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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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import pandas as pd |
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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{imperial-kochmar-2023-basahacorpus, |
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title = "{B}asaha{C}orpus: An Expanded Linguistic Resource for Readability Assessment in {C}entral {P}hilippine Languages", |
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author = "Imperial, Joseph Marvin and |
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Kochmar, Ekaterina", |
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editor = "Bouamor, Houda and |
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Pino, Juan and |
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Bali, Kalika", |
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booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2023", |
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address = "Singapore", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2023.emnlp-main.388", |
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doi = "10.18653/v1/2023.emnlp-main.388", |
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pages = "6302--6309", |
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} |
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""" |
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_DATASETNAME = "basaha_corpus" |
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_DESCRIPTION = """ |
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BasahaCorpus contains short stories in four Central Philippine languages \ |
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(Minasbate, Rinconada, Kinaray-a, and Hiligaynon) for low-resource \ |
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readability assessment. Each dataset per language contains stories \ |
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distributed over the first three grade levels (L1, L2, and L3) in \ |
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the Philippine education context. The grade levels of the dataset \ |
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have been provided by an expert from Let's Read Asia. |
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""" |
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_HOMEPAGE = "https://github.com/imperialite/BasahaCorpus-HierarchicalCrosslingualARA" |
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_LANGUAGES = [ |
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"msb", |
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"rin", |
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"kar", |
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"hil", |
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] |
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_LICENSE = Licenses.CC_BY_NC_SA_4_0.value |
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_LOCAL = False |
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_URLS = { |
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"msb": "https://raw.githubusercontent.com/imperialite/BasahaCorpus-HierarchicalCrosslingualARA/main/data/features/min_features.csv", |
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"rin": "https://raw.githubusercontent.com/imperialite/BasahaCorpus-HierarchicalCrosslingualARA/main/data/features/rin_features.csv", |
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"kar": "https://raw.githubusercontent.com/imperialite/BasahaCorpus-HierarchicalCrosslingualARA/main/data/features/kar_features.csv", |
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"hil": "https://raw.githubusercontent.com/imperialite/BasahaCorpus-HierarchicalCrosslingualARA/main/data/features/hil_features.csv", |
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} |
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_SUPPORTED_TASKS = [Tasks.READABILITY_ASSESSMENT] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class BasahaCorpusDataset(datasets.GeneratorBasedBuilder): |
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""" |
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BasahaCorpus comprises short stories in four Central Philippine |
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languages (Minasbate, Rinconada, Kinaray-a, and Hiligaynon) |
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for low-resource readability assessment. Each language dataset |
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includes stories from the first three grade levels (L1, L2, and L3) |
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in the Philippine education context, as classified by an expert |
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from Let's Read Asia. |
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""" |
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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 = [SEACrowdConfig(name=f"{_DATASETNAME}_{lang}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}_{lang}",) for lang in _LANGUAGES] + [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_{lang}_seacrowd_text", |
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version=datasets.Version(_SEACROWD_VERSION), |
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description=f"{_DATASETNAME} SEACrowd schema", |
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schema="seacrowd_text", |
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subset_id=f"{_DATASETNAME}_{lang}", |
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) |
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for lang in _LANGUAGES |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_msb_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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"book_title": datasets.Value("string"), |
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"word_count": datasets.Value("int64"), |
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"sentence_count": datasets.Value("int64"), |
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"phrase_count_per_sentence": datasets.Value("float64"), |
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"average_word_len": datasets.Value("float64"), |
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"average_sentence_len": datasets.Value("float64"), |
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"average_syllable_count": datasets.Value("float64"), |
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"polysyll_count": datasets.Value("int64"), |
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"consonant_cluster_density": datasets.Value("float64"), |
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"v_density": datasets.Value("float64"), |
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"cv_density": datasets.Value("float64"), |
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"vc_density": datasets.Value("float64"), |
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"cvc_density": datasets.Value("float64"), |
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"vcc_density": datasets.Value("float64"), |
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"cvcc_density": datasets.Value("float64"), |
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"ccvc_density": datasets.Value("float64"), |
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"ccv_density": datasets.Value("float64"), |
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"ccvcc_density": datasets.Value("float64"), |
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"ccvccc_density": datasets.Value("float64"), |
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"tag_bigram_sim": datasets.Value("float64"), |
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"bik_bigram_sim": datasets.Value("float64"), |
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"ceb_bigram_sim": datasets.Value("float64"), |
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"hil_bigram_sim": datasets.Value("float64"), |
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"rin_bigram_sim": datasets.Value("float64"), |
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"min_bigram_sim": datasets.Value("float64"), |
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"kar_bigram_sim": datasets.Value("float64"), |
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"tag_trigram_sim": datasets.Value("float64"), |
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"bik_trigram_sim": datasets.Value("float64"), |
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"ceb_trigam_sim": datasets.Value("float64"), |
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"hil_trigam_sim": datasets.Value("float64"), |
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"rin_trigam_sim": datasets.Value("float64"), |
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"min_trigam_sim": datasets.Value("float64"), |
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"kar_trigam_sim": datasets.Value("float64"), |
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"grade_level": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "seacrowd_text": |
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features = schemas.text_features(["1", "2", "3"]) |
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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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lang = self.config.name.split("_")[2] |
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if lang in _LANGUAGES: |
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data_path = Path(dl_manager.download_and_extract(_URLS[lang])) |
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else: |
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data_path = [Path(dl_manager.download_and_extract(_URLS[lang])) for lang in _LANGUAGES] |
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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_path, |
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"split": "train", |
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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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"""Yields examples as (key, example) tuples.""" |
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df = pd.read_csv(filepath, index_col=None) |
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for index, row in df.iterrows(): |
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if self.config.schema == "source": |
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example = row.to_dict() |
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elif self.config.schema == "seacrowd_text": |
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example = { |
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"id": str(index), |
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"text": str(row["book_title"]), |
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"label": str(row["grade_level"]), |
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} |
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yield index, example |
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