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"""MasakhaNEWS: News Topic Classification for African languages""" |
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import datasets |
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import pandas |
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import pandas as pd |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """ |
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@inproceedings{lawallanre-2023-geoNLPSent, |
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author = "Olanrewaju", |
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month = "Nov", |
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year = "2023", |
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address = "Lagos, Nigeria", |
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} |
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""" |
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_DESCRIPTION = """\ |
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geoNLPSent is dataset of transport tweets extrcted from twitter |
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The language is: |
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- English (eng) |
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""" |
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_URL = "https://github.com/lawallanre00490038/GeoNLP/raw/main/data/" |
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_TRAINING_FILE = "train.tsv" |
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_DEV_FILE = "dev.tsv" |
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_TEST_FILE = "test.tsv" |
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class GeoNLPSentiConfig(datasets.BuilderConfig): |
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"""BuilderConfig for GeoNLPsenti""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for GeoNLPsenti. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(GeoNLPSentiConfig, self).__init__(**kwargs) |
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class GeoNLPSenti(datasets.GeneratorBasedBuilder): |
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"""GeoNLPsenti dataset.""" |
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BUILDER_CONFIGS = [ |
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GeoNLPSentiConfig(name="en", version=datasets.Version("1.0.0"), description="Nollysenti English dataset") |
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] |
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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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"label": datasets.features.ClassLabel( |
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names=["Positive", "Negative", "Neutral"] |
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), |
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"review": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://github.com/lawallanre00490038/GeoNLP", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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urls_to_download = { |
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"train": f"{_URL}{self.config.name}/{_TRAINING_FILE}", |
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"dev": f"{_URL}{self.config.name}/{_DEV_FILE}", |
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"test": f"{_URL}{self.config.name}/{_TEST_FILE}", |
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} |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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] |
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def _generate_examples(self, filepath): |
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logger.info("⏳ Generating examples from = %s", filepath) |
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df = pd.read_csv(filepath, sep='\t') |
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df = df.dropna() |
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N = df.shape[0] |
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for id_ in range(N): |
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yield id_, { |
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"label": df['sentiment'].iloc[id_], |
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"review": df['tweet'].iloc[id_], |
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} |
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