def test_core(self):
data_type = namedtuple('data_type', ['doc_id', 'field1', 'field2'])
mock_file = StringFile('''
123\tsome field\tanother field
123\t repeated entry \tshouldn't filter
456\tanother query\tsomething
'''.lstrip())
expected_results = [
data_type('123', 'some field', 'another field'),
data_type('123', ' repeated entry ', 'shouldn\'t filter'),
data_type('456', 'another query', 'something'),
]
queries = TsvQueries(mock_file, data_type)
self.assertEqual(queries.queries_path(), 'MOCK')
self.assertEqual(list(queries.queries_iter()), expected_results)
docs = TsvDocs(mock_file, data_type)
self.assertEqual(docs.docs_path(), 'MOCK')
self.assertEqual(list(docs.docs_iter()), expected_results)
docpairs = TsvDocPairs(mock_file, data_type)
self.assertEqual(docpairs.docpairs_path(), 'MOCK')
self.assertEqual(list(docpairs.docpairs_iter()), expected_results)
def test_flex_columns(self):
class data_type(NamedTuple):
doc_id: str
field1: str
field2: Tuple[str, ...]
mock_file = StringFile('''
123\tsome field\tanother field
123\ttoo few fields
456\tanother query\tsomething
456\tanother query\tsomething\ttoo many fields\teven more
'''.strip())
expected_results = [
data_type('123', 'some field', ('another field', )),
data_type('123', 'too few fields', ()),
data_type('456', 'another query', ('something', )),
data_type('456', 'another query',
('something', 'too many fields', 'even more')),
]
queries = TsvQueries(mock_file, data_type)
self.assertEqual(queries.queries_path(), 'MOCK')
self.assertEqual(list(queries.queries_iter()), expected_results)
docs = TsvDocs(mock_file, data_type)
self.assertEqual(docs.docs_path(), 'MOCK')
self.assertEqual(list(docs.docs_iter()), expected_results)
docpairs = TsvDocPairs(mock_file, data_type)
self.assertEqual(docpairs.docpairs_path(), 'MOCK')
self.assertEqual(list(docpairs.docpairs_iter()), expected_results)
def _init():
documentation = YamlDocumentation('docs/antique.yaml')
base_path = ir_datasets.util.home_path() / NAME
dlc = DownloadConfig.context(NAME, base_path, dua=DUA)
collection = TsvDocs(dlc['docs'],
namespace=NAME,
lang='en',
count_hint=ir_datasets.util.count_hint(NAME))
subsets = {}
for subset in ('train', 'test'):
qrels = TrecQrels(dlc[f'{subset}/qrels'], QREL_DEFS)
queries = TsvQueries(dlc[f'{subset}/queries'],
namespace=NAME,
lang='en')
subsets[subset] = Dataset(collection, queries, qrels)
# Split the training data into training and validation data
validation_qids = Lazy(lambda: VALIDATION_QIDS)
subsets['train/split200-train'] = Dataset(
FilteredQueries(subsets['train'].queries_handler(),
validation_qids,
mode='exclude'),
FilteredQrels(subsets['train'].qrels_handler(),
validation_qids,
mode='exclude'), subsets['train'])
subsets['train/split200-valid'] = Dataset(
FilteredQueries(subsets['train'].queries_handler(),
validation_qids,
mode='include'),
FilteredQrels(subsets['train'].qrels_handler(),
validation_qids,
mode='include'), subsets['train'])
# Separate test set removing the "offensive (and noisy)" questions
disallow_list = dlc['disallow_list']
def disllow_qids():
with disallow_list.stream() as stream:
stream = io.TextIOWrapper(stream)
return {l.rstrip() for l in stream}
disllow_qids = Lazy(disllow_qids)
subsets['test/non-offensive'] = Dataset(
FilteredQueries(subsets['test'].queries_handler(),
disllow_qids,
mode='exclude'),
FilteredQrels(subsets['test'].qrels_handler(),
disllow_qids,
mode='exclude'), subsets['test'])
ir_datasets.registry.register(NAME, Dataset(collection,
documentation('_')))
for s in sorted(subsets):
ir_datasets.registry.register(f'{NAME}/{s}',
Dataset(subsets[s], documentation(s)))
return collection, subsets
def _docs_initializer(lang_code):
if lang_code not in _docs_cache:
dlc = _dlc().context("clirmatrix_docs", base_path)
docs = TsvDocs(GzipExtract(dlc[f'docs/{lang_code}']),
namespace=f'{NAME}/{lang_code}',
lang=lang_code)
_docs_cache[lang_code] = docs
return _docs_cache[lang_code]
def test_too_few_columns(self):
data_type = namedtuple('data_type', ['doc_id', 'field1', 'field2'])
mock_file = StringFile('''
123\tsome field\tanother field
123\ttoo few fields
456\tanother query\tsomething
'''.strip())
queries = TsvQueries(mock_file, data_type)
with self.assertRaises(RuntimeError):
list(queries.queries_iter())
docs = TsvDocs(mock_file, data_type)
with self.assertRaises(RuntimeError):
list(docs.docs_iter())
docpairs = TsvDocPairs(mock_file, data_type)
with self.assertRaises(RuntimeError):
list(docpairs.docpairs_iter())
def _init():
base_path = ir_datasets.util.home_path() / NAME
dlc = ir_datasets.util.DownloadConfig.context(NAME, base_path)
