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Github: datasets/msmarco_passage.py

ir_datasets: MSMARCO (passage)

Index
  1. msmarco-passage
  2. msmarco-passage/dev
  3. msmarco-passage/dev/judged
  4. msmarco-passage/dev/small
  5. msmarco-passage/eval
  6. msmarco-passage/eval/small
  7. msmarco-passage/train
  8. msmarco-passage/train/judged
  9. msmarco-passage/train/medical
  10. msmarco-passage/train/split200-train
  11. msmarco-passage/train/split200-valid
  12. msmarco-passage/train/triples-small
  13. msmarco-passage/train/triples-v2
  14. msmarco-passage/trec-dl-2019
  15. msmarco-passage/trec-dl-2019/judged
  16. msmarco-passage/trec-dl-2020
  17. msmarco-passage/trec-dl-2020/judged
  18. msmarco-passage/trec-dl-hard
  19. msmarco-passage/trec-dl-hard/fold1
  20. msmarco-passage/trec-dl-hard/fold2
  21. msmarco-passage/trec-dl-hard/fold3
  22. msmarco-passage/trec-dl-hard/fold4
  23. msmarco-passage/trec-dl-hard/fold5

"msmarco-passage"

A passage ranking benchmark with a collection of 8.8 million passages and question queries. Most relevance judgments are shallow (typically at most 1-2 per query), but the TREC Deep Learning track adds deep judgments. Evaluation typically conducted using MRR@10.

Note that the original document source files for this collection contain a double-encoding error that cause strange sequences like "å¬" and "ðºð". These are automatically corrrected (properly converting previous examples to "公" and "🇺🇸").

docs
8.8M docs

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

Citation

ir_datasets.bib:

\cite{Bajaj2016Msmarco}

Bibtex:

@inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/dev"

Official dev set.

scoreddocs are the top 1000 results from BM25. These are used for the "re-ranking" setting. Note that these are sub-sampled to about 1/8 of the total avaialable dev queries by the MSMARCO authors for faster evaluation. The BM25 scores from scoreddocs are not available (all have a score of 0).

Official evaluation measures: RR@10

queries
101K queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/dev")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/dev queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/dev')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/dev")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/dev docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/dev')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
59K qrels
Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
1Labeled by crowd worker as relevant59K100.0%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/dev")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/dev qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/dev')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [RR@10]
)

You can find more details about PyTerrier experiments here.

Citation

ir_datasets.bib:

\cite{Bajaj2016Msmarco}

Bibtex:

@inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/dev/judged"

Subset of msmarco-passage/dev that only includes queries that have at least one qrel.

Official evaluation measures: RR@10

queries
56K queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/dev/judged")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/dev/judged queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/dev/judged')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/dev/judged")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/dev/judged docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/dev/judged')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
59K qrels

Inherits qrels from msmarco-passage/dev

Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
1Labeled by crowd worker as relevant59K100.0%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/dev/judged")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/dev/judged qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/dev/judged')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [RR@10]
)

You can find more details about PyTerrier experiments here.

Citation

ir_datasets.bib:

\cite{Bajaj2016Msmarco}

Bibtex:

@inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/dev/small"

Official "small" version of the dev set, consisting of 6,980 queries (6.9% of the full dev set).

Official evaluation measures: RR@10

queries
7.0K queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/dev/small")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/dev/small queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/dev/small')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/dev/small")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/dev/small docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/dev/small')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
7.4K qrels
Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
1Labeled by crowd worker as relevant7.4K100.0%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/dev/small")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/dev/small qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/dev/small')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [RR@10]
)

You can find more details about PyTerrier experiments here.

scoreddocs
6.7M scoreddocs
Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/dev/small")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/dev/small scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{Bajaj2016Msmarco}

Bibtex:

@inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/eval"

Official eval set for submission to MS MARCO leaderboard. Relevance judgments are hidden.

scoreddocs are the top 1000 results from BM25. These are used for the "re-ranking" setting. Note that these are sub-sampled to about 1/8 of the total avaialable eval queries by the MSMARCO authors for faster evaluation. The BM25 scores from scoreddocs are not available (all have a score of 0).

Official evaluation measures: RR@10

queries
101K queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/eval")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/eval queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/eval')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/eval")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/eval docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/eval')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

Citation

ir_datasets.bib:

\cite{Bajaj2016Msmarco}

Bibtex:

@inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/eval/small"

Official "small" version of the eval set, consisting of 6,837 queries (6.8% of the full eval set).

