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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 = '''
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
'''
all_data = [
'arguana',
'climate-fever',
'cqadupstack-android',
'cqadupstack-english',
'cqadupstack-gaming',
'cqadupstack-gis',
'cqadupstack-mathematica',
'cqadupstack-physics',
'cqadupstack-programmers',
'cqadupstack-stats',
'cqadupstack-tex',
'cqadupstack-unix',
'cqadupstack-webmasters',
'cqadupstack-wordpress',
'dbpedia-entity',
'fever',
'fiqa',
'hotpotqa',
'nfcorpus',
'quora',
'scidocs',
'scifact',
'trec-covid',
'webis-touche2020',
'nq'
]
_DESCRIPTION = 'dataset load script for BEIR corpus'
_DATASET_URLS = {
data: {
'train': f'https://huggingface.co/datasets/Tevatron/beir-corpus/resolve/main/{data}.jsonl.gz',
} for data in all_data
}
class BeirCorpus(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
version=datasets.Version('1.1.0'),
name=data,
description=f'BEIR dataset corpus {data}.'
) for data in all_data
]
def _info(self):
features = datasets.Features({
'docid': datasets.Value('string'),
'title': datasets.Value('string'),
'text': 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/beir-cellar/beir',
# License for the dataset if available
license='',
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data = self.config.name
downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[data])
splits = [
datasets.SplitGenerator(
name='train',
gen_kwargs={
'filepath': downloaded_files['train'],
},
),
]
return splits
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
for line in f:
data = json.loads(line)
yield data['docid'], data |