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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
import json
import datasets
_CITATION = '''
@article{lawrie2023overview,
title={Overview of the TREC 2022 NeuCLIR track},
author={Lawrie, Dawn and MacAvaney, Sean and Mayfield, James and McNamee, Paul and Oard, Douglas W and Soldaini, Luca and Yang, Eugene},
journal={arXiv preprint arXiv:2304.12367},
year={2023}
}
'''
_LANGUAGES = [
'rus',
'fas',
'zho',
]
_DESCRIPTION = 'dataset load script for NeuCLIR 2022'
_DATASET_URLS = {
lang: {
'test': f'https://huggingface.co/datasets/MTEB/neuclir-2022/resolve/main/neuclir-{lang}/queries.jsonl.gz',
} for lang in _LANGUAGES
}
_DATASET_CORPUS_URLS = {
f'corpus-{lang}': {
'corpus': f'https://huggingface.co/datasets/MTEB/neuclir-2022/resolve/main/neuclir-{lang}/corpus.jsonl.gz'
} for lang in _LANGUAGES
}
class MLDR(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [datasets.BuilderConfig(
version=datasets.Version('1.0.0'),
name=lang, description=f'MLDR dataset in language {lang}.'
) for lang in _LANGUAGES
] + [
datasets.BuilderConfig(
version=datasets.Version('1.0.0'),
name=f'corpus-{lang}', description=f'corpus of MLDR dataset in language {lang}.'
) for lang in _LANGUAGES
]
def _info(self):
name = self.config.name
if name.startswith('corpus-'):
features = datasets.Features({
'_id': datasets.Value('string'),
'text': datasets.Value('string'),
})
else:
features = datasets.Features({
'_id': datasets.Value('string'),
'query': datasets.Value('string'),
})
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
supervised_keys=None,
# Homepage of the dataset for documentation
homepage='https://github.com/FlagOpen/FlagEmbedding',
# License for the dataset if available
license='mit',
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
name = self.config.name
if name.startswith('corpus-'):
downloaded_files = dl_manager.download_and_extract(_DATASET_CORPUS_URLS[name])
splits = [
datasets.SplitGenerator(
name='corpus',
gen_kwargs={
'filepath': downloaded_files['corpus'],
},
),
]
else:
downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[name])
splits = [
datasets.SplitGenerator(
name='test',
gen_kwargs={
'filepath': downloaded_files['test'],
},
),
]
return splits
def _generate_examples(self, filepath):
name = self.config.name
if name.startswith('corpus-'):
with open(filepath, encoding='utf-8') as f:
for line in f:
data = json.loads(line)
yield data['docid'], data
else:
with open(filepath, encoding="utf-8") as f:
for line in f:
data = json.loads(line)
qid = data['query_id']
yield qid, data |