Official evaluation measures: RR@10

queries
6.8K queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/eval/small")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/eval/small queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/eval/small')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/eval/small")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/eval/small docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/eval/small')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

scoreddocs
6.5M scoreddocs
Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/eval/small")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/eval/small scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{Bajaj2016Msmarco}

Bibtex:

@inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/train"

Official train set.

Not all queries have relevance judgments. Use msmarco-passage/train/judged for a filtered list that only includes documents that have at least one qrel.

scoreddocs are the top 1000 results from BM25. These are used for the "re-ranking" setting. Note that these are sub-sampled to about 1/8 of the total avaialable train queries by the MSMARCO authors for faster evaluation. The BM25 scores from scoreddocs are not available (all have a score of 0).

docpairs provides access to the "official" sequence for pairwise training.

Official evaluation measures: RR@10

queries
809K queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
533K qrels
Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
1Labeled by crowd worker as relevant533K100.0%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [RR@10]
)

You can find more details about PyTerrier experiments here.

scoreddocs
478M scoreddocs
Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

docpairs
270M docpairs
Document Pair type:
GenericDocPair: (namedtuple)
  1. query_id: str
  2. doc_id_a: str
  3. doc_id_b: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train")
for docpair in dataset.docpairs_iter():
    docpair # namedtuple<query_id, doc_id_a, doc_id_b>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train docpairs
[query_id]    [doc_id_a]    [doc_id_b]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{Bajaj2016Msmarco}

Bibtex:

@inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/train/judged"

Subset of msmarco-passage/train that only includes queries that have at least one qrel.

Official evaluation measures: RR@10

queries
503K queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/judged")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/judged queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/judged')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/judged")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/judged docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/judged')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
533K qrels

Inherits qrels from msmarco-passage/train

Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
1Labeled by crowd worker as relevant533K100.0%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/judged")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/judged qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/judged')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [RR@10]
)

You can find more details about PyTerrier experiments here.

scoreddocs
478M scoreddocs
Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/judged")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/judged scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

docpairs
270M docpairs

Inherits docpairs from msmarco-passage/train

Document Pair type:
GenericDocPair: (namedtuple)
  1. query_id: str
  2. doc_id_a: str
  3. doc_id_b: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/judged")
for docpair in dataset.docpairs_iter():
    docpair # namedtuple<query_id, doc_id_a, doc_id_b>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/judged docpairs
[query_id]    [doc_id_a]    [doc_id_b]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{Bajaj2016Msmarco}

Bibtex:

@inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/train/medical"

Subset of msmarco-passage/train that only includes queries that have a layman or expert medical term. Note that this includes about 20% false matches due to terms with multiple senses.

Official evaluation measures: RR@10

queries
79K queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/medical")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/medical queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/medical')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/medical")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/medical docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/medical')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
55K qrels
Query relevance judgment type:
GenericQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int

Relevance levels

Rel.DefinitionCount%
1Labeled by crowd worker as relevant55K100.0%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/medical")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/medical qrels --format tsv
[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/medical')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [RR@10]
)

You can find more details about PyTerrier experiments here.

scoreddocs
49M scoreddocs
Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/medical")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/medical scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

docpairs
29M docpairs
Document Pair type:
GenericDocPair: (namedtuple)
  1. query_id: str
  2. doc_id_a: str
  3. doc_id_b: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/medical")
for docpair in dataset.docpairs_iter():
    docpair # namedtuple<query_id, doc_id_a, doc_id_b>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/medical docpairs
[query_id]    [doc_id_a]    [doc_id_b]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{MacAvaney2020MedMarco,Bajaj2016Msmarco}

Bibtex:

@inproceedings{MacAvaney2020MedMarco, author = {MacAvaney, Sean and Cohan, Arman and Goharian, Nazli}, title = {SLEDGE-Zero: A Zero-Shot Baseline for COVID-19 Literature Search}, booktitle = {EMNLP}, year = {2020} } @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/train/split200-train"

Subset of msmarco-passage/train without 200 queries that are meant to be used as a small validation set. From various works.