documentation = YamlDocumentation(f'docs/{NAME}.yaml')
collection = TsvDocs(GzipExtract(dlc['docs']),
doc_cls=DprW100Doc,
namespace=NAME,
lang='en',
skip_first_line=True,
docstore_size_hint=12827215492,
count_hint=ir_datasets.util.count_hint(NAME))
base = Dataset(collection, documentation('_'))
subsets = {}
nq_dev_manager = DprW100Manager(GzipExtract(dlc['nq-dev']),
base_path / 'nq-dev')
subsets['natural-questions/dev'] = Dataset(
collection, DprW100Queries(nq_dev_manager.file_ref('queries.tsv')),
TrecQrels(nq_dev_manager.file_ref('qrels'), QREL_DEFS),
documentation('natural-questions/dev'))
nq_train_manager = DprW100Manager(GzipExtract(dlc['nq-train']),
base_path / 'nq-train')
subsets['natural-questions/train'] = Dataset(
collection, DprW100Queries(nq_train_manager.file_ref('queries.tsv')),
TrecQrels(nq_train_manager.file_ref('qrels'), QREL_DEFS),
documentation('natural-questions/train'))
tqa_dev_manager = DprW100Manager(GzipExtract(dlc['tqa-dev']),
base_path / 'tqa-dev',
passage_id_key='psg_id')
subsets['trivia-qa/dev'] = Dataset(
collection, DprW100Queries(tqa_dev_manager.file_ref('queries.tsv')),
TrecQrels(tqa_dev_manager.file_ref('qrels'), QREL_DEFS),
documentation('trivia-qa/dev'))
tqa_train_manager = DprW100Manager(GzipExtract(dlc['tqa-train']),
base_path / 'tqa-train',
passage_id_key='psg_id')
subsets['trivia-qa/train'] = Dataset(
collection, DprW100Queries(tqa_train_manager.file_ref('queries.tsv')),
TrecQrels(tqa_train_manager.file_ref('qrels'), QREL_DEFS),
documentation('trivia-qa/train'))
ir_datasets.registry.register(NAME, base)
for s in sorted(subsets):
ir_datasets.registry.register(f'{NAME}/{s}', subsets[s])
return base, subsets
def _init():
base_path = ir_datasets.util.home_path() / NAME
dlc = ir_datasets.util.DownloadConfig.context(NAME, base_path)
documentation = YamlDocumentation(f'docs/{NAME}.yaml')
base_dlc = TarExtractAll(dlc['source'], base_path / 'lotte_extracted')
base = Dataset(documentation('_'))
subsets = {}
domains = [
('lifestyle', ),
('recreation', ),
('science', ),
('technology', ),
('writing', ),
('pooled', ),
]
for (domain, ) in domains:
for split in ['dev', 'test']:
corpus = TsvDocs(RelativePath(
base_dlc, f'lotte/{domain}/{split}/collection.tsv'),
lang='en')
subsets[f'{domain}/{split}'] = Dataset(
corpus, documentation(f'{domain}/{split}'))
for qtype in ['search', 'forum']:
subsets[f'{domain}/{split}/{qtype}'] = Dataset(
corpus,
TsvQueries(RelativePath(
base_dlc,
f'lotte/{domain}/{split}/questions.{qtype}.tsv'),
lang='en'),
LotteQrels(
RelativePath(
base_dlc,
f'lotte/{domain}/{split}/qas.{qtype}.jsonl')),
documentation(f'{domain}/{split}/{qtype}'))
ir_datasets.registry.register(NAME, base)
for s in sorted(subsets):
ir_datasets.registry.register(f'{NAME}/{s}', subsets[s])
return base, subsets
def _init():
documentation = YamlDocumentation(f'docs/{NAME}.yaml')
base_path = ir_datasets.util.home_path() / NAME
dlc = DownloadConfig.context(NAME, base_path, dua=DUA)
migrator = Migrator(base_path / 'irds_version.txt',
'v2',
affected_files=[
base_path / 'collection.tsv',
base_path / 'collection.tsv.pklz4'
],
message=f'Migrating {NAME} (fixing passage encoding)')
collection = TsvDocs(Cache(
FixEncoding(TarExtract(dlc['collectionandqueries'], 'collection.tsv')),
base_path / 'collection.tsv'),
namespace='msmarco',
lang='en',
docstore_size_hint=14373971970,
count_hint=ir_datasets.util.count_hint(NAME))
collection = migrator(collection)
subsets = {}
subsets['train'] = Dataset(
collection,
TsvQueries(Cache(TarExtract(dlc['queries'], 'queries.train.tsv'),
base_path / 'train/queries.tsv'),
namespace='msmarco',
lang='en'),
TrecQrels(dlc['train/qrels'], QRELS_DEFS),
TsvDocPairs(GzipExtract(dlc['train/docpairs'])),
TrecScoredDocs(
Cache(
ExtractQidPid(
TarExtract(dlc['train/scoreddocs'], 'top1000.train.txt')),
base_path / 'train/ms.run')),
)
subsets['train/triples-v2'] = Dataset(
collection,
subsets['train'].queries_handler(),
subsets['train'].qrels_handler(),
TsvDocPairs(GzipExtract(dlc['train/docpairs/v2'])),
subsets['train'].scoreddocs_handler(),
)
subsets['train/triples-small'] = Dataset(
collection,
subsets['train'].queries_handler(),
subsets['train'].qrels_handler(),
TsvDocPairs(
Cache(
MapSmallTriplesQidPid(
TarExtract(dlc['train/docpairs/small'],
'triples.train.small.tsv'),
TarExtract(dlc['collectionandqueries'], 'collection.tsv'),
subsets['train'].queries_handler()),
base_path / 'train/small.triples.qidpid.tsv')),
subsets['train'].scoreddocs_handler(),
)
subsets['dev'] = Dataset(
collection,
TsvQueries(Cache(TarExtract(dlc['queries'], 'queries.dev.tsv'),
