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