Official evaluation measures: RR@10

queries
809K queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/split200-train")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/split200-train queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/split200-train')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/split200-train")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/split200-train docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/split200-train')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
533K qrels
Query relevance judgment type:
GenericQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int

Relevance levels

Rel.DefinitionCount%
1Labeled by crowd worker as relevant533K100.0%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/split200-train")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/split200-train qrels --format tsv
[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/split200-train')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [RR@10]
)

You can find more details about PyTerrier experiments here.

scoreddocs
478M scoreddocs
Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/split200-train")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/split200-train scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

docpairs
270M docpairs
Document Pair type:
GenericDocPair: (namedtuple)
  1. query_id: str
  2. doc_id_a: str
  3. doc_id_b: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/split200-train")
for docpair in dataset.docpairs_iter():
    docpair # namedtuple<query_id, doc_id_a, doc_id_b>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/split200-train docpairs
[query_id]    [doc_id_a]    [doc_id_b]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{Bajaj2016Msmarco}

Bibtex:

@inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/train/split200-valid"

Subset of msmarco-passage/train with only 200 queries that are meant to be used as a small validation set. From various works.

Official evaluation measures: RR@10

queries
200 queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/split200-valid")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/split200-valid queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/split200-valid')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/split200-valid")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/split200-valid docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/split200-valid')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
131 qrels
Query relevance judgment type:
GenericQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int

Relevance levels

Rel.DefinitionCount%
1Labeled by crowd worker as relevant131 100.0%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/split200-valid")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/split200-valid qrels --format tsv
[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/split200-valid')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [RR@10]
)

You can find more details about PyTerrier experiments here.

scoreddocs
119K scoreddocs
Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/split200-valid")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/split200-valid scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

docpairs
64K docpairs
Document Pair type:
GenericDocPair: (namedtuple)
  1. query_id: str
  2. doc_id_a: str
  3. doc_id_b: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/split200-valid")
for docpair in dataset.docpairs_iter():
    docpair # namedtuple<query_id, doc_id_a, doc_id_b>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/split200-valid docpairs
[query_id]    [doc_id_a]    [doc_id_b]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{Bajaj2016Msmarco}

Bibtex:

@inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/train/triples-small"

Version of msmarco-passage/train, but with the "small" triples file (a 10% sample of the full file).

Note that to save on storage space (27GB), the contents of the file are mapped to their corresponding query and document IDs. This process takes a few minutes to run the first time the triples are requested.

Official evaluation measures: RR@10

queries
809K queries

Inherits queries from msmarco-passage/train

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/triples-small")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/triples-small queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/triples-small')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/triples-small")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/triples-small docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/triples-small')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
533K qrels

Inherits qrels from msmarco-passage/train

Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
1Labeled by crowd worker as relevant533K100.0%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/triples-small")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/triples-small qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/triples-small')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [RR@10]
)

You can find more details about PyTerrier experiments here.

scoreddocs
478M scoreddocs

Inherits scoreddocs from msmarco-passage/train

Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/triples-small")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/triples-small scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

docpairs
40M docpairs
Document Pair type:
GenericDocPair: (namedtuple)
  1. query_id: str
  2. doc_id_a: str
  3. doc_id_b: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/triples-small")
for docpair in dataset.docpairs_iter():
    docpair # namedtuple<query_id, doc_id_a, doc_id_b>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/triples-small docpairs
[query_id]    [doc_id_a]    [doc_id_b]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{Bajaj2016Msmarco}

Bibtex:

@inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/train/triples-v2"

Version of msmarco-passage/train, but with version 2 of the triples file.

This version of the triples file includes rows that were accidently missing from version 1 of the file (see discussion here).

Note that this is sorted by the IDs in the file, so you probably would not want to use it unless you first shuffle it before usage. We opened an issue suggesting that a third version of the file is provided that is shuffled so that the order is consistent across groups using the data, but at this time, no such file exists in an official capacity.

Official evaluation measures: RR@10

queries
809K queries

Inherits queries from msmarco-passage/train

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/triples-v2")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/triples-v2 queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/triples-v2')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/triples-v2")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/triples-v2 docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/triples-v2')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
533K qrels

Inherits qrels from msmarco-passage/train

Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
1Labeled by crowd worker as relevant533K100.0%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/triples-v2")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/triples-v2 qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/train/triples-v2')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [RR@10]
)

You can find more details about PyTerrier experiments here.

scoreddocs
478M scoreddocs

Inherits scoreddocs from msmarco-passage/train

Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/triples-v2")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/triples-v2 scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

docpairs
398M docpairs
Document Pair type:
GenericDocPair: (namedtuple)
  1. query_id: str
  2. doc_id_a: str
  3. doc_id_b: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/train/triples-v2")
for docpair in dataset.docpairs_iter():
    docpair # namedtuple<query_id, doc_id_a, doc_id_b>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/train/triples-v2 docpairs
[query_id]    [doc_id_a]    [doc_id_b]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{Bajaj2016Msmarco}

Bibtex:

@inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/trec-dl-2019"

Queries from the TREC Deep Learning (DL) 2019 shared task, which were sampled from msmarco-passage/eval. A subset of these queries were judged by NIST assessors, (filtered list available in msmarco-passage/trec-dl-2019/judged).

