ir_datasets : NFCorpus (NutritionFacts)

import ir_datasets
dataset = ir_datasets.load("nfcorpus")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, abstract>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus docs



[doc_id]    [url]    [title]    [abstract]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus')
# Index nfcorpus
indexer = pt.IterDictIndexer('./indices/nfcorpus')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'abstract'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.nfcorpus')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

\cite{Boteva2016Nfcorpus}

Bibtex:

@inproceedings{Boteva2016Nfcorpus, title="A Full-Text Learning to Rank Dataset for Medical Information Retrieval", author = "Vera Boteva and Demian Gholipour and Artem Sokolov and Stefan Riezler", booktitle = "Proceedings of the European Conference on Information Retrieval ({ECIR})", location = "Padova, Italy", publisher = "Springer", year = 2016 }

{
  "docs": {
    "count": 5371,
    "fields": {
      "doc_id": {
        "max_len": 8,
        "common_prefix": "MED-"
      }
    }
  }
}

`"nfcorpus/dev"`

Official dev set. Queries include both title and combinted "all" text field (titles, descriptions, topics, transcripts and comments)

325 queries

Language: en

Query type:

NfCorpusQuery: (namedtuple)

query_id: str
title: str
all: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/dev")
for query in dataset.queries_iter():
    query # namedtuple<query_id, title, all>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/dev queries



[query_id]    [title]    [all]
...

You can find more details about the CLI here.

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

You can find more details about PyTerrier retrieval here.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.nfcorpus.dev.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

5.4K docs

Inherits docs from nfcorpus

Language: en

Document type:

NfCorpusDoc: (namedtuple)

doc_id: str
url: str
title: str
abstract: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/dev")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, abstract>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/dev docs



[doc_id]    [url]    [title]    [abstract]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/dev')
# Index nfcorpus
indexer = pt.IterDictIndexer('./indices/nfcorpus')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'abstract'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.nfcorpus.dev')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

15K qrels

Query relevance judgment type:

TrecQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int
iteration: str

Relevance levels

Rel.	Definition	Count	%
0	Marginally relevant, based on topic containment.	`0`	0.0%
1	A link exists from the query to another query that directly links to the document.	`3.2K`	22.0%
2	A direct link from the query to the document the cited sources section of a page.	`11K`	74.5%

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/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 nfcorpus/dev qrels --format tsv



[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/dev')
index_ref = pt.IndexRef.of('./indices/nfcorpus') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics('title'),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.nfcorpus.dev.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Boteva2016Nfcorpus}

Bibtex:

{
  "docs": {
    "count": 5371,
    "fields": {
      "doc_id": {
        "max_len": 8,
        "common_prefix": "MED-"
      }
    }
  },
  "queries": {
    "count": 325
  },
  "qrels": {
    "count": 14589,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "3": 521,
          "2": 10864,
          "1": 3204
        }
      }
    }
  }
}

`"nfcorpus/dev/nontopic"`

Official dev set, filtered to exclude queries from topic pages.

144 queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/dev/nontopic")
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 nfcorpus/dev/nontopic queries



[query_id]    [text]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/dev/nontopic')
index_ref = pt.IndexRef.of('./indices/nfcorpus') # 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.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.nfcorpus.dev.nontopic.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

5.4K docs

Inherits docs from nfcorpus

Language: en

Document type:

NfCorpusDoc: (namedtuple)

doc_id: str
url: str
title: str
abstract: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/dev/nontopic")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, abstract>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/dev/nontopic docs



[doc_id]    [url]    [title]    [abstract]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/dev/nontopic')
# Index nfcorpus
indexer = pt.IterDictIndexer('./indices/nfcorpus')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'abstract'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.nfcorpus.dev.nontopic')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

4.4K qrels

Query relevance judgment type:

GenericQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int

Relevance levels

Rel.	Definition	Count	%
0	Marginally relevant, based on topic containment.	`0`	0.0%
1	A link exists from the query to another query that directly links to the document.	`699`	16.1%
2	A direct link from the query to the document the cited sources section of a page.	`3.1K`	72.0%

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/dev/nontopic")
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 nfcorpus/dev/nontopic qrels --format tsv



[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/dev/nontopic')
index_ref = pt.IndexRef.of('./indices/nfcorpus') # 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(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.nfcorpus.dev.nontopic.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Boteva2016Nfcorpus}

Bibtex:

{
  "docs": {
    "count": 5371,
    "fields": {
      "doc_id": {
        "max_len": 8,
        "common_prefix": "MED-"
      }
    }
  },
  "queries": {
    "count": 144
  },
  "qrels": {
    "count": 4353,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "3": 521,
          "2": 3133,
          "1": 699
        }
      }
    }
  }
}

`"nfcorpus/dev/video"`

Official dev set, filtered to only include queries from video pages.

