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from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
_CITATION = """\
@article{hidayatullah2020attention,
title={Attention-based cnn-bilstm for dialect identification on javanese text},
author={Hidayatullah, Ahmad Fathan and Cahyaningtyas, Siwi and Pamungkas, Rheza Daffa},
journal={Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control},
pages={317--324},
year={2020}
}
"""
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_DATASETNAME = "jadi_ide"
_DESCRIPTION = """\
The JaDi-Ide dataset is a Twitter dataset for Javanese dialect identification, containing 16,498
data samples. The dialect is classified into `Standard Javanese`, `Ngapak Javanese`, and `East
Javanese` dialects.
"""
_HOMEPAGE = "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data"
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx",
}
# TODO check supported tasks
_SUPPORTED_TASKS = [Tasks.EMOTION_CLASSIFICATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class JaDi_Ide(datasets.GeneratorBasedBuilder):
"""The JaDi-Ide dataset is a Twitter dataset for Javanese dialect identification, containing 16,498
data samples."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name="jadi_ide_source",
version=SOURCE_VERSION,
description="JaDi-Ide source schema",
schema="source",
subset_id="jadi_ide",
),
SEACrowdConfig(
name="jadi_ide_seacrowd_text",
version=SEACROWD_VERSION,
description="JaDi-Ide Nusantara schema",
schema="seacrowd_text",
subset_id="jadi_ide",
),
]
DEFAULT_CONFIG_NAME = "jadi_ide_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string")
}
)
elif self.config.schema == "seacrowd_text":
features = schemas.text_features(["Jawa Timur", "Jawa Standar", "Jawa Ngapak"])
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."""
# Dataset does not have predetermined split, putting all as TRAIN
urls = _URLS[_DATASETNAME]
base_dir = Path(dl_manager.download(urls))
data_files = {"train": base_dir}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_files["train"],
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_excel(filepath)
df.columns = ["id", "text", "label"]
if self.config.schema == "source":
for idx, row in enumerate(df.itertuples()):
ex = {
"id": str(idx),
"text": row.text,
"label": row.label,
}
yield idx, ex
elif self.config.schema == "seacrowd_text":
for idx, row in enumerate(df.itertuples()):
ex = {
"id": str(idx),
"text": row.text,
"label": row.label,
}
yield idx, ex
else:
raise ValueError(f"Invalid config: {self.config.name}")
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