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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 datasets.download.download_manager import DownloadManager |
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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 Licenses, Tasks |
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_CITATION = """ |
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@inproceedings{flores-radev-2022-look, |
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title = "Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in {F}ilipino", |
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author = "Flores, Lorenzo Jaime and |
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Radev, Dragomir", |
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booktitle = "Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)", |
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month = dec, |
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year = "2022", |
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address = "Abu Dhabi, United Arab Emirates (Hybrid)", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.sustainlp-1.5", |
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pages = "29--35", |
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} |
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""" |
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_LOCAL = False |
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_LANGUAGES = ["fil"] |
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_DATASETNAME = "filipino_slang_norm" |
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_DESCRIPTION = """\ |
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This dataset contains 398 abbreviated and/or contracted Filipino words used in |
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Facebook comments made on weather advisories from a Philippine weather bureau. |
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volunteers. |
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""" |
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_HOMEPAGE = "https://github.com/ljyflores/efficient-spelling-normalization-filipino" |
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_LICENSE = Licenses.UNKNOWN.value |
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_URLS = { |
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"train": "https://github.com/ljyflores/efficient-spelling-normalization-filipino/raw/main/data/train_words.csv", |
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"test": "https://github.com/ljyflores/efficient-spelling-normalization-filipino/raw/main/data/test_words.csv", |
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} |
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_SUPPORTED_TASKS = [Tasks.MULTILEXNORM] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class FilipinoSlangNormDataset(datasets.GeneratorBasedBuilder): |
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"""Filipino Slang Norm dataset by Flores and Radev (2022)""" |
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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 = "t2t" |
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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=_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=_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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"src_sent": datasets.Value("string"), |
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"norm_sent": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": |
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features = schemas.text2text_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: DownloadManager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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data_files = { |
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"train": Path(dl_manager.download_and_extract(_URLS["train"])), |
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"test": Path(dl_manager.download_and_extract(_URLS["test"])), |
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} |
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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={ |
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"filepath": data_files["train"], |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": data_files["test"], |
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"split": "test", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: |
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"""Yield examples as (key, example) tuples""" |
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with open(filepath, encoding="utf-8") as f: |
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for guid, line in enumerate(f): |
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src_sent, norm_sent = line.strip("\n").split(",") |
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if self.config.schema == "source": |
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example = { |
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"id": str(guid), |
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"src_sent": src_sent, |
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"norm_sent": norm_sent, |
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} |
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elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": |
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example = { |
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"id": str(guid), |
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"text_1": src_sent, |
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"text_2": norm_sent, |
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"text_1_name": "src_sent", |
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"text_2_name": "norm_sent", |
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} |
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yield guid, example |
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