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loading script default

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  1. story.py +131 -0
story.py ADDED
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+ import csv
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+ import os
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+
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+ import datasets
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+
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+ _CITATION = """\
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+ @Dataset{wisdomify:storyteller,
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+ title = {Korean proverb definitions and examples},
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+ author={Jongyoon Kim, Yubin Kim, Yongtaek Im
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+ },
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+ year={2021}
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ This dataset is designed to provide forward and reverse dictionary of Korean proverbs.
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+ """
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+
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+ # TODO: Add a link to an official homepage for the dataset here
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+ _HOMEPAGE = ""
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+
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+ # TODO: Add the licence for the dataset here if you can find it
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+ _LICENSE = ""
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+
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+ # TODO: Add link to the official dataset URLs here
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+ # If it is dropbox link, you must set 1 for query parameter "dl".
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+ _URLs = {
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+ 'definition': "https://www.dropbox.com/s/4uh564afaimtob3/definition.zip?dl=1",
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+ 'example': "https://www.dropbox.com/s/adlt9n6x5gjs0a6/example.zip?dl=1",
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+ }
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+
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+
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+ class Story(datasets.GeneratorBasedBuilder):
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+ # version must be "x.y.z' form
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+ VERSION = datasets.Version("0.0.0")
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+
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+ BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(name="definition", version=VERSION, description="definition"),
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+ datasets.BuilderConfig(name="example", version=VERSION, description="example"),
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+ ]
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+
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+ # This config is applied when user load dataset without "name".
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+ DEFAULT_CONFIG_NAME = "definition"
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+
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+ def _info(self):
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+ # This method specifies the datasets.DatasetInfo object which contains information
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+ # and typings for the dataset
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+
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+ if self.config.name == "definition":
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+ # These are the features of your dataset like images, labels ...
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+ features = datasets.Features(
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+ {
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+ "wisdom": datasets.Value("string"),
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+ "def": datasets.Value("string"),
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+
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+ }
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+ )
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+ elif self.config.name == "example":
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+ features = datasets.Features(
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+ {
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+ "wisdom": datasets.Value("string"),
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+ "eg": datasets.Value("string"),
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+ }
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+ )
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+ else:
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+ raise NotImplementedError(f"Wrong name: {self.config.name}")
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+
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=features,
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+ supervised_keys=None,
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+ homepage=_HOMEPAGE,
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+ license=_LICENSE,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+ # This method is used when user loads dataset.
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+ # dl_manager can be used to download and extract the dataset
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+ # and also can set split depending onf the configuration
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+
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+ # Downloading data with _URLs
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+ downloaded_files = dl_manager.download_and_extract(_URLs[self.config.name])
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+
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+ dtp = 'def' if self.config.name == "definition" else 'eg'
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+
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+ train_path = os.path.join(downloaded_files, f'train_wisdom2{dtp}.tsv')
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+ val_path = os.path.join(downloaded_files, f'val_wisdom2{dtp}.tsv')
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+ test_path = os.path.join(downloaded_files, f'test_wisdom2{dtp}.tsv')
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+
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+ return [
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+ # These gen_kwargs will be passed to _generate_examples
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ gen_kwargs={"filepath": train_path, "split": "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": val_path, "split": "validation"},
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ gen_kwargs={"filepath": test_path, "split": "test"},
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+ ),
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+ ]
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+
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+ def _generate_examples(self, filepath, split):
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+ # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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+ """ Yields examples as (key, example) tuples. """
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+ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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+ # The `key` is here for legacy reason (tfds) and is not important in itself.
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+
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+ with open(filepath, encoding="utf-8") as f:
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+ tsv_reader = csv.reader(f, delimiter="\t")
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+ for id_, row in enumerate(tsv_reader):
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+ if id_ == 0:
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+ continue # first row shows column info
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+
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+ if self.config.name == "definition":
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+ yield id_, {
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+ "wisdom": row[0],
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+ "def": row[1],
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+ }
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+ elif self.config.name == "example":
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+ yield id_, {
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+ "wisdom": row[0],
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+ "eg": row[1],
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+ }
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+ else:
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+ raise NotImplementedError(f"Wrong name: {self.config.name}")