Upload msmarco-passage-ranking.py
Browse files- msmarco-passage-ranking.py +86 -0
msmarco-passage-ranking.py
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# coding=utf-8
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# Lint as: python3
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"""Passage Ranking fintune dataset."""
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import json
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import datasets
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_CITATION = """
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@misc{bajaj2018ms,
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title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
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author={Payal Bajaj and Daniel Campos and Nick Craswell and Li Deng and Jianfeng Gao and Xiaodong Liu
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and Rangan Majumder and Andrew McNamara and Bhaskar Mitra and Tri Nguyen and Mir Rosenberg and Xia Song
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and Alina Stoica and Saurabh Tiwary and Tong Wang},
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year={2018},
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eprint={1611.09268},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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"""
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_DESCRIPTION = "MSMARCO Passage Ranking datas"
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_DATASET_URLS = {
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'train': "https://modelscope.cn/api/v1/datasets/zyznull/msmarco-passage-ranking/repo/files?Revision=master&FilePath=train.jsonl.gz",
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'dev': "https://modelscope.cn/api/v1/datasets/zyznull/msmarco-passage-ranking/repo/files?Revision=master&FilePath=dev.jsonl.gz",
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}
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class MsMarcoPassage(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("0.0.1")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(version=VERSION,
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description="MS MARCO passage train/dev datasets"),
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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'), 'title': 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'), 'title': 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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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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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
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