base_path / 'dev/queries.tsv'),
namespace='msmarco',
lang='en'),
TrecQrels(dlc['dev/qrels'], QRELS_DEFS),
)
subsets['dev/small'] = Dataset(
collection,
TsvQueries(Cache(
TarExtract(dlc['collectionandqueries'], 'queries.dev.small.tsv'),
base_path / 'dev/small/queries.tsv'),
namespace='msmarco',
lang='en'),
TrecQrels(
Cache(
TarExtract(dlc['collectionandqueries'], 'qrels.dev.small.tsv'),
base_path / 'dev/small/qrels'), QRELS_DEFS),
TrecScoredDocs(
Cache(
ExtractQidPid(TarExtract(dlc['dev/scoreddocs'],
'top1000.dev')),
base_path / 'dev/ms.run')),
)
subsets['eval'] = Dataset(
collection,
TsvQueries(Cache(TarExtract(dlc['queries'], 'queries.eval.tsv'),
base_path / 'eval/queries.tsv'),
namespace='msmarco',
lang='en'),
)
subsets['eval/small'] = Dataset(
collection,
TsvQueries(Cache(
TarExtract(dlc['collectionandqueries'], 'queries.eval.small.tsv'),
base_path / 'eval/small/queries.tsv'),
namespace='msmarco',
lang='en'),
TrecScoredDocs(
Cache(
ExtractQidPid(
TarExtract(dlc['eval/scoreddocs'], 'top1000.eval')),
base_path / 'eval/ms.run')),
)
subsets['trec-dl-2019'] = Dataset(
collection,
TrecQrels(dlc['trec-dl-2019/qrels'], TREC_DL_QRELS_DEFS),
TsvQueries(Cache(GzipExtract(dlc['trec-dl-2019/queries']),
base_path / 'trec-dl-2019/queries.tsv'),
namespace='msmarco',
lang='en'),
TrecScoredDocs(
Cache(ExtractQidPid(GzipExtract(dlc['trec-dl-2019/scoreddocs'])),
base_path / 'trec-dl-2019/ms.run')),
)
subsets['trec-dl-2020'] = Dataset(
collection,
TsvQueries(GzipExtract(dlc['trec-dl-2020/queries']),
namespace='msmarco',
lang='en'),
TrecQrels(dlc['trec-dl-2020/qrels'], TREC_DL_QRELS_DEFS),
TrecScoredDocs(
Cache(ExtractQidPid(GzipExtract(dlc['trec-dl-2020/scoreddocs'])),
base_path / 'trec-dl-2020/ms.run')),
)
# A few subsets that are contrainted to just the queries/qrels/docpairs that have at least
# 1 relevance assessment
train_judged = Lazy(
lambda: {q.query_id
for q in subsets['train'].qrels_iter()})
subsets['train/judged'] = Dataset(
FilteredQueries(subsets['train'].queries_handler(), train_judged),
FilteredScoredDocs(subsets['train'].scoreddocs_handler(),
train_judged),
subsets['train'],
)
dev_judged = Lazy(
lambda: {q.query_id
for q in subsets['dev'].qrels_iter()})
subsets['dev/judged'] = Dataset(
FilteredQueries(subsets['dev'].queries_handler(), dev_judged),
subsets['dev'],
)
dl19_judged = Lazy(
lambda: {q.query_id
for q in subsets['trec-dl-2019'].qrels_iter()})
subsets['trec-dl-2019/judged'] = Dataset(
FilteredQueries(subsets['trec-dl-2019'].queries_handler(),
dl19_judged),
FilteredScoredDocs(subsets['trec-dl-2019'].scoreddocs_handler(),
dl19_judged),
subsets['trec-dl-2019'],
)
dl20_judged = Lazy(
lambda: {q.query_id
for q in subsets['trec-dl-2020'].qrels_iter()})
subsets['trec-dl-2020/judged'] = Dataset(
FilteredQueries(subsets['trec-dl-2020'].queries_handler(),
dl20_judged),
FilteredScoredDocs(subsets['trec-dl-2020'].scoreddocs_handler(),
dl20_judged),
subsets['trec-dl-2020'],
)
# split200 -- 200 queries held out from the training data for validation
split200 = Lazy(lambda: SPLIT200_QIDS)
subsets['train/split200-train'] = Dataset(
FilteredQueries(subsets['train'].queries_handler(),
split200,
mode='exclude'),
FilteredScoredDocs(subsets['train'].scoreddocs_handler(),
split200,
mode='exclude'),
FilteredQrels(subsets['train'].qrels_handler(),
split200,
mode='exclude'),
FilteredDocPairs(subsets['train'].docpairs_handler(),
split200,
mode='exclude'),
subsets['train'],
)
subsets['train/split200-valid'] = Dataset(
FilteredQueries(subsets['train'].queries_handler(),
split200,
mode='include'),
FilteredScoredDocs(subsets['train'].scoreddocs_handler(),
split200,
mode='include'),
FilteredQrels(subsets['train'].qrels_handler(),
split200,
mode='include'),
FilteredDocPairs(subsets['train'].docpairs_handler(),
split200,
mode='include'),
subsets['train'],
)
# Medical subset
def train_med():
with dlc['medmarco_ids'].stream() as stream:
stream = codecs.getreader('utf8')(stream)
return {l.rstrip() for l in stream}
train_med = Lazy(train_med)
subsets['train/medical'] = Dataset(
FilteredQueries(subsets['train'].queries_handler(), train_med),
FilteredScoredDocs(subsets['train'].scoreddocs_handler(), train_med),
FilteredDocPairs(subsets['train'].docpairs_handler(), train_med),
FilteredQrels(subsets['train'].qrels_handler(), train_med),
subsets['train'],
)
# DL-Hard
dl_hard_qrels_migrator = Migrator(
base_path / 'trec-dl-hard' / 'irds_version.txt',
'v3',
affected_files=[base_path / 'trec-dl-hard' / 'qrels'],
message='Updating trec-dl-hard qrels')
hard_qids = Lazy(lambda: DL_HARD_QIDS)
