Delete imdb

imdb

@@ -1,34 +0,0 @@
-import csv
-import datasets
-from datasets.tasks import TextClassification
-
-_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/linxinyuan/imdb/resolve/main/train.csv"
-_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/linxinyuan/imdb/resolve/main/test.csv"
-
-class imdb(datasets.GeneratorBasedBuilder):
-    def _info(self):
-        return datasets.DatasetInfo(
-            description="imdb",
-            features=datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']),
-                }
-            ),
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
-        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
-        ]
-        
-    def _generate_examples(self, filepath):
-        with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.reader(csv_file, delimiter="\t")
-            for id_, row in enumerate(csv_reader):
-                yield id_, {"text": row[1], "label": (int)(row[0])}
-