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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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_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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names = [ |
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"arts_&_culture", "business_&_entrepreneurs", "celebrity_&_pop_culture", "diaries_&_daily_life", "family", |
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"fashion_&_style", "film_tv_&_video", "fitness_&_health", "food_&_dining", "gaming", |
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"learning_&_educational", "music", "news_&_social_concern", "other_hobbies", "relationships", |
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"science_&_technology", "sports", "travel_&_adventure", "youth_&_student_life" |
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] |
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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": datasets.Sequence(datasets.features.ClassLabel(names=names)), |
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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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