ir_datasets: NFCorpus (NutritionFacts)"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."
Language: en
Examples:
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.
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
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-" } } } } Official dev set. Queries include both title and combinted "all" text field (titles, descriptions, topics, transcripts and comments)
Language: en
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.
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.
Inherits docs from nfcorpus
Language: en
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.
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
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.
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.
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-" } } }, "queries": { "count": 325 }, "qrels": { "count": 14589, "fields": { "relevance": { "counts_by_value": { "3": 521, "2": 10864, "1": 3204 } } } } } Official dev set, filtered to exclude queries from topic pages.
Language: en
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.
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.
Inherits docs from nfcorpus
Language: en
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.
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
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.
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.
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-" } } }, "queries": { "count": 144 }, "qrels": { "count": 4353, "fields": { "relevance": { "counts_by_value": { "3": 521, "2": 3133, "1": 699 } } } } } Official dev set, filtered to only include queries from video pages.
Language: en
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.
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.
Inherits docs from nfcorpus
Language: en
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.
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
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.
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.
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-" } } }, "queries": { "count": 102 }, "qrels": { "count": 3068, "fields": { "relevance": { "counts_by_value": { "3": 411, "2": 1979, "1": 678 } } } } } Official test set. Queries include both title and combinted "all" text field (titles, descriptions, topics, transcripts and comments)
Language: en
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.
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.
Inherits docs from nfcorpus
Language: en
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.
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
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.
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.
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-" } } }, "queries": { "count": 325 }, "qrels": { "count": 15820, "fields": { "relevance": { "counts_by_value": { "3": 576, "1": 3486, "2": 11758 } } } } } Official test set, filtered to exclude queries from topic pages.
Language: en
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.
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.
Inherits docs from nfcorpus
Language: en
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.
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
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.
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.
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-" } } }, "queries": { "count": 144 }, "qrels": { "count": 4540, "fields": { "relevance": { "counts_by_value": { "3": 576, "1": 676, "2": 3288 } } } } } Official test set, filtered to only include queries from video pages.
Language: en
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.
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.
Inherits docs from nfcorpus
Language: en
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.
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
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.
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.
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-" } } }, "queries": { "count": 102 }, "qrels": { "count": 3108, "fields": { "relevance": { "counts_by_value": { "3": 464, "2": 2034, "1": 610 } } } } } Official train set. Queries include both title and combinted "all" text field (titles, descriptions, topics, transcripts and comments)
Language: en
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.
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.
Inherits docs from nfcorpus
Language: en
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.
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
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.
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.
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-" } } }, "queries": { "count": 2594 }, "qrels": { "count": 139350, "fields": { "relevance": { "counts_by_value": { "3": 4279, "2": 106296, "1": 28775 } } } } } Official train set, filtered to exclude queries from topic pages.
Language: en
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.
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.
Inherits docs from nfcorpus
Language: en
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.
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
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.
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.
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-" } } }, "queries": { "count": 1141 }, "qrels": { "count": 37383, "fields": { "relevance": { "counts_by_value": { "3": 4279, "2": 26384, "1": 6720 } } } } } Official train set, filtered to only include queries from video pages.
Language: en
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.
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.
Inherits docs from nfcorpus
Language: en
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.
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
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.
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.
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-" } } }, "queries": { "count": 812 }, "qrels": { "count": 27465, "fields": { "relevance": { "counts_by_value": { "3": 3536, "2": 17669, "1": 6260 } } } } }