ir_datasets: MedlineMedical articles from Medline. This collection was used by TREC Genomics 2004-05 (2004 version of dataset) and by TREC Precision Medicine 2017-18 (2017 version).
3M Medline articles including titles and abstracts, used for the TREC 2004-05 Genomics track.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2004")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, abstract>
You can find more details about the Python API here.
ir_datasets export medline/2004 docs
[doc_id] [title] [abstract]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:medline/2004')
# Index medline/2004
indexer = pt.IterDictIndexer('./indices/medline_2004')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'abstract'])
You can find more details about PyTerrier indexing here.
from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.medline.2004')
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
{
"docs": {
"count": 3672808,
"fields": {
"doc_id": {
"max_len": 8,
"common_prefix": ""
}
}
}
}
The TREC Genomics Track 2004 benchmark. Contains 50 queries with article-level relevance judgments.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2004/trec-genomics-2004")
for query in dataset.queries_iter():
query # namedtuple<query_id, title, need, context>
You can find more details about the Python API here.
ir_datasets export medline/2004/trec-genomics-2004 queries
[query_id] [title] [need] [context]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:medline/2004/trec-genomics-2004')
index_ref = pt.IndexRef.of('./indices/medline_2004') # 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.medline.2004.trec-genomics-2004.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 medline/2004
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2004/trec-genomics-2004")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, abstract>
You can find more details about the Python API here.
ir_datasets export medline/2004/trec-genomics-2004 docs
[doc_id] [title] [abstract]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:medline/2004/trec-genomics-2004')
# Index medline/2004
indexer = pt.IterDictIndexer('./indices/medline_2004')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'abstract'])
You can find more details about PyTerrier indexing here.
from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.medline.2004.trec-genomics-2004')
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 | not relevant | 0 | 0.0% |
| 1 | possibly relevant | 4.6K | 56.0% |
| 2 | definitely relevant | 3.6K | 44.0% |
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2004/trec-genomics-2004")
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 medline/2004/trec-genomics-2004 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:medline/2004/trec-genomics-2004')
index_ref = pt.IndexRef.of('./indices/medline_2004') # 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.medline.2004.trec-genomics-2004.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{Hersh2004TrecGenomics, title={TREC 2004 Genomics Track Overview}, author={William R. Hersh and Ravi Teja Bhuptiraju and Laura Ross and Phoebe Johnson and Aaron M. Cohen and Dale F. Kraemer}, booktitle={TREC}, year={2004} }{
"docs": {
"count": 3672808,
"fields": {
"doc_id": {
"max_len": 8,
"common_prefix": ""
}
}
},
"queries": {
"count": 50
},
"qrels": {
"count": 8268,
"fields": {
"relevance": {
"counts_by_value": {
"2": 3639,
"1": 4629
}
}
}
}
}
The TREC Genomics Track 2005 benchmark. Contains 50 queries with article-level relevance judgments.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2004/trec-genomics-2005")
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
You can find more details about the Python API here.
ir_datasets export medline/2004/trec-genomics-2005 queries
[query_id] [text]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:medline/2004/trec-genomics-2005')
index_ref = pt.IndexRef.of('./indices/medline_2004') # 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.medline.2004.trec-genomics-2005.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 medline/2004
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2004/trec-genomics-2005")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, abstract>
You can find more details about the Python API here.
ir_datasets export medline/2004/trec-genomics-2005 docs
[doc_id] [title] [abstract]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:medline/2004/trec-genomics-2005')
# Index medline/2004
indexer = pt.IterDictIndexer('./indices/medline_2004')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'abstract'])
You can find more details about PyTerrier indexing here.
from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.medline.2004.trec-genomics-2005')
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 | not relevant | 35K | 88.5% |
| 1 | possibly relevant | 2.1K | 5.2% |
| 2 | definitely relevant | 2.5K | 6.3% |
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2004/trec-genomics-2005")
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 medline/2004/trec-genomics-2005 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:medline/2004/trec-genomics-2005')
index_ref = pt.IndexRef.of('./indices/medline_2004') # 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.medline.2004.trec-genomics-2005.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{Hersh2005TrecGenomics, title={TREC 2005 Genomics Track Overview}, author={William Hersh and Aaron Cohen and Jianji Yang and Ravi Teja Bhupatiraju and Phoebe Roberts and Marti Hearst}, booktitle={TREC}, year={2007} }{
"docs": {
"count": 3672808,
"fields": {
"doc_id": {
"max_len": 8,
"common_prefix": ""
}
}
},
"queries": {
"count": 50
},
"qrels": {
"count": 39958,
"fields": {
"relevance": {
"counts_by_value": {
"0": 35374,
"2": 2525,
"1": 2059
}
}
}
}
}
26M Medline and AACR/ASCO Proceedings articles including titles and abstracts. This collection is used for the TREC 2017-18 TREC Precision Medicine track.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2017")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, abstract>
You can find more details about the Python API here.
