Datasets:
Tasks:
Text Classification
Sub-tasks:
text-scoring
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Source Datasets:
original
License:
File size: 7,013 Bytes
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# 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.
"""This is humorous headline dataset called Humicroedit introduced in the Task-7 of SemEval 2020."""
import csv
import os
import datasets
_CITATION = """\
@article{hossain2019president,
title={" President Vows to Cut< Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines},
author={Hossain, Nabil and Krumm, John and Gamon, Michael},
journal={arXiv preprint arXiv:1906.00274},
year={2019}
}
"""
_DESCRIPTION = """\
This new dataset is designed to assess the funniness of edited news headlines.
"""
_HOMEPAGE = "https://www.cs.rochester.edu/u/nhossain/humicroedit.html"
_LICENSE = ""
_URL = "https://cs.rochester.edu/u/nhossain/semeval-2020-task-7-dataset.zip"
class Humicroedit(datasets.GeneratorBasedBuilder):
"""This is humorous headline dataset called Humicroedit introduced in the Task-7 of SemEval 2020."""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="subtask-1", description="This part of the dataset covers the data for subtask-1"),
datasets.BuilderConfig(name="subtask-2", description="This part of the dataset covers the data for subtask-2"),
]
def _info(self):
if self.config.name == "subtask-1":
features = datasets.Features(
{
"id": datasets.Value("string"),
"original": datasets.Value("string"),
"edit": datasets.Value("string"),
"grades": datasets.Value("string"),
"meanGrade": datasets.Value("float"),
# These are the features of your dataset like images, labels ...
}
)
else:
features = datasets.Features(
{
"id": datasets.Value("string"),
"original1": datasets.Value("string"),
"edit1": datasets.Value("string"),
"grades1": datasets.Value("string"),
"meanGrade1": datasets.Value("float"),
"original2": datasets.Value("string"),
"edit2": datasets.Value("string"),
"grades2": datasets.Value("string"),
"meanGrade2": datasets.Value("float"),
"label": datasets.ClassLabel(names=["equal", "sentence1", "sentence2"]),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_URL)
ROOT = "semeval-2020-task-7-dataset"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, ROOT, self.config.name, "train.csv"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, ROOT, self.config.name, "test.csv"), "split": "test"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, ROOT, self.config.name, "dev.csv"),
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split("funlines"),
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, ROOT, self.config.name, "train_funlines.csv"),
"split": "funlines",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
label_names = ["equal", "sentence1", "sentence2"]
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.reader(
csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
)
next(csv_reader)
for id_, row in enumerate(csv_reader):
if self.config.name == "subtask-1":
row_id, original, edit, grades, meanGrade = row
yield id_, {
"id": row_id,
"original": original,
"edit": edit,
"grades": grades,
"meanGrade": meanGrade,
}
else:
row_id, original1, edit1, grades1, meanGrade1, original2, edit2, grades2, meanGrade2, label = row
yield id_, {
"id": row_id,
"original1": original1,
"edit1": edit1,
"grades1": grades1,
"meanGrade1": meanGrade1,
"original2": original2,
"edit2": edit2,
"grades2": grades2,
"meanGrade2": meanGrade2,
"label": label_names[int(label)],
}
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