ir_datasets : ANTIQUE

import ir_datasets
dataset = ir_datasets.load("antique")
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 antique docs



[doc_id]    [text]
...

You can find more details about the CLI here.

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

You can find more details about PyTerrier indexing here.

\cite{Hashemi2020Antique}

Bibtex:

@inproceedings{Hashemi2020Antique, title={ANTIQUE: A Non-Factoid Question Answering Benchmark}, author={Helia Hashemi and Mohammad Aliannejadi and Hamed Zamani and Bruce Croft}, booktitle={ECIR}, year={2020} }

{
  "docs": {
    "count": 403666,
    "fields": {
      "doc_id": {
        "max_len": 10,
        "common_prefix": ""
      }
    }
  }
}

`"antique/test"`

Official test set of the ANTIQUE dataset.

200 queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

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



[query_id]    [text]
...

You can find more details about the CLI here.

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

404K docs

Inherits docs from antique

Language: en

Document type:

GenericDoc: (namedtuple)

doc_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/test")
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 antique/test docs



[doc_id]    [text]
...

You can find more details about the CLI here.

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

You can find more details about PyTerrier indexing here.

qrels

6.6K qrels

Query relevance judgment type:

TrecQrel: (namedtuple)

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

Relevance levels

Rel.	Definition	Count	%
1	It is completely out of context or does not make any sense.	`1.6K`	24.9%
2	It does not answer the question or if it does, it provides anunreasonable answer, however, it is not out of context. Therefore, you cannot accept it as an answer to the question.	`2.4K`	36.7%
3	It can be an answer to the question, however, it is notsufficiently convincing. There should be an answer with much better quality for the question.	`1.2K`	18.2%
4	It looks reasonable and convincing. Its quality is on parwith or better than the "Possibly Correct Answer". Note that it does not have to provide the same answer as the "PossiblyCorrect Answer".	`1.3K`	20.2%

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/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 antique/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:antique/test')
index_ref = pt.IndexRef.of('./indices/antique') # 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.

\cite{Hashemi2020Antique}

Bibtex:

{
  "docs": {
    "count": 403666,
    "fields": {
      "doc_id": {
        "max_len": 10,
        "common_prefix": ""
      }
    }
  },
  "queries": {
    "count": 200
  },
  "qrels": {
    "count": 6589,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "4": 1334,
          "1": 1642,
          "2": 2417,
          "3": 1196
        }
      }
    }
  }
}

`"antique/test/non-offensive"`

antique/test without a set of queries deemed by the authors of ANTIQUE to be "offensive (and noisy)."

176 queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/test/non-offensive")
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 antique/test/non-offensive queries



[query_id]    [text]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:antique/test/non-offensive')
index_ref = pt.IndexRef.of('./indices/antique') # 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

404K docs

Inherits docs from antique

Language: en

Document type:

GenericDoc: (namedtuple)

doc_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/test/non-offensive")
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 antique/test/non-offensive docs



[doc_id]    [text]
...

You can find more details about the CLI here.

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

You can find more details about PyTerrier indexing here.

qrels

5.8K qrels

Query relevance judgment type:

GenericQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int

Relevance levels

Rel.	Definition	Count	%
1	It is completely out of context or does not make any sense.	`1.4K`	24.5%
2	It does not answer the question or if it does, it provides anunreasonable answer, however, it is not out of context. Therefore, you cannot accept it as an answer to the question.	`2.1K`	36.5%
3	It can be an answer to the question, however, it is notsufficiently convincing. There should be an answer with much better quality for the question.	`1.0K`	18.2%
4	It looks reasonable and convincing. Its quality is on parwith or better than the "Possibly Correct Answer". Note that it does not have to provide the same answer as the "PossiblyCorrect Answer".	`1.2K`	20.8%

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/test/non-offensive")
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 antique/test/non-offensive 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:antique/test/non-offensive')
index_ref = pt.IndexRef.of('./indices/antique') # 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.

\cite{Hashemi2020Antique}

Bibtex:

{
  "docs": {
    "count": 403666,
    "fields": {
      "doc_id": {
        "max_len": 10,
        "common_prefix": ""
      }
    }
  },
  "queries": {
    "count": 176
  },
  "qrels": {
    "count": 5752,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "4": 1195,
          "1": 1407,
          "2": 2101,
          "3": 1049
        }
      }
    }
  }
}

`"antique/train"`

Official train set of the ANTIQUE dataset.

2.4K queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/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 antique/train queries



[query_id]    [text]
...

You can find more details about the CLI here.

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

404K docs

Inherits docs from antique

Language: en

Document type:

GenericDoc: (namedtuple)

doc_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/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 antique/train docs



[doc_id]    [text]
...

You can find more details about the CLI here.

