Datasets:
germeval_14

	# coding=utf-8
	# Copyright 2020 HuggingFace Datasets 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
	"""The GermEval 2014 NER Shared Task dataset."""


	import csv

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@inproceedings{benikova-etal-2014-nosta,
	title = {NoSta-D Named Entity Annotation for German: Guidelines and Dataset},
	author = {Benikova, Darina and
	Biemann, Chris and
	Reznicek, Marc},
	booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)},
	month = {may},
	year = {2014},
	address = {Reykjavik, Iceland},
	publisher = {European Language Resources Association (ELRA)},
	url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/276_Paper.pdf},
	pages = {2524--2531},
	}
	"""

	_DESCRIPTION = """\
	The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties:\
	- The data was sampled from German Wikipedia and News Corpora as a collection of citations.\
	- The dataset covers over 31,000 sentences corresponding to over 590,000 tokens.\
	- The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines,\
	using four main NER categories with sub-structure, and annotating embeddings among NEs\
	such as [ORG FC Kickers [LOC Darmstadt]].
	"""

	_URLS = {
	"train": "https://drive.google.com/uc?export=download&id=1Jjhbal535VVz2ap4v4r_rN1UEHTdLK5P",
	"dev": "https://drive.google.com/uc?export=download&id=1ZfRcQThdtAR5PPRjIDtrVP7BtXSCUBbm",
	"test": "https://drive.google.com/uc?export=download&id=1u9mb7kNJHWQCWyweMDRMuTFoOHOfeBTH",
	}


	class GermEval14Config(datasets.BuilderConfig):
	"""BuilderConfig for GermEval 2014."""

	def __init__(self, **kwargs):
	"""BuilderConfig for GermEval 2014.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(GermEval14Config, self).__init__(**kwargs)


	class GermEval14(datasets.GeneratorBasedBuilder):
	"""GermEval 2014 NER Shared Task dataset."""

	BUILDER_CONFIGS = [
	GermEval14Config(
	name="germeval_14", version=datasets.Version("2.0.0"), description="GermEval 2014 NER Shared Task dataset"
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"source": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"B-LOC",
	"I-LOC",
	"B-LOCderiv",
	"I-LOCderiv",
	"B-LOCpart",
	"I-LOCpart",
	"B-ORG",
	"I-ORG",
	"B-ORGderiv",
	"I-ORGderiv",
	"B-ORGpart",
	"I-ORGpart",
	"B-OTH",
	"I-OTH",
	"B-OTHderiv",
	"I-OTHderiv",
	"B-OTHpart",
	"I-OTHpart",
	"B-PER",
	"I-PER",
	"B-PERderiv",
	"I-PERderiv",
	"B-PERpart",
	"I-PERpart",
	]
	)
	),
	"nested_ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"B-LOC",
	"I-LOC",
	"B-LOCderiv",
	"I-LOCderiv",
	"B-LOCpart",
	"I-LOCpart",
	"B-ORG",
	"I-ORG",
	"B-ORGderiv",
	"I-ORGderiv",
	"B-ORGpart",
	"I-ORGpart",
	"B-OTH",
	"I-OTH",
	"B-OTHderiv",
	"I-OTHderiv",
	"B-OTHpart",
	"I-OTHpart",
	"B-PER",
	"I-PER",
	"B-PERderiv",
	"I-PERderiv",
	"B-PERpart",
	"I-PERpart",
	]
	)
	),
	}
	),
	supervised_keys=None,
	homepage="https://sites.google.com/site/germeval2014ner/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	downloaded_files = dl_manager.download_and_extract(_URLS)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _generate_examples(self, filepath):
	logger.info("⏳ Generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	data = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
	current_source = ""
	current_tokens = []
	current_ner_tags = []
	current_nested_ner_tags = []
	sentence_counter = 0
	for row in data:
	if row:
	if row[0] == "#":
	current_source = " ".join(row[1:])
	continue
	id_, token, label, nested_label = row[:4]
	current_tokens.append(token)
	current_ner_tags.append(label)
	current_nested_ner_tags.append(nested_label)
	else:
	# New sentence
	if not current_tokens:
	# Consecutive empty lines will cause empty sentences
	continue
	assert len(current_tokens) == len(current_ner_tags), "💔 between len of tokens & labels"
	assert len(current_ner_tags) == len(
	current_nested_ner_tags
	), "💔 between len of labels & nested labels"
	assert current_source, "💥 Source for new sentence was not set"
	sentence = (
	sentence_counter,
	{
	"id": str(sentence_counter),
	"tokens": current_tokens,
	"ner_tags": current_ner_tags,
	"nested_ner_tags": current_nested_ner_tags,
	"source": current_source,
	},
	)
	sentence_counter += 1
	current_tokens = []
	current_ner_tags = []
	current_nested_ner_tags = []
	current_source = ""
	yield sentence
	# Don't forget last sentence in dataset 🧐
	yield sentence_counter, {
	"id": str(sentence_counter),
	"tokens": current_tokens,
	"ner_tags": current_ner_tags,
	"nested_ner_tags": current_nested_ner_tags,
	"source": current_source,
	}