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"""TODO: Add a description here.""" |
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import datasets |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {QA-SRL: Question-Answer Driven Semantic Role Labeling}, |
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authors={Luheng He, Mike Lewis, Luke Zettlemoyer}, |
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year={2015} |
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publisher = {cs.washington.edu}, |
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howpublished={\\url{https://dada.cs.washington.edu/qasrl/#page-top}}, |
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} |
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""" |
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_DESCRIPTION = """\ |
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The dataset contains question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence. |
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There were 2 datsets used in the paper, newswire and wikipedia. Unfortunately the newswiredataset is built from CoNLL-2009 English training set that is covered under license |
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Thus, we are providing only Wikipedia training set here. Please check README.md for more details on newswire dataset. |
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For the Wikipedia domain, randomly sampled sentences from the English Wikipedia (excluding questions and sentences with fewer than 10 or more than 60 words) were taken. |
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This new dataset is designed to solve this great NLP task and is crafted with a lot of care. |
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""" |
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_HOMEPAGE = "https://dada.cs.washington.edu/qasrl/#page-top" |
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_LICENSE = "" |
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_URLs = { |
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"wiki_train": "https://dada.cs.washington.edu/qasrl/data/wiki1.train.qa", |
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"wiki_dev": "https://dada.cs.washington.edu/qasrl/data/wiki1.dev.qa", |
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"wiki_test": "https://dada.cs.washington.edu/qasrl/data/wiki1.test.qa", |
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} |
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class QaSrl(datasets.GeneratorBasedBuilder): |
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"""QA-SRL: Question-Answer Driven Semantic Role Labeling (qa_srl) corpus""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="plain_text", version=VERSION, description="This provides WIKIPEDIA dataset for qa_srl corpus" |
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), |
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] |
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DEFAULT_CONFIG_NAME = ( |
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"plain_text" |
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) |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"sentence": datasets.Value("string"), |
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"sent_id": datasets.Value("string"), |
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"predicate_idx": datasets.Value("int32"), |
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"predicate": datasets.Value("string"), |
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"question": datasets.Sequence(datasets.Value("string")), |
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"answers": datasets.Sequence(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=_HOMEPAGE, |
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license=_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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"""Returns SplitGenerators.""" |
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train_fpath = dl_manager.download(_URLs["wiki_train"]) |
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dev_fpath = dl_manager.download(_URLs["wiki_dev"]) |
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test_fpath = dl_manager.download(_URLs["wiki_test"]) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": train_fpath, |
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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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"filepath": dev_fpath, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": test_fpath, |
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}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""Yields examples.""" |
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with open(filepath, encoding="utf-8") as f: |
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qa_counter = 0 |
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sent_id, predicates_cnt = f.readline().rstrip("\n").split("\t") |
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while True: |
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sentence = f.readline().rstrip("\n") |
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predicates_counter = int(predicates_cnt) |
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while predicates_counter != 0: |
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predicates_counter -= 1 |
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predicate_details = f.readline().rstrip("\n").split("\t") |
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predicate_idx, predicate, qa_pairs_cnt = ( |
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predicate_details[0], |
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predicate_details[1], |
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predicate_details[2], |
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) |
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pairs = int(qa_pairs_cnt) |
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while pairs != 0: |
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pairs -= 1 |
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line = f.readline().rstrip("\n").split("\t") |
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question = line[:8] |
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answers_list = line[8:] |
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qa_counter += 1 |
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if "###" in answers_list[0]: |
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answers = [answer.strip() for answer in answers_list[0].split("###")] |
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else: |
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answers = answers_list |
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yield qa_counter, { |
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"sentence": sentence, |
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"sent_id": sent_id, |
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"predicate_idx": predicate_idx, |
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"predicate": predicate, |
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"question": question, |
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"answers": answers, |
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} |
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f.readline() |
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nextline = f.readline() |
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if not nextline: |
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break |
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else: |
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sent_id, predicates_cnt = nextline.rstrip("\n").split("\t") |
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