Datasets:
turkish_ner

	# 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.
	"""TODO: Add a description here."""


	import os

	import datasets


	logger = datasets.logging.get_logger(__name__)


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings@article{DBLP:journals/corr/SahinTYES17,
	author = {H. Bahadir Sahin and
	Caglar Tirkaz and
	Eray Yildiz and
	Mustafa Tolga Eren and
	Omer Ozan Sonmez},
	title = {Automatically Annotated Turkish Corpus for Named Entity Recognition
	and Text Categorization using Large-Scale Gazetteers},
	journal = {CoRR},
	volume = {abs/1702.02363},
	year = {2017},
	url = {http://arxiv.org/abs/1702.02363},
	archivePrefix = {arXiv},
	eprint = {1702.02363},
	timestamp = {Mon, 13 Aug 2018 16:46:36 +0200},
	biburl = {https://dblp.org/rec/journals/corr/SahinTYES17.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	Turkish Wikipedia Named-Entity Recognition and Text Categorization
	(TWNERTC) dataset is a collection of automatically categorized and annotated
	sentences obtained from Wikipedia. The authors constructed large-scale
	gazetteers by using a graph crawler algorithm to extract
	relevant entity and domain information
	from a semantic knowledge base, Freebase.
	The constructed gazetteers contains approximately
	300K entities with thousands of fine-grained entity types
	under 77 different domains.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://data.mendeley.com/datasets/cdcztymf4k/1"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "Creative Commons Attribution 4.0 International"

	_URL = "https://data.mendeley.com/public-files/datasets/cdcztymf4k/files/5557ef78-7d53-4a01-8241-3173c47bbe10/file_downloaded"


	_FILE_NAME_ZIP = "TWNERTC_TC_Coarse Grained NER_DomainIndependent_NoiseReduction.zip"
	_FILE_NAME = "TWNERTC_TC_Coarse Grained NER_DomainIndependent_NoiseReduction.DUMP"


	class TurkishNER(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"domain": datasets.ClassLabel(
	names=[
	"architecture",
	"basketball",
	"book",
	"business",
	"education",
	"fictional_universe",
	"film",
	"food",
	"geography",
	"government",
	"law",
	"location",
	"military",
	"music",
	"opera",
	"organization",
	"people",
	"religion",
	"royalty",
	"soccer",
	"sports",
	"theater",
	"time",
	"travel",
	"tv",
	]
	),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"B-PERSON",
	"I-PERSON",
	"B-ORGANIZATION",
	"I-ORGANIZATION",
	"B-LOCATION",
	"I-LOCATION",
	"B-MISC",
	"I-MISC",
	]
	)
	),
	}
	),
	supervised_keys=None,
	# 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):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.extract(os.path.join(dl_manager.download_and_extract(_URL), _FILE_NAME_ZIP))
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": (os.path.join(data_dir, _FILE_NAME)),
	"split": "train",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	logger.info("⏳ Generating examples from = %s", filepath)

	with open(filepath, encoding="utf-8") as f:
	id_ = -1
	for line in f:
	if line == "" or line == "\n":
	continue
	else:
	splits = line.split("\t")
	id_ += 1
	yield id_, {
	"id": str(id_),
	"domain": splits[0],
	"tokens": splits[2].split(" "),
	"ner_tags": splits[1].split(" "),
	}