Datasets:
german_legal_entity_recognition

	import os
	import warnings

	import datasets


	_DESCRIPTION = """\
	"""
	_HOMEPAGE_URL = "https://github.com/elenanereiss/Legal-Entity-Recognition"
	_CITATION = """\
	@inproceedings{leitner2019fine,
	author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},
	title = {{Fine-grained Named Entity Recognition in Legal Documents}},
	booktitle = {Semantic Systems. The Power of AI and Knowledge
	Graphs. Proceedings of the 15th International Conference
	(SEMANTiCS 2019)},
	year = 2019,
	editor = {Maribel Acosta and Philippe Cudré-Mauroux and Maria
	Maleshkova and Tassilo Pellegrini and Harald Sack and York
	Sure-Vetter},
	keywords = {aip},
	publisher = {Springer},
	series = {Lecture Notes in Computer Science},
	number = {11702},
	address = {Karlsruhe, Germany},
	month = 9,
	note = {10/11 September 2019},
	pages = {272--287},
	pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}}
	"""
	_DATA_URL = "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/dataset_courts.zip"
	_VERSION = "1.0.0"
	_COURTS = ["bag", "bfh", "bgh", "bpatg", "bsg", "bverfg", "bverwg"]
	_COURTS_FILEPATHS = {court: f"{court}.conll" for court in _COURTS}
	_ALL = "all"


	class GermanLegalEntityRecognitionConfig(datasets.BuilderConfig):
	def __init__(self, args, courts=None, *kwargs):
	super().__init__(args, version=datasets.Version(_VERSION, ""), *kwargs)
	self.courts = courts

	@property
	def filepaths(self):
	return [_COURTS_FILEPATHS[court] for court in self.courts]


	class GermanLegalEntityRecognition(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	GermanLegalEntityRecognitionConfig(name=court, courts=[court], description=f"Court. {court}.")
	for court in _COURTS
	] + [GermanLegalEntityRecognitionConfig(name=_ALL, courts=_COURTS, description="All courts included.")]
	BUILDER_CONFIG_CLASS = GermanLegalEntityRecognitionConfig
	DEFAULT_CONFIG_NAME = _ALL

	def _info(self):
	warnings.warn(
	"Dataset 'german_legal_entity_recognition' is deprecated and will be deleted. Use 'elenanereiss/german-ler' instead.",
	FutureWarning,
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"B-AN",
	"B-EUN",
	"B-GRT",
	"B-GS",
	"B-INN",
	"B-LD",
	"B-LDS",
	"B-LIT",
	"B-MRK",
	"B-ORG",
	"B-PER",
	"B-RR",
	"B-RS",
	"B-ST",
	"B-STR",
	"B-UN",
	"B-VO",
	"B-VS",
	"B-VT",
	"I-AN",
	"I-EUN",
	"I-GRT",
	"I-GS",
	"I-INN",
	"I-LD",
	"I-LDS",
	"I-LIT",
	"I-MRK",
	"I-ORG",
	"I-PER",
	"I-RR",
	"I-RS",
	"I-ST",
	"I-STR",
	"I-UN",
	"I-VO",
	"I-VS",
	"I-VT",
	"O",
	]
	)
	),
	},
	),
	supervised_keys=None,
	homepage=_HOMEPAGE_URL,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	path = dl_manager.download_and_extract(_DATA_URL)
	return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"datapath": path})]

	def _generate_examples(self, datapath):
	sentence_counter = 0
	for filepath in self.config.filepaths:
	filepath = os.path.join(datapath, filepath)
	with open(filepath, encoding="utf-8") as f:
	current_words = []
	current_labels = []
	for row in f:
	row = row.rstrip()
	row_split = row.split()
	if len(row_split) == 2:
	token, label = row_split
	current_words.append(token)
	current_labels.append(label)
	else:
	if not current_words:
	continue
	assert len(current_words) == len(current_labels), "word len doesnt match label length"
	sentence = (
	sentence_counter,
	{
	"id": str(sentence_counter),
	"tokens": current_words,
	"ner_tags": current_labels,
	},
	)
	sentence_counter += 1
	current_words = []
	current_labels = []
	yield sentence

	# if something remains:
	if current_words:
	sentence = (
	sentence_counter,
	{
	"id": str(sentence_counter),
	"tokens": current_words,
	"ner_tags": current_labels,
	},
	)
	yield sentence