Datasets:

rcds
/

wikipedia-for-mask-filling

	# dataset loading script for huggingface
	import datasets
	import json
	try:
	import lzma as xz
	except ImportError:
	import pylzma as xz

	datasets.logging.set_verbosity_info()
	logger = datasets.logging.get_logger(__name__)

	_DESCRIPTION = """\
	"""

	_HOMEPAGE = "https://skatinger.github.io/master_thesis/"

	_LICENSE = ""

	_CITATION = ""

	_TYPES = ["original", "paraphrased"]

	_SIZES = [4096, 512]

	_URLS = {
	"original_4096": "data/original_4096.jsonl.xz",
	"original_512": "data/original_512.jsonl.xz",
	"paraphrased_4096": "data/paraphrased_4096.jsonl.xz",
	"paraphrased_512": "data/paraphrased_512.jsonl.xz"
	}


	class WikipediaForMaskFillingConfig(datasets.BuilderConfig):
	"""BuilderConfig for WikipediaForMaskFilling.

	features: list[string], list of the features that will appear in the
	feature dict. Should not include "label".
	**kwargs: keyword arguments forwarded to super
	"""

	def __init__(self, type:str, size=4096, **kwargs):
	"""BuilderConfig for WikipediaForMaskFilling.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""

	# Version history:
	# 1.0.0: first version
	super(WikipediaForMaskFillingConfig, self).__init__(**kwargs)
	self.size = size
	self.type = type


	class WikipediaForMaskFilling(datasets.GeneratorBasedBuilder):
	"""WikipediaForMaskFilling dataset."""

	BUILDER_CONFIGS = [
	WikipediaForMaskFillingConfig(
	name="original_4096",
	version=datasets.Version("1.0.0"),
	description="Part of the dataset with original texts and masks, with text chunks split into size of max 4096 tokens (Longformer).",
	size=4096,
	type="original"
	),
	WikipediaForMaskFillingConfig(
	name="original_512",
	version=datasets.Version("1.0.0"),
	description="text chunks split into size of max 512 tokens (roberta).",
	size=512,
	type="original"
	),
	WikipediaForMaskFillingConfig(
	name="paraphrased_4096",
	version=datasets.Version("1.0.0"),
	description="Part of the dataset with paraphrased texts and masks, with text chunks split into size of max 4096 tokens (Longformer).",
	size=4096,
	type="paraphrased"
	),
	WikipediaForMaskFillingConfig(
	name="paraphrased_512",
	version=datasets.Version("1.0.0"),
	description="Paraphrased text chunks split into size of max 512 tokens (roberta).",
	size=512,
	type="paraphrased"
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("int32"),
	"sequence_number": datasets.Value("int32"),
	"title": datasets.Value("string"),
	"type": datasets.Value("string"),
	"size": datasets.Value("int32"),
	"texts": datasets.Value("string"),
	"masks": datasets.Sequence(datasets.Value("string")),
	}
	),
	# No default supervised_keys (as we have to pass both question
	# and context as input).
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	type = self.config.type
	size = self.config.size
	filepath = dl_manager.download(f"data/{type}_{size}.jsonl.xz")

	return [
	datasets.SplitGenerator(name='train', gen_kwargs={"filepath": filepath}),
	]

	def _generate_examples(self, filepath):
	id_ = 0
	if filepath:
	logger.info("Generating examples from = %s", filepath)
	try:
	with xz.open(open(filepath,'rb'), 'rt', encoding='utf-8') as f:
	json_list = list(f)

	for json_str in json_list:
	data = json.loads(json_str)
	if data is not None and isinstance(data, dict):
	yield id_, {
	"id": data["id"],
	"sequence_number": data["sequence_number"],
	"title": data["title"],
	"type": data["type"],
	"size": data["size"],
	"texts": data["texts"],
	"masks": data["masks"]
	}
	id_ +=1

	except Exception:
	logger.exception("Error while processing file %s", filepath)