dl_hard_base_queries = TsvQueries([
Cache(GzipExtract(dlc['trec-dl-2019/queries']),
base_path / 'trec-dl-2019/queries.tsv'),
Cache(GzipExtract(dlc['trec-dl-2020/queries']),
base_path / 'trec-dl-2020/queries.tsv')
],
namespace='msmarco',
lang='en')
subsets['trec-dl-hard'] = Dataset(
collection, FilteredQueries(dl_hard_base_queries, hard_qids),
dl_hard_qrels_migrator(
TrecQrels(dlc['trec-dl-hard/qrels'], TREC_DL_QRELS_DEFS)),
documentation('trec-dl-hard'))
hard_qids = Lazy(lambda: DL_HARD_QIDS_BYFOLD['1'])
subsets['trec-dl-hard/fold1'] = Dataset(
collection, FilteredQueries(dl_hard_base_queries, hard_qids),
FilteredQrels(subsets['trec-dl-hard'], hard_qids),
documentation('trec-dl-hard/fold1'))
hard_qids = Lazy(lambda: DL_HARD_QIDS_BYFOLD['2'])
subsets['trec-dl-hard/fold2'] = Dataset(
collection, FilteredQueries(dl_hard_base_queries, hard_qids),
FilteredQrels(subsets['trec-dl-hard'], hard_qids),
documentation('trec-dl-hard/fold2'))
hard_qids = Lazy(lambda: DL_HARD_QIDS_BYFOLD['3'])
subsets['trec-dl-hard/fold3'] = Dataset(
collection, FilteredQueries(dl_hard_base_queries, hard_qids),
FilteredQrels(subsets['trec-dl-hard'], hard_qids),
documentation('trec-dl-hard/fold3'))
hard_qids = Lazy(lambda: DL_HARD_QIDS_BYFOLD['4'])
subsets['trec-dl-hard/fold4'] = Dataset(
collection, FilteredQueries(dl_hard_base_queries, hard_qids),
FilteredQrels(subsets['trec-dl-hard'], hard_qids),
documentation('trec-dl-hard/fold4'))
hard_qids = Lazy(lambda: DL_HARD_QIDS_BYFOLD['5'])
subsets['trec-dl-hard/fold5'] = Dataset(
collection, FilteredQueries(dl_hard_base_queries, hard_qids),
FilteredQrels(subsets['trec-dl-hard'], hard_qids),
documentation('trec-dl-hard/fold5'))
ir_datasets.registry.register(NAME, Dataset(collection,
documentation('_')))
for s in sorted(subsets):
ir_datasets.registry.register(f'{NAME}/{s}',
Dataset(subsets[s], documentation(s)))
return collection, subsets
# What to the relevance levels in qrels mean?
QREL_DEFS = {
1: 'relevant',
0: 'not relevant',
}
# Specify where to find the content. Here it's just from the repository, but it could be anywhere.
DL_DOCS = ir_datasets.util.RequestsDownload(
'https://raw.githubusercontent.com/seanmacavaney/dummy-irds-ext/master/data/docs.tsv'
)
DL_QUERIES = ir_datasets.util.RequestsDownload(
'https://raw.githubusercontent.com/seanmacavaney/dummy-irds-ext/master/data/queries.tsv'
)
DL_QRELS = ir_datasets.util.RequestsDownload(
'https://raw.githubusercontent.com/seanmacavaney/dummy-irds-ext/master/data/qrels'
)
# where the content is cached
base_path = ir_datasets.util.home_path() / NAME
# Dataset definition: it provides docs, queries, and qrels
dataset = ir_datasets.Dataset(
TsvDocs(ir_datasets.util.Cache(DL_DOCS, base_path / 'docs.tsv')),
TsvQueries(ir_datasets.util.Cache(DL_QUERIES, base_path / 'queries.tsv')),
TrecQrels(ir_datasets.util.Cache(DL_QRELS, base_path / 'qrels'),
QREL_DEFS),
)
# Register the dataset with ir_datasets
ir_datasets.registry.register(NAME, dataset)
def _init():
base_path = ir_datasets.util.home_path() / NAME
dlc = DownloadConfig.context(NAME, base_path)
documentation = YamlDocumentation(f'docs/{NAME}.yaml')
main_dlc = dlc['main']
collection = TsvDocs(Cache(
TarExtract(main_dlc, 'nfcorpus/raw/doc_dump.txt'),
base_path / 'collection.tsv'),
doc_cls=NfCorpusDoc,
namespace=NAME)
subsets = {}
def read_lines(file):
file = Cache(TarExtract(main_dlc, f'nfcorpus/raw/{file}'),
base_path / file)
with file.stream() as stream:
stream = codecs.getreader('utf8')(stream)
return {l.rstrip() for l in stream}
nontopic_qid_filter = Lazy(lambda: read_lines('nontopics.ids'))
video_qid_filter = Lazy(lambda: read_lines('all_videos.ids'))
subsets['train'] = Dataset(
collection,
ZipQueries([
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/train.titles.queries'),
base_path / 'train/queries.titles.tsv'),
namespace=NAME),
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/train.all.queries'),
base_path / 'train/queries.all.tsv'),
namespace=NAME),
], [(0, 0), (0, 1), (1, 1)], NfCorpusQuery),
TrecQrels(
Cache(TarExtract(main_dlc, 'nfcorpus/train.3-2-1.qrel'),
base_path / 'train/qrels'), QRELS_DEFS),
documentation('train'),
)
subsets['train/nontopic'] = Dataset(
collection,
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/train.nontopic-titles.queries'),
base_path / 'train/nontopic/queries.tsv'),
namespace=NAME),
FilteredQrels(subsets['train'].qrels_handler(),
nontopic_qid_filter,
mode='include'),
documentation('train/nontopic'),
)
subsets['train/video'] = Dataset(
collection,
ZipQueries([
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/train.vid-titles.queries'),