Official evaluation measures: nDCG@10, RR(rel=2), AP(rel=2)

queries
200 queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2019")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2019 queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2019')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2019")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2019 docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2019')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
9.3K qrels
Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
0Irrelevant: The passage has nothing to do with the query.5.2K55.7%
1Related: The passage seems related to the query but does not answer it.1.6K17.3%
2Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information.1.8K19.5%
3Perfectly relevant: The passage is dedicated to the query and contains the exact answer.697 7.5%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2019")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2019 qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2019')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [nDCG@10, RR(rel=2), AP(rel=2)]
)

You can find more details about PyTerrier experiments here.

scoreddocs
190K scoreddocs
Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2019")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2019 scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{Craswell2019TrecDl,Bajaj2016Msmarco}

Bibtex:

@inproceedings{Craswell2019TrecDl, title={Overview of the TREC 2019 deep learning track}, author={Nick Craswell and Bhaskar Mitra and Emine Yilmaz and Daniel Campos and Ellen Voorhees}, booktitle={TREC 2019}, year={2019} } @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/trec-dl-2019/judged"

Subset of msmarco-passage/trec-dl-2019, only including queries with qrels.

Official evaluation measures: nDCG@10, RR(rel=2), AP(rel=2)

queries
43 queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2019/judged")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2019/judged queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2019/judged')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2019/judged")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2019/judged docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2019/judged')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
9.3K qrels

Inherits qrels from msmarco-passage/trec-dl-2019

Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
0Irrelevant: The passage has nothing to do with the query.5.2K55.7%
1Related: The passage seems related to the query but does not answer it.1.6K17.3%
2Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information.1.8K19.5%
3Perfectly relevant: The passage is dedicated to the query and contains the exact answer.697 7.5%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2019/judged")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2019/judged qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2019/judged')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [nDCG@10, RR(rel=2), AP(rel=2)]
)

You can find more details about PyTerrier experiments here.

scoreddocs
41K scoreddocs
Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2019/judged")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2019/judged scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{Craswell2019TrecDl,Bajaj2016Msmarco}

Bibtex:

@inproceedings{Craswell2019TrecDl, title={Overview of the TREC 2019 deep learning track}, author={Nick Craswell and Bhaskar Mitra and Emine Yilmaz and Daniel Campos and Ellen Voorhees}, booktitle={TREC 2019}, year={2019} } @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/trec-dl-2020"

Queries from the TREC Deep Learning (DL) 2020 shared task, which were sampled from msmarco-passage/eval. A subset of these queries were judged by NIST assessors, (filtered list available in msmarco-passage/trec-dl-2020/judged).

Official evaluation measures: nDCG@10, RR(rel=2), AP(rel=2)

queries
200 queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2020")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2020 queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2020')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2020")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2020 docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2020')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
11K qrels
Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
0Irrelevant: The passage has nothing to do with the query.7.8K68.3%
1Related: The passage seems related to the query but does not answer it.1.9K17.0%
2Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information.1.0K9.0%
3Perfectly relevant: The passage is dedicated to the query and contains the exact answer.646 5.7%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2020")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2020 qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2020')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [nDCG@10, RR(rel=2), AP(rel=2)]
)

You can find more details about PyTerrier experiments here.

scoreddocs
191K scoreddocs
Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2020")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2020 scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{Craswell2020TrecDl,Bajaj2016Msmarco}

Bibtex:

@inproceedings{Craswell2020TrecDl, title={Overview of the TREC 2020 deep learning track}, author={Nick Craswell and Bhaskar Mitra and Emine Yilmaz and Daniel Campos}, booktitle={TREC}, year={2020} } @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/trec-dl-2020/judged"

Subset of msmarco-passage/trec-dl-2020, only including queries with qrels.

