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Merge branch 'main' of https://huggingface.co/datasets/zyznull/dureader-retrieval-ranking into main

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  1. dureader-retrieval-ranking.py +83 -0
dureader-retrieval-ranking.py ADDED
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+ # coding=utf-8
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+
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+ # Lint as: python3
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+ """Dureader Retrieval dataset."""
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+
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+ import json
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+
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+ import datasets
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+
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+ _CITATION = """
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+ @article{Qiu2022DuReader\_retrievalAL,
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+ title={DuReader\_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine},
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+ author={Yifu Qiu and Hongyu Li and Yingqi Qu and Ying Chen and Qiaoqiao She and Jing Liu and Hua Wu and Haifeng Wang},
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+ journal={ArXiv},
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+ year={2022},
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+ volume={abs/2203.10232}
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+ }
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+ """
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+
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+ _DESCRIPTION = "Dureader-Retrieval datas"
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+
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+ _DATASET_URLS = {
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+ 'train': "https://huggingface.co/datasets/zyznull/dureader-retrieval-ranking/resolve/main/train.jsonl.gz",
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+ 'dev': "https://huggingface.co/datasets/zyznull/dureader-retrieval-ranking/resolve/main/dev.jsonl.gz"
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+ }
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+
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+
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+ class DureaderRetrieval(datasets.GeneratorBasedBuilder):
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+ VERSION = datasets.Version("0.0.1")
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+
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+ BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(version=VERSION,
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+ description="Dureader Retrieval train/dev datasets"),
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+ ]
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+
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+ def _info(self):
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+ features = datasets.Features({
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+ 'query_id': datasets.Value('string'),
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+ 'query': datasets.Value('string'),
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+ 'positive_passages': [
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+ {'docid': datasets.Value('string'), 'text': datasets.Value('string')}
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+ ],
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+ 'negative_passages': [
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+ {'docid': datasets.Value('string'), 'text': datasets.Value('string')}
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+ ],
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+ })
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+ return datasets.DatasetInfo(
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+ # This is the description that will appear on the datasets page.
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+ description=_DESCRIPTION,
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+ # This defines the different columns of the dataset and their types
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+ features=features, # Here we define them above because they are different between the two configurations
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+ supervised_keys=None,
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+ # Homepage of the dataset for documentation
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+ homepage="",
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+ # License for the dataset if available
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+ license="",
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+ # Citation for the dataset
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
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+ splits = [
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+ datasets.SplitGenerator(
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+ name=split,
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+ gen_kwargs={
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+ "files": [downloaded_files[split]] if isinstance(downloaded_files[split], str) else downloaded_files[split],
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+ },
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+ ) for split in downloaded_files
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+ ]
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+ return splits
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+
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+ def _generate_examples(self, files):
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+ """Yields examples."""
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+ for filepath in files:
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+ with open(filepath, encoding="utf-8") as f:
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+ for line in f:
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+ data = json.loads(line)
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+ if data.get('negative_passages') is None:
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+ data['negative_passages'] = []
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+ if data.get('positive_passages') is None:
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+ data['positive_passages'] = []
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+ yield data['query_id'], data