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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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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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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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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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