102 queries

Language: en

Query type:

NfCorpusVideoQuery: (namedtuple)

query_id: str
title: str
desc: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/dev/video")
for query in dataset.queries_iter():
    query # namedtuple<query_id, title, desc>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/dev/video queries



[query_id]    [title]    [desc]
...

You can find more details about the CLI here.

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

You can find more details about PyTerrier retrieval here.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.nfcorpus.dev.video.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

5.4K docs

Inherits docs from nfcorpus

Language: en

Document type:

NfCorpusDoc: (namedtuple)

doc_id: str
url: str
title: str
abstract: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/dev/video")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, abstract>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/dev/video docs



[doc_id]    [url]    [title]    [abstract]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/dev/video')
# Index nfcorpus
indexer = pt.IterDictIndexer('./indices/nfcorpus')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'abstract'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.nfcorpus.dev.video')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

3.1K qrels

Query relevance judgment type:

GenericQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int

Relevance levels

Rel.	Definition	Count	%
0	Marginally relevant, based on topic containment.	`0`	0.0%
1	A link exists from the query to another query that directly links to the document.	`678`	22.1%
2	A direct link from the query to the document the cited sources section of a page.	`2.0K`	64.5%

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/dev/video")
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 nfcorpus/dev/video qrels --format tsv



[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/dev/video')
index_ref = pt.IndexRef.of('./indices/nfcorpus') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics('title'),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.nfcorpus.dev.video.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Boteva2016Nfcorpus}

Bibtex:

{
  "docs": {
    "count": 5371,
    "fields": {
      "doc_id": {
        "max_len": 8,
        "common_prefix": "MED-"
      }
    }
  },
  "queries": {
    "count": 102
  },
  "qrels": {
    "count": 3068,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "3": 411,
          "2": 1979,
          "1": 678
        }
      }
    }
  }
}

`"nfcorpus/test"`

Official test set. Queries include both title and combinted "all" text field (titles, descriptions, topics, transcripts and comments)

325 queries

Language: en

Query type:

NfCorpusQuery: (namedtuple)

query_id: str
title: str
all: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/test")
for query in dataset.queries_iter():
    query # namedtuple<query_id, title, all>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/test queries



[query_id]    [title]    [all]
...

You can find more details about the CLI here.

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

You can find more details about PyTerrier retrieval here.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.nfcorpus.test.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

5.4K docs

Inherits docs from nfcorpus

Language: en

Document type:

NfCorpusDoc: (namedtuple)

doc_id: str
url: str
title: str
abstract: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/test")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, abstract>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/test docs



[doc_id]    [url]    [title]    [abstract]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/test')
# Index nfcorpus
indexer = pt.IterDictIndexer('./indices/nfcorpus')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'abstract'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.nfcorpus.test')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

16K qrels

Query relevance judgment type:

TrecQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int
iteration: str

Relevance levels

Rel.	Definition	Count	%
0	Marginally relevant, based on topic containment.	`0`	0.0%
1	A link exists from the query to another query that directly links to the document.	`3.5K`	22.0%
2	A direct link from the query to the document the cited sources section of a page.	`12K`	74.3%

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/test")
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 nfcorpus/test qrels --format tsv



[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/test')
index_ref = pt.IndexRef.of('./indices/nfcorpus') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics('title'),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.nfcorpus.test.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Boteva2016Nfcorpus}

Bibtex:

{
  "docs": {
    "count": 5371,
    "fields": {
      "doc_id": {
        "max_len": 8,
        "common_prefix": "MED-"
      }
    }
  },
  "queries": {
    "count": 325
  },
  "qrels": {
    "count": 15820,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "3": 576,
          "1": 3486,
          "2": 11758
        }
      }
    }
  }
}

`"nfcorpus/test/nontopic"`

Official test set, filtered to exclude queries from topic pages.

