Create cola.py

cola.py

@@ -0,0 +1,33 @@
+import csv
+import datasets
+from datasets.tasks import TextClassification
+
+_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/linxinyuan/cola/resolve/main/train.csv"
+_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/linxinyuan/cola/resolve/main/test.csv"
+
+class mind(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="cola",
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=['0', '1']),
+                }
+            ),
+            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[3], "label": (int)(row[1])}