Delete loading script

kor_nli.py

@@ -1,121 +0,0 @@
-"""TODO(kor_nli): Add a description here."""
-
-
-import os
-
-import datasets
-
-
-# TODO(kor_nli): BibTeX citation
-_CITATION = """\
-@article{ham2020kornli,
-  title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
-  author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
-  journal={arXiv preprint arXiv:2004.03289},
-  year={2020}
-}
-"""
-
-# TODO(kor_nli):
-_DESCRIPTION = """ Korean Natural  Language Inference datasets
-"""
-_URL = "data.zip"
-
-
-class KorNLIConfig(datasets.BuilderConfig):
-    """BuilderConfig for KorNLI."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for KorNLI.
-
-        Args:
-
-          **kwargs: keyword arguments forwarded to super.
-        """
-        # Version 1.1.0 remove empty document and summary strings.
-        super(KorNLIConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
-
-
-class KorNli(datasets.GeneratorBasedBuilder):
-    """TODO(kor_nli): Short description of my dataset."""
-
-    # TODO(kor_nli): Set up version.
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        KorNLIConfig(name="multi_nli", description="Korean multi NLI datasets"),
-        KorNLIConfig(name="snli", description="Korean SNLI dataset"),
-        KorNLIConfig(name="xnli", description="Korean XNLI dataset"),
-    ]
-
-    def _info(self):
-        # TODO(kor_nli): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    # These are the features of your dataset like images, labels ...
-                    "premise": datasets.Value("string"),
-                    "hypothesis": datasets.Value("string"),
-                    "label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]),
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://github.com/kakaobrain/KorNLUDatasets",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(kor_nli): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        dl_dir = dl_manager.download_and_extract(_URL)
-        dl_dir = os.path.join(dl_dir, "KorNLI")
-        if self.config.name == "multi_nli":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(dl_dir, "multinli.train.ko.tsv")},
-                ),
-            ]
-        elif self.config.name == "snli":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(dl_dir, "snli_1.0_train.ko.tsv")},
-                ),
-            ]
-        else:
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(dl_dir, "xnli.dev.ko.tsv")},
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={"filepath": os.path.join(dl_dir, "xnli.test.ko.tsv")},
-                ),
-            ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(kor_nli): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            next(f)  # skip headers
-            columns = ("premise", "hypothesis", "label")
-            for id_, row in enumerate(f):
-                row = row.strip().split("\t")
-                if len(row) != 3:
-                    continue
-                row = dict(zip(columns, row))
-                yield id_, row