base_path / 'train/video/queries.titles.tsv'),
namespace=NAME),
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/train.vid-desc.queries'),
base_path / 'train/video/queries.desc.tsv'),
namespace=NAME),
], [(0, 0), (0, 1), (1, 1)], NfCorpusVideoQuery),
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/train.nontopic-titles.queries'),
base_path / 'train/video/queries.tsv'),
NfCorpusVideoQuery,
namespace=NAME),
FilteredQrels(subsets['train'].qrels_handler(),
video_qid_filter,
mode='include'),
documentation('train/video'),
)
subsets['dev'] = Dataset(
collection,
ZipQueries([
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/dev.titles.queries'),
base_path / 'dev/queries.titles.tsv'),
namespace=NAME),
TsvQueries(Cache(TarExtract(main_dlc, 'nfcorpus/dev.all.queries'),
base_path / 'dev/queries.all.tsv'),
namespace=NAME),
], [(0, 0), (0, 1), (1, 1)], NfCorpusQuery),
TrecQrels(
Cache(TarExtract(main_dlc, 'nfcorpus/dev.3-2-1.qrel'),
base_path / 'dev/qrels'), QRELS_DEFS),
documentation('dev'),
)
subsets['dev/nontopic'] = Dataset(
collection,
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/dev.nontopic-titles.queries'),
base_path / 'dev/nontopic/queries.tsv'),
namespace=NAME),
FilteredQrels(subsets['dev'].qrels_handler(),
nontopic_qid_filter,
mode='include'),
documentation('dev/nontopic'),
)
subsets['dev/video'] = Dataset(
collection,
ZipQueries([
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/dev.vid-titles.queries'),
base_path / 'dev/video/queries.titles.tsv'),
namespace=NAME),
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/dev.vid-desc.queries'),
base_path / 'dev/video/queries.desc.tsv'),
namespace=NAME),
], [(0, 0), (0, 1), (1, 1)], NfCorpusVideoQuery),
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/dev.nontopic-titles.queries'),
base_path / 'dev/video/queries.tsv'),
NfCorpusVideoQuery,
namespace=NAME),
FilteredQrels(subsets['dev'].qrels_handler(),
video_qid_filter,
mode='include'),
documentation('dev/video'),
)
subsets['test'] = Dataset(
collection,
ZipQueries([
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/test.titles.queries'),
base_path / 'test/queries.titles.tsv'),
namespace=NAME),
TsvQueries(Cache(TarExtract(main_dlc, 'nfcorpus/test.all.queries'),
base_path / 'test/queries.all.tsv'),
namespace=NAME),
], [(0, 0), (0, 1), (1, 1)], NfCorpusQuery),
TrecQrels(
Cache(TarExtract(main_dlc, 'nfcorpus/test.3-2-1.qrel'),
base_path / 'test/qrels'), QRELS_DEFS),
documentation('test'),
)
subsets['test/nontopic'] = Dataset(
collection,
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/test.nontopic-titles.queries'),
base_path / 'test/nontopic/queries.tsv'),
namespace=NAME),
FilteredQrels(subsets['test'].qrels_handler(),
nontopic_qid_filter,
mode='include'),
documentation('test/nontopic'),
)
subsets['test/video'] = Dataset(
collection,
ZipQueries([
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/test.vid-titles.queries'),
base_path / 'test/video/queries.titles.tsv'),
namespace=NAME),
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/test.vid-desc.queries'),
base_path / 'test/video/queries.desc.tsv'),
namespace=NAME),
], [(0, 0), (0, 1), (1, 1)], NfCorpusVideoQuery),
TsvQueries(Cache(
TarExtract(main_dlc, 'nfcorpus/test.nontopic-titles.queries'),
base_path / 'test/video/queries.tsv'),
NfCorpusVideoQuery,
namespace=NAME),
FilteredQrels(subsets['test'].qrels_handler(),
video_qid_filter,
mode='include'),
documentation('test/video'),
)
ir_datasets.registry.register(NAME, Dataset(collection,
documentation('_')))
for s in sorted(subsets):
ir_datasets.registry.register(f'{NAME}/{s}', subsets[s])
return collection, subsets
def _init():
subsets = {}
base_path = ir_datasets.util.home_path() / NAME
dlc = DownloadConfig.context(NAME, base_path)
documentation = YamlDocumentation(f'docs/{NAME}.yaml')
collection = TrecDocs(dlc['benchmark'],
parser='tut',
path_globs=['**/docs_grp_*.txt'],
namespace=NAME,
lang='en',
count_hint=ir_datasets.util.count_hint(NAME))
topics_and_qrels = TarExtractAll(
dlc['benchmark'],
base_path / "topics_and_qrels",
path_globs=['**/topics.*.txt', '**/qrels.*.txt'])
val_runs = TarExtractAll(dlc['dlfiles'],
base_path / "val_runs",
path_globs=['**/run.trip.BM25.*.val.txt'])
test_runs = TarExtractAll(dlc['dlfiles_runs_test'],
base_path / "test_runs",
path_globs=['**/run.trip.BM25.*.test.txt'])
base = Dataset(collection, documentation('_'))
subsets['logs'] = Dataset(
TsvDocs(Cache(
FixAllarticles(TarExtract(dlc['logs'], 'logs/allarticles.txt')),
base_path / 'allarticles-fixed.tsv'),
doc_cls=TripClickPartialDoc,
lang='en',
