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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 (DEFAULT_SEACROWD_VIEW_NAME,
DEFAULT_SOURCE_VIEW_NAME, Tasks)
_DATASETNAME = "indo_puisi"
_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
_CITATION = """
"""
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_DESCRIPTION = """\
Puisi is an Indonesian poetic form. The dataset was collected by scraping various websites. It contains 7223 Indonesian puisi along with the title and author.
"""
_HOMEPAGE = "https://github.com/ilhamfp/puisi-pantun-generator"
_LICENSE = "Creative Commons Attribution Share-Alike 4.0 International"
_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
_URLS = {
"train": "https://raw.githubusercontent.com/ilhamfp/puisi-pantun-generator/main/data/puisi.csv",
}
class IndoPuisi(datasets.GeneratorBasedBuilder):
"""IndoPuisi contains 7223 Indonesian puisi along with the title and author."""
BUILDER_CONFIGS = (
SEACrowdConfig(
name="indo_puisi_source",
version=_SOURCE_VERSION,
description="Indo puisi source schema",
schema="source",
subset_id="indo_puisi",
),
SEACrowdConfig(
name="indo_puisi_seacrowd_ssp",
version=_SEACROWD_VERSION,
description="Indo puisi Nusantara schema",
schema="seacrowd_ssp",
subset_id="indo_puisi",
),
)
DEFAULT_CONFIG_NAME = "indo_puisi_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"puisi": datasets.Value("string"),
"title": datasets.Value("string"),
"author": datasets.Value("string"),
"puisi_with_header": datasets.Value("string"),
}
)
elif self.config.schema == "seacrowd_ssp":
features = schemas.self_supervised_pretraining.features
else:
raise ValueError(f"Invalid config schema: {self.config.schema}")
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."""
train_csv_path = Path(dl_manager.download(_URLS["train"]))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": train_csv_path},
),
]
def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
if self.config.schema != "source" and self.config.schema != "seacrowd_ssp":
raise ValueError(f"Invalid config schema: {self.config.schema}")
df = pd.read_csv(filepath).reset_index()
if self.config.name == "indo_puisi_source":
for row in df.itertuples():
ex = {
"id": str(row.index),
"puisi": str(row.puisi).rstrip(),
"title": row.title,
"author": row.author,
"puisi_with_header": str(row.puisi_with_header).rstrip(),
}
yield row.index, ex
elif self.config.name == "indo_puisi_seacrowd_ssp":
for row in df.itertuples():
ex = {"id": str(row.index), "text": str(row.puisi).rstrip()}
yield row.index, ex
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