File size: 3,466 Bytes
7a02a00 c47bac1 1a06a57 7a02a00 c47bac1 7a02a00 c47bac1 7a02a00 c47bac1 7a02a00 c47bac1 7a02a00 c47bac1 7a02a00 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
# 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/ornl/resolve/main/train.csv"
_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/Rodekool/ornl/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, 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}
|