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import os |
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import datasets |
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_DESCRIPTION = """\ |
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SimpleQuestions is a dataset for simple QA, which consists |
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of a total of 108,442 questions written in natural language by human |
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English-speaking annotators each paired with a corresponding fact, |
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formatted as (subject, relationship, object), that provides the answer |
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but also a complete explanation. Fast have been extracted from the |
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Knowledge Base Freebase (freebase.com). We randomly shuffle these |
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questions and use 70% of them (75910) as training set, 10% as |
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validation set (10845), and the remaining 20% as test set. |
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""" |
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_HOMEPAGE_URL = "https://research.fb.com/downloads/babi/" |
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_CITATION = """\ |
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@misc{bordes2015largescale, |
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title={Large-scale Simple Question Answering with Memory Networks}, |
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author={Antoine Bordes and Nicolas Usunier and Sumit Chopra and Jason Weston}, |
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year={2015}, |
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eprint={1506.02075}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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""" |
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_URL = "https://www.dropbox.com/s/tohrsllcfy7rch4/SimpleQuestions_v2.tgz?dl=1" |
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class SimpleQuestionsV2Config(datasets.BuilderConfig): |
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def __init__(self, *args, data_type=None, **kwargs): |
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super().__init__(*args, version=datasets.Version("1.0.0", ""), **kwargs) |
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self.data_type = data_type |
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class SimpleQuestionsV2(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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SimpleQuestionsV2Config(name="annotated", data_type="annotated", description="Annotated dataset"), |
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SimpleQuestionsV2Config(name="freebase2m", data_type="freebase2m", description="Freebase subset 2M"), |
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SimpleQuestionsV2Config(name="freebase5m", data_type="freebase5m", description="Freebase subset 5M"), |
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] |
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BUILDER_CONFIG_CLASS = SimpleQuestionsV2Config |
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DEFAULT_CONFIG_NAME = "annotated" |
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def _info(self): |
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if self.config.data_type == "annotated": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"subject_entity": datasets.Value("string"), |
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"relationship": datasets.Value("string"), |
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"object_entity": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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}, |
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) |
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else: |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"subject_entity": datasets.Value("string"), |
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"relationship": datasets.Value("string"), |
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"object_entities": 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_URL, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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path = dl_manager.download_and_extract(_URL) |
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if self.config.data_type == "annotated": |
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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={"datapath": os.path.join(path, "SimpleQuestions_v2", "annotated_fb_data_train.txt")}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"datapath": os.path.join(path, "SimpleQuestions_v2", "annotated_fb_data_train.txt")}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"datapath": os.path.join(path, "SimpleQuestions_v2", "annotated_fb_data_train.txt")}, |
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), |
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] |
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elif self.config.data_type == "freebase2m": |
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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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"datapath": os.path.join( |
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path, |
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"SimpleQuestions_v2", |
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"freebase-subsets", |
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"freebase-FB2M.txt", |
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) |
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}, |
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) |
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] |
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elif self.config.data_type == "freebase5m": |
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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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"datapath": os.path.join( |
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path, |
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"SimpleQuestions_v2", |
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"freebase-subsets", |
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"freebase-FB5M.txt", |
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) |
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}, |
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) |
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] |
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else: |
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raise Exception("Unknown data type. Try one of: annotated, freebase2m and freebase5m") |
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def _generate_examples(self, datapath): |
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if self.config.data_type == "annotated": |
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with open(datapath, encoding="utf-8") as f: |
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for sentence_counter, row in enumerate(f): |
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row = row.split("\t") |
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result = ( |
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sentence_counter, |
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{ |
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"id": str(sentence_counter), |
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"subject_entity": row[0], |
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"relationship": row[1], |
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"object_entity": row[2], |
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"question": row[3], |
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}, |
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) |
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yield result |
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else: |
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with open(datapath, encoding="utf-8") as f: |
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for sentence_counter, row in enumerate(f): |
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row = row.split("\t") |
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result = ( |
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sentence_counter, |
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{ |
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"id": str(sentence_counter), |
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"subject_entity": row[0], |
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"relationship": row[1], |
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"object_entities": row[2].split(), |
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}, |
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) |
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yield result |
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