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