Datasets:
Commit
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f26037b
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Parent(s):
88075f6
Create web_archive_classification.py
Browse files- web_archive_classification.py +208 -0
web_archive_classification.py
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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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import datasets
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import csv
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_CITATION = """\
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TODO
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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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_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
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_URL = "https://bl.iro.bl.uk/downloads/78e2421a-70ea-426d-8a67-57e4a8b23019?locale=en"
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class WebArchiveClassificationDataset(datasets.GeneratorBasedBuilder):
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"""Web Archive Classification Dataset"""
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VERSION = datasets.Version("1.1.0")
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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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185 |
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license=_LICENSE,
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186 |
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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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190 |
+
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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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198 |
+
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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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}
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