NFCorpus (NutritionFacts)

`"nfcorpus"`

"NFCorpus is a full-text English retrieval data set for Medical Information Retrieval. It contains a total of 3,244 natural language queries (written in non-technical English, harvested from the NutritionFacts.org site) with 169,756 automatically extracted relevance judgments for 9,964 medical documents (written in a complex terminology-heavy language), mostly from PubMed."

Dataset website

Dataset paper

docs Citation Metadata

5.4K docs

Language: en

Document type:

NfCorpusDoc: (namedtuple)

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

Examples:

Python API CLI PyTerrier

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.

ir_datasets export nfcorpus docs



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

You can find more details about the CLI here.

{
  "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)

queries docs qrels Citation Metadata

325 queries

Language: en

Query type:

NfCorpusQuery: (namedtuple)

query_id: str
title: str
all: str

Examples:

Python API CLI PyTerrier

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.

ir_datasets export nfcorpus/dev queries



[query_id]    [title]    [all]
...

You can find more details about the CLI here.

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%

{
  "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.

queries docs qrels Citation Metadata

144 queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

Python API CLI PyTerrier

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.

ir_datasets export nfcorpus/dev/nontopic queries



[query_id]    [text]
...

You can find more details about the CLI here.

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%

{
  "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.

queries docs qrels Citation Metadata

102 queries

Language: en

Query type:

NfCorpusVideoQuery: (namedtuple)

query_id: str
title: str
desc: str

Examples:

Python API CLI PyTerrier

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.

ir_datasets export nfcorpus/dev/video queries



[query_id]    [title]    [desc]
...

You can find more details about the CLI here.

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%

{
  "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)

queries docs qrels Citation Metadata

325 queries

Language: en

Query type:

NfCorpusQuery: (namedtuple)

query_id: str
title: str
all: str

Examples:

Python API CLI PyTerrier

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.

ir_datasets export nfcorpus/test queries



[query_id]    [title]    [all]
...

You can find more details about the CLI here.

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%

{
  "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.

queries docs qrels Citation Metadata

144 queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

Python API CLI PyTerrier

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.

ir_datasets export nfcorpus/test/nontopic queries



[query_id]    [text]
...

You can find more details about the CLI here.

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%

{
  "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.

queries docs qrels Citation Metadata

102 queries

Language: en

Query type:

NfCorpusVideoQuery: (namedtuple)

query_id: str
title: str
desc: str

Examples:

Python API CLI PyTerrier

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.

ir_datasets export nfcorpus/test/video queries



[query_id]    [title]    [desc]
...

You can find more details about the CLI here.

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%

{
  "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)

queries docs qrels Citation Metadata

2.6K queries

Language: en

Query type:

NfCorpusQuery: (namedtuple)

query_id: str
title: str
all: str

Examples:

Python API CLI PyTerrier

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.

ir_datasets export nfcorpus/train queries



[query_id]    [title]    [all]
...

You can find more details about the CLI here.

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%

{
  "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.

queries docs qrels Citation Metadata

1.1K queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

Python API CLI PyTerrier

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.

ir_datasets export nfcorpus/train/nontopic queries



[query_id]    [text]
...

You can find more details about the CLI here.

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%

{
  "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.

queries docs qrels Citation Metadata

812 queries

Language: en

Query type:

NfCorpusVideoQuery: (namedtuple)

query_id: str
title: str
desc: str

Examples:

Python API CLI PyTerrier

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.

ir_datasets export nfcorpus/train/video queries



[query_id]    [title]    [desc]
...

You can find more details about the CLI here.

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%

{
  "docs": {
    "count": 5371,
    "fields": {
      "doc_id": {
        "max_len": 8,
        "common_prefix": "MED-"
      }
    }
  },
  "queries": {
    "count": 812
  },
  "qrels": {
    "count": 27465,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "3": 3536,
          "2": 17669,
          "1": 6260
        }
      }
    }
  }
}

ir_datasets: NFCorpus (NutritionFacts)

"nfcorpus"

"nfcorpus/dev"

"nfcorpus/dev/nontopic"

"nfcorpus/dev/video"

"nfcorpus/test"

"nfcorpus/test/nontopic"

"nfcorpus/test/video"

"nfcorpus/train"

"nfcorpus/train/nontopic"

"nfcorpus/train/video"

`ir_datasets`: NFCorpus (NutritionFacts)

`"nfcorpus"`

`"nfcorpus/dev"`

`"nfcorpus/dev/nontopic"`

`"nfcorpus/dev/video"`

`"nfcorpus/test"`

`"nfcorpus/test/nontopic"`

`"nfcorpus/test/video"`

`"nfcorpus/train"`

`"nfcorpus/train/nontopic"`

`"nfcorpus/train/video"`