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indonesian_news_dataset.py

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+import pickle
+from pathlib import Path
+from typing import List
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@misc{andreaschandra2020,
+  author    = {Chandra, Andreas},
+  title     = {Indonesian News Dataset},
+  year      = {2020},
+  howpublished = {Online},
+  url       = {https://github.com/andreaschandra/indonesian-news},
+  note      = {Accessed: 2024-02-13},
+}
+"""
+
+_LANGUAGES = ["ind"]
+
+_DATASETNAME = "indonesian_news_dataset"
+
+_DESCRIPTION = """An imbalanced dataset to classify Indonesian News articles.
+The dataset contains 5 class labels: bola, news, bisnis, tekno, and otomotif.
+The dataset comprises of around 6k train and 2.5k test examples, with the more prevalent classes
+(bola and news) having roughly 10x the number of train and test examples than the least prevalent class (otomotif).
+"""
+
+_HOMEPAGE = "https://github.com/andreaschandra/indonesian-news"
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_URLS = {
+    f"{_DATASETNAME}_train": "https://drive.usercontent.google.com/u/0/uc?id=1wCwPMKSyTciv8I3g9xGdUfEraA1SydG6&export=download",
+    f"{_DATASETNAME}_test": "https://drive.usercontent.google.com/u/0/uc?id=1AFW_5KQFW86jlFO16S9bt564Y86WoJjV&export=download",
+}
+
+_SUPPORTED_TASKS = [Tasks.TOPIC_MODELING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+_TAGS = ["bola", "news", "bisnis", "tekno", "otomotif"]
+
+_LOCAL = False
+
+
+class IndonesianNewsDataset(datasets.GeneratorBasedBuilder):
+    """The dataset contains 5 Indonesian News articles with imbalanced classes"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SEACROWD_SCHEMA_NAME = "text"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features({"index": datasets.Value("string"), "news": datasets.Value("string"), "label": datasets.Value("string")})
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_features(_TAGS)
+
+        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."""
+        train_dir = Path(dl_manager.download(_URLS[f"{_DATASETNAME}_train"]))
+        test_dir = Path(dl_manager.download(_URLS[f"{_DATASETNAME}_test"]))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_dir,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str):
+        """Yields examples as (key, example) tuples."""
+
+        with open(filepath, "rb") as file:
+            news_file = pickle.load(file)
+
+        news_list = news_file[0]
+        label_list = news_file[1]
+
+        if self.config.schema == "source":
+            for idx, (news, label) in enumerate(zip(news_list, label_list)):
+                example = {"index": str(idx), "news": news, "label": label}
+                yield idx, example
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            for idx, (news, label) in enumerate(zip(news_list, label_list)):
+                example = {"id": str(idx), "text": news, "label": label}
+                yield idx, example
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")