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basaha_corpus.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{imperial-kochmar-2023-basahacorpus,
+    title = "{B}asaha{C}orpus: An Expanded Linguistic Resource for Readability Assessment in {C}entral {P}hilippine Languages",
+    author = "Imperial, Joseph Marvin  and
+      Kochmar, Ekaterina",
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.emnlp-main.388",
+    doi = "10.18653/v1/2023.emnlp-main.388",
+    pages = "6302--6309",
+    }
+"""
+
+_DATASETNAME = "basaha_corpus"
+
+_DESCRIPTION = """
+BasahaCorpus contains short stories in four Central Philippine languages \
+    (Minasbate, Rinconada, Kinaray-a, and Hiligaynon) for low-resource \
+    readability assessment. Each dataset per language contains stories \
+    distributed over the first three grade levels (L1, L2, and L3) in \
+    the Philippine education context. The grade levels of the dataset \
+    have been provided by an expert from Let's Read Asia.
+"""
+_HOMEPAGE = "https://github.com/imperialite/BasahaCorpus-HierarchicalCrosslingualARA"
+
+_LANGUAGES = [
+    "msb",
+    "rin",
+    "kar",
+    "hil",
+]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.CC_BY_NC_SA_4_0.value
+
+_LOCAL = False
+
+_URLS = {
+    # Minasbate, Rinconada, Kinaray-a, and Hiligaynon (from the _DESCRIPTION)
+    "msb": "https://raw.githubusercontent.com/imperialite/BasahaCorpus-HierarchicalCrosslingualARA/main/data/features/min_features.csv",
+    "rin": "https://raw.githubusercontent.com/imperialite/BasahaCorpus-HierarchicalCrosslingualARA/main/data/features/rin_features.csv",
+    "kar": "https://raw.githubusercontent.com/imperialite/BasahaCorpus-HierarchicalCrosslingualARA/main/data/features/kar_features.csv",
+    "hil": "https://raw.githubusercontent.com/imperialite/BasahaCorpus-HierarchicalCrosslingualARA/main/data/features/hil_features.csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.READABILITY_ASSESSMENT]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class BasahaCorpusDataset(datasets.GeneratorBasedBuilder):
+    """
+    BasahaCorpus comprises short stories in four Central Philippine
+    languages (Minasbate, Rinconada, Kinaray-a, and Hiligaynon)
+    for low-resource readability assessment. Each language dataset
+    includes stories from the first three grade levels (L1, L2, and L3)
+    in the Philippine education context, as classified by an expert
+    from Let's Read Asia.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    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] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{lang}_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_text",
+            subset_id=f"{_DATASETNAME}_{lang}",
+        )
+        for lang in _LANGUAGES
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_msb_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+
+            features = datasets.Features(
+                {
+                    "book_title": datasets.Value("string"),
+                    "word_count": datasets.Value("int64"),
+                    "sentence_count": datasets.Value("int64"),
+                    "phrase_count_per_sentence": datasets.Value("float64"),
+                    "average_word_len": datasets.Value("float64"),
+                    "average_sentence_len": datasets.Value("float64"),
+                    "average_syllable_count": datasets.Value("float64"),
+                    "polysyll_count": datasets.Value("int64"),
+                    "consonant_cluster_density": datasets.Value("float64"),
+                    "v_density": datasets.Value("float64"),
+                    "cv_density": datasets.Value("float64"),
+                    "vc_density": datasets.Value("float64"),
+                    "cvc_density": datasets.Value("float64"),
+                    "vcc_density": datasets.Value("float64"),
+                    "cvcc_density": datasets.Value("float64"),
+                    "ccvc_density": datasets.Value("float64"),
+                    "ccv_density": datasets.Value("float64"),
+                    "ccvcc_density": datasets.Value("float64"),
+                    "ccvccc_density": datasets.Value("float64"),
+                    "tag_bigram_sim": datasets.Value("float64"),
+                    "bik_bigram_sim": datasets.Value("float64"),
+                    "ceb_bigram_sim": datasets.Value("float64"),
+                    "hil_bigram_sim": datasets.Value("float64"),
+                    "rin_bigram_sim": datasets.Value("float64"),
+                    "min_bigram_sim": datasets.Value("float64"),
+                    "kar_bigram_sim": datasets.Value("float64"),
+                    "tag_trigram_sim": datasets.Value("float64"),
+                    "bik_trigram_sim": datasets.Value("float64"),
+                    "ceb_trigam_sim": datasets.Value("float64"),
+                    "hil_trigam_sim": datasets.Value("float64"),
+                    "rin_trigam_sim": datasets.Value("float64"),
+                    "min_trigam_sim": datasets.Value("float64"),
+                    "kar_trigam_sim": datasets.Value("float64"),
+                    "grade_level": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(["1", "2", "3"])
+
+        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."""
+
+        lang = self.config.name.split("_")[2]
+
+        if lang in _LANGUAGES:
+            data_path = Path(dl_manager.download_and_extract(_URLS[lang]))
+        else:
+            data_path = [Path(dl_manager.download_and_extract(_URLS[lang])) for lang in _LANGUAGES]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_path,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        df = pd.read_csv(filepath, index_col=None)
+
+        for index, row in df.iterrows():
+
+            if self.config.schema == "source":
+                example = row.to_dict()
+
+            elif self.config.schema == "seacrowd_text":
+
+                example = {
+                    "id": str(index),
+                    "text": str(row["book_title"]),
+                    "label": str(row["grade_level"]),
+                }
+
+            yield index, example