144 queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/test/nontopic")
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 nfcorpus/test/nontopic queries



[query_id]    [text]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/test/nontopic')
index_ref = pt.IndexRef.of('./indices/nfcorpus') # 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.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.nfcorpus.test.nontopic.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

5.4K docs

Inherits docs from nfcorpus

Language: en

Document type:

NfCorpusDoc: (namedtuple)

doc_id: str
url: str
title: str
abstract: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/test/nontopic")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, abstract>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/test/nontopic docs



[doc_id]    [url]    [title]    [abstract]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/test/nontopic')
# Index nfcorpus
indexer = pt.IterDictIndexer('./indices/nfcorpus')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'abstract'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.nfcorpus.test.nontopic')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

4.5K qrels

Query relevance judgment type:

GenericQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int

Relevance levels

Rel.	Definition	Count	%
0	Marginally relevant, based on topic containment.	`0`	0.0%
1	A link exists from the query to another query that directly links to the document.	`676`	14.9%
2	A direct link from the query to the document the cited sources section of a page.	`3.3K`	72.4%

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/test/nontopic")
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 nfcorpus/test/nontopic qrels --format tsv



[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/test/nontopic')
index_ref = pt.IndexRef.of('./indices/nfcorpus') # 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(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.nfcorpus.test.nontopic.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Boteva2016Nfcorpus}

Bibtex:

{
  "docs": {
    "count": 5371,
    "fields": {
      "doc_id": {
        "max_len": 8,
        "common_prefix": "MED-"
      }
    }
  },
  "queries": {
    "count": 144
  },
  "qrels": {
    "count": 4540,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "3": 576,
          "1": 676,
          "2": 3288
        }
      }
    }
  }
}

`"nfcorpus/test/video"`

Official test set, filtered to only include queries from video pages.

102 queries

Language: en

Query type:

NfCorpusVideoQuery: (namedtuple)

query_id: str
title: str
desc: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/test/video")
for query in dataset.queries_iter():
    query # namedtuple<query_id, title, desc>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/test/video queries



[query_id]    [title]    [desc]
...

You can find more details about the CLI here.

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

You can find more details about PyTerrier retrieval here.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.nfcorpus.test.video.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

5.4K docs

Inherits docs from nfcorpus

Language: en

Document type:

NfCorpusDoc: (namedtuple)

doc_id: str
url: str
title: str
abstract: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/test/video")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, abstract>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/test/video docs



[doc_id]    [url]    [title]    [abstract]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/test/video')
# Index nfcorpus
indexer = pt.IterDictIndexer('./indices/nfcorpus')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'abstract'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.nfcorpus.test.video')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

3.1K qrels

Query relevance judgment type:

GenericQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int

Relevance levels

Rel.	Definition	Count	%
0	Marginally relevant, based on topic containment.	`0`	0.0%
1	A link exists from the query to another query that directly links to the document.	`610`	19.6%
2	A direct link from the query to the document the cited sources section of a page.	`2.0K`	65.4%

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/test/video")
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 nfcorpus/test/video qrels --format tsv



[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/test/video')
index_ref = pt.IndexRef.of('./indices/nfcorpus') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics('title'),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.nfcorpus.test.video.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Boteva2016Nfcorpus}

Bibtex:

{
  "docs": {
    "count": 5371,
    "fields": {
      "doc_id": {
        "max_len": 8,
        "common_prefix": "MED-"
      }
    }
  },
  "queries": {
    "count": 102
  },
  "qrels": {
    "count": 3108,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "3": 464,
          "2": 2034,
          "1": 610
        }
      }
    }
  }
}

`"nfcorpus/train"`

Official train set. Queries include both title and combinted "all" text field (titles, descriptions, topics, transcripts and comments)

2.6K queries

Language: en

Query type:

NfCorpusQuery: (namedtuple)

query_id: str
title: str
all: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/train")
for query in dataset.queries_iter():
    query # namedtuple<query_id, title, all>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/train queries



[query_id]    [title]    [all]
...

You can find more details about the CLI here.

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

You can find more details about PyTerrier retrieval here.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.nfcorpus.train.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

5.4K docs

Inherits docs from nfcorpus

Language: en

Document type:

NfCorpusDoc: (namedtuple)

doc_id: str
url: str
title: str
abstract: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/train")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, abstract>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/train docs



[doc_id]    [url]    [title]    [abstract]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/train')
# Index nfcorpus
indexer = pt.IterDictIndexer('./indices/nfcorpus')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'abstract'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.nfcorpus.train')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

139K qrels

Query relevance judgment type:

TrecQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int
iteration: str

Relevance levels

Rel.	Definition	Count	%
0	Marginally relevant, based on topic containment.	`0`	0.0%
1	A link exists from the query to another query that directly links to the document.	`29K`	20.6%
2	A direct link from the query to the document the cited sources section of a page.	`106K`	76.3%

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/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 nfcorpus/train qrels --format tsv



[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/train')
index_ref = pt.IndexRef.of('./indices/nfcorpus') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics('title'),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.nfcorpus.train.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Boteva2016Nfcorpus}

Bibtex:

{
  "docs": {
    "count": 5371,
    "fields": {
      "doc_id": {
        "max_len": 8,
        "common_prefix": "MED-"
      }
    }
  },
  "queries": {
    "count": 2594
  },
  "qrels": {
    "count": 139350,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "3": 4279,
          "2": 106296,
          "1": 28775
        }
      }
    }
  }
}

`"nfcorpus/train/nontopic"`

Official train set, filtered to exclude queries from topic pages.

