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"""MsMarco Passage 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 = "dataset load script for MSMARCO Passage query variation" |
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_DATASET_URLS = { |
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'dev': "https://huggingface.co/datasets/spacemanidol/msmarco-passage-query-variation/blob/main/dev.jsonl", |
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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 with query variation"), |
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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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"query-inject-det": datasets.Value('string'), |
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"query-synonym": datasets.Value('string'), |
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"query-lemmatize": datasets.Value('string'), |
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"query-stem":datasets.Value('string'), |
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"query-random-char-swap": datasets.Value('string'), |
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"query-char-keyboard": datasets.Value('string'), |
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"query-char-delete": datasets.Value('string'), |
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"query-reorder-words": datasets.Value('string'), |
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"query-backtranslation": datasets.Value('string'), |
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"query-paraphrase": 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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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage="", |
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license="", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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if self.config.data_files: |
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downloaded_files = self.config.data_files |
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else: |
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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) 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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