Create QC.py

QC.py

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+# Lint as: python3
+"""QC question classification dataset."""
+
+
+import csv
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+_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/QC/raw/main/test.csv"
+_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/QC/raw/main/test.csv"
+
+
+CATEGORY_MAPPING = {0: 'LOC_city',
+ 1: 'HUM_desc',
+ 2: 'DESC_def',
+ 3: 'NUM_date',
+ 4: 'NUM_dist',
+ 5: 'HUM_gr',
+ 6: 'ENTY_plant',
+ 7: 'DESC_reason',
+ 8: 'HUM_ind',
+ 9: 'NUM_weight',
+ 10: 'NUM_other',
+ 11: 'ENTY_substance',
+ 12: 'LOC_other',
+ 13: 'NUM_speed',
+ 14: 'LOC_mount',
+ 15: 'NUM_temp',
+ 16: 'NUM_period',
+ 17: 'NUM_count',
+ 18: 'ENTY_animal',
+ 19: 'DESC_desc',
+ 20: 'ENTY_food',
+ 21: 'LOC_state',
+ 22: 'ENTY_termeq',
+ 23: 'NUM_money',
+ 24: 'ENTY_currency',
+ 25: 'LOC_country',
+ 26: 'ENTY_event',
+ 27: 'ENTY_other',
+ 28: 'DESC_manner',
+ 29: 'ENTY_color',
+ 30: 'ENTY_product',
+ 31: 'HUM_title',
+ 32: 'ENTY_body',
+ 33: 'ENTY_veh',
+ 34: 'ENTY_lang',
+ 35: 'ENTY_instru',
+ 36: 'ABBR_abb',
+ 37: 'ABBR_exp',
+ 38: 'ENTY_dismed',
+ 39: 'NUM_perc',
+ 40: 'ENTY_sport',
+ 41: 'ENTY_techmeth'}
+
+class AGNews(datasets.GeneratorBasedBuilder):
+    """AG News topic classification dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=CATEGORY_MAPPING.values()),
+                }
+            ),
+            homepage="https://cogcomp.seas.upenn.edu/Data/QA/QC/",
+            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)
+        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):
+        """Generate QC News examples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
+            )
+            for id_, row in enumerate(csv_reader):
+                text, label = row
+                label = CATEGORY_MAPPING[label]
+                yield id_, {"text": text, "label": label}