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import json |
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import csv |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """ """ |
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_DESCRIPTION = """ GuiaCat is a dataset consisting of 5.750 restaurant reviews in Catalan, with 5 associated scores and a label of sentiment. The data was provided by GuiaCat and curated by the BSC. """ |
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_HOMEPAGE = """ https://huggingface.co/datasets/projecte-aina/Parafraseja/ """ |
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_URL = "https://huggingface.co/datasets/projecte-aina/Parafraseja/resolve/main/" |
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_TRAINING_FILE = "train.csv" |
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_DEV_FILE = "dev.csv" |
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_TEST_FILE = "test.csv" |
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class GuiaCatConfig(datasets.BuilderConfig): |
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""" Builder config for the reviews_finder dataset """ |
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def __init__(self, **kwargs): |
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"""BuilderConfig for reviews_finder. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(GuiaCatConfig, self).__init__(**kwargs) |
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class GuiaCat(datasets.GeneratorBasedBuilder): |
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""" GuiaCat Dataset """ |
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BUILDER_CONFIGS = [ |
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GuiaCatConfig( |
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name="GuiaCat", |
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version=datasets.Version("1.0.0"), |
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description="GuiaCat dataset", |
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), |
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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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"text": datasets.Value("string"), |
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"label": datasets.features.ClassLabel |
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(names= |
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[ |
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"molt bo", |
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"bo", |
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"regular", |
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"dolent", |
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"molt dolent" |
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] |
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), |
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} |
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), |
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homepage=_HOMEPAGE, |
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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}{_TRAINING_FILE}", |
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"dev": f"{_URL}{_DEV_FILE}", |
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"test": f"{_URL}{_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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"""This function returns the examples in the raw (text) form.""" |
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logger.info("generating examples from = %s", filepath) |
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with open(filepath) as f: |
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read = csv.reader(f) |
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data = [item for item in read] |
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for id_, article in enumerate(data): |
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text = article[5] |
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label = article[6] |
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yield id_, { |
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"text": text, |
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"label": label, |
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} |
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