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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
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks
_DATASETNAME = "melayu_sabah"
_DESCRIPTION = """\
Korpus Variasi Bahasa Melayu: Sabah is a language corpus sourced from various folklores in Melayu Sabah dialect.
"""
_CITATION = """\
@misc{melayusabah,
author = {Hiroki Nomoto},
title = {Melayu_Sabah},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/matbahasa/Melayu_Sabah}},
commit = {90a46c8268412ccc1f29cdcbbd47354474f12d50}
}
"""
_HOMEPAGE = "https://github.com/matbahasa/Melayu_Sabah"
_LANGUAGES = ["msi"]
_LICENSE = Licenses.CC_BY_4_0.value
_LOCAL = False
_URLS = {
"sabah201701": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201701.txt",
"sabah201702": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201702.txt",
"sabah201901": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201901.txt",
"sabah201902": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201902.txt",
"sabah201903": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201903.txt",
"sabah201904": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201904.txt",
"sabah201905": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201905.txt",
"sabah201906": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201906.txt",
"sabah201907": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201907.txt",
"sabah201908": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201908.txt",
"sabah201909": "https://raw.githubusercontent.com/matbahasa/Melayu_Sabah/master/Sabah201909.txt",
}
_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class MelayuSabah(datasets.GeneratorBasedBuilder):
"""Korpus Variasi Bahasa Melayu:
Sabah is a language corpus sourced from various folklores in Melayu Sabah dialect."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
SEACROWD_SCHEMA_NAME = TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=SOURCE_VERSION,
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=f"{_DATASETNAME}",
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
version=SEACROWD_VERSION,
description=f"{_DATASETNAME} SEACrowd schema",
schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
subset_id=f"{_DATASETNAME}",
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
}
)
elif self.config.schema == "seacrowd_ssp":
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."""
urls = [_URLS[key] for key in _URLS.keys()]
data_path = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_path[0], "split": "train", "other_path": data_path[1:]},
)
]
def _generate_examples(self, filepath: Path, split: str, other_path: List) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
filepaths = [filepath] + other_path
data = []
for filepath in filepaths[:2]:
with open(filepath, "r") as f:
sentences = [line.rstrip() for line in f.readlines()]
sentences = [sentence.split("\t")[-1] for sentence in sentences]
data.append("\n".join(sentences))
for filepath in filepaths[2:]:
with open(filepath, "r") as f:
data.append([line.rstrip() for line in f.readlines()])
for id, text in enumerate(data):
yield id, {"id": id, "text": text}
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