Datasets:

vesteinn
/

sosialurin-faroese-ner

	# coding=utf-8
	# Copyright 2020 HuggingFace Datasets Authors.
	# Modified by Vésteinn Snæbjarnarson 2021
	#
	# 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






	LABELS = [
	'B-Date',
	'B-Location',
	'B-Miscellaneous',
	'B-Money',
	'B-Organization',
	'B-Percent',
	'B-Person',
	'B-Time',
	'I-Date',
	'I-Location',
	'I-Miscellaneous',
	'I-Money',
	'I-Organization',
	'I-Percent',
	'I-Person',
	'I-Time',
	'O',
	]



	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@misc{sosialurin-ner,
	title = {},
	author = {},
	url = {},
	year = {2022} }
	"""

	_DESCRIPTION = """\
	The corpus that has been created consists of ca. 100.000 words of text from the [Faroese] newspaper Sosialurin. Each word is tagged with named entity information
	"""

	_URL = "https://huggingface.co/datasets/vesteinn/sosialurin-faroese-ner/raw/main/"
	_TRAINING_FILE = "sosialurin.faroese.ner.train.txt"


	class SosialurinNERConfig(datasets.BuilderConfig):
	"""BuilderConfig for sosialurin-faroese-ner"""

	def __init__(self, **kwargs):
	"""BuilderConfig for sosialurin-faroese-ner.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(SosialurinNERConfig, self).__init__(**kwargs)


	class SosialurinNER(datasets.GeneratorBasedBuilder):
	"""sosialurin-faroese-ner dataset."""

	BUILDER_CONFIGS = [
	SosialurinNERConfig(name="sosialurin-faroese-ner", version=datasets.Version("0.1.0"), description="sosialurin-faroese-ner dataset"),
	]

	def _info(self):
	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=LABELS
	)
	),
	}
	),
	supervised_keys=None,
	homepage="",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls_to_download = {
	"train": f"{_URL}{_TRAINING_FILE}",
	}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

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

	def _generate_examples(self, filepath):
	logger.info("⏳ Generating examples from = %s", filepath)
	with open(filepath, encoding="utf-8") as f:
	guid = 0
	tokens = []
	ner_tags = []
	for line in f:
	if line.startswith("-DOCSTART-") or line == "" or line == "\n":
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}
	guid += 1
	tokens = []
	ner_tags = []
	else:
	# tokens are tab separated
	splits = line.split("\t")
	tokens.append(splits[0])
	try:
	ner_tags.append(splits[1].rstrip())
	except:
	print(splits)
	raise
	# last example
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}