1.1K queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/train/nontopic")
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 nfcorpus/train/nontopic queries



[query_id]    [text]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/train/nontopic')
index_ref = pt.IndexRef.of('./indices/nfcorpus') # 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.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.nfcorpus.train.nontopic.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

5.4K docs

Inherits docs from nfcorpus

Language: en

Document type:

NfCorpusDoc: (namedtuple)

doc_id: str
url: str
title: str
abstract: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/train/nontopic")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, abstract>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/train/nontopic docs



[doc_id]    [url]    [title]    [abstract]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/train/nontopic')
# Index nfcorpus
indexer = pt.IterDictIndexer('./indices/nfcorpus')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'abstract'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.nfcorpus.train.nontopic')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

37K qrels

Query relevance judgment type:

GenericQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int

Relevance levels

Rel.	Definition	Count	%
0	Marginally relevant, based on topic containment.	`0`	0.0%
1	A link exists from the query to another query that directly links to the document.	`6.7K`	18.0%
2	A direct link from the query to the document the cited sources section of a page.	`26K`	70.6%

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/train/nontopic")
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 nfcorpus/train/nontopic qrels --format tsv



[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/train/nontopic')
index_ref = pt.IndexRef.of('./indices/nfcorpus') # 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(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.nfcorpus.train.nontopic.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Boteva2016Nfcorpus}

Bibtex:

{
  "docs": {
    "count": 5371,
    "fields": {
      "doc_id": {
        "max_len": 8,
        "common_prefix": "MED-"
      }
    }
  },
  "queries": {
    "count": 1141
  },
  "qrels": {
    "count": 37383,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "3": 4279,
          "2": 26384,
          "1": 6720
        }
      }
    }
  }
}

`"nfcorpus/train/video"`

Official train set, filtered to only include queries from video pages.

812 queries

Language: en

Query type:

NfCorpusVideoQuery: (namedtuple)

query_id: str
title: str
desc: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/train/video")
for query in dataset.queries_iter():
    query # namedtuple<query_id, title, desc>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/train/video queries



[query_id]    [title]    [desc]
...

You can find more details about the CLI here.

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

You can find more details about PyTerrier retrieval here.

from datamaestro import prepare_dataset
topics = prepare_dataset('irds.nfcorpus.train.video.queries')  # AdhocTopics
for topic in topics.iter():
    print(topic)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocTopics.

docs

5.4K docs

Inherits docs from nfcorpus

Language: en

Document type:

NfCorpusDoc: (namedtuple)

doc_id: str
url: str
title: str
abstract: str

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/train/video")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, abstract>

You can find more details about the Python API here.

CLI

ir_datasets export nfcorpus/train/video docs



[doc_id]    [url]    [title]    [abstract]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/train/video')
# Index nfcorpus
indexer = pt.IterDictIndexer('./indices/nfcorpus')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'abstract'])

You can find more details about PyTerrier indexing here.

from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.nfcorpus.train.video')
for doc in dataset.iter_documents():
    print(doc)  # an AdhocDocumentStore
    break

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocDocumentStore

qrels

27K qrels

Query relevance judgment type:

GenericQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int

Relevance levels

Rel.	Definition	Count	%
0	Marginally relevant, based on topic containment.	`0`	0.0%
1	A link exists from the query to another query that directly links to the document.	`6.3K`	22.8%
2	A direct link from the query to the document the cited sources section of a page.	`18K`	64.3%

Examples:

import ir_datasets
dataset = ir_datasets.load("nfcorpus/train/video")
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 nfcorpus/train/video qrels --format tsv



[query_id]    [doc_id]    [relevance]
...

You can find more details about the CLI here.

import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:nfcorpus/train/video')
index_ref = pt.IndexRef.of('./indices/nfcorpus') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics('title'),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.nfcorpus.train.video.qrels')  # AdhocAssessments
for topic_qrels in qrels.iter():
    print(topic_qrels)  # An AdhocTopic

This examples requires that experimaestro-ir be installed. For more information about the returned object, see the documentation about AdhocAssessments.

\cite{Boteva2016Nfcorpus}

Bibtex: