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Github: datasets/wapo.py

ir_datasets: Washington Post

Index
  1. wapo
  2. wapo/v2
  3. wapo/v2/trec-core-2018
  4. wapo/v2/trec-news-2018
  5. wapo/v2/trec-news-2019
  6. wapo/v3/trec-news-2020

Data Access Information

To use this dataset, you need a copy of Washington Post Collection, provided by NIST.

Your organization may already have a copy. If this is the case, you may only need to complete a new "Individual Argeement". Otherwise, your organization will need to file the "Organizational agreement" with NIST. It can take some time to process, but you will end up with a password-protected download link.

The source file required is WashingtonPost.v2.tar.gz.

ir_datasets expects the above file to be copied/linked under ~/.ir_datasets/wapo/WashingtonPost.v2.tar.gz.

"wapo"

The Washington Post collection.

"wapo/v2"

Version 2 of the Washington Post collection, consisting of articles published between 2012-2017.

The collection is obtained from NIST by requesting it from NIST here.

body contains all body text in plain text format, including paragrphs and multi-media captions. body_paras_html contains only source paragraphs and contains HTML markup. body_media contains images, videos, tweets, and galeries, along with a link to the content and a textual caption.

docs
595K docs

Language: en

Document type:
WapoDoc: (namedtuple)
  1. doc_id: str
  2. url: str
  3. title: str
  4. author: str
  5. published_date: int
  6. kicker: str
  7. body: str
  8. body_paras_html: Tuple[str, ...]
  9. body_media: Tuple[
    WapoDocMedia: (namedtuple)
    1. type: str
    2. url: str
    3. text: str
    , ...]

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("wapo/v2")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, author, published_date, kicker, body, body_paras_html, body_media>

You can find more details about the Python API here.

CLI 
ir_datasets export wapo/v2 docs
[doc_id]    [url]    [title]    [author]    [published_date]    [kicker]    [body]    [body_paras_html]    [body_media]
...

You can find more details about the CLI here.

PyTerrier 
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:wapo/v2')
# Index wapo/v2
indexer = pt.IterDictIndexer('./indices/wapo_v2', meta={"docno": 36})
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'author', 'kicker', 'body'])

You can find more details about PyTerrier indexing here.

Metadata 
{
  "docs": {
    "count": 595037,
    "fields": {
      "doc_id": {
        "max_len": 36,
        "common_prefix": ""
      }
    }
  }
}

"wapo/v2/trec-core-2018"

The TREC Common Core 2018 benchmark.

  • Queries: TREC-style (keyword, description, narrative)
  • Relevance: Deeply-annotated
  • Shared Task Website
queries
50 queries

Language: en

Query type:
TrecQuery: (namedtuple)
  1. query_id: str
  2. title: str
  3. description: str
  4. narrative: str

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("wapo/v2/trec-core-2018")
for query in dataset.queries_iter():
    query # namedtuple<query_id, title, description, narrative>

You can find more details about the Python API here.

CLI 
ir_datasets export wapo/v2/trec-core-2018 queries
[query_id]    [title]    [description]    [narrative]
...

You can find more details about the CLI here.

PyTerrier 
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:wapo/v2/trec-core-2018')
index_ref = pt.IndexRef.of('./indices/wapo_v2') # 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.

docs
595K docs

Inherits docs from wapo/v2

Language: en

Document type:
WapoDoc: (namedtuple)
  1. doc_id: str
  2. url: str
  3. title: str
  4. author: str
  5. published_date: int
  6. kicker: str
  7. body: str
  8. body_paras_html: Tuple[str, ...]
  9. body_media: Tuple[
    WapoDocMedia: (namedtuple)
    1. type: str
    2. url: str
    3. text: str
    , ...]

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("wapo/v2/trec-core-2018")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, author, published_date, kicker, body, body_paras_html, body_media>

You can find more details about the Python API here.

CLI 
ir_datasets export wapo/v2/trec-core-2018 docs
[doc_id]    [url]    [title]    [author]    [published_date]    [kicker]    [body]    [body_paras_html]    [body_media]
...

You can find more details about the CLI here.

PyTerrier 
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:wapo/v2/trec-core-2018')
# Index wapo/v2
indexer = pt.IterDictIndexer('./indices/wapo_v2', meta={"docno": 36})
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'author', 'kicker', 'body'])

You can find more details about PyTerrier indexing here.

qrels
26K qrels
Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
0not relevant22K85.0%
1relevant2.1K7.9%
2highly relevant1.9K7.1%

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("wapo/v2/trec-core-2018")
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 wapo/v2/trec-core-2018 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:wapo/v2/trec-core-2018')
index_ref = pt.IndexRef.of('./indices/wapo_v2') # 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.

Metadata 
{
  "docs": {
    "count": 595037,
    "fields": {
      "doc_id": {
        "max_len": 36,
        "common_prefix": ""
      }
    }
  },
  "queries": {
    "count": 50
  },
  "qrels": {
    "count": 26233,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "0": 22285,
          "2": 1865,
          "1": 2083
        }
      }
    }
  }
}

"wapo/v2/trec-news-2018"

The TREC News 2018 Background Linking task. The task is to find relevant background information for the provided articles.

queries
50 queries

Language: en

Query type:
TrecBackgroundLinkingQuery: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. url: str

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("wapo/v2/trec-news-2018")
for query in dataset.queries_iter():
    query # namedtuple<query_id, doc_id, url>

You can find more details about the Python API here.

CLI 
ir_datasets export wapo/v2/trec-news-2018 queries
[query_id]    [doc_id]    [url]
...

You can find more details about the CLI here.

PyTerrier 
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:wapo/v2/trec-news-2018')
index_ref = pt.IndexRef.of('./indices/wapo_v2') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('doc_id'))

You can find more details about PyTerrier retrieval here.

docs
595K docs

Inherits docs from wapo/v2

Language: en

Document type:
WapoDoc: (namedtuple)
  1. doc_id: str
  2. url: str
  3. title: str
  4. author: str
  5. published_date: int
  6. kicker: str
  7. body: str
  8. body_paras_html: Tuple[str, ...]
  9. body_media: Tuple[
    WapoDocMedia: (namedtuple)
    1. type: str
    2. url: str
    3. text: str
    , ...]

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("wapo/v2/trec-news-2018")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, author, published_date, kicker, body, body_paras_html, body_media>

You can find more details about the Python API here.

CLI 
ir_datasets export wapo/v2/trec-news-2018 docs
[doc_id]    [url]    [title]    [author]    [published_date]    [kicker]    [body]    [body_paras_html]    [body_media]
...

You can find more details about the CLI here.

PyTerrier 
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:wapo/v2/trec-news-2018')
# Index wapo/v2
indexer = pt.IterDictIndexer('./indices/wapo_v2', meta={"docno": 36})
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'author', 'kicker', 'body'])

You can find more details about PyTerrier indexing here.

qrels
8.5K qrels
Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
0The document provides little or no useful background information.6.5K76.0%
2The document provides some useful background or contextual information that would help the user understand the broader story context of the target article.1.2K14.0%
4The document provides significantly useful background ...584 6.9%
8The document provides essential useful background ...164 1.9%
16The document _must_ appear in the sidebar otherwise critical context is missing.106 1.2%

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("wapo/v2/trec-news-2018")
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 wapo/v2/trec-news-2018 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:wapo/v2/trec-news-2018')
index_ref = pt.IndexRef.of('./indices/wapo_v2') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics('doc_id'),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

Citation

ir_datasets.bib:

\cite{Soboroff2018News}

Bibtex:

@inproceedings{Soboroff2018News, title={TREC 2018 News Track Overview}, author={Ian Soboroff and Shudong Huang and Donna Harman}, booktitle={TREC}, year={2018} }
Metadata 
{
  "docs": {
    "count": 595037,
    "fields": {
      "doc_id": {
        "max_len": 36,
        "common_prefix": ""
      }
    }
  },
  "queries": {
    "count": 50
  },
  "qrels": {
    "count": 8508,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "16": 106,
          "2": 1189,
          "0": 6465,
          "4": 584,
          "8": 164
        }
      }
    }
  }
}

"wapo/v2/trec-news-2019"

The TREC News 2019 Background Linking task. The task is to find relevant background information for the provided articles.

queries
60 queries

Language: en

Query type:
TrecBackgroundLinkingQuery: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. url: str

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("wapo/v2/trec-news-2019")
for query in dataset.queries_iter():
    query # namedtuple<query_id, doc_id, url>

You can find more details about the Python API here.

CLI 
ir_datasets export wapo/v2/trec-news-2019 queries
[query_id]    [doc_id]    [url]
...

You can find more details about the CLI here.

PyTerrier 
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:wapo/v2/trec-news-2019')
index_ref = pt.IndexRef.of('./indices/wapo_v2') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('doc_id'))

You can find more details about PyTerrier retrieval here.

docs
595K docs

Inherits docs from wapo/v2

Language: en

Document type:
WapoDoc: (namedtuple)
  1. doc_id: str
  2. url: str
  3. title: str
  4. author: str
  5. published_date: int
  6. kicker: str
  7. body: str
  8. body_paras_html: Tuple[str, ...]
  9. body_media: Tuple[
    WapoDocMedia: (namedtuple)
    1. type: str
    2. url: str
    3. text: str
    , ...]

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("wapo/v2/trec-news-2019")
for doc in dataset.docs_iter():
    doc # namedtuple<doc_id, url, title, author, published_date, kicker, body, body_paras_html, body_media>

You can find more details about the Python API here.

CLI 
ir_datasets export wapo/v2/trec-news-2019 docs
[doc_id]    [url]    [title]    [author]    [published_date]    [kicker]    [body]    [body_paras_html]    [body_media]
...

You can find more details about the CLI here.

PyTerrier 
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:wapo/v2/trec-news-2019')
# Index wapo/v2
indexer = pt.IterDictIndexer('./indices/wapo_v2', meta={"docno": 36})
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['url', 'title', 'author', 'kicker', 'body'])

You can find more details about PyTerrier indexing here.

qrels
16K qrels
Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
0The document provides little or no useful background information.13K80.6%
2The document provides some useful background or contextual information that would help the user understand the broader story context of the target article.1.7K10.7%
4The document provides significantly useful background ...660 4.2%
8The document provides essential useful background ...431 2.8%
16The document _must_ appear in the sidebar otherwise critical context is missing.273 1.7%

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("wapo/v2/trec-news-2019")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI 
ir_datasets export wapo/v2/trec-news-2019 qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier 
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:wapo/v2/trec-news-2019')
index_ref = pt.IndexRef.of('./indices/wapo_v2') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
    [pipeline],
    dataset.get_topics('doc_id'),
    dataset.get_qrels(),
    [MAP, nDCG@20]
)

You can find more details about PyTerrier experiments here.

Citation

ir_datasets.bib:

\cite{Soboroff2019News}

Bibtex:

@inproceedings{Soboroff2019News, title={TREC 2019 News Track Overview}, author={Ian Soboroff and Shudong Huang and Donna Harman}, booktitle={TREC}, year={2019} }
Metadata 
{
  "docs": {
    "count": 595037,
    "fields": {
      "doc_id": {
        "max_len": 36,
        "common_prefix": ""
      }
    }
  },
  "queries": {
    "count": 60
  },
  "qrels": {
    "count": 15655,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "2": 1677,
          "0": 12614,
          "8": 431,
          "16": 273,
          "4": 660
        }
      }
    }
  }
}

"wapo/v3/trec-news-2020"

The TREC News 2020 Background Linking task. The task is to find relevant background information for the provided articles.

If you have a copy of the v3 dataset, we would appreciate a pull request to add support!

queries
50 queries

Language: en

Query type:
TrecBackgroundLinkingQuery: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. url: str

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("wapo/v3/trec-news-2020")
for query in dataset.queries_iter():
    query # namedtuple<query_id, doc_id, url>

You can find more details about the Python API here.

CLI 
ir_datasets export wapo/v3/trec-news-2020 queries
[query_id]    [doc_id]    [url]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

qrels
18K qrels
Query relevance judgment type:
TrecQrel: (namedtuple)
  1. query_id: str
  2. doc_id: str
  3. relevance: int
  4. iteration: str

Relevance levels

Rel.DefinitionCount%
0The document provides little or no useful background information.15K86.4%
2The document provides some useful background or contextual information that would help the user understand the broader story context of the target article.1.6K9.0%
4The document provides significantly useful background ...631 3.6%
8The document provides essential useful background ...132 0.7%
16The document _must_ appear in the sidebar otherwise critical context is missing.50 0.3%

Examples:

Python API 
import ir_datasets
dataset = ir_datasets.load("wapo/v3/trec-news-2020")
for qrel in dataset.qrels_iter():
    qrel # namedtuple<query_id, doc_id, relevance, iteration>

You can find more details about the Python API here.

CLI 
ir_datasets export wapo/v3/trec-news-2020 qrels --format tsv
[query_id]    [doc_id]    [relevance]    [iteration]
...

You can find more details about the CLI here.

PyTerrier

No example available for PyTerrier

Metadata 
{
  "queries": {
    "count": 50
  },
  "qrels": {
    "count": 17764,
    "fields": {
      "relevance": {
        "counts_by_value": {
          "0": 15348,
          "2": 1603,
          "4": 631,
          "8": 132,
          "16": 50
        }
      }
    }
  }
}