Official evaluation measures: nDCG@10, RR(rel=2), AP(rel=2)

queries
54 queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2020/judged")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2020/judged queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2020/judged')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2020/judged")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2020/judged docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2020/judged')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
11K qrels

Inherits qrels from msmarco-passage/trec-dl-2020

Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
0Irrelevant: The passage has nothing to do with the query.7.8K68.3%
1Related: The passage seems related to the query but does not answer it.1.9K17.0%
2Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information.1.0K9.0%
3Perfectly relevant: The passage is dedicated to the query and contains the exact answer.646 5.7%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2020/judged")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2020/judged qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-2020/judged')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [nDCG@10, RR(rel=2), AP(rel=2)]
)

You can find more details about PyTerrier experiments here.

scoreddocs
50K scoreddocs
Scored Document type:
GenericScoredDoc: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. score: float

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-2020/judged")
for scoreddoc in dataset.scoreddocs_iter():
    scoreddoc # namedtuple<query_id, doc_id, score>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-2020/judged scoreddocs --format tsv
[query_id]    [doc_id]    [score]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Citation

ir_datasets.bib:

\cite{Craswell2020TrecDl,Bajaj2016Msmarco}

Bibtex:

@inproceedings{Craswell2020TrecDl, title={Overview of the TREC 2020 deep learning track}, author={Nick Craswell and Bhaskar Mitra and Emine Yilmaz and Daniel Campos}, booktitle={TREC}, year={2020} } @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/trec-dl-hard"

A more challenging subset of msmarco-passage/trec-dl-2019 and msmarco-document/trec-dl-2020.

Official evaluation measures: nDCG@10, RR(rel=2)

queries
50 queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
4.3K qrels
Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
0Irrelevant: The passage has nothing to do with the query.2.5K57.8%
1Related: The passage seems related to the query but does not answer it.810 19.0%
2Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information.634 14.9%
3Perfectly relevant: The passage is dedicated to the query and contains the exact answer.350 8.2%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [nDCG@10, RR(rel=2)]
)

You can find more details about PyTerrier experiments here.

Citation

ir_datasets.bib:

\cite{Mackie2021DlHard,Bajaj2016Msmarco}

Bibtex:

@article{Mackie2021DlHard, title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset}, author={Iain Mackie and Jeffrey Dalton and Andrew Yates}, journal={ArXiv}, year={2021}, volume={abs/2105.07975} } @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/trec-dl-hard/fold1"

Fold 1 of msmarco-passage/trec-dl-hard

Official evaluation measures: nDCG@10, RR(rel=2)

queries
10 queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold1")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold1 queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold1')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold1")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold1 docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold1')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
1.1K qrels
Query relevance judgment type:
GenericQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int

Relevance levels

Rel.DefinitionCount%
0Irrelevant: The passage has nothing to do with the query.582 54.3%
1Related: The passage seems related to the query but does not answer it.197 18.4%
2Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information.181 16.9%
3Perfectly relevant: The passage is dedicated to the query and contains the exact answer.112 10.4%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold1")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold1 qrels --format tsv
[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold1')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [nDCG@10, RR(rel=2)]
)

You can find more details about PyTerrier experiments here.

Citation

ir_datasets.bib:

\cite{Mackie2021DlHard,Bajaj2016Msmarco}

Bibtex:

@article{Mackie2021DlHard, title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset}, author={Iain Mackie and Jeffrey Dalton and Andrew Yates}, journal={ArXiv}, year={2021}, volume={abs/2105.07975} } @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/trec-dl-hard/fold2"

Fold 2 of msmarco-passage/trec-dl-hard

Official evaluation measures: nDCG@10, RR(rel=2)

queries
10 queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold2")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold2 queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold2')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold2")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold2 docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold2')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
898 qrels
Query relevance judgment type:
GenericQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int

Relevance levels

Rel.DefinitionCount%
0Irrelevant: The passage has nothing to do with the query.611 68.0%
1Related: The passage seems related to the query but does not answer it.151 16.8%
2Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information.99 11.0%
3Perfectly relevant: The passage is dedicated to the query and contains the exact answer.37 4.1%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold2")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold2 qrels --format tsv
[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold2')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [nDCG@10, RR(rel=2)]
)

You can find more details about PyTerrier experiments here.

