Delete loading script

urdu_fake_news.py

@@ -1,97 +0,0 @@
-"""Urdu Fake News Dataset"""
-
-
-import glob
-import os
-
-import datasets
-
-
-_CITATION = """
-@article{MaazUrdufake2020,
-author = {Amjad, Maaz and Sidorov, Grigori and Zhila, Alisa and  G’{o}mez-Adorno, Helena and Voronkov, Ilia  and Gelbukh, Alexander},
-title = {Bend the Truth: A Benchmark Dataset for Fake News Detection in Urdu and Its Evaluation},
-journal={Journal of Intelligent & Fuzzy Systems},
-volume={39},
-number={2},
-pages={2457-2469},
-doi = {10.3233/JIFS-179905},
-year={2020},
-publisher={IOS Press}
-}
-"""
-
-_DESCRIPTION = """
-Urdu fake news datasets that contain news of 5 different news domains.
-These domains are Sports, Health, Technology, Entertainment, and Business.
-The real news are collected by combining manual approaches.
-"""
-
-_URL = "https://github.com/MaazAmjad/Datasets-for-Urdu-news/blob/master/"
-_URL += "Urdu%20Fake%20News%20Dataset.zip?raw=true"
-
-
-class UrduFakeNews(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    category_list = [
-        "bus",
-        "hlth",
-        "sp",
-        "tch",
-        "sbz",
-    ]
-
-    def _info(self):
-        labels_list = ["Fake", "Real"]
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "news": datasets.Value("string"),
-                    "label": datasets.ClassLabel(names=labels_list),
-                    "category": datasets.ClassLabel(names=self.category_list),
-                }
-            ),
-            homepage="https://github.com/MaazAmjad/Datasets-for-Urdu-news",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        dl_path = dl_manager.download_and_extract(_URL)
-        input_path = os.path.join(dl_path, "1.Corpus")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"pattern": os.path.join(input_path, "Train", "*", "*.txt")},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"pattern": os.path.join(input_path, "Test", "*", "*.txt")},
-            ),
-        ]
-
-    def _generate_examples(self, pattern=None):
-        """Yields examples."""
-        for filename in sorted(glob.glob(pattern)):
-
-            with open(filename, encoding="utf-8") as f:
-                news = ""
-                for line in f:
-                    if line == "\n":
-                        continue
-                    news += line
-
-            name = os.path.basename(filename)
-            key = name.rstrip(".txt")
-
-            _class = 1 if ("Real" in filename) else 0
-            _class_name = "Real" if ("Real" in filename) else "Fake"
-            category = "".join([i for i in key if not i.isdigit()])
-            if category == "":
-                continue
-            category = self.category_list.index(category)
-
-            yield f"{_class_name}_{key}", {"news": news, "label": _class, "category": category}