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Github: datasets/antique.py

ir_datasets: ANTIQUE

Index
  1. antique
  2. antique/test
  3. antique/test/non-offensive
  4. antique/train
  5. antique/train/split200-train
  6. antique/train/split200-valid

"antique"

"ANTIQUE is a non-factoid quesiton answering dataset based on the questions and answers of Yahoo! Webscope L6."

  • Documents: Short answer passages (from Yahoo Answers)
  • Queries: Natural language questions (from Yahoo Answers)
  • Dataset Paper
docs

Language: en

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

Examples:

Python API
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.

PyTerrier
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.

Citation

ir_datasets.bib:

\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} }

"antique/test"

Official test set of the ANTIQUE dataset.

queries

Language: en

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

Examples:

Python API
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.

PyTerrier
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

Language: en

Note: Uses docs from antique

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

Examples:

Python API
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.

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

Relevance levels

Rel.Definition
1It is completely out of context or does not make any sense.
2It 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.
3It can be an answer to the question, however, it is notsufficiently convincing. There should be an answer with much better quality for the question.
4It 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".

Examples:

Python API
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.

PyTerrier
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.

Citation

ir_datasets.bib:

\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} }

"antique/test/non-offensive"

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

queries

Language: en

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

Examples:

Python API
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.

PyTerrier
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

Language: en

Note: Uses docs from antique

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

Examples:

Python API
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.

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

Relevance levels

Rel.Definition
1It is completely out of context or does not make any sense.
2It 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.
3It can be an answer to the question, however, it is notsufficiently convincing. There should be an answer with much better quality for the question.
4It 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".

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("antique/test/non-offensive")
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/non-offensive 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: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.

Citation

ir_datasets.bib:

\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} }

"antique/train"

Official train set of the ANTIQUE dataset.

queries

Language: en

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

Examples:

Python API
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.

PyTerrier
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

Language: en

Note: Uses docs from antique

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

Examples:

Python API
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.

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

Relevance levels

Rel.Definition
1It is completely out of context or does not make any sense.
2It 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.
3It can be an answer to the question, however, it is notsufficiently convincing. There should be an answer with much better quality for the question.
4It 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".

Examples:

Python API
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.

PyTerrier
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.

Citation

ir_datasets.bib:

\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} }

"antique/train/split200-train"

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

queries

Language: en

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

Examples:

Python API
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.

PyTerrier
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

Language: en

Note: Uses docs from antique

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

Examples:

Python API
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.

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

Relevance levels

Rel.Definition
1It is completely out of context or does not make any sense.
2It 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.
3It can be an answer to the question, however, it is notsufficiently convincing. There should be an answer with much better quality for the question.
4It 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".

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("antique/train/split200-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/split200-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: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.

Citation

ir_datasets.bib:

\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} }

"antique/train/split200-valid"

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

queries

Language: en

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

Examples:

Python API
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.

PyTerrier
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

Language: en

Note: Uses docs from antique

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

Examples:

Python API
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.

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

Relevance levels

Rel.Definition
1It is completely out of context or does not make any sense.
2It 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.
3It can be an answer to the question, however, it is notsufficiently convincing. There should be an answer with much better quality for the question.
4It 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".

Examples:

Python API
import ir_datasets
dataset = ir_datasets.load("antique/train/split200-valid")
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/split200-valid 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: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.

Citation

ir_datasets.bib:

\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} }