Upload wolf_data_processing.py

wolf_data_processing.py

@@ -0,0 +1,75 @@
+# coding=utf-8
+# --- dataset class --
+import csv
+import datasets
+from datasets import load_dataset
+from datasets.tasks import TextClassification
+
+_TRAIN_DOWNLOAD_URL = 'https://raw.githubusercontent.com/ZhangLe59151/goginBE/main/wolf_t.csv' #'./corpus.json'
+_TEST_DOWNLOAD_URL = 'https://raw.githubusercontent.com/ZhangLe59151/goginBE/main/wolf_v.csv'
+
+_DESCRIPTION = 'My Third Dataset'
+_HOMEPAGE = ''
+_LICENSE = ''
+_CITATION = '''\\n@inproceedings{Casanueva2020,
+    author      = Mulin,
+    title       = Second Dataset,
+    year        = {2021},
+    month       = {Sep},
+    note        = {},
+    url         = {},
+    booktitle   = {}
+}'''
+class MulinThird(datasets.GeneratorBasedBuilder):
+  VERSION = datasets.Version('1.1.0')
+  def _info(self):
+    features = datasets.Features(
+      {'text': datasets.Value('string'),
+      'label': datasets.features.ClassLabel(
+        names=[
+          'Ask for ideas',
+          'Explain ideas',
+          'Ask action',
+          'Reject answer',
+          'Ask to do',
+          'Explain actions',
+          'Polite',
+          'Reject',
+          'Encourage',
+          'Check emotion',
+          'Explain ideas,Encourage',
+          'Explain ideas,Polite',
+          'Reject answer,Reject',
+          'Ask for ideas,Ask to do',
+          'Explain actions,Polite',
+          'Explain ideas,Explain actions',
+          'Polite,Encourage',
+          'Polite,Explain actions',
+          'Polite,Ask to do',
+          'Polite,Encourage,Explain ideas,Explain actions',
+          'Explain ideas,Polite,Explain actions,Encourage'])})
+    return datasets.DatasetInfo(
+        description=_DESCRIPTION,
+        features=features,
+        supervised_keys=None,
+        homepage=_HOMEPAGE,
+        license=_LICENSE,
+        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)
+    print(train_path)
+    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 f:
+      csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True)
+      next(csv_reader)
+      for id_, row in enumerate(csv_reader):
+        id, label, text = row
+        yield id_, {"text": text, "label": label}