Create 20ng_not_enough_data.py

20ng_not_enough_data.py

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+# Lint as: python3
+"""20ng  classification dataset."""
+
+
+import csv
+
+import datasets
+from datasets.tasks import TextClassification
+import sys
+csv.field_size_limit(sys.maxsize)
+
+_DESCRIPTION = """\
+This data collection contains all the data used in our learning question classification experiments(see [1]), which has question class definitions, the training and testing question sets, examples of preprocessing the questions, feature definition scripts and examples of semantically related word features. 
+This work has been done by Xin Li and Dan Roth and supported by [2].
+"""
+
+_CITATION = """"""
+
+_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/20ng_not_enough_data/resolve/main/train.csv"
+_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/20ng_not_enough_data/resolve/main/test.csv"
+_VALID_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/20ng_not_enough_data/raw/main/validaton.csv"
+
+
+CATEGORY_MAPPING = {'comp.sys.mac.hardware': 0,
+ 'comp.graphics': 1,
+ 'sci.space': 2,
+ 'talk.politics.guns': 3,
+ 'sci.med': 4,
+ 'comp.sys.ibm.pc.hardware': 5,
+ 'comp.os.ms-windows.misc': 6,
+ 'rec.motorcycles': 7,
+ 'misc.forsale': 8,
+ 'alt.atheism': 9,
+ 'rec.autos': 10,
+ 'sci.electronics': 11,
+ 'comp.windows.x': 12,
+ 'rec.sport.hockey': 13,
+ 'rec.sport.baseball': 14,
+ 'talk.politics.mideast': 15,
+ 'sci.crypt': 16,
+ 'soc.religion.christian': 17,
+ 'talk.politics.misc': 18,
+ 'talk.religion.misc': 19}
+
+class NG(datasets.GeneratorBasedBuilder):
+    """20ng  classification dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=list(CATEGORY_MAPPING.keys())),
+                    "labeled_mask": datasets.Value("bool"),
+                }
+            ),
+            homepage="",
+            citation=_CITATION,
+            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)
+        valid_path = dl_manager.download_and_extract(_VALID_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}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate examples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file, quotechar='"', delimiter=";", quoting=csv.QUOTE_ALL, skipinitialspace=True
+            )
+            _ = next(csv_reader) # skip header
+            for id_, row in enumerate(csv_reader):
+                text, label, label_mask = row
+                label = CATEGORY_MAPPING.get(label, label)
+                yield id_, {"text": text, "label": label, "labeled_mask": label_mask}