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ornl20.py

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+# 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 nlp # old loader
+import datasets
+from datasets.tasks import TextClassification
+
+
+_DESCRIPTION = """\
+still a WIP, Dataset originally comes from Open Data van de Rechtspraak"
+"""
+
+_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/Rodekool/ornl20/resolve/main/train.csv"
+_TEST_DOWNLOAD_URL  = "https://huggingface.co/datasets/Rodekool/ornl20/resolve/main/test.csv"
+
+
+class ORnl(datasets.GeneratorBasedBuilder):
+    """Open Data van de Rechtspraak dutch topic classification dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "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",
+        )
+
+    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 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, ... 25, 26] ->
+                # Re-map to [0, 1, ... 24, 25].
+                
+                label = int(label) - 1
+                text = " ".join((title, description))
+                yield id_, {"text": text, "label": label}