nlp to datasets

ornl.py

@@ -20,7 +20,7 @@ from __future__ import absolute_import, division, print_function
 
 import csv
 
-import nlp
+# import nlp # old loader
 import datasets
 from datasets.tasks import TextClassification
 
@@ -33,16 +33,16 @@ _TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/Rodekool/ornl/resolve/mai
 _TEST_DOWNLOAD_URL  = "https://huggingface.co/datasets/Rodekool/ornl/resolve/main/test.csv"
 
 
-class ORnl(nlp.GeneratorBasedBuilder):
+class ORnl(datasets.GeneratorBasedBuilder):
     """Open Data van de Rechtspraak dutch topic classification dataset."""
 
     def _info(self):
-        return nlp.DatasetInfo(
+        return datasets.DatasetInfo(
             description=_DESCRIPTION,
-            features=nlp.Features(
+            features=datasets.Features(
                 {
-                    "text": nlp.Value("string"),
-                    "label": nlp.features.ClassLabel(names=['Ambtenarenrecht', 'Arbeidsrecht', 'Belastingrecht', 'Omgevingsrecht', 'Personen- en familierecht', 'Socialezekerheidsrecht', 'Verbintenissenrecht', 'Vreemdelingenrecht']),
+                    "text": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=['Ambtenarenrecht', 'Arbeidsrecht', 'Belastingrecht', 'Omgevingsrecht', 'Personen- en familierecht', 'Socialezekerheidsrecht', 'Verbintenissenrecht', 'Vreemdelingenrecht']),
                 }
             ),
             homepage="https://www.rechtspraak.nl/Uitspraken/paginas/open-data.aspx",
@@ -52,8 +52,8 @@ class ORnl(nlp.GeneratorBasedBuilder):
         train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
         test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
         return [
-            nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={"filepath": test_path}),
+            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):