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# coding=utf-8
# Lint as: python3
"""Passage Ranking fintune dataset."""
import json
import datasets
_CITATION = """
@article{Qiu2022DuReader\_retrievalAL,
title={DuReader\_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine},
author={Yifu Qiu and Hongyu Li and Yingqi Qu and Ying Chen and Qiaoqiao She and Jing Liu and Hua Wu and Haifeng Wang},
journal={ArXiv},
year={2022},
volume={abs/2203.10232}
}
"""
_DESCRIPTION = "DuReader-retrieval datas"
_DATASET_URLS = {
'corpus': "https://huggingface.co/datasets/zyznull/dureader-retrieval-corpus/resolve/main/passage_collection.tsv.gz",
'train_query': "https://huggingface.co/datasets/zyznull/dureader-retrieval-corpus/resolve/main/queries.train.tsv.gz",
'dev_query': "https://huggingface.co/datasets/zyznull/dureader-retrieval-corpus/resolve/main/queries.dev.tsv.gz",
}
class Dureader(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.0.1")
BUILDER_CONFIGS = [
datasets.BuilderConfig(version=VERSION,
description="MS MARCO passage corpus"),
]
def _info(self):
features = datasets.Features({
'_id': 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="",
# License for the dataset if available
license="",
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
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 i, line in enumerate(f):
line = line.strip().split('\t')
item = {'_id': line[0], 'text': line[1]}
yield i, item
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