count_hint=ir_datasets.util.count_hint(f'{NAME}/logs')),
TripClickQlogs(
TarExtractAll(dlc['logs'],
base_path / 'logs',
path_globs=['**/*.json'])), documentation('logs'))
### Train
subsets['train/head'] = Dataset(
collection,
TrecQueries(RelativePath(topics_and_qrels,
'benchmark/topics/topics.head.train.txt'),
qtype=GenericQuery,
qtype_map=QTYPE_MAP,
namespace=NAME,
lang='en'),
TrecQrels(
RelativePath(topics_and_qrels,
'benchmark/qrels/qrels.raw.head.train.txt'),
QREL_DEFS), documentation('train/head'))
subsets['train/head/dctr'] = Dataset(
TrecQrels(
RelativePath(topics_and_qrels,
'benchmark/qrels/qrels.dctr.head.train.txt'),
QREL_DCTR_DEFS), subsets['train/head'],
documentation('train/head/dctr'))
subsets['train/torso'] = Dataset(
collection,
TrecQueries(RelativePath(topics_and_qrels,
'benchmark/topics/topics.torso.train.txt'),
qtype=GenericQuery,
qtype_map=QTYPE_MAP,
namespace=NAME,
lang='en'),
TrecQrels(
RelativePath(topics_and_qrels,
'benchmark/qrels/qrels.raw.torso.train.txt'),
QREL_DEFS), documentation('train/torso'))
subsets['train/tail'] = Dataset(
collection,
TrecQueries(RelativePath(topics_and_qrels,
'benchmark/topics/topics.tail.train.txt'),
qtype=GenericQuery,
qtype_map=QTYPE_MAP,
namespace=NAME,
lang='en'),
TrecQrels(
RelativePath(topics_and_qrels,
'benchmark/qrels/qrels.raw.tail.train.txt'),
QREL_DEFS), documentation('train/tail'))
train_queries = ConcatQueries([
subsets['train/head'].queries_handler(),
subsets['train/torso'].queries_handler(),
subsets['train/tail'].queries_handler(),
])
train_docpairs = DocPairGenerator(
TarExtract(dlc['dlfiles'], 'dlfiles/triples.train.tsv'), collection,
train_queries, base_path / 'train.docpairs')
subsets['train'] = Dataset(
collection, train_queries,
ConcatQrels([
subsets['train/head'].qrels_handler(),
subsets['train/torso'].qrels_handler(),
subsets['train/tail'].qrels_handler(),
]), TsvDocPairs(train_docpairs), documentation('train'))
subsets['train/hofstaetter-triples'] = Dataset(
collection, train_queries, subsets['train'].qrels_handler(),
TsvDocPairs(dlc['hofstaetter-triples']),
documentation('train/hofstaetter-triples'))
### Val
subsets['val/head'] = Dataset(
collection,
TrecQueries(RelativePath(topics_and_qrels,
'benchmark/topics/topics.head.val.txt'),
qtype=GenericQuery,
qtype_map=QTYPE_MAP,
namespace=NAME,
lang='en'),
TrecQrels(
RelativePath(topics_and_qrels,
'benchmark/qrels/qrels.raw.head.val.txt'), QREL_DEFS),
TrecScoredDocs(
RelativePath(val_runs, 'dlfiles/run.trip.BM25.head.val.txt')),
documentation('val/head'))
subsets['val/head/dctr'] = Dataset(
TrecQrels(
RelativePath(topics_and_qrels,
'benchmark/qrels/qrels.dctr.head.val.txt'),
QREL_DCTR_DEFS), subsets['val/head'],
documentation('val/head/dctr'))
subsets['val/torso'] = Dataset(
collection,
TrecQueries(RelativePath(topics_and_qrels,
'benchmark/topics/topics.torso.val.txt'),
qtype=GenericQuery,
qtype_map=QTYPE_MAP,
namespace=NAME,
lang='en'),
TrecQrels(
RelativePath(topics_and_qrels,
'benchmark/qrels/qrels.raw.torso.val.txt'),
QREL_DEFS),
TrecScoredDocs(
RelativePath(val_runs, 'dlfiles/run.trip.BM25.torso.val.txt')),
documentation('val/torso'))
subsets['val/tail'] = Dataset(
collection,
TrecQueries(RelativePath(topics_and_qrels,
'benchmark/topics/topics.tail.val.txt'),
qtype=GenericQuery,
qtype_map=QTYPE_MAP,
namespace=NAME,
lang='en'),
TrecQrels(
RelativePath(topics_and_qrels,
'benchmark/qrels/qrels.raw.tail.val.txt'), QREL_DEFS),
TrecScoredDocs(
RelativePath(val_runs, 'dlfiles/run.trip.BM25.tail.val.txt')),
documentation('val/tail'))
subsets['val'] = Dataset(
collection,
ConcatQueries([
subsets['val/head'].queries_handler(),
subsets['val/torso'].queries_handler(),
subsets['val/tail'].queries_handler(),
]),
ConcatQrels([
subsets['val/head'].qrels_handler(),
subsets['val/torso'].qrels_handler(),
subsets['val/tail'].qrels_handler(),
]),
ConcatScoreddocs([
subsets['val/head'].scoreddocs_handler(),
subsets['val/torso'].scoreddocs_handler(),
subsets['val/tail'].scoreddocs_handler(),
]), documentation('val'))
### Test
subsets['test/head'] = Dataset(
collection,
TrecQueries(RelativePath(topics_and_qrels,
'benchmark/topics/topics.head.test.txt'),
qtype=GenericQuery,
qtype_map=QTYPE_MAP,
namespace=NAME,
lang='en'),
TrecScoredDocs(
RelativePath(test_runs, 'runs_test/run.trip.BM25.head.test.txt')),
documentation('val/head'))
subsets['test/torso'] = Dataset(
collection,
TrecQueries(RelativePath(topics_and_qrels,
'benchmark/topics/topics.torso.test.txt'),
qtype=GenericQuery,
qtype_map=QTYPE_MAP,
namespace=NAME,
lang='en'),
TrecScoredDocs(
RelativePath(test_runs, 'runs_test/run.trip.BM25.torso.test.txt')),
