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	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""Fine-grained Named Entity Recognition in Legal Documents"""

	from __future__ import absolute_import, division, print_function

	import datasets

	_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}
	}
	"""

	_DESCRIPTION = """\
	We describe a dataset developed for Named Entity Recognition in German federal court decisions.
	It consists of approx. 67,000 sentences with over 2 million tokens.
	The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes:
	person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law,
	ordinance, European legal norm, regulation, contract, court decision, and legal literature.
	The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions.
	The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format,
	was developed for training an NER service for German legal documents in the EU project Lynx.
	"""

	_URL = "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler.conll"


	class Ler(datasets.GeneratorBasedBuilder):
	"""
	We describe a dataset developed for Named Entity Recognition in German federal court decisions.
	It consists of approx. 67,000 sentences with over 2 million tokens.
	The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes:
	person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law,
	ordinance, European legal norm, regulation, contract, court decision, and legal literature.
	The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions.
	The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format,
	was developed for training an NER service for German legal documents in the EU project Lynx.
	"""
	VERSION = datasets.Version("1.0.0")

	def _info(self):
	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=datasets.Features(
	{
	"id": datasets.Value("int32"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"ner_tags": datasets.Sequence(
	datasets.ClassLabel(
	names=[
	"O",
	"B-PER",
	"I-PER",
	"B-RR",
	"I-RR",
	"B-AN",
	"I-AN",
	"B-LD",
	"I-LD",
	"B-ST",
	"I-ST",
	"B-STR",
	"I-STR",
	"B-LDS",
	"I-LDS",
	"B-ORG",
	"I-ORG",
	"B-UN",
	"I-UN",
	"B-INN",
	"I-INN",
	"B-GRT",
	"I-GRT",
	"B-MRK",
	"I-MRK",
	"B-GS",
	"I-GS",
	"B-VO",
	"I-VO",
	"B-EUN",
	"I-EUN",
	"B-VS",
	"I-VS",
	"B-VT",
	"I-VT",
	"B-RS",
	"I-RS",
	"B-LIT",
	"I-LIT",
	]
	)
	),
	}
	),
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=datasets.info.SupervisedKeysData(input="tokens", output="ner_tags"),
	# Homepage of the dataset for documentation
	homepage="https://github.com/elenanereiss/Legal-Entity-Recognition",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	dl_file = dl_manager.download(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": dl_file},
	),
	]

	def _generate_examples(self, filepath):
	""" Yields examples. """
	with open(filepath, "r", encoding="utf-8") as f:
	guid = 0
	tokens = []
	ner_tags = []
	for line in f:
	if line == "" or line == "\n":
	if tokens:
	yield guid, {"id": guid, "tokens": tokens, "ner_tags": ner_tags}
	guid += 1
	tokens = []
	ner_tags = []
	else:
	# conll2002 tokens are space separated
	splits = line.split(" ")
	tokens.append(splits[0])
	ner_tags.append(splits[1].rstrip())