ir_datasets export medline/2017 docs
[doc_id] [title] [abstract]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:medline/2017')
# Index medline/2017
indexer = pt.IterDictIndexer('./indices/medline_2017')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'abstract'])
You can find more details about PyTerrier indexing here.
from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.medline.2017')
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
{
"docs": {
"count": 26740025,
"fields": {
"doc_id": {
"max_len": 15,
"common_prefix": ""
}
}
}
}
The TREC Precision Medicine (PM) Track 2017 benchmark. Contains 30 queries containing disease, gene, and target demographic information.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2017/trec-pm-2017")
for query in dataset.queries_iter():
query # namedtuple<query_id, disease, gene, demographic, other>
You can find more details about the Python API here.
ir_datasets export medline/2017/trec-pm-2017 queries
[query_id] [disease] [gene] [demographic] [other]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:medline/2017/trec-pm-2017')
index_ref = pt.IndexRef.of('./indices/medline_2017') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('disease'))
You can find more details about PyTerrier retrieval here.
from datamaestro import prepare_dataset
topics = prepare_dataset('irds.medline.2017.trec-pm-2017.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 medline/2017
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2017/trec-pm-2017")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, abstract>
You can find more details about the Python API here.
ir_datasets export medline/2017/trec-pm-2017 docs
[doc_id] [title] [abstract]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:medline/2017/trec-pm-2017')
# Index medline/2017
indexer = pt.IterDictIndexer('./indices/medline_2017')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'abstract'])
You can find more details about PyTerrier indexing here.
from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.medline.2017.trec-pm-2017')
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 | not relevant | 19K | 82.9% |
| 1 | possibly relevant | 1.9K | 8.2% |
| 2 | definitely relevant | 2.0K | 8.9% |
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2017/trec-pm-2017")
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 medline/2017/trec-pm-2017 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:medline/2017/trec-pm-2017')
index_ref = pt.IndexRef.of('./indices/medline_2017') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
[pipeline],
dataset.get_topics('disease'),
dataset.get_qrels(),
[MAP, nDCG@20]
)
You can find more details about PyTerrier experiments here.
from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.medline.2017.trec-pm-2017.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{Roberts2017TrecPm, title={Overview of the TREC 2017 Precision Medicine Track}, author={Kirk Roberts and Dina Demner-Fushman and Ellen Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar and Shubham Pant}, booktitle={TREC}, year={2017} }{
"docs": {
"count": 26740025,
"fields": {
"doc_id": {
"max_len": 15,
"common_prefix": ""
}
}
},
"queries": {
"count": 30
},
"qrels": {
"count": 22642,
"fields": {
"relevance": {
"counts_by_value": {
"0": 18767,
"1": 1853,
"2": 2022
}
}
}
}
}
The TREC Precision Medicine (PM) Track 2018 benchmark. Contains 50 queries containing disease, gene, and target demographic information.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2017/trec-pm-2018")
for query in dataset.queries_iter():
query # namedtuple<query_id, disease, gene, demographic>
You can find more details about the Python API here.
ir_datasets export medline/2017/trec-pm-2018 queries
[query_id] [disease] [gene] [demographic]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:medline/2017/trec-pm-2018')
index_ref = pt.IndexRef.of('./indices/medline_2017') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('disease'))
You can find more details about PyTerrier retrieval here.
from datamaestro import prepare_dataset
topics = prepare_dataset('irds.medline.2017.trec-pm-2018.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 medline/2017
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2017/trec-pm-2018")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, abstract>
You can find more details about the Python API here.
ir_datasets export medline/2017/trec-pm-2018 docs
[doc_id] [title] [abstract]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:medline/2017/trec-pm-2018')
# Index medline/2017
indexer = pt.IterDictIndexer('./indices/medline_2017')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'abstract'])
You can find more details about PyTerrier indexing here.
from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.medline.2017.trec-pm-2018')
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 | not relevant | 17K | 75.1% |
| 1 | possibly relevant | 2.1K | 9.6% |
| 2 | definitely relevant | 3.4K | 15.3% |
Examples:
import ir_datasets
dataset = ir_datasets.load("medline/2017/trec-pm-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.
ir_datasets export medline/2017/trec-pm-2018 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:medline/2017/trec-pm-2018')
index_ref = pt.IndexRef.of('./indices/medline_2017') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
[pipeline],
dataset.get_topics('disease'),
dataset.get_qrels(),
[MAP, nDCG@20]
)
You can find more details about PyTerrier experiments here.
from datamaestro import prepare_dataset
qrels = prepare_dataset('irds.medline.2017.trec-pm-2018.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{Roberts2018TrecPm, title={Overview of the TREC 2018 Precision Medicine Track}, author={Kirk Roberts and Dina Demner-Fushman and Ellen Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar}, booktitle={TREC}, year={2018} }{
"docs": {
"count": 26740025,
"fields": {
"doc_id": {
"max_len": 15,
"common_prefix": ""
}
}
},
"queries": {
"count": 50
},
"qrels": {
"count": 22429,
"fields": {
"relevance": {
"counts_by_value": {
"0": 16841,
"2": 3442,
"1": 2146
}
}
}
}
}