documentation('test/torso'))
subsets['test/tail'] = Dataset(
collection,
TrecQueries(RelativePath(topics_and_qrels,
'benchmark/topics/topics.tail.test.txt'),
qtype=GenericQuery,
qtype_map=QTYPE_MAP,
namespace=NAME,
lang='en'),
TrecScoredDocs(
RelativePath(test_runs, 'runs_test/run.trip.BM25.tail.test.txt')),
documentation('test/tail'))
subsets['test'] = Dataset(
collection,
ConcatQueries([
subsets['test/head'].queries_handler(),
subsets['test/torso'].queries_handler(),
subsets['test/tail'].queries_handler(),
]),
ConcatScoreddocs([
subsets['test/head'].scoreddocs_handler(),
subsets['test/torso'].scoreddocs_handler(),
subsets['test/tail'].scoreddocs_handler(),
]), documentation('test'))
ir_datasets.registry.register(NAME, base)
for s in sorted(subsets):
ir_datasets.registry.register(f'{NAME}/{s}', subsets[s])
return base, subsets
def _init():
documentation = YamlDocumentation(f'docs/{NAME}.yaml')
base_path = ir_datasets.util.home_path() / NAME
dlc = DownloadConfig.context(NAME, base_path)
subsets = {}
train_qrels = ir_datasets.registry['msmarco-passage/train'].qrels_handler()
train_docparis = TsvDocPairs(dlc['train/triples'])
dev_qrels = TrecQrels(dlc['dev/qrels'], QRELS_DEFS)
dev_small_qrels = TrecQrels(dlc['dev/qrels-small'], QRELS_DEFS)
small_dev_qids = Lazy(
lambda: {q.query_id
for q in dev_small_qrels.qrels_iter()})
for lang in ['es', 'fr', 'pt', 'it', 'id', 'de', 'ru', 'zh']:
collection = TsvDocs(
dlc[f'{lang}/docs'],
namespace=f'mmarco/{lang}',
lang=lang,
count_hint=ir_datasets.util.count_hint(f'{NAME}/{lang}'))
subsets[f'{lang}'] = Dataset(collection, documentation(f'{lang}'))
subsets[f'{lang}/train'] = Dataset(
collection,
TsvQueries(dlc[f'{lang}/queries/train'],
namespace=f'mmarco/{lang}',
lang=lang), train_qrels, train_docparis,
documentation(f'{lang}/train'))
subsets[f'{lang}/dev'] = Dataset(
collection,
TsvQueries(dlc[f'{lang}/queries/dev'],
namespace=f'mmarco/{lang}',
lang=lang), dev_qrels, documentation(f'{lang}/dev'))
subsets[f'{lang}/dev/small'] = Dataset(
collection,
FilteredQueries(subsets[f'{lang}/dev'].queries_handler(),
small_dev_qids,
mode='include'), dev_small_qrels,
TrecScoredDocs(dlc[f'{lang}/scoreddocs/dev'])
if lang not in ('zh', 'pt') else None,
documentation(f'{lang}/dev/small'))
if lang in ('zh', 'pt'):
subsets[f'{lang}/dev/v1.1'] = Dataset(
collection,
TsvQueries(dlc[f'{lang}/queries/dev/v1.1'],
namespace=f'mmarco/{lang}',
lang=lang), dev_qrels,
documentation(f'{lang}/dev/v1.1'))
subsets[f'{lang}/dev/small/v1.1'] = Dataset(
collection,
FilteredQueries(subsets[f'{lang}/dev/v1.1'].queries_handler(),
small_dev_qids,
mode='include'), dev_small_qrels,
TrecScoredDocs(dlc[f'{lang}/scoreddocs/dev/v1.1']),
documentation(f'{lang}/dev/v1.1'))
if lang in ('pt', ):
subsets[f'{lang}/train/v1.1'] = Dataset(
collection,
TsvQueries(dlc[f'{lang}/queries/train/v1.1'],
namespace=f'mmarco/{lang}',
lang=lang), train_qrels, train_docparis,
documentation(f'{lang}/train/v1.1'))
for lang in [
'ar', 'zh', 'dt', 'fr', 'de', 'hi', 'id', 'it', 'ja', 'pt', 'ru',
'es', 'vi'
]:
collection = TsvDocs(
dlc[f'v2/{lang}/docs'],
namespace=f'mmarco/{lang}',
lang=lang,
count_hint=ir_datasets.util.count_hint(f'{NAME}/v2/{lang}'))
subsets[f'v2/{lang}'] = Dataset(collection,
documentation(f'v2/{lang}'))
subsets[f'v2/{lang}/train'] = Dataset(
collection,
TsvQueries(dlc[f'v2/{lang}/queries/train'],
namespace=f'mmarco/v2/{lang}',
lang=lang), train_qrels, train_docparis,
documentation(f'v2/{lang}/train'))
subsets[f'v2/{lang}/dev'] = Dataset(
collection,
TsvQueries(dlc[f'v2/{lang}/queries/dev'],
namespace=f'v2/mmarco/{lang}',
lang=lang), dev_qrels, documentation(f'v2/{lang}/dev'))
subsets[f'v2/{lang}/dev/small'] = Dataset(
collection,
FilteredQueries(subsets[f'v2/{lang}/dev'].queries_handler(),
small_dev_qids,
mode='include'), dev_small_qrels,
TrecScoredDocs(dlc[f'v2/{lang}/scoreddocs/dev'],
negate_score=True),
documentation(f'v2/{lang}/dev/small'))
ir_datasets.registry.register(NAME, Dataset(documentation('_')))
for s in sorted(subsets):
ir_datasets.registry.register(f'{NAME}/{s}', subsets[s])
return collection, subsets
def _init():
documentation = YamlDocumentation('docs/msmarco-passage.yaml')
base_path = ir_datasets.util.home_path() / 'msmarco-passage'
dlc = DownloadConfig.context('msmarco-passage', base_path, dua=DUA)
collection = TsvDocs(Cache(
FixEncoding(TarExtract(dlc['collectionandqueries'], 'collection.tsv')),
base_path / 'collection.tsv'),
namespace='msmarco')
subsets = {}
subsets['train'] = Dataset(
collection,
TsvQueries(Cache(TarExtract(dlc['queries'], 'queries.train.tsv'),
