Create ornl.py

ornl.py

@@ -0,0 +1,80 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace NLP Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""Open Data Rechtspraak dutch topic classification dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import nlp
+
+
+_DESCRIPTION = """\
+still a WIP, Dataset originally comes from Open Data van de Rechtspraak"
+"""
+
+_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/Rodekool/ornl/resolve/main/train.csv"
+_TEST_DOWNLOAD_URL  = "https://huggingface.co/datasets/Rodekool/ornl/resolve/main/test.csv"
+
+
+class ORnl(nlp.GeneratorBasedBuilder):
+    """Open Data van de Rechtspraak dutch topic classification dataset."""
+
+    def _info(self):
+        return nlp.DatasetInfo(
+            description=_DESCRIPTION,
+            features=nlp.Features(
+                {
+                    "text": nlp.Value("string"),
+                    "label": nlp.features.ClassLabel(names=['Ambtenarenrecht', 'Arbeidsrecht', 'Belastingrecht', 'Omgevingsrecht', 'Personen- en familierecht', 'Socialezekerheidsrecht', 'Verbintenissenrecht', 'Vreemdelingenrecht']),
+                }
+            ),
+            homepage="https://www.rechtspraak.nl/Uitspraken/paginas/open-data.aspx",
+        )
+
+    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 [
+            nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            nlp.SplitGenerator(name=nlp.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate Rechtspraak 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):
+                label, title, description = row
+                
+                # Original labels are [1, 2, 3, 4, 5, 6, 7, 8] ->
+                #                   ['Ambtenarenrecht',
+                #                    'Arbeidsrecht',
+                #                    'Belastingrecht',
+                #                    'Omgevingsrecht',
+                #                    'Personen- en familierecht',
+                #                    'Socialezekerheidsrecht',
+                #                    'Verbintenissenrecht',
+                #                    'Vreemdelingenrecht']
+                # Re-map to [0, 1, 2, 3, 4, 5, 6, 7].
+                
+                label = int(label) - 1
+                text = " ".join((title, description))
+                yield id_, {"text": text, "label": label}