Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
Tagalog
Size:
1K<n<10K
License:
# 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 | |