Datasets:

clarin-pl
/

kpwr-ner

	# coding=utf-8
	# Copyright 2021 The TensorFlow Datasets Authors and the 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
	"""KPWR-NER tagging dataset."""

	import csv
	from typing import List, Tuple, Dict, Generator

	import datasets

	_DESCRIPTION = """KPWR-NER tagging dataset."""

	_URLS = {
	"train": "https://huggingface.co/datasets/clarin-pl/kpwr-ner/resolve/main/data/kpwr-ner-n82-train-tune.iob",
	"test": "https://huggingface.co/datasets/clarin-pl/kpwr-ner/resolve/main/data/kpwr-ner-n82-test.iob",
	}

	_HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/294"

	_NER_TAGS = [
	"B-nam_adj",
	"B-nam_adj_city",
	"B-nam_adj_country",
	"B-nam_adj_person",
	"B-nam_eve",
	"B-nam_eve_human",
	"B-nam_eve_human_cultural",
	"B-nam_eve_human_holiday",
	"B-nam_eve_human_sport",
	"B-nam_fac_bridge",
	"B-nam_fac_goe",
	"B-nam_fac_goe_stop",
	"B-nam_fac_park",
	"B-nam_fac_road",
	"B-nam_fac_square",
	"B-nam_fac_system",
	"B-nam_liv_animal",
	"B-nam_liv_character",
	"B-nam_liv_god",
	"B-nam_liv_habitant",
	"B-nam_liv_person",
	"B-nam_loc",
	"B-nam_loc_astronomical",
	"B-nam_loc_country_region",
	"B-nam_loc_gpe_admin1",
	"B-nam_loc_gpe_admin2",
	"B-nam_loc_gpe_admin3",
	"B-nam_loc_gpe_city",
	"B-nam_loc_gpe_conurbation",
	"B-nam_loc_gpe_country",
	"B-nam_loc_gpe_district",
	"B-nam_loc_gpe_subdivision",
	"B-nam_loc_historical_region",
	"B-nam_loc_hydronym",
	"B-nam_loc_hydronym_lake",
	"B-nam_loc_hydronym_ocean",
	"B-nam_loc_hydronym_river",
	"B-nam_loc_hydronym_sea",
	"B-nam_loc_land",
	"B-nam_loc_land_continent",
	"B-nam_loc_land_island",
	"B-nam_loc_land_mountain",
	"B-nam_loc_land_peak",
	"B-nam_loc_land_region",
	"B-nam_num_house",
	"B-nam_num_phone",
	"B-nam_org_company",
	"B-nam_org_group",
	"B-nam_org_group_band",
	"B-nam_org_group_team",
	"B-nam_org_institution",
	"B-nam_org_nation",
	"B-nam_org_organization",
	"B-nam_org_organization_sub",
	"B-nam_org_political_party",
	"B-nam_oth",
	"B-nam_oth_currency",
	"B-nam_oth_data_format",
	"B-nam_oth_license",
	"B-nam_oth_position",
	"B-nam_oth_tech",
	"B-nam_oth_www",
	"B-nam_pro",
	"B-nam_pro_award",
	"B-nam_pro_brand",
	"B-nam_pro_media",
	"B-nam_pro_media_periodic",
	"B-nam_pro_media_radio",
	"B-nam_pro_media_tv",
	"B-nam_pro_media_web",
	"B-nam_pro_model_car",
	"B-nam_pro_software",
	"B-nam_pro_software_game",
	"B-nam_pro_title",
	"B-nam_pro_title_album",
	"B-nam_pro_title_article",
	"B-nam_pro_title_book",
	"B-nam_pro_title_document",
	"B-nam_pro_title_song",
	"B-nam_pro_title_treaty",
	"B-nam_pro_title_tv",
	"B-nam_pro_vehicle",
	"I-nam_adj_country",
	"I-nam_eve",
	"I-nam_eve_human",
	"I-nam_eve_human_cultural",
	"I-nam_eve_human_holiday",
	"I-nam_eve_human_sport",
	"I-nam_fac_bridge",
	"I-nam_fac_goe",
	"I-nam_fac_goe_stop",
	"I-nam_fac_park",
	"I-nam_fac_road",
	"I-nam_fac_square",
	"I-nam_fac_system",
	"I-nam_liv_animal",
	"I-nam_liv_character",
	"I-nam_liv_god",
	"I-nam_liv_person",
	"I-nam_loc",
	"I-nam_loc_astronomical",
	"I-nam_loc_country_region",
	"I-nam_loc_gpe_admin1",
	"I-nam_loc_gpe_admin2",
	"I-nam_loc_gpe_admin3",
	"I-nam_loc_gpe_city",
	"I-nam_loc_gpe_conurbation",
	"I-nam_loc_gpe_country",
	"I-nam_loc_gpe_district",
	"I-nam_loc_gpe_subdivision",
	"I-nam_loc_historical_region",
	"I-nam_loc_hydronym",
	"I-nam_loc_hydronym_lake",
	"I-nam_loc_hydronym_ocean",
	"I-nam_loc_hydronym_river",
	"I-nam_loc_hydronym_sea",
	"I-nam_loc_land",
	"I-nam_loc_land_continent",
	"I-nam_loc_land_island",
	"I-nam_loc_land_mountain",
	"I-nam_loc_land_peak",
	"I-nam_loc_land_region",
	"I-nam_num_house",
	"I-nam_num_phone",
	"I-nam_org_company",
	"I-nam_org_group",
	"I-nam_org_group_band",
	"I-nam_org_group_team",
	"I-nam_org_institution",
	"I-nam_org_nation",
	"I-nam_org_organization",
	"I-nam_org_organization_sub",
	"I-nam_org_political_party",
	"I-nam_oth",
	"I-nam_oth_currency",
	"I-nam_oth_data_format",
	"I-nam_oth_license",
	"I-nam_oth_position",
	"I-nam_oth_tech",
	"I-nam_oth_www",
	"I-nam_pro",
	"I-nam_pro_award",
	"I-nam_pro_brand",
	"I-nam_pro_media",
	"I-nam_pro_media_periodic",
	"I-nam_pro_media_radio",
	"I-nam_pro_media_tv",
	"I-nam_pro_media_web",
	"I-nam_pro_model_car",
	"I-nam_pro_software",
	"I-nam_pro_software_game",
	"I-nam_pro_title",
	"I-nam_pro_title_album",
	"I-nam_pro_title_article",
	"I-nam_pro_title_book",
	"I-nam_pro_title_document",
	"I-nam_pro_title_song",
	"I-nam_pro_title_treaty",
	"I-nam_pro_title_tv",
	"I-nam_pro_vehicle",
	"O",
	]


