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"""Victorian.""" |
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from typing import List |
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from functools import partial |
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import datasets |
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import pandas |
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VERSION = datasets.Version("1.0.0") |
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_ORIGINAL_FEATURE_NAMES = [ |
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"text", |
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"author" |
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] |
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_BASE_FEATURE_NAMES = [ |
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"text", |
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"author" |
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] |
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DESCRIPTION = "Victorian dataset from the Gungor thesis.\"." |
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_HOMEPAGE = "https://scholarworks.iupui.edu/server/api/core/bitstreams/708a9870-915e-4d59-b54d-938af563c196/content" |
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_URLS = ("https://scholarworks.iupui.edu/server/api/core/bitstreams/708a9870-915e-4d59-b54d-938af563c196/content") |
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_CITATION = """ |
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@phdthesis{gungor2018benchmarking, |
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title={Benchmarking authorship attribution techniques using over a thousand books by fifty victorian era novelists}, |
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author={Gungor, Abdulmecit}, |
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year={2018}, |
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school={Purdue University} |
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}""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/victorian_authorship/resolve/main/train.csv", |
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} |
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features_types_per_config = { |
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"authorship": { |
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"text": datasets.Value("string"), |
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"author": datasets.ClassLabel(num_classes=51) |
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} |
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} |
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features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
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class VictorianConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(VictorianConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Victorian(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "authorship" |
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BUILDER_CONFIGS = [ |
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VictorianConfig(name="authorship", |
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description="authorship"), |
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] |
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def _info(self): |
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info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, |
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features=features_per_config[self.config.name]) |
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return info |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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downloads = dl_manager.download_and_extract(urls_per_split) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}) |
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] |
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def _generate_examples(self, filepath: str): |
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print(f"reading {filepath}") |
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data = pandas.read_csv(filepath, encoding="latin-1") |
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print(data.columns) |
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for row_id, row in data.iterrows(): |
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data_row = dict(row) |
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yield row_id, data_row |
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