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Tevatron
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beir-corpus

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beir-corpus / beir-corpus.py

Xueguang Ma

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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