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Delete loading script
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qa_zre.py
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"""A dataset reducing relation extraction to simple reading comprehension questions"""
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import csv
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import os
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import datasets
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_CITATION = """\
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@inproceedings{levy-etal-2017-zero,
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title = "Zero-Shot Relation Extraction via Reading Comprehension",
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author = "Levy, Omer and
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Seo, Minjoon and
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Choi, Eunsol and
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Zettlemoyer, Luke",
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booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
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month = aug,
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year = "2017",
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address = "Vancouver, Canada",
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publisher = "Association for Computational Linguistics",
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url = "https://www.aclweb.org/anthology/K17-1034",
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doi = "10.18653/v1/K17-1034",
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pages = "333--342",
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}
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"""
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_DESCRIPTION = """\
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A dataset reducing relation extraction to simple reading comprehension questions
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"""
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_DATA_URL = "http://nlp.cs.washington.edu/zeroshot/relation_splits.tar.bz2"
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class QaZre(datasets.GeneratorBasedBuilder):
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"""QA-ZRE: Reducing relation extraction to simple reading comprehension questions"""
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VERSION = datasets.Version("0.1.0")
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"relation": datasets.Value("string"),
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"question": datasets.Value("string"),
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"subject": datasets.Value("string"),
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"context": datasets.Value("string"),
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"answers": datasets.features.Sequence(datasets.Value("string")),
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}
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),
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# If there's a common (input, target) tuple from the features,
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# specify them here. They'll be used if as_supervised=True in
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# builder.as_dataset.
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage="http://nlp.cs.washington.edu/zeroshot",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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dl_dir = dl_manager.download_and_extract(_DATA_URL)
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dl_dir = os.path.join(dl_dir, "relation_splits")
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"filepaths": [os.path.join(dl_dir, "test." + str(i)) for i in range(10)],
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"filepaths": [os.path.join(dl_dir, "dev." + str(i)) for i in range(10)],
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepaths": [os.path.join(dl_dir, "train." + str(i)) for i in range(10)],
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},
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),
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]
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def _generate_examples(self, filepaths):
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"""Yields examples."""
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for file_idx, filepath in enumerate(filepaths):
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with open(filepath, encoding="utf-8") as f:
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data = csv.reader(f, delimiter="\t")
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for idx, row in enumerate(data):
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yield f"{file_idx}_{idx}", {
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"relation": row[0],
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"question": row[1],
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"subject": row[2],
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"context": row[3],
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"answers": row[4:],
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}
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