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from typing import Dict, List, Tuple
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
_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}}
}
"""
_LANGUAGES = ["fil"]
# copied from https://huggingface.co/datasets/dengue_filipino/blob/main/dengue_filipino.py
_URL = "https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/dengue/dengue_raw.zip"
_DATASETNAME = "dengue_filipino"
_DESCRIPTION = """\
Benchmark dataset for low-resource multi-label 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.
"""
_HOMEPAGE = "https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks"
_LICENSE = Licenses.UNKNOWN.value
_SUPPORTED_TASKS = [Tasks.DOMAIN_KNOWLEDGE_MULTICLASSIFICATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
_LOCAL = False
class DengueFilipinoDataset(datasets.GeneratorBasedBuilder):
"""Dengue Dataset Low-Resource Multi-label Text Classification Dataset in Filipino"""
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=datasets.Version(_SOURCE_VERSION),
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=f"{_DATASETNAME}",
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_text_multi",
version=datasets.Version(_SEACROWD_VERSION),
description=f"{_DATASETNAME} SEACrowd schema text multi",
schema="seacrowd_text_multi",
subset_id=f"{_DATASETNAME}",
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
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"]),
}
)
elif self.config.schema == "seacrowd_text_multi":
features = schemas.text_multi_features(["0", "1"])
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"split": "test",
},
),
]
def _generate_examples(self, split: str) -> Tuple[int, Dict]:
dataset = datasets.load_dataset(_DATASETNAME, split=split)
for id, data in enumerate(dataset):
if self.config.schema == "source":
yield id, {
"text": data["text"],
"absent": data["absent"],
"dengue": data["dengue"],
"health": data["health"],
"mosquito": data["mosquito"],
"sick": data["sick"],
}
elif self.config.schema == "seacrowd_text_multi":
yield id, {
"id": id,
"text": data["text"],
"labels": [
data["absent"],
data["dengue"],
data["health"],
data["mosquito"],
data["sick"],
],
}
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