import ir_datasets
dataset = ir_datasets.load("vaswani")
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 vaswani 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:vaswani')
index_ref = pt.IndexRef.of('./indices/vaswani') # 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.vaswani.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.