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from pathlib import Path |
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from typing import Dict, List, Tuple |
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from seacrowd.utils.constants import Tasks |
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from seacrowd.utils import schemas |
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import datasets |
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from seacrowd.utils.configs import SEACrowdConfig |
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_CITATION = """\ |
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@inproceedings{siallagan2022sampiran, |
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title={Poetry Generation for Indonesian Pantun: Comparison Between SeqGAN and GPT-2}, |
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author={Emmanuella Anggi Siallagan and Ika Alfina}, |
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booktitle={Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) Vol 1x No x February 2023 (Minor Revision)}, |
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year={2023}, |
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} |
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""" |
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_DATASETNAME = "sampiran" |
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_DESCRIPTION = """\ |
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Sampiran is a dataset for pantun generation. It consists of 7.8K Indonesian pantun, collected from various sources (online). |
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Pantun is a traditional Malay poem consisting of four lines: two lines of deliverance and two lines of message. |
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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. |
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""" |
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_LANGUAGES = ["ind"] |
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_LOCAL = False |
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_HOMEPAGE = "https://github.com/ir-nlp-csui/sampiran" |
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_LICENSE = "AGPL-3.0" |
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_URLS = "https://raw.githubusercontent.com/ir-nlp-csui/sampiran/main/sampiran.txt" |
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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 SampiranDataset(datasets.GeneratorBasedBuilder): |
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"""Sampiran is a dataset for pantun generation. It consists of 7.8K Indonesian pantun, |
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collected from various sources (online).""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name="sampiran_source", |
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version=SOURCE_VERSION, |
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description="sampiran source schema", |
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schema="source", |
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subset_id="sampiran", |
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), |
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SEACrowdConfig( |
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name="sampiran_seacrowd_ssp", |
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version=SEACROWD_VERSION, |
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description="sampiran Nusantara schema", |
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schema="seacrowd_ssp", |
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subset_id="sampiran", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "sampiran_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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"pantun": 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( |
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self, dl_manager: datasets.DownloadManager |
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) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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filepath = Path(dl_manager.download(_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": filepath}, |
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), |
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] |
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def _read_data(self, filepath: Path) -> List[Dict]: |
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"""Reads the data from the source file and returns a list of dicts.""" |
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def _generate_examples(self, filepath: Path, split: str = None) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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if self.config.schema != "source" and self.config.schema != "seacrowd_ssp": |
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raise ValueError(f"Invalid config schema: {self.config.schema}") |
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if self.config.name == "sampiran_source": |
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with open(filepath, encoding="utf-8") as f: |
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for id_, row in enumerate(f): |
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ex = { |
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"id": str(id_), |
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"pantun": str(row).rstrip(), |
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} |
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yield id_, ex |
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elif self.config.name == "sampiran_seacrowd_ssp": |
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with open(filepath, encoding="utf-8") as f: |
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for id_, row in enumerate(f): |
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ex = {"id": str(id_), "text": str(row).rstrip()} |
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yield id_, ex |
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