Datasets:

strombergnlp
/

dkstance

	# coding=utf-8
	# Copyright 2022 Leon Derczynski, 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
	"""Danish Stance Dataset DAST"""

	from collections import defaultdict
	import glob
	import json
	import os
	import sys

	import datasets

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """\
	@inproceedings{lillie-etal-2019-joint,
	title = "Joint Rumour Stance and Veracity Prediction",
	author = "Lillie, Anders Edelbo and
	Middelboe, Emil Refsgaard and
	Derczynski, Leon",
	booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
	month = sep # "{--}" # oct,
	year = "2019",
	address = "Turku, Finland",
	publisher = {Link{\"o}ping University Electronic Press},
	url = "https://aclanthology.org/W19-6122",
	pages = "208--221",
	}
	"""

	_DESCRIPTION = """\
	This dataset presents a series of stories on Reddit and the conversation around
	them, annotated for stance. Stories are also annotated for veracity.

	For more details see https://aclanthology.org/W19-6122/
	"""

	_URL = "dast.jsonl"


	class DastConfig(datasets.BuilderConfig):
	"""BuilderConfig for IPM NEL"""

	def __init__(self, **kwargs):
	"""BuilderConfig for IPM NEL.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(DastConfig, self).__init__(**kwargs)



	class Dast(datasets.GeneratorBasedBuilder):


	"""Dast dataset."""

	BUILDER_CONFIGS = [
	DastConfig(name="dkstance", version=datasets.Version("1.0.0"), description="Danish Stance"),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"native_id": datasets.Value("string"),
	"text": datasets.Value("string"),
	"parent_id": datasets.Value("string"),
	"parent_text": datasets.Value("string"),
	"parent_stance": datasets.features.ClassLabel(
	names=[
	"Supporting",
	"Denying",
	"Querying",
	"Commenting",
	]
	),
	"source_id": datasets.Value("string"),
	"source_text": datasets.Value("string"),
	"source_stance": datasets.features.ClassLabel(
	names=[
	"Supporting",
	"Denying",
	"Querying",
	"Commenting",
	]
	),

	}
	),
	supervised_keys=None,
	homepage="https://aclanthology.org/W19-6122/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	downloaded_file = dl_manager.download_and_extract(_URL)
	print(downloaded_file)
	data_files = {
	"dast": downloaded_file,
	}

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files['dast'], "split":"train"}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files['dast'], "split":"validation"}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files['dast'], "split":"test"}),
	]


	def unpack(self, entry, parent_id = None, source_id = None):

	if isinstance(entry, dict):
	e = entry['comment']
	original_id = e['comment_id']
	text = e['text']
	parent_id = e['parent_id']
	parent_stance = e['SDQC_Parent']
	source_id = e['submission_id']
	source_stance = e['SDQC_Submission']

	self.texts[original_id] = text

	instance = {
	"id":self.guid,
	"native_id":original_id,
	"text":text,
	"parent_id":parent_id,
	"parent_text":self.texts[parent_id],
	"parent_stance":parent_stance,
	"source_id":source_id,
	"source_text":self.texts[source_id],
	"source_stance":source_stance,
	}

	self.id_mapper[e['comment_id']] = self.guid
	self.guid += 1
	yield instance

	elif isinstance(entry, list):
	for sub_entry in entry:
	yield from self.unpack(sub_entry, parent_id=parent_id, source_id=source_id)


	def process_block(self, block):

	j = json.loads(block)
	s = j['redditSubmission']
	descr = s['RumourDescription']
	source_id = s['submission_id']
	#print(i, '', descr, '', '', s['title'], s['SourceSDQC'])
	self.id_mapper[source_id] = self.guid
	self.guid += 1
	self.texts[source_id] = s['title']

	yield from self.unpack(j['branches'], source_id = 0, parent_id = 0)


	def _generate_examples(self, filepath, split):
	logger.info("⏳ Generating %s examples from = %s", (split, filepath))

	def _deleted():
	return "[deleted]"

	self.guid = 0
	self.id_mapper = {}
	self.texts = defaultdict(_deleted)

	partition_sources = ()
	if split == 'train':
	partition_sources = ('8sjevz', 'a0954m', 'a1gsmt', 'a2fpjr', 'a6o3us', 'ax70y5', 'axnshu', 'b23eat', 'b2xrgd', 'b72gok', 'b7aybw', 'b7ohqt', 'bb9iqt')
	elif split == 'validation':
	partition_sources = ('6v1ivh', '76y6rb', '7r9ouo', '8192oe', '83l9nm', '8agt1s', '8clb74', '8k6lcb')
	elif split == 'test':
	partition_sources = ('3qc12m', '3ud5z9', '53u5j7', '5emjyw', '5pfq1r', '5t1h6y', '60il0b', '67c2zf', '6jqtkm', '6nz7dy', '6szxwj', '6tm5kp')

	with open(filepath, 'r', encoding="utf-8") as dastfile:
	for line in dastfile:
	instances = self.process_block(line.strip())
	for instance in instances:
	if instance['source_id'] in partition_sources:
	yield instance['id'], instance