Datasets:

rcds
/

swiss_law_area_prediction

	import csv
	import json
	import lzma
	import os

	import datasets
	try:
	import lzma as xz
	except ImportError:
	import pylzma as xz


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {A great new dataset},
	author={huggingface, Inc.
	},
	year={2020}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	This dataset contains court decision for law area prediction task.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = ""

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = ""

	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLS = {
	"main": "https://huggingface.co/datasets/rcds/law_area_prediction/resolve/main/data/sub_areas/huggingface"
	}

	def get_url(config_name):
	if config_name == "main":
	return _URLS["main"]


	class LawAreaPrediction(datasets.GeneratorBasedBuilder):
	"""This dataset contains court decision for law area prediction task."""

	VERSION = datasets.Version("1.1.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="main", version=VERSION, description="Whole dataset"),
	]

	DEFAULT_CONFIG_NAME = "main" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	if self.config.name == "main": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"decision_id": datasets.Value("string"),
	"facts": datasets.Value("string"),
	"considerations": datasets.Value("string"),
	"law_area": datasets.Value("string"),
	"law_sub_area": datasets.Value("string"),
	"language": datasets.Value("string"),
	"year": datasets.Value("int32"),
	"court": datasets.Value("string"),
	"chamber": datasets.Value("string"),
	"canton": datasets.Value("string"),
	"region": datasets.Value("string")

	# These are the features of your dataset like images, labels ...
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	# homepage=_HOMEPAGE,
	# License for the dataset if available
	# license=_LICENSE,
	# Citation for the dataset
	# citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	urls = get_url(self.config.name)
	filepath_train = dl_manager.download(os.path.join(urls, "train.jsonl.xz"))
	filepath_validation = dl_manager.download(os.path.join(urls, "validation.jsonl.xz"))
	filepath_test = dl_manager.download(os.path.join(urls, "test.jsonl.xz"))

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": filepath_train,
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": filepath_validation,
	"split": "validation",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": filepath_test,
	"split": "test"
	},
	)
	]

	def belongs_to_law_area(self, law_sub_area):
	area_map = {
	"public": ['Tax', 'Urban Planning and Environmental', 'Expropriation', 'Public Administration', 'Other Fiscal'],
	"civil": ['Rental and Lease', 'Employment Contract', 'Bankruptcy', 'Family', 'Competition and Antitrust', 'Intellectual Property'],
	'criminal': ['Substantive Criminal', 'Criminal Procedure']
	}
	if law_sub_area in area_map[self.config.name]:
	return True
	# raise Error if law_sub_area not found in any area
	for area in area_map:
	if law_sub_area in area_map[area]:
	return False
	raise ValueError("law_sub_area not found in any area")


	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	line_counter = 0
	try:
	with xz.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
	for id, line in enumerate(f):
	line_counter += 1
	if line:
	data = json.loads(line)
	if self.config.name == "main":
	yield id, {
	"decision_id": data["decision_id"],
	"facts": data["facts"],
	"considerations": data["considerations"],
	"law_area": data["law_area"],
	"law_sub_area": data["label"],
	"language": data["language"],
	"year": data["year"],
	"court": data["court"],
	"chamber": data["chamber"],
	"canton": data["canton"],
	"region": data["region"]
	}
	except lzma.LZMAError as e:
	print(split, e)
	if line_counter == 0:
	raise e