Datasets:
Tasks:
Text Classification
Sub-tasks:
fact-checking
Languages:
Danish
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
License:
File size: 6,871 Bytes
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# 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 |