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+ # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """British Library Web Classification Dataset."""
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+
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+ import datasets
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+ import csv
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+
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+ _CITATION = """\
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+ TODO
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+ """
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+
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+ _DESCRIPTION = """\
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+ The dataset comprises a manually curated selective archive produced by UKWA which includes the classification of sites into a two-tiered subject hierarchy.
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+ """
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+ _HOMEPAGE = "https://doi.org/10.5259/ukwa.ds.1/classification/1"
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+
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+ _LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
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+
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+ _URL = "https://bl.iro.bl.uk/downloads/78e2421a-70ea-426d-8a67-57e4a8b23019?locale=en"
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+
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+
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+ class WebArchiveClassificationDataset(datasets.GeneratorBasedBuilder):
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+ """Web Archive Classification Dataset"""
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+
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+ VERSION = datasets.Version("1.1.0")
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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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+ "primary_category": datasets.ClassLabel(
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+ names=[
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+ "Arts & Humanities",
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+ "Business, Economy & Industry",
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+ "Company Web Sites",
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+ "Computer Science, Information Technology and Web Technology",
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+ "Crime, Criminology, Police and Prisons",
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+ "Digital Society",
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+ "Education & Research",
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+ "Environment",
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+ "Government, Law & Politics",
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+ "History",
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+ "Law and Legal System",
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+ "Libraries, Archives and Museums",
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+ "Life Sciences",
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+ "Literature",
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+ "Medicine & Health",
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+ "Politics, Political Theory and Political Systems",
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+ "Popular Science",
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+ "Publishing, Printing and Bookselling",
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+ "Religion",
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+ "Science & Technology",
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+ "Social Problems and Welfare",
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+ "Society & Culture",
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+ "Sports and Recreation",
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+ "Travel & Tourism",
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+ ]
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+ ),
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+ "secondary_category": datasets.ClassLabel(
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+ names=[
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+ "Architecture",
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+ "Art and Design",
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+ "Comedy and Humour",
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+ "Dance",
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+ "Family History / Genealogy",
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+ "Film / Cinema",
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+ "Geography",
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+ "History",
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+ "Languages",
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+ "Literature",
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+ "Live Art",
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+ "Local History",
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+ "Music",
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+ "News and Contemporary Events",
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+ "Oral History in the UK",
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+ "Philosophy and Ethics",
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+ "Publishing, Printing and Bookselling",
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+ "Religion",
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+ "TV and Radio",
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+ "Theatre",
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+ "Agriculture, Fishing, and Forestry",
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+ "Banking, Insurance, Accountancy and Financial Economics",
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+ "Business Studies and Management Theory",
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+ "Company Web Sites",
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+ "Credit Crunch",
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+ "Economic Development, Enterprise and Aid",
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+ "Economics and Economic Theory",
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+ "Employment, Unemployment and Labour Economics",
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+ "Energy",
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+ "Industries",
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+ "Marketing and Market Research",
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+ "Trade, Commerce, and Globalisation",
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+ "Transport and Infrastructure",
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+ "Cambridge Network",
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+ "Video Games",
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+ "Governing the Police",
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+ "Blogs",
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+ "Dictionaries, Encyclopaedias, and Reference Works",
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+ "Further Education",
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+ "Higher Education",
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+ "Libraries, Archives and Museums",
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+ "Library Key Issues",
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+ "Lifelong Learning",
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+ "Preschool Education",
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+ "School Education",
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+ "Special Needs Education",
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+ "Vocational Education",
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+ "Indian Ocean Tsunami December 2004",
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+ "Central Government",
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+ "Civil Rights, Pressure Groups, and Trade Unions",
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+ "Crime, Criminology, Police and Prisons",
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+ "Devolved Government",
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+ "European Parliament Elections 2009",
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+ "Inter-Governmental Agencies",
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+ "International Relations, Diplomacy, and Peace",
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+ "Law and Legal System",
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+ "Local Government",
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+ "London Mayoral Election 2008",
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+ "Political Parties",
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+ "Politics, Political Theory and Political Systems",
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+ "Public Inquiries",
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+ "Scottish Parliamentary Election - 2007",
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+ "Spending Cuts 2010: Impact on Social Welfare",
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+ "UK General Election 2005",
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+ "Slavery and Abolition in the Caribbean",
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+ "Religion, politics and law since 2005",
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+ "Evolving role of libraries in the UK",
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+ "History of Libraries Collection",
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+ "Darwin 200",
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+ "19th Century English Literature",
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+ "Alternative Medicine / Complementary Medicine",
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+ "Conditions and Diseases",
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+ "Health Organisations and Services",
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+ "Medicines, Treatments and Therapies",
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+ "Men's Issues",
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+ "Mental Health",
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+ "Pandemic Influenza",
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+ "Personal Experiences of Illness",
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+ "Public Health and Safety",
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+ "Women's Issues",
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+ "Political Action and Communication",
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+ "E-publishing Trends",
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+ "Free Church",
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+ "Quakers",
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+ "Computer Science, Information Technology and Web Technology",
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+ "Engineering",
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+ "Environment",
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+ "Life Sciences",
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+ "Mathematics",
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+ "Physical Sciences",
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+ "Popular Science",
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+ "Zoology, Veterinary Science and Animal Health",
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+ "Communities",
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+ "Digital Society",
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+ "Food and Drink",
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+ "London Terrorist Attack 7th July 2005",
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+ "Queen's Diamond Jubilee, 2012",
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+ "Social Problems and Welfare",
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+ "Sociology, Anthropology and Population Studies",
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+ "Sports and Recreation",
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+ "Travel & Tourism",
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+ "British Countryside",
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+ "Olympic & Paralympic Games 2012",
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+ "Cornwall",
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+ ]
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+ ),
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+ "title": datasets.Value("string"),
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+ "url": 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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+ homepage=_HOMEPAGE,
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+ license=_LICENSE,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+
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+ csv_file = dl_manager.download_and_extract(_URL)
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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={"csv_file": csv_file},
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+ ),
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+ ]
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+
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+ def _generate_examples(self, csv_file):
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+ with open(csv_file) as f:
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+ reader = csv.DictReader(f, dialect="excel-tab")
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+ for id_, row in enumerate(reader):
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+ yield id_, {
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+ "primary_category": row["Primary Category"],
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+ "secondary_category": row["Secondary Category"],
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+ "title": row["Title"],
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+ "url": row["URL"],
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+ }