base_path / 'train/queries.tsv'),
namespace='msmarco'),
TrecQrels(dlc['train/qrels'], QRELS_DEFS),
TsvDocPairs(GzipExtract(dlc['train/docpairs'])),
TrecScoredDocs(
Cache(
ExtractQidPid(
TarExtract(dlc['train/scoreddocs'], 'top1000.train.txt')),
base_path / 'train/ms.run')),
)
subsets['dev'] = Dataset(
collection,
TsvQueries(Cache(TarExtract(dlc['queries'], 'queries.dev.tsv'),
base_path / 'dev/queries.tsv'),
namespace='msmarco'),
TrecQrels(dlc['dev/qrels'], QRELS_DEFS),
TrecScoredDocs(
Cache(
ExtractQidPid(TarExtract(dlc['dev/scoreddocs'],
'top1000.dev')),
base_path / 'dev/ms.run')),
)
subsets['dev/small'] = Dataset(
collection,
TsvQueries(Cache(
TarExtract(dlc['collectionandqueries'], 'queries.dev.small.tsv'),
base_path / 'dev/small/queries.tsv'),
namespace='msmarco'),
TrecQrels(
Cache(
TarExtract(dlc['collectionandqueries'], 'qrels.dev.small.tsv'),
base_path / 'dev/small/qrels'), QRELS_DEFS),
)
subsets['eval'] = Dataset(
collection,
TsvQueries(Cache(TarExtract(dlc['queries'], 'queries.eval.tsv'),
base_path / 'eval/queries.tsv'),
namespace='msmarco'),
TrecScoredDocs(
Cache(
ExtractQidPid(
TarExtract(dlc['eval/scoreddocs'], 'top1000.eval')),
base_path / 'eval/ms.run')),
)
subsets['eval/small'] = Dataset(
collection,
TsvQueries(Cache(
TarExtract(dlc['collectionandqueries'], 'queries.eval.small.tsv'),
base_path / 'eval/small/queries.tsv'),
namespace='msmarco'),
)
subsets['trec-dl-2019'] = Dataset(
collection,
TrecQrels(dlc['trec-dl-2019/qrels'], TREC_DL_QRELS_DEFS),
TsvQueries(Cache(GzipExtract(dlc['trec-dl-2019/queries']),
base_path / 'trec-dl-2019/queries.tsv'),
namespace='msmarco'),
TrecScoredDocs(
Cache(ExtractQidPid(GzipExtract(dlc['trec-dl-2019/scoreddocs'])),
base_path / 'trec-dl-2019/ms.run')),
)
subsets['trec-dl-2020'] = Dataset(
collection,
TsvQueries(GzipExtract(dlc['trec-dl-2020/queries']),
namespace='msmarco'),
TrecScoredDocs(
Cache(ExtractQidPid(GzipExtract(dlc['trec-dl-2020/scoreddocs'])),
base_path / 'trec-dl-2020/ms.run')),
)
# A few subsets that are contrainted to just the queries/qrels/docpairs that have at least
# 1 relevance assessment
train_judged = Lazy(
lambda: {q.query_id
for q in subsets['train'].qrels_iter()})
subsets['train/judged'] = Dataset(
FilteredQueries(subsets['train'].queries_handler(), train_judged),
FilteredScoredDocs(subsets['train'].scoreddocs_handler(),
train_judged),
subsets['train'],
)
dev_judged = Lazy(
lambda: {q.query_id
for q in subsets['dev'].qrels_iter()})
subsets['dev/judged'] = Dataset(
FilteredQueries(subsets['dev'].queries_handler(), dev_judged),
FilteredScoredDocs(subsets['dev'].scoreddocs_handler(), dev_judged),
subsets['dev'],
)
dl19_judged = Lazy(
lambda: {q.query_id
for q in subsets['trec-dl-2019'].qrels_iter()})
subsets['trec-dl-2019/judged'] = Dataset(
FilteredQueries(subsets['trec-dl-2019'].queries_handler(),
dl19_judged),
FilteredScoredDocs(subsets['trec-dl-2019'].scoreddocs_handler(),
dl19_judged),
subsets['trec-dl-2019'],
)
# split200 -- 200 queries held out from the training data for validation
split200 = Lazy(lambda: SPLIT200_QIDS)
subsets['train/split200-train'] = Dataset(
FilteredQueries(subsets['train'].queries_handler(),
split200,
mode='exclude'),
FilteredScoredDocs(subsets['train'].scoreddocs_handler(),
split200,
mode='exclude'),
FilteredQrels(subsets['train'].qrels_handler(),
split200,
mode='exclude'),
FilteredDocPairs(subsets['train'].docpairs_handler(),
split200,
mode='exclude'),
subsets['train'],
)
subsets['train/split200-valid'] = Dataset(
FilteredQueries(subsets['train'].queries_handler(),
split200,
mode='include'),
FilteredScoredDocs(subsets['train'].scoreddocs_handler(),
split200,
mode='include'),
FilteredQrels(subsets['train'].qrels_handler(),
split200,
mode='include'),
FilteredDocPairs(subsets['train'].docpairs_handler(),
split200,
mode='include'),
subsets['train'],
)
# Medical subset
def train_med():
with dlc['medmarco_ids'].stream() as stream:
stream = codecs.getreader('utf8')(stream)
return {l.rstrip() for l in stream}
train_med = Lazy(train_med)
subsets['train/medical'] = Dataset(
FilteredQueries(subsets['train'].queries_handler(), train_med),
FilteredScoredDocs(subsets['train'].scoreddocs_handler(), train_med),
FilteredDocPairs(subsets['train'].docpairs_handler(), train_med),
FilteredQrels(subsets['train'].qrels_handler(), train_med),
subsets['train'],
)
ir_datasets.registry.register('msmarco-passage',
Dataset(collection, documentation('_')))
for s in sorted(subsets):
ir_datasets.registry.register(f'msmarco-passage/{s}',
Dataset(subsets[s], documentation(s)))
return collection, subsets