Upload ph_fake_news_corpus.py with huggingface_hub

ph_fake_news_corpus.py

@@ -0,0 +1,116 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+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
+
+_CITATION = """
+@inproceedings{hernandez-devaraj-2019-phfakenews,
+  author    = {Fernandez, Aaron Carl T. and Devaraj, Madhavi},
+  title     = {Computing the Linguistic-Based Cues of Fake News in the Philippines Towards its Detection},
+  booktitle = {Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics},
+  publisher = {Association for Computing Machinery},
+  year      = {2019},
+  url       = {https://dl.acm.org/doi/abs/10.1145/3326467.3326490},
+  doi       = {10.1145/3326467.3326490},
+  pages     = {1-9},
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["eng"]
+_DATASETNAME = "ph_fake_news_corpus"
+_DESCRIPTION = """
+The Philippine Fake News Corpus consists of news headlines and content from various "credible" and "non-credible"
+national news outlets. "Credible" sources were national broadsheets available in the National Library of the
+Philippines, while "non-credible" sources were sources included in lists of websites with fake or unverified content
+provided by government and private institutions.
+"""
+
+_HOMEPAGE = "https://github.com/aaroncarlfernandez/Philippine-Fake-News-Corpus"
+_LICENSE = Licenses.UNKNOWN.value
+_URL = "https://github.com/aaroncarlfernandez/Philippine-Fake-News-Corpus/raw/master/Philippine%20Fake%20News%20Corpus.zip/"
+
+_SUPPORTED_TASKS = [Tasks.FACT_CHECKING]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class PhilippineFakeNewsDataset(datasets.GeneratorBasedBuilder):
+    """
+    Dataset of English news articles from the Philippines manually annotated as "credible" or
+    "non-credible" based on source.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        features = datasets.Features(
+            {
+                "Headline": datasets.Value("string"),
+                "Content": datasets.Value("string"),
+                "Authors": datasets.Value("string"),
+                "Date": datasets.Value("string"),
+                "URL": datasets.Value("string"),
+                "Brand": datasets.Value("string"),
+                "Label": datasets.Value("string"),
+            }
+        )
+
+        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 = dl_manager.download_and_extract(_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "Philippine Fake News Corpus.csv"),
+                    "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, header="infer", encoding="utf-8")
+        for index, example in df.iterrows():
+            yield index, example.to_dict()