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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{xu-etal-2020-matinf, |
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title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization", |
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author = "Xu, Canwen and |
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Pei, Jiaxin and |
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Wu, Hongtao and |
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Liu, Yiyu and |
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Li, Chenliang", |
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booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", |
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month = jul, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2020.acl-main.330", |
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pages = "3586--3596", |
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} |
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""" |
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_DESCRIPTION = """\ |
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MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization. |
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MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question |
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descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, |
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question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to |
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inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the |
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merits held by MATINF. |
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""" |
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class MatinfConfig(datasets.BuilderConfig): |
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"""BuilderConfig for MATINF.""" |
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def __init__( |
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self, |
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text_features, |
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label_column, |
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label_classes=None, |
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**kwargs, |
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): |
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"""BuilderConfig for MATINF. |
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Args: |
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text_features: `dict[string, string]`, map from the name of the feature |
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dict for each text field to the name of the column in the tsv file |
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label_column: `string`, name of the column in the tsv file corresponding |
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to the label |
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label_classes: `list[string]`, the list of classes if the label is |
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categorical. If not provided, then the label will be of type |
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`datasets.Value('float32')`. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(MatinfConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs) |
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self.text_features = text_features |
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self.label_column = label_column |
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self.label_classes = label_classes |
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class Matinf(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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MatinfConfig( |
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name="age_classification", |
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text_features=["question", "description"], |
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label_column="class", |
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label_classes=["0-1岁", "1-2岁", "2-3岁"], |
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), |
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MatinfConfig( |
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name="topic_classification", |
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text_features=["question", "description"], |
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label_column="class", |
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label_classes=[ |
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"产褥期保健", |
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"儿童过敏", |
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"动作发育", |
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"婴幼保健", |
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"婴幼心理", |
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"婴幼早教", |
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"婴幼期喂养", |
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"婴幼营养", |
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"孕期保健", |
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"家庭教育", |
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"幼儿园", |
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"未准父母", |
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"流产和不孕", |
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"疫苗接种", |
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"皮肤护理", |
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"宝宝上火", |
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"腹泻", |
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"婴幼常见病", |
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], |
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), |
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MatinfConfig( |
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name="summarization", |
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text_features=["description", "question"], |
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label_column=None, |
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), |
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MatinfConfig( |
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name="qa", |
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text_features=["question", "answer"], |
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label_column=None, |
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), |
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] |
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@property |
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def manual_download_instructions(self): |
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return ( |
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"To use MATINF you have to download it manually. Please fill this google form (" |
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"https://forms.gle/nkH4LVE4iNQeDzsc9). You will receive a download link and a password once you " |
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"complete the form. Please extract all files in one folder and load the dataset with: " |
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"`datasets.load_dataset('matinf', data_dir='path/to/folder/folder_name')`" |
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) |
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def _info(self): |
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features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features} |
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if self.config.label_classes: |
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features["label"] = datasets.features.ClassLabel(names=self.config.label_classes) |
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features["id"] = datasets.Value("int32") |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features(features), |
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homepage="https://github.com/WHUIR/MATINF", |
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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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data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) |
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if not os.path.exists(data_dir): |
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raise FileNotFoundError( |
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f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('matinf', data_dir=...)` that includes files unzipped from the MATINF zip. Manual download instructions: {self.manual_download_instructions}" |
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) |
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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={"filepath": os.path.join(data_dir, "train.csv")}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": os.path.join(data_dir, "test.csv")}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": os.path.join(data_dir, "dev.csv")}, |
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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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label_classes = self.config.label_classes |
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with open(filepath, encoding="utf8") as f: |
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reader = csv.DictReader(f) |
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for n, row in enumerate(reader): |
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example = {feat: row[feat] for feat in self.config.text_features} |
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example["id"] = row["id"] |
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if self.config.label_column: |
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label = row[self.config.label_column] |
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if label_classes and label not in label_classes: |
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continue |
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example["label"] = label |
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for value in example.values(): |
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if value is None: |
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break |
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else: |
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yield example["id"], example |
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