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bhinneka_korpus.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+from seacrowd.utils import schemas
+
+_CITATION = """\
+@misc{lopo2024constructing,
+      title={Constructing and Expanding Low-Resource and Underrepresented Parallel Datasets for Indonesian Local Languages}, 
+      author={Joanito Agili Lopo and Radius Tanone},
+      year={2024},
+      eprint={2404.01009},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DATASETNAME = "bhinneka_korpus"
+_DESCRIPTION = """The Bhinneka Korpus dataset was parallel dataset for five Indonesian Local Languages conducted 
+through a volunteer-driven translation strategy, encompassing sentences in the Indonesian-English pairs and lexical 
+terms. The dataset consist of parallel data with 16,000 sentences in total, details with 4,000 sentence pairs for two 
+Indonesia local language and approximately 3,000 sentences for other languages, and one lexicon dataset creation for 
+Beaye language. In addition, since beaye is a undocumented language, we don't have any information yet about the use 
+of language code. Therefore, we used "day" (a code for land dayak language family) to represent the language."""
+
+_HOMEPAGE = "https://github.com/joanitolopo/bhinneka-korpus"
+_LICENSE = Licenses.APACHE_2_0.value
+_URLS = "https://raw.githubusercontent.com/joanitolopo/bhinneka-korpus/main/"
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+_LANGUAGES = ["abs", "aoz", "day", "mak", "mkn"]
+LANGUAGES_TO_FILENAME_MAP = {
+    "abs": "ambonese-malay",
+    "aoz": "uab-meto",
+    "day": "beaye",
+    "mak": "makassarese",
+    "mkn": "kupang-malay",
+}
+
+_LOCAL = False
+
+
+class BhinnekaKorpusDataset(datasets.GeneratorBasedBuilder):
+    """A Collection of Multilingual Parallel Datasets for 5 Indonesian Local Languages."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SEACROWD_SCHEMA_NAME = "t2t"
+
+    dataset_names = sorted([f"{_DATASETNAME}_{lang}" for lang in _LANGUAGES])
+    BUILDER_CONFIGS = []
+    for name in dataset_names:
+        source_config = SEACrowdConfig(
+            name=f"{name}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=name
+        )
+        BUILDER_CONFIGS.append(source_config)
+        seacrowd_config = SEACrowdConfig(
+            name=f"{name}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=name
+        )
+        BUILDER_CONFIGS.append(seacrowd_config)
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_day_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        schema = self.config.schema
+        features = datasets.Features(
+            {
+                "source_sentence": datasets.Value("string"),
+                "target_sentence": datasets.Value("string"),
+                "source_lang": datasets.Value("string"),
+                "target_lang": datasets.Value("string")
+            } if schema == "source" else schemas.text2text_features
+            if schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}" else None
+        )
+        if features is None:
+            raise ValueError("Invalid config schema")
+
+        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."""
+        data_dir = []
+        lang = self.config.name.split("_")[2]
+        if lang in _LANGUAGES:
+            data_dir.append(Path(dl_manager.download(_URLS + f"{LANGUAGES_TO_FILENAME_MAP[lang]}/{lang}.xlsx")))
+        else:
+            raise ValueError("Invalid language name")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir[0],
+                    "split": "train",
+                    "language": lang
+                }
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str, language: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        dfs = pd.read_excel(filepath, index_col=0, engine="openpyxl")
+        source_sents = dfs["ind"]
+        target_sents = dfs[language]
+
+        for idx, (source, target) in enumerate(zip(source_sents.values, target_sents.values)):
+            if self.config.schema == "source":
+                example = {
+                    "source_sentence": source,
+                    "target_sentence": target,
+                    "source_lang": "ind",
+                    "target_lang": language
+                }
+            elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+                example = {
+                    "id": str(idx),
+                    "text_1": source,
+                    "text_2": target,
+                    "text_1_name": "ind",
+                    "text_2_name": language,
+                }
+            yield idx, example