Upload datasaur-MmRmNmMwYzE-ODFhMDk2ZTI.py with huggingface_hub

datasaur-MmRmNmMwYzE-ODFhMDk2ZTI.py

@@ -0,0 +1,49 @@
+
+import csv
+import pandas as pd
+import datasets
+
+from sklearn.model_selection import train_test_split
+from datasets.tasks import TextClassification
+
+_DATASET_LABELS = ['tech', 'politics', 'business', 'entertainment', 'sport']
+
+class Custom(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description='',
+            features=datasets.Features(
+                {
+                    'text': datasets.Value('string'),
+                    'label': datasets.features.ClassLabel(
+                        names=_DATASET_LABELS
+                    ),
+                }
+            ),
+            homepage='',
+            citation='',
+            task_templates=[
+                TextClassification(text_column='text', label_column='label')
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_path = dl_manager.download_and_extract('data.csv')
+        records = pd.read_csv(data_path)
+        train_df, val_df = train_test_split(records, test_size=0.2, random_state=42)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={'df': train_df}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={'df': val_df}
+            ),
+        ]
+
+    def _generate_examples(self, df):
+        for id_, row in df.iterrows():
+            text, label = row['text'], row['label'],
+            label = _DATASET_LABELS.index(label)
+            yield id_, {'text': text, 'label': label}