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"""tweetyface dataset.""" |
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import json |
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import datasets |
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_DESCRIPTION = """\ |
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Dataset containing Tweets from prominent Twitter Users in various languages. \ |
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The dataset has been created utilizing a crawler for the Twitter API.\n \ |
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""" |
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_HOMEPAGE = "https://github.com/ml-projects-kiel/OpenCampus-ApplicationofTransformers" |
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URL = "https://raw.githubusercontent.com/ml-projects-kiel/OpenCampus-ApplicationofTransformers/qa/data/" |
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_URLs = { |
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"english": { |
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"train": URL + "tweetyface_en/train.json", |
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"validation": URL + "tweetyface_en/validation.json", |
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}, |
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"german": { |
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"train": URL + "tweetyface_de/train.json", |
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"validation": URL + "tweetyface_de/validation.json", |
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}, |
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} |
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_VERSION = "0.3.0" |
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_LICENSE = """ |
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Apache License Version 2.0 |
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""" |
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class TweetyFaceConfig(datasets.BuilderConfig): |
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"""BuilderConfig for TweetyFace.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for TweetyFace. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(TweetyFaceConfig, self).__init__(**kwargs) |
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class TweetyFace(datasets.GeneratorBasedBuilder): |
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"""tweetyface""" |
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BUILDER_CONFIGS = [ |
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TweetyFaceConfig( |
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name=lang, |
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description=f"{lang.capitalize()} Twitter Users", |
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version=datasets.Version(_VERSION), |
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) |
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for lang in _URLs.keys() |
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] |
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def _info(self): |
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if self.config.name == "english": |
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names = [ |
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"MKBHD", |
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"elonmusk", |
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"alyankovic", |
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"Cristiano", |
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"katyperry", |
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"neiltyson", |
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"BillGates", |
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"BillNye", |
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"GretaThunberg", |
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"BarackObama", |
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"Trevornoah", |
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] |
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else: |
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names = [ |
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"OlafScholz", |
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"Karl_Lauterbach", |
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"janboehm", |
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"Markus_Soeder", |
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] |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION + self.config.description, |
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features=datasets.Features( |
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{ |
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"text": datasets.Value("string"), |
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"label": datasets.features.ClassLabel(names=names), |
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"idx": datasets.Value("string"), |
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"ref_tweet": datasets.Value("bool"), |
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"reply_tweet": datasets.Value("bool"), |
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} |
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), |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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my_urls = _URLs[self.config.name] |
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data_dir = dl_manager.download_and_extract(my_urls) |
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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={"filepath": data_dir["train"]}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"filepath": data_dir["validation"]}, |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form by iterating on all the files.""" |
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with open(filepath, encoding="utf-8") as f: |
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for row in f: |
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data = json.loads(row) |
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idx = data["idx"] |
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yield idx, data |
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