Datasets:
nkjp-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.
	"""NKJP-NER"""


	import csv
	import os

	import datasets
	from datasets.tasks import TextClassification


	_CITATION = """\
	@book{przepiorkowski2012narodowy,
	title={Narodowy korpus jezyka polskiego},
	author={Przepi{\'o}rkowski, Adam},
	year={2012},
	publisher={Naukowe PWN}
	}
	"""

	_DESCRIPTION = """\
	The NKJP-NER is based on a human-annotated part of National Corpus of Polish (NKJP). We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity.
	"""

	_HOMEPAGE = "https://klejbenchmark.com/tasks/"

	_LICENSE = "GNU GPL v.3"

	_URLs = "https://klejbenchmark.com/static/data/klej_nkjp-ner.zip"


	class NkjpNer(datasets.GeneratorBasedBuilder):
	"""NKJP-NER"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"sentence": datasets.Value("string"),
	"target": datasets.ClassLabel(
	names=[
	"geogName",
	"noEntity",
	"orgName",
	"persName",
	"placeName",
	"time",
	]
	),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	task_templates=[TextClassification(text_column="sentence", label_column="target")],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URLs)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "train.tsv"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": os.path.join(data_dir, "test_features.tsv"), "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(data_dir, "dev.tsv"),
	"split": "dev",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
	for id_, row in enumerate(reader):
	yield id_, {
	"sentence": row["sentence"],
	"target": -1 if split == "test" else row["target"],
	}