Datasets:
File size: 4,195 Bytes
9918791 |
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 |
# coding=utf-8
# Copyright 2020 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
"""DanFEVER: A FEVER dataset for Danish"""
import csv
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{norregaard-derczynski-2021-danfever,
title = "{D}an{FEVER}: claim verification dataset for {D}anish",
author = "N{\o}rregaard, Jeppe and
Derczynski, Leon",
booktitle = "Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may # " 31--2 " # jun,
year = "2021",
address = "Reykjavik, Iceland (Online)",
publisher = {Link{\"o}ping University Electronic Press, Sweden},
url = "https://aclanthology.org/2021.nodalida-main.47",
pages = "422--428",
abstract = "We present a dataset, DanFEVER, intended for multilingual misinformation research. The dataset is in Danish and has the same format as the well-known English FEVER dataset. It can be used for testing methods in multilingual settings, as well as for creating models in production for the Danish language.",
}
"""
_DESCRIPTION = """\
"""
_URL = "https://media.githubusercontent.com/media/StrombergNLP/danfever/main/tsv/da_fever.tsv"
class DanFeverConfig(datasets.BuilderConfig):
"""BuilderConfig for DanFever"""
def __init__(self, **kwargs):
"""BuilderConfig DanFever.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(DanFeverConfig, self).__init__(**kwargs)
class DanFever(datasets.GeneratorBasedBuilder):
"""DanFever dataset."""
BUILDER_CONFIGS = [
DanFeverConfig(name="DanFever", version=datasets.Version("1.0.0"), description="FEVER dataset for Danish"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"claim": datasets.Value("string"),
"label": datasets.features.ClassLabel(
names=[
"Refuted",
"Supported",
"NotEnoughInfo",
]
),
"evidence_extract": datasets.Value("string"),
"verifiable": datasets.features.ClassLabel(
names=[
"NotVerifiable",
"Verifiable",
]
),
"evidence": datasets.Value("string"),
"original_id": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://stromberg.ai/publication/danfever/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_file = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
data_reader = csv.DictReader(f, delimiter="\t", quotechar='"')
guid = 0
for instance in data_reader:
instance.pop('nr.')
instance["original_id"] = instance.pop('id')
instance["id"] = str(guid)
yield guid, instance
guid += 1
|