds')
    qid           query    docno      score  rank
0     1  terrier breeds  1406578  22.686367     0
1     1  terrier breeds  5785957  22.611134     1
2     1  terrier breeds  7455374  22.592781     2
3     1  terrier breeds  3984886  22.242958     3
4     1  terrier breeds  3984893  22.009525     4
...

Benchmarks

TREC DL 2019

Code

from ir_measures import nDCG, RR, MAP, R
import pyterrier as pt
from pyterrier_pisa import PisaIndex
index = PisaIndex.from_hf('macavaney/msmarco-passage.pisa')
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2019/judged')
pt.Experiment(
  [index.bm25(), index.qld(), index.dph(), index.pl2()],
  dataset.get_topics(),
  dataset.get_qrels(),
  [nDCG@10, nDCG, RR(rel=2), MAP(rel=2), R(rel=2)@1000],
  ['BM25', 'QLD', 'DPH', 'PL2'],
  round=4,
)

	name	nDCG@10	nDCG	RR(rel=2)	AP(rel=2)	R(rel=2)@1000
0	BM25	0.4989	0.6023	0.6804	0.3031	0.7555
1	QLD	0.468	0.5984	0.6047	0.3037	0.7601
2	DPH	0.4975	0.5907	0.6674	0.3009	0.7436
3	PL2	0.4503	0.5681	0.6495	0.2679	0.7304

TREC DL 2020

Code

from ir_measures import nDCG, RR, MAP, R
import pyterrier as pt
from pyterrier_pisa import PisaIndex
index = PisaIndex.from_hf('macavaney/msmarco-passage.pisa')
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2020/judged')
pt.Experiment(
  [index.bm25(), index.qld(), index.dph(), index.pl2()],
  dataset.get_topics(),
  dataset.get_qrels(),
  [nDCG@10, nDCG, RR(rel=2), MAP(rel=2), R(rel=2)@1000],
  ['BM25', 'QLD', 'DPH', 'PL2'],
  round=4,
)

	name	nDCG@10	nDCG	RR(rel=2)	AP(rel=2)	R(rel=2)@1000
0	BM25	0.4793	0.5963	0.6529	0.2974	0.8048
1	QLD	0.4511	0.587	0.5812	0.2879	0.8125
2	DPH	0.4586	0.5704	0.6123	0.2779	0.798
3	PL2	0.4552	0.5609	0.5788	0.2666	0.7772

MS MARCO Dev (small)

Code

from ir_measures import RR, R
import pyterrier as pt
from pyterrier_pisa import PisaIndex
index = PisaIndex.from_hf('macavaney/msmarco-passage.pisa')
dataset = pt.get_dataset('irds:msmarco-passage/dev/small')
pt.Experiment(
  [index.bm25(), index.qld(), index.dph(), index.pl2()],
  dataset.get_topics(),
  dataset.get_qrels(),
  [RR@10, R@1000],
  ['BM25', 'QLD', 'DPH', 'PL2'],
  round=4,
)

	name	RR@10	R@1000
0	BM25	0.185	0.8677
1	QLD	0.1683	0.8542
2	DPH	0.1782	0.8605
3	PL2	0.1741	0.8607

Reproduction

>>> import pyterrier_pisa
>>> import pyterrier as pt
>>> idx = pyterrier_pisa.PisaIndex('msmarco-passage.pisa')
>>> idx.indexer().index(pt.get_dataset('irds:msmarco-passage').get_corpus_iter())

Metadata

{
  "type": "sparse_index",
  "format": "pisa",
  "package_hint": "pyterrier-pisa",
  "stemmer": "porter2"
}