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""" TweetTopicMultilingual Dataset """ |
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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_DESCRIPTION = """[TweetTopicMultilingual](TBA)""" |
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_VERSION = "0.0.3" |
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_CITATION = """TBA""" |
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_HOME_PAGE = "https://cardiffnlp.github.io" |
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_NAME = "tweet_topic_multilingual" |
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_ROOT_URL = f"https://huggingface.co/datasets/cardiffnlp/{_NAME}/resolve/main/dataset" |
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_LANGUAGES = ["en", "es", "ja", "gr"] |
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_CLASS_MAPPING = { |
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"en": [ |
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"Arts & Culture", |
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"Business & Entrepreneurs", |
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"Celebrity & Pop Culture", |
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"Diaries & Daily Life", |
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"Family", |
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"Fashion & Style", |
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"Film, TV & Video", |
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"Fitness & Health", |
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"Food & Dining", |
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"Learning & Educational", |
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"News & Social Concern", |
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"Relationships", |
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"Science & Technology", |
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"Youth & Student Life", |
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"Music", |
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"Gaming", |
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"Sports", |
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"Travel & Adventure", |
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"Other Hobbies" |
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], |
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"gr": [ |
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"Τέχνες & Πολιτισμός", |
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"Επιχειρήσεις & Επιχειρηματίες", |
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"Διασημότητες & Ποπ κουλτούρα", |
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"Ημερολόγια & Καθημερινή ζωή", |
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"Οικογένεια", |
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"Μόδα & Στυλ", |
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"Ταινίες, τηλεόραση & βίντεο", |
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"Γυμναστική & Υεία", |
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"Φαγητό & Δείπνο", |
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"Μάθηση & Εκπαίδευση", |
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"Ειδήσεις & Κοινωνία", |
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"Σχέσεις", |
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"Επιστήμη & Τεχνολογία", |
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"Νεανική & Φοιτητική ζωή", |
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"Μουσική", |
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"Παιχνίδια", |
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"Αθλητισμός", |
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"Ταξίδια & Περιπέτεια", |
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"Άλλα χόμπι" |
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], |
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"es": [ |
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"Arte y cultura", |
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"Negocios y emprendedores", |
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"Celebridades y cultura pop", |
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"Diarios y vida diaria", |
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"Familia", |
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"Moda y estilo", |
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"Cine, televisión y video", |
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"Estado físico y salud", |
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"Comida y comedor", |
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"Aprendizaje y educación", |
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"Noticias e interés social", |
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"Relaciones", |
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"Ciencia y Tecnología", |
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"Juventud y Vida Estudiantil", |
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"Música", |
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"Juegos", |
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"Deportes", |
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"Viajes y aventuras", |
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"Otros pasatiempos" |
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], |
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"ja": [ |
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"アート&カルチャー", |
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"ビジネス", |
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"芸能", |
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"日常", |
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"家族", |
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"ファッション", |
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"映画&ラジオ", |
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"フィットネス&健康", |
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"料理", |
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"教育関連", |
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"社会", |
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"人間関係", |
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"サイエンス", |
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"学校", |
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"音楽", |
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"ゲーム", |
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"スポーツ", |
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"旅行", |
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"その他" |
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] |
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} |
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_URL = {} |
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for lan in _LANGUAGES: |
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_URL[lan] = {split: f"{_ROOT_URL}/{lan}/{lan}_{split}.jsonl" for split in ["train", "test", "validation"]} |
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_URL["en_2022"] = {split: f"{_ROOT_URL}/en_2022/{split}.jsonl" for split in ["train", "validation"]} |
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for lan in _LANGUAGES: |
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_URL.update({ |
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f"{lan}_cross_validation_{n}": { |
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split: f"{_ROOT_URL}/{lan}/cross_validation/{lan}_{split}_{n}.jsonl" |
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for split in ["train", "test", "validation"] |
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} for n in range(5) |
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}) |
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class Config(datasets.BuilderConfig): |
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"""BuilderConfig""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(Config, self).__init__(**kwargs) |
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class TweetTopicMultilingual(datasets.GeneratorBasedBuilder): |
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"""Dataset.""" |
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BUILDER_CONFIGS = [ |
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Config(name=i, version=datasets.Version(_VERSION), description=_DESCRIPTION) for i in _URL.keys() |
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] |
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def _split_generators(self, dl_manager): |
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downloaded_file = dl_manager.download_and_extract(_URL[self.config.name]) |
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splits = _URL[self.config.name].keys() |
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return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_file[i]}) for i in splits] |
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def _generate_examples(self, filepath): |
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_key = 0 |
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logger.info("generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as f: |
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_list = [json.loads(i) for i in f.read().split("\n") if len(i) > 0] |
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for i in _list: |
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yield _key, i |
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_key += 1 |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"label_name_flatten": datasets.Value("string"), |
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"label": datasets.Sequence(datasets.features.ClassLabel(names=_CLASS_MAPPING["en"])), |
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"label_name": datasets.Sequence(datasets.Value("string")) |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOME_PAGE, |
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citation=_CITATION, |
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) |
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