Citation

ir_datasets.bib:

\cite{Mackie2021DlHard,Bajaj2016Msmarco}

Bibtex:

@article{Mackie2021DlHard, title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset}, author={Iain Mackie and Jeffrey Dalton and Andrew Yates}, journal={ArXiv}, year={2021}, volume={abs/2105.07975} } @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/trec-dl-hard/fold3"

Fold 3 of msmarco-passage/trec-dl-hard

Official evaluation measures: nDCG@10, RR(rel=2)

queries
10 queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold3")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold3 queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold3')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold3")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold3 docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold3')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
444 qrels
Query relevance judgment type:
GenericQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int

Relevance levels

Rel.DefinitionCount%
0Irrelevant: The passage has nothing to do with the query.342 77.0%
1Related: The passage seems related to the query but does not answer it.43 9.7%
2Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information.36 8.1%
3Perfectly relevant: The passage is dedicated to the query and contains the exact answer.23 5.2%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold3")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold3 qrels --format tsv
[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold3')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [nDCG@10, RR(rel=2)]
)

You can find more details about PyTerrier experiments here.

Citation

ir_datasets.bib:

\cite{Mackie2021DlHard,Bajaj2016Msmarco}

Bibtex:

@article{Mackie2021DlHard, title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset}, author={Iain Mackie and Jeffrey Dalton and Andrew Yates}, journal={ArXiv}, year={2021}, volume={abs/2105.07975} } @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/trec-dl-hard/fold4"

Fold 4 of msmarco-passage/trec-dl-hard

Official evaluation measures: nDCG@10, RR(rel=2)

queries
10 queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold4")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold4 queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold4')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold4")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold4 docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold4')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
716 qrels
Query relevance judgment type:
GenericQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int

Relevance levels

Rel.DefinitionCount%
0Irrelevant: The passage has nothing to do with the query.396 55.3%
1Related: The passage seems related to the query but does not answer it.137 19.1%
2Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information.151 21.1%
3Perfectly relevant: The passage is dedicated to the query and contains the exact answer.32 4.5%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold4")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold4 qrels --format tsv
[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold4')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [nDCG@10, RR(rel=2)]
)

You can find more details about PyTerrier experiments here.

Citation

ir_datasets.bib:

\cite{Mackie2021DlHard,Bajaj2016Msmarco}

Bibtex:

@article{Mackie2021DlHard, title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset}, author={Iain Mackie and Jeffrey Dalton and Andrew Yates}, journal={ArXiv}, year={2021}, volume={abs/2105.07975} } @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata

"msmarco-passage/trec-dl-hard/fold5"

Fold 5 of msmarco-passage/trec-dl-hard

Official evaluation measures: nDCG@10, RR(rel=2)

queries
10 queries

Language: en

Query type:
GenericQuery: (namedtuple)
  1. query_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold5")
for query in dataset.queries_iter():
    query # namedtuple<query_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold5 queries
[query_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold5')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics())

You can find more details about PyTerrier retrieval here.

docs
8.8M docs

Inherits docs from msmarco-passage

Language: en

Document type:
GenericDoc: (namedtuple)
  1. doc_id: str
  2. text: str

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold5")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, text>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold5 docs
[doc_id]    [text]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold5')
# Index msmarco-passage
indexer = pt.IterDictIndexer('./indices/msmarco-passage')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['text'])

You can find more details about PyTerrier indexing here.

qrels
1.1K qrels
Query relevance judgment type:
GenericQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int

Relevance levels

Rel.DefinitionCount%
0Irrelevant: The passage has nothing to do with the query.531 47.2%
1Related: The passage seems related to the query but does not answer it.282 25.0%
2Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information.167 14.8%
3Perfectly relevant: The passage is dedicated to the query and contains the exact answer.146 13.0%

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("msmarco-passage/trec-dl-hard/fold5")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance>

You can find more details about the Python API here.

CLI
ir_datasets export msmarco-passage/trec-dl-hard/fold5 qrels --format tsv
[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

PyTerrier
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:msmarco-passage/trec-dl-hard/fold5')
index_ref = pt.IndexRef.of('./indices/msmarco-passage') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics(),
    dataset.get_qrels(),
    [nDCG@10, RR(rel=2)]
)

You can find more details about PyTerrier experiments here.

Citation

ir_datasets.bib:

\cite{Mackie2021DlHard,Bajaj2016Msmarco}

Bibtex:

@article{Mackie2021DlHard, title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset}, author={Iain Mackie and Jeffrey Dalton and Andrew Yates}, journal={ArXiv}, year={2021}, volume={abs/2105.07975} } @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} }
Metadata