ir_datasets: Highwire (TREC Genomics 2006-07)Medical document collection from Highwire Press. Includes 162,259 scientific articles from 49 journals.
This dataset is used for the TREC 2006-07 TREC Genomics track.
Note that these documents are split into passages based on paragraph tags in the HTML.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("highwire")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, journal, title, spans>
You can find more details about the Python API here.
ir_datasets export highwire docs
[doc_id] [journal] [title] [spans]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:highwire')
# Index highwire
indexer = pt.IterDictIndexer('./indices/highwire')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['journal', 'title'])
You can find more details about PyTerrier indexing here.
from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.highwire')
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": 162259,
"fields": {
"doc_id": {
"max_len": 8,
"common_prefix": ""
}
}
}
}
The TREC Genomics Track 2006 benchmark. Contains 28 queries with passage-level relevance judgments.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("highwire/trec-genomics-2006")
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
You can find more details about the Python API here.
ir_datasets export highwire/trec-genomics-2006 queries
[query_id] [text]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:highwire/trec-genomics-2006')
index_ref = pt.IndexRef.of('./indices/highwire') # 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.highwire.trec-genomics-2006.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 highwire
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("highwire/trec-genomics-2006")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, journal, title, spans>
You can find more details about the Python API here.
ir_datasets export highwire/trec-genomics-2006 docs
[doc_id] [journal] [title] [spans]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:highwire/trec-genomics-2006')
# Index highwire
indexer = pt.IterDictIndexer('./indices/highwire')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['journal', 'title'])
You can find more details about PyTerrier indexing here.
from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.highwire.trec-genomics-2006')
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 | 25K | 89.1% |
| 1 | POSSIBLY | 1.2K | 4.4% |
| 2 | DEFINITELY | 1.8K | 6.5% |
Examples:
import ir_datasets
dataset = ir_datasets.load("highwire/trec-genomics-2006")
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, start, length, relevance>
You can find more details about the Python API here.
ir_datasets export highwire/trec-genomics-2006 qrels --format tsv
[query_id] [doc_id] [start] [length] [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:highwire/trec-genomics-2006')
index_ref = pt.IndexRef.of('./indices/highwire') # 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.highwire.trec-genomics-2006.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{Hersh2006TrecGenomics, title={TREC 2006 Genomics Track Overview}, author={William Hersh and Aaron M. Cohen and Phoebe Roberts and Hari Krishna Rekapalli}, booktitle={TREC}, year={2006} }{
"docs": {
"count": 162259,
"fields": {
"doc_id": {
"max_len": 8,
"common_prefix": ""
}
}
},
"queries": {
"count": 28
},
"qrels": {
"count": 27999,
"fields": {
"relevance": {
"counts_by_value": {
"0": 24934,
"1": 1237,
"2": 1828
}
}
}
}
}
The TREC Genomics Track 2007 benchmark. Contains 36 queries with passage-level relevance judgments.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("highwire/trec-genomics-2007")
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
You can find more details about the Python API here.
ir_datasets export highwire/trec-genomics-2007 queries
[query_id] [text]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:highwire/trec-genomics-2007')
index_ref = pt.IndexRef.of('./indices/highwire') # 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.highwire.trec-genomics-2007.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 highwire
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("highwire/trec-genomics-2007")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, journal, title, spans>
You can find more details about the Python API here.
ir_datasets export highwire/trec-genomics-2007 docs
[doc_id] [journal] [title] [spans]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:highwire/trec-genomics-2007')
# Index highwire
indexer = pt.IterDictIndexer('./indices/highwire')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['journal', 'title'])
You can find more details about PyTerrier indexing here.
from datamaestro import prepare_dataset
dataset = prepare_dataset('irds.highwire.trec-genomics-2007')
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 | 32K | 87.5% |
| 1 | RELEVANT | 4.5K | 12.5% |
Examples:
import ir_datasets
dataset = ir_datasets.load("highwire/trec-genomics-2007")
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, start, length, relevance>
You can find more details about the Python API here.
ir_datasets export highwire/trec-genomics-2007 qrels --format tsv
[query_id] [doc_id] [start] [length] [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:highwire/trec-genomics-2007')
index_ref = pt.IndexRef.of('./indices/highwire') # 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.highwire.trec-genomics-2007.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{Hersh2007TrecGenomics, title={TREC 2007 Genomics Track Overview}, author={William Hersh and Aaron Cohen and Lynn Ruslen and Phoebe Roberts}, booktitle={TREC}, year={2007} }{
"docs": {
"count": 162259,
"fields": {
"doc_id": {
"max_len": 8,
"common_prefix": ""
}
}
},
"queries": {
"count": 36
},
"qrels": {
"count": 35996,
"fields": {
"relevance": {
"counts_by_value": {
"0": 31501,
"1": 4495
}
}
}
}
}