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# coding=utf-8
# Copyright 2022 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.
from pathlib import Path
from typing import Dict, List, Tuple
from seacrowd.utils.constants import Tasks
from seacrowd.utils import schemas
import datasets
from seacrowd.utils.configs import SEACrowdConfig
# TODO: Add BibTeX citation
_CITATION = """\
@inproceedings{siallagan2022sampiran,
title={Poetry Generation for Indonesian Pantun: Comparison Between SeqGAN and GPT-2},
author={Emmanuella Anggi Siallagan and Ika Alfina},
booktitle={Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) Vol 1x No x February 2023 (Minor Revision)},
year={2023},
}
"""
_DATASETNAME = "sampiran"
_DESCRIPTION = """\
Sampiran is a dataset for pantun generation. It consists of 7.8K Indonesian pantun, collected from various sources (online).
Pantun is a traditional Malay poem consisting of four lines: two lines of deliverance and two lines of message.
This dataset filtered the gathered Pantun to follow the general rules of Pantun; four lines with ABAB rhyme and eight to twelve syllables per line.
"""
_LANGUAGES = ["ind"]
_LOCAL = False
_HOMEPAGE = "https://github.com/ir-nlp-csui/sampiran"
_LICENSE = "AGPL-3.0"
_URLS = "https://raw.githubusercontent.com/ir-nlp-csui/sampiran/main/sampiran.txt"
_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class SampiranDataset(datasets.GeneratorBasedBuilder):
"""Sampiran is a dataset for pantun generation. It consists of 7.8K Indonesian pantun,
collected from various sources (online)."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name="sampiran_source",
version=SOURCE_VERSION,
description="sampiran source schema",
schema="source",
subset_id="sampiran",
),
SEACrowdConfig(
name="sampiran_seacrowd_ssp",
version=SEACROWD_VERSION,
description="sampiran Nusantara schema",
schema="seacrowd_ssp",
subset_id="sampiran",
),
]
DEFAULT_CONFIG_NAME = "sampiran_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"pantun": datasets.Value("string"),
}
)
elif self.config.schema == "seacrowd_ssp":
# e.g. features = schemas.kb_features
# TODO: Choose your seacrowd schema here
features = schemas.self_supervised_pretraining.features
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."""
filepath = Path(dl_manager.download(_URLS))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": filepath},
),
]
def _read_data(self, filepath: Path) -> List[Dict]:
"""Reads the data from the source file and returns a list of dicts."""
def _generate_examples(self, filepath: Path, split: str = None) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
if self.config.schema != "source" and self.config.schema != "seacrowd_ssp":
raise ValueError(f"Invalid config schema: {self.config.schema}")
# Read the file line by line
if self.config.name == "sampiran_source":
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
ex = {
"id": str(id_),
"pantun": str(row).rstrip(),
}
yield id_, ex
elif self.config.name == "sampiran_seacrowd_ssp":
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
ex = {"id": str(id_), "text": str(row).rstrip()}
yield id_, ex
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