Datasets:

wardenga
/

lsoie

	# -- coding: utf-8 --
	"""LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction."""
	import os
	import datasets
	from datasets.info import SupervisedKeysData
	from zipfile import ZipFile
	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@article{lsoie-2021,
	title={{LSOIE}: A Large-Scale Dataset for Supervised Open Information Extraction},
	author={{Solawetz}, Jacob and {Larson}, Stefan},
	journal={arXiv preprint arXiv:2101.11177},
	year={2019},
	url="https://arxiv.org/pdf/2101.11177.pdf"
	}
	"""

	_DESCRIPTION = """
	The Large Scale Open Information Extraction Dataset (LSOIE), is a dataset 20
	times larger than the next largest human-annotated Open Information Extraction
	(OIE) dataset. LSOIE is a built upon the QA-SRL 2.0 dataset.
	"""

	_URL = "https://github.com/Jacobsolawetz/large-scale-oie/"
	_URLS = {
	"zip": _URL+"raw/master/dataset_creation/lsoie_data/lsoie_data.zip"
	}
	_ARCHIVE_FILES = [
	"lsoie_science_train.conll",
	"lsoie_science_dev.conll",
	"lsoie_science_test.conll",
	"lsoie_wiki_train.conll",
	"lsoie_wiki_dev.conll",
	"lsoie_wiki_test.conll",
	]


	class LsoieConfig(datasets.BuilderConfig):
	"""BuilderConfig for LSOIE."""

	def __init__(self,subset="wiki", **kwargs):
	"""BuilderConfig for LSOIE.
	Args:
	subset: str - either "wiki" or "science"
	**kwargs: keyword arguments forwarded to super.
	"""
	super(LsoieConfig, self).__init__(**kwargs)
	self.subset=subset


	class Lsoie(datasets.GeneratorBasedBuilder):
	"""LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction"""

	BUILDER_CONFIGS = [
	LsoieConfig(
	name="wiki",
	description="LSOIE dataset from wikipedia and wikinews",
	subset="wiki",
	),
	LsoieConfig(
	name="sci",
	description="LSOIE dataset build over scientific domain",
	subset="science",
	),
	]

	DEFAULT_CONFIG_NAME = "wiki"

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"word_ids": datasets.Sequence(datasets.Value("int16")),
	"words": datasets.Sequence(datasets.Value("string")),
	"pred": datasets.Value("string"),
	"pred_ids": datasets.Sequence(datasets.Value("int16")),
	"head_pred_id": datasets.Value("int16"),
	"sent_id": datasets.Value("int16"),
	"run_id": datasets.Value("int16"),
	"label": datasets.Sequence(datasets.Value("string")),
	}
	),
	supervised_keys=SupervisedKeysData(input="word_ids",output="label"),
	homepage=_URL,
	citation=_CITATION,
	#there is no default task for open information extraction yet
	#task_templates=[
	# OpenInformationExtraction(
	# question_column="question", context_column="context", answers_column="answers"
	# )
	#],
	)

	def _split_generators(self, dl_manager):
	downloaded_archive = dl_manager.download(_URLS)['zip']
	#name_pre=os.path.join("lsoie_data","lsoie_")+self.config.subset+"_"
	name_pre="lsoie_"+self.config.subset+"_"
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN,
	gen_kwargs={
	"archive_path": downloaded_archive,
	"file_name": name_pre+"train.conll",
	}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION,
	gen_kwargs={
	"archive_path": downloaded_archive,
	"file_name": name_pre+"dev.conll",
	}),
	datasets.SplitGenerator(name=datasets.Split.TEST,
	gen_kwargs={
	"archive_path": downloaded_archive,
	"file_name": name_pre+"test.conll",
	}),
	]

	def _generate_examples(self,archive_path,file_name):
	"""This functions returns the samples in a raw format"""
	logger.info("generating examples from archive:{}".format(archive_path))
	columns={'word_ids':int,
	'words':str,
	'pred':str,
	'pred_ids':lambda x: [ num for num in x.strip('[]').split(',')],
	'head_pred_id': int,
	'sent_id':int,
	'run_id': int,
	'label':str}
	list_columns=["word_ids","words","label"]
	sep="\t"
	key=0
	sentence=dict()
	for column in list_columns:
	sentence[column]=[]
	with ZipFile(archive_path) as zipfile:
	with zipfile.open('lsoie_data/'+file_name,mode='r') as file:
	for line in file:
	line=line.decode("utf-8").strip('\n').split(sep=sep)
	if line[0]=='':
	yield key, sentence
	key+=1
	for column in list_columns:
	sentence[column]=[]
	continue
	for column, val in zip(columns.keys(),line):
	val=columns[column](val)
	if column in list_columns:
	sentence[column].append(val)
	else:
	sentence[column]=val