Datasets:

biglam
/

unsilence_voc

	# coding=utf-8
	# Copyright 2022 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
	"""UnSilenceVOC dataset"""


	import datasets
	import re
	from datasets import ClassLabel, Sequence, Value


	_CITATION = """\
	TODO
	"""

	_DESCRIPTION = """\
	TODO
	"""

	NE_MAIN_LABELS = [
	"B-Organization",
	"B-Organization,B-Place",
	"B-Organization,I-Person",
	"B-Organization,I-Place",
	"B-Person",
	"B-Person,B-Place",
	"B-Person,I-Place",
	"B-Place",
	"I-Organization",
	"I-Organization,B-Place",
	"I-Organization,I-Person",
	"I-Organization,I-Person,B-Place",
	"I-Organization,I-Person,I-Place",
	"I-Organization,I-Place",
	"I-Person",
	"I-Person,B-Place",
	"I-Person,I-Place",
	"I-Place",
	"O",
	]

	NE_PER_NAME = ["I-ProperName", "O", "B-ProperName", ""]
	NE_PER_GENDER = [
	"B-Group",
	"B-Man",
	"B-Man,B-Unspecified",
	"B-Man,I-Woman",
	"B-Unspecified",
	"B-Unspecified,I-Woman",
	"B-Woman",
	"I-Group",
	"I-Man",
	"I-Man,I-Unspecified",
	"I-Man,I-Woman",
	"I-Unspecified",
	"I-Unspecified,I-Woman",
	"I-Woman",
	"NE-PER-GENDER",
	"O",
	]

	NE_PER_LEGAL_STATUS = [
	"B-Enslaved",
	"B-Freed",
	"B-Unspecified",
	"I-Enslaved",
	"I-Freed",
	"I-Unspecified",
	"NE-PER-LEGAL-STATUS",
	"O",
	]

	NE_PER_ROLE = [
	"B-Acting_Notary",
	"B-Beneficiary",
	"B-Notary",
	"B-Other",
	"B-Testator",
	"B-Testator_Beneficiary",
	"B-Witness",
	"I-Acting_Notary",
	"I-Beneficiary",
	"I-Beneficiary,B-Other",
	"I-Beneficiary,I-Other",
	"I-Notary",
	"I-Other",
	"I-Testator",
	"I-Testator_Beneficiary",
	"I-Witness",
	"NE-PER-ROLE",
	"O",
	]

	NE_ORG_BENEFICIARY = [
	"B-No",
	"B-Yes",
	"I-No",
	"I-Yes",
	"NE-ORG-BENEFICIARY",
	"O",
	]

	_BASE_URL = (
	"https://raw.githubusercontent.com/budh333/UnSilence_VOC/main/processed_data"
	)

	_URLS = {
	"train": f"{_BASE_URL}/train-nl.tsv",
	"test": f"{_BASE_URL}/test-nl.tsv",
	"dev": f"{_BASE_URL}/dev-nl.tsv",
	}


	class UnSilenceVOC(datasets.GeneratorBasedBuilder):
	"""UnSilence VOC dataset."""

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"tokens": Sequence(datasets.Value("string")),
	"NE-MAIN": Sequence(
	ClassLabel(names=NE_MAIN_LABELS),
	),
	"NE-PER-NAME": Sequence(ClassLabel(names=NE_PER_NAME)),
	"NE-PER-GENDER": Sequence(ClassLabel(names=NE_PER_GENDER)),
	"NE-PER-LEGAL-STATUS": Sequence(
	ClassLabel(names=NE_PER_LEGAL_STATUS)
	),
	"NE-PER-ROLE": Sequence(ClassLabel(names=NE_PER_ROLE)),
	"NE-ORG-BENEFICIARY": Sequence(
	ClassLabel(names=NE_ORG_BENEFICIARY)
	),
	"MISC": Value("string"),
	"document_id": datasets.Value("string"),
	}
	),
	homepage="TODO",
	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"]},
	)
	]

	def _generate_examples(self, filepath):
	document_id_re = re.compile(r"# document_path = ..(\/.*.txt)")
	with open(filepath, encoding="utf-8") as f:
	guid = 0
	tokens = []
	NE_MAIN_LABELS = []
	NE_PER_NAME = []
	NE_PER_GENDER = []
	NE_PER_LEGAL_STATUS = []
	NE_PER_ROLE = []
	NE_ORG_BENEFICIARY = []
	MISC = []

	for line in f:
	if line.startswith("TOKEN"):
	continue
	if line.startswith("#") or line.startswith("\t"):
	document_id_match = re.search(document_id_re, line)
	if document_id_match:
	document_id = document_id_match.groups(0)[0]

	if not tokens:
	continue
	yield guid, {
	"tokens": tokens,
	"NE-MAIN": NE_MAIN_LABELS,
	"NE-PER-NAME": NE_PER_NAME,
	"NE-PER-GENDER": NE_PER_GENDER,
	"NE-PER-LEGAL-STATUS": NE_PER_LEGAL_STATUS,
	"NE-PER-ROLE": NE_PER_ROLE,
	"NE-ORG-BENEFICIARY": NE_ORG_BENEFICIARY,
	"MISC": MISC,
	"document_id": document_id,
	}
	guid += 1
	tokens = []
	NE_MAIN_LABELS = []
	NE_PER_NAME = []
	NE_PER_GENDER = []
	NE_PER_LEGAL_STATUS = []
	NE_PER_ROLE = []
	NE_ORG_BENEFICIARY = []
	MISC = []
	else:
	# tokens are tab separated
	splits = line.split("\t")
	tokens.append(splits[0])
	NE_MAIN_LABELS.append(splits[1])
	NE_PER_NAME.append(splits[2])
	NE_PER_GENDER.append(splits[3])
	NE_PER_LEGAL_STATUS.append(splits[4])
	NE_PER_ROLE.append(splits[5])
	NE_ORG_BENEFICIARY.append(splits[6])
	MISC.append(splits[-1].replace("\n", ""))

	# last example
	yield guid, {
	"tokens": tokens,
	"NE-MAIN": NE_MAIN_LABELS,
	"NE-PER-NAME": NE_PER_NAME,
	"NE-PER-GENDER": NE_PER_GENDER,
	"NE-PER-LEGAL-STATUS": NE_PER_LEGAL_STATUS,
	"NE-PER-ROLE": NE_PER_ROLE,
	"NE-ORG-BENEFICIARY": NE_ORG_BENEFICIARY,
	"MISC": MISC,
	"document_id": "document_id",
	}