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

You can find more details about PyTerrier indexing here.

qrels

27K qrels

Query relevance judgment type:

TrecQrel: (namedtuple)

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

Relevance levels

Rel.	Definition	Count	%
1	It is completely out of context or does not make any sense.	`1.3K`	4.6%
2	It does not answer the question or if it does, it provides anunreasonable answer, however, it is not out of context. Therefore, you cannot accept it as an answer to the question.	`6.3K`	23.1%
3	It can be an answer to the question, however, it is notsufficiently convincing. There should be an answer with much better quality for the question.	`8.1K`	29.5%
4	It looks reasonable and convincing. Its quality is on parwith or better than the "Possibly Correct Answer". Note that it does not have to provide the same answer as the "PossiblyCorrect Answer".	`12K`	42.8%

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/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 antique/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:antique/train')
index_ref = pt.IndexRef.of('./indices/antique') # 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.

\cite{Hashemi2020Antique}

Bibtex:

{
  "docs": {
    "count": 403666,
    "fields": {
      "doc_id": {
        "max_len": 10,
        "common_prefix": ""
      }
    }
  },
  "queries": {
    "count": 2426
  },
  "qrels": {
    "count": 27422,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "4": 11733,
          "3": 8080,
          "2": 6337,
          "1": 1272
        }
      }
    }
  }
}

`"antique/train/split200-train"`

antique/train without the 200 queries used by antique/train/split200-valid.

2.2K queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/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 antique/train/split200-train queries



[query_id]    [text]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:antique/train/split200-train')
index_ref = pt.IndexRef.of('./indices/antique') # 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

404K docs

Inherits docs from antique

Language: en

Document type:

GenericDoc: (namedtuple)

doc_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/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 antique/train/split200-train docs



[doc_id]    [text]
...

You can find more details about the CLI here.

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

You can find more details about PyTerrier indexing here.

qrels

25K qrels

Query relevance judgment type:

GenericQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int

Relevance levels

Rel.	Definition	Count	%
1	It is completely out of context or does not make any sense.	`1.2K`	4.6%
2	It does not answer the question or if it does, it provides anunreasonable answer, however, it is not out of context. Therefore, you cannot accept it as an answer to the question.	`5.8K`	23.1%
3	It can be an answer to the question, however, it is notsufficiently convincing. There should be an answer with much better quality for the question.	`7.4K`	29.5%
4	It looks reasonable and convincing. Its quality is on parwith or better than the "Possibly Correct Answer". Note that it does not have to provide the same answer as the "PossiblyCorrect Answer".	`11K`	42.7%

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/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 antique/train/split200-train 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:antique/train/split200-train')
index_ref = pt.IndexRef.of('./indices/antique') # 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.

\cite{Hashemi2020Antique}

Bibtex:

{
  "docs": {
    "count": 403666,
    "fields": {
      "doc_id": {
        "max_len": 10,
        "common_prefix": ""
      }
    }
  },
  "queries": {
    "count": 2226
  },
  "qrels": {
    "count": 25229,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "4": 10782,
          "3": 7447,
          "2": 5829,
          "1": 1171
        }
      }
    }
  }
}

`"antique/train/split200-valid"`

A held-out subset of 200 queries from antique/train. Use in conjunction with antique/train/split200-train.

200 queries

Language: en

Query type:

GenericQuery: (namedtuple)

query_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/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 antique/train/split200-valid queries



[query_id]    [text]
...

You can find more details about the CLI here.

import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:antique/train/split200-valid')
index_ref = pt.IndexRef.of('./indices/antique') # 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

404K docs

Inherits docs from antique

Language: en

Document type:

GenericDoc: (namedtuple)

doc_id: str
text: str

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/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 antique/train/split200-valid docs



[doc_id]    [text]
...

You can find more details about the CLI here.

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

You can find more details about PyTerrier indexing here.

qrels

2.2K qrels

Query relevance judgment type:

GenericQrel: (namedtuple)

query_id: str
doc_id: str
relevance: int

Relevance levels

Rel.	Definition	Count	%
1	It is completely out of context or does not make any sense.	`101`	4.6%
2	It does not answer the question or if it does, it provides anunreasonable answer, however, it is not out of context. Therefore, you cannot accept it as an answer to the question.	`508`	23.2%
3	It can be an answer to the question, however, it is notsufficiently convincing. There should be an answer with much better quality for the question.	`633`	28.9%
4	It looks reasonable and convincing. Its quality is on parwith or better than the "Possibly Correct Answer". Note that it does not have to provide the same answer as the "PossiblyCorrect Answer".	`951`	43.4%

Examples:

import ir_datasets
dataset = ir_datasets.load("antique/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 antique/train/split200-valid 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:antique/train/split200-valid')
index_ref = pt.IndexRef.of('./indices/antique') # 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.

\cite{Hashemi2020Antique}

Bibtex: