uploading files

task1a.py

@@ -0,0 +1,42 @@
+import csv
+import datasets
+
+_DOWNLOAD_URL = "https://huggingface.co/datasets/mrojas/task1a/resolve/main/data.csv"
+
+class Task1a(datasets.GeneratorBasedBuilder):
+    """Task1a classification dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {   
+                    "filename": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names = ["0", "1"]),
+                }
+            )
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download_and_extract(_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": path, "is_test": False}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": path, "is_test": True}),
+        ]
+
+    def _generate_examples(self, filepath, is_test, test_size = 0.3):
+        """Generate examples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+            train_threshold = 122
+            csv_reader = csv.reader(
+                csv_file
+            )
+            # next(csv_reader, None)  # skip the headers
+            for id_, row in enumerate(csv_reader):
+                if id_ > 0:
+                    text, label = row
+                    current_row = id_, {"text": text, "label": int(label)}
+                    if (id_ < train_threshold) & (not is_test):
+                        yield current_row
+                    if (id_ >= train_threshold) & (is_test):
+                        yield current_row