	class KPWRNER(datasets.GeneratorBasedBuilder):
	def _info(self) -> datasets.DatasetInfo:
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"tokens": datasets.Sequence(datasets.Value("string")),
	"lemmas": datasets.Sequence(datasets.Value("string")),
	"orth": datasets.Sequence(datasets.Value("string")),
	"ner": datasets.Sequence(
	datasets.features.ClassLabel(
	names=_NER_TAGS, num_classes=len(_NER_TAGS)
	)
	),
	}
	),
	homepage=_HOMEPAGE,
	)

	def _split_generators(
	self, dl_manager: datasets.DownloadManager
	) -> List[datasets.SplitGenerator]:
	urls_to_download = _URLS
	downloaded_files = dl_manager.download_and_extract(urls_to_download)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": downloaded_files["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": downloaded_files["test"]},
	),
	]

	def _generate_examples(
	self, filepath: str
	) -> Generator[Tuple[int, Dict[str, str]], None, None]:
	with open(filepath, "r", encoding="utf-8") as f:
	reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)

	tokens = []
	lemma = []
	orth = []
	ner = []
	gid = 0

	for line in reader:
	if not line:
	yield gid, {
	"tokens": tokens,
	"lemmas": lemma,
	"orth": orth,
	"ner": ner,
	}
	gid += 1
	tokens = []
	lemma = []
	orth = []
	ner = []

	elif len(line) == 1: # ignore DOCS
	continue

	else:
	tokens.append(line[0])
	lemma.append(line[1])
	orth.append(line[2])
	ner.append(line[3])