Upload fakenews_ph.py with huggingface_hub

fakenews_ph.py

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+import csv
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+from datasets.download.download_manager import DownloadManager
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = r"""
+@inproceedings{cruz-etal-2020-localization,
+    title = "Localization of Fake News Detection via Multitask Transfer Learning",
+    author = "Cruz, Jan Christian Blaise  and
+      Tan, Julianne Agatha  and
+      Cheng, Charibeth",
+    editor = "Calzolari, Nicoletta  and
+      B{\'e}chet, Fr{\'e}d{\'e}ric  and
+      Blache, Philippe  and
+      Choukri, Khalid  and
+      Cieri, Christopher  and
+      Declerck, Thierry  and
+      Goggi, Sara  and
+      Isahara, Hitoshi  and
+      Maegaard, Bente  and
+      Mariani, Joseph  and
+      Mazo, H{\'e}l{\`e}ne  and
+      Moreno, Asuncion  and
+      Odijk, Jan  and
+      Piperidis, Stelios",
+    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2020.lrec-1.316",
+    pages = "2596--2604",
+    language = "English",
+    ISBN = "979-10-95546-34-4",
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["fil"]
+_DATASETNAME = "fakenews_ph"
+_DESCRIPTION = """\
+Fake news articles were sourced from online sites that were tagged as fake
+news sites by the non-profit independent media fact-checking organization
+Verafiles and the National Union of Journalists in the Philippines (NUJP).
+Real news articles were sourced from mainstream news websites in the
+Philippines, including Pilipino Star Ngayon, Abante, and Bandera.
+"""
+
+_HOMEPAGE = "https://github.com/jcblaisecruz02/Tagalog-fake-news"
+_LICENSE = Licenses.GPL_3_0.value
+_URL = "https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/fakenews/fakenews.zip"
+
+_SUPPORTED_TASKS = [Tasks.HOAX_NEWS_CLASSIFICATION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class FakeNewsFilipinoDataset(datasets.GeneratorBasedBuilder):
+    """Fake News Filipino Dataset from https://huggingface.co/datasets/fake_news_filipino"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "text"
+    LABEL_CLASSES = ["0", "1"]
+
+    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=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "article": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=self.LABEL_CLASSES),
+                }
+            )
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_features(self.LABEL_CLASSES)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
+        """Return SplitGenerators."""
+        data_dir = Path(dl_manager.download_and_extract(_URL))
+        train_path = data_dir / "fakenews" / "full.csv"
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": train_path, "split": "train"},
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yield examples as (key, example) tuples"""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file,
+                quotechar='"',
+                delimiter=",",
+                quoting=csv.QUOTE_ALL,
+                skipinitialspace=True,
+            )
+            next(csv_reader)
+            for id_, row in enumerate(csv_reader):
+                label, article = row
+                if self.config.schema == "source":
+                    yield id_, {"label": label, "article": article}
+                if self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+                    yield id_, {"id": id_, "label": label, "text": article}