Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
Tagalog
Size:
1K<n<10K
License:
File size: 4,553 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.
"""Dengue Dataset Low-Resource Multiclass Text Classification Dataset in Filipino"""
import csv
import os
import datasets
_DESCRIPTION = """\
Benchmark dataset for low-resource multiclass classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets.
"""
_CITATION = """\
@INPROCEEDINGS{8459963,
author={E. D. {Livelo} and C. {Cheng}},
booktitle={2018 IEEE International Conference on Agents (ICA)},
title={Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies},
year={2018},
volume={},
number={},
pages={2-7},
doi={10.1109/AGENTS.2018.8459963}}
}
"""
_HOMEPAGE = "https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
_URL = "https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/dengue/dengue_raw.zip"
class DengueFilipino(datasets.GeneratorBasedBuilder):
"""Dengue Dataset Low-Resource Multiclass Text Classification Dataset in Filipino"""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"text": datasets.Value("string"),
"absent": datasets.features.ClassLabel(names=["0", "1"]),
"dengue": datasets.features.ClassLabel(names=["0", "1"]),
"health": datasets.features.ClassLabel(names=["0", "1"]),
"mosquito": datasets.features.ClassLabel(names=["0", "1"]),
"sick": datasets.features.ClassLabel(names=["0", "1"]),
}
)
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)
train_path = os.path.join(data_dir, "dengue", "train.csv")
test_path = os.path.join(data_dir, "dengue", "train.csv")
validation_path = os.path.join(data_dir, "dengue", "valid.csv")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": train_path,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": test_path,
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": validation_path,
"split": "dev",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
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):
try:
text, absent, dengue, health, mosquito, sick = row
payload = {
"text": text,
"absent": absent,
"dengue": dengue,
"health": health,
"mosquito": mosquito,
"sick": sick,
}
yield id_, payload
except ValueError:
pass
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