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from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
from seacrowd.utils import schemas
import pandas as pd
_CITATION = """\
@INPROCEEDINGS{8629151,
author={Aliyah Salsabila, Nikmatun and Ardhito Winatmoko, Yosef and Akbar Septiandri, Ali and Jamal, Ade},
booktitle={2018 International Conference on Asian Language Processing (IALP)},
title={Colloquial Indonesian Lexicon},
year={2018},
volume={},
number={},
pages={226-229},
doi={10.1109/IALP.2018.8629151}}
"""
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_DATASETNAME = "kamus_alay"
_DESCRIPTION = """\
Kamus Alay provide a lexicon for text normalization of Indonesian colloquial words.
It contains 3,592 unique colloquial words-also known as “bahasa alay” -and manually annotated them
with the normalized form. We built this lexicon from Instagram comments provided by Septiandri & Wibisono (2017)
"""
_HOMEPAGE = "https://ieeexplore.ieee.org/abstract/document/8629151"
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://raw.githubusercontent.com/nasalsabila/kamus-alay/master/colloquial-indonesian-lexicon.csv",
}
_SUPPORTED_TASKS = [Tasks.MORPHOLOGICAL_INFLECTION]
# Dataset does not have versioning
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class KamusAlay(datasets.GeneratorBasedBuilder):
"""Kamus Alay is a dataset of lexicon for text normalization of Indonesian colloquial word"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
label_classes = [
"abreviasi",
"afiksasi",
"akronim",
"anaptiksis",
"coinage",
"elongasi",
"homofon",
"metatesis",
"modifikasi vokal",
"monoftongisasi",
"naturalisasi",
"pungtuasi",
"reduplikasi",
"salah ketik",
"subtitusi",
"word-value letter",
"zeroisasi",
]
BUILDER_CONFIGS = [
SEACrowdConfig(
name="kamus_alay_source",
version=SOURCE_VERSION,
description="Kamus Alay source schema",
schema="source",
subset_id="kamus_alay",
),
SEACrowdConfig(
name="kamus_alay_seacrowd_pairs_multi",
version=SEACROWD_VERSION,
description="Kamus Alay Nusantara schema",
schema="seacrowd_pairs_multi",
subset_id="kamus_alay",
),
]
DEFAULT_CONFIG_NAME = "kamus_alay_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"slang": datasets.Value("string"),
"formal": datasets.Value("string"),
"in_dictionary": datasets.Value("bool"),
"context": datasets.Value("string"),
"categories": datasets.Sequence(datasets.Value("string")),
}
)
elif self.config.schema == "seacrowd_pairs_multi":
features = schemas.pairs_multi_features(self.label_classes)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
urls = _URLS[_DATASETNAME]
data_dir = Path(dl_manager.download(urls))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir,
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
# Dataset does not have id, using row index as id
df = pd.read_csv(filepath, encoding="ISO-8859-1").reset_index()
df.columns = ["id", "slang", "formal", "is_in_dictionary", "example", "category1", "category2", "category3"]
if self.config.schema == "source":
for row in df.itertuples():
ex = {
"slang": row.slang,
"formal": row.formal,
"in_dictionary": row.is_in_dictionary,
"context": row.example,
"categories": [c for c in (row.category1, row.category2, row.category3) if c != "0"],
}
yield row.id, ex
elif self.config.schema == "seacrowd_pairs_multi":
for row in df.itertuples():
ex = {
"id": str(row.id),
"text_1": row.formal,
"text_2": row.slang,
"label": [c for c in (row.category1, row.category2, row.category3) if c != "0"],
}
yield row.id, ex
else:
raise ValueError(f"Invalid config: {self.config.name}")
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