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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
"""Wikipedia NQ dataset."""
import json
import datasets
_CITATION = """
@inproceedings{karpukhin-etal-2020-dense,
title = "Dense Passage Retrieval for Open-Domain Question Answering",
author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov,
Sergey and Chen, Danqi and Yih, Wen-tau",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.550",
doi = "10.18653/v1/2020.emnlp-main.550",
pages = "6769--6781",
}
"""
_DESCRIPTION = "dataset load script for Wikipedia NQ"
_DATASET_URLS = {
'train': "https://huggingface.co./datasets/Tevatron/wikipedia-nq/resolve/main/nq-train.jsonl.gz",
'dev': "https://huggingface.co./datasets/Tevatron/wikipedia-nq/resolve/main/nq-dev.jsonl.gz",
'test': "https://huggingface.co./datasets/Tevatron/wikipedia-nq/resolve/main/nq-test.jsonl.gz",
}
class WikipediaNq(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.0.1")
BUILDER_CONFIGS = [
datasets.BuilderConfig(version=VERSION,
description="Wikipedia NQ train/dev/test datasets"),
]
def _info(self):
features = datasets.Features({
'query_id': datasets.Value('string'),
'query': datasets.Value('string'),
'answers': [datasets.Value('string')],
'positive_passages': [
{'docid': datasets.Value('string'), 'text': datasets.Value('string'),
'title': datasets.Value('string')}
],
'negative_passages': [
{'docid': datasets.Value('string'), 'text': datasets.Value('string'),
'title': 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="",
# License for the dataset if available
license="",
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
if self.config.data_files:
downloaded_files = self.config.data_files
else:
downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
splits = [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"files": [downloaded_files[split]] if isinstance(downloaded_files[split], str) else downloaded_files[split],
},
) for split in downloaded_files
]
return splits
def _generate_examples(self, files):
"""Yields examples."""
for filepath in files:
with open(filepath, encoding="utf-8") as f:
for line in f:
data = json.loads(line)
if data.get('negative_passages') is None:
data['negative_passages'] = []
if data.get('positive_passages') is None:
data['positive_passages'] = []
if data.get('answers') is None:
data['answers'] = []
yield data['query_id'], data
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