Datasets:
File size: 7,076 Bytes
607626b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""Dataset for explainable fake news detection of public health claims."""
import csv
import os
import datasets
_CITATION = """\
@inproceedings{kotonya-toni-2020-explainable,
title = "Explainable Automated Fact-Checking for Public Health Claims",
author = "Kotonya, Neema and Toni, Francesca",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods
in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.623",
pages = "7740--7754",
}
"""
_DESCRIPTION = """\
PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of
public health claims. Each instance in the PUBHEALTH dataset has an associated
veracity label (true, false, unproven, mixture). Furthermore each instance in the
dataset has an explanation text field. The explanation is a justification for which
the claim has been assigned a particular veracity label.
The dataset was created to explore fact-checking of difficult to verify claims i.e.,
those which require expertise from outside of the journalistics domain, in this case
biomedical and public health expertise.
It was also created in response to the lack of fact-checking datasets which provide
gold standard natural language explanations for verdicts/labels.
NOTE: There are missing labels in the dataset and we have replaced them with -1.
"""
_DATA_URL = "https://drive.google.com/uc?export=download&id=1eTtRs5cUlBP5dXsx-FTAlmXuB6JQi2qj"
_TEST_FILE_NAME = "PUBHEALTH/test.tsv"
_TRAIN_FILE_NAME = "PUBHEALTH/train.tsv"
_VAL_FILE_NAME = "PUBHEALTH/dev.tsv"
class HealthFact(datasets.GeneratorBasedBuilder):
"""Dataset for explainable fake news detection of public health claims."""
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=datasets.Features(
{
"claim_id": datasets.Value("string"),
"claim": datasets.Value("string"),
"date_published": datasets.Value("string"),
"explanation": datasets.Value("string"),
"fact_checkers": datasets.Value("string"),
"main_text": datasets.Value("string"),
"sources": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["false", "mixture", "true", "unproven"]),
"subjects": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/neemakot/Health-Fact-Checking/blob/master/data/DATASHEET.md",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_DATA_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, _TRAIN_FILE_NAME),
"split": datasets.Split.TRAIN,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, _TEST_FILE_NAME),
"split": datasets.Split.TEST,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, _VAL_FILE_NAME),
"split": datasets.Split.VALIDATION,
},
),
]
def _generate_examples(self, filepath, split):
with open(filepath, encoding="utf-8") as f:
label_list = ["false", "mixture", "true", "unproven"]
data = csv.reader(f, delimiter="\t")
next(data, None) # skip the headers
for row_id, row in enumerate(data):
row = [x if x != "nan" else "" for x in row] # nan values changed to empty string
if split != "test":
if len(row) <= 9:
elements = ["" for x in range(9 - len(row))]
row = row + elements
(
claim_id,
claim,
date_published,
explanation,
fact_checkers,
main_text,
sources,
label,
subjects,
) = row
if label not in label_list: # remove stray labels in dev.tsv, train.tsv
label = -1
else:
if len(row) <= 10:
elements = ["" for x in range(10 - len(row))]
row = row + elements
(
_,
claim_id,
claim,
date_published,
explanation,
fact_checkers,
main_text,
sources,
label,
subjects,
) = row
if label not in label_list: # remove stray labels in test.tsv
label = -1
if label == "":
label = -1
yield row_id, {
"claim_id": claim_id,
"claim": claim,
"date_published": date_published,
"explanation": explanation,
"fact_checkers": fact_checkers,
"main_text": main_text,
"sources": sources,
"label": label,
"subjects": subjects,
}
|