Upload bg-fake-news.py

bg-fake-news.py

@@ -0,0 +1,98 @@
+import json
+import textwrap
+
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = "Fake news detection dataset."
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+_FEATURES = datasets.Features({
+                "title": datasets.Value("string"),
+                "url": datasets.Value("string"),
+                "date_published": datasets.Value("string"),
+                "content": datasets.Value("string"),
+                "fake_news":  datasets.features.ClassLabel(names=["fake", "real"])
+            })
+
+class FakeNewsConfig(datasets.BuilderConfig):
+    """BuilderConfig for FakeNews"""
+
+    def __init__(self, data_url, citation, url, text_features, **kwargs):
+        """
+        Args:
+            text_features: `dict[string, string]`, map from the name of the feature
+        dict for each text field to the name of the column in the tsv file
+            label_column:
+            label_classes
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(FakeNewsConfig, self).__init__(version=datasets.Version("0.1.0", ""), **kwargs)
+        self.text_features = text_features
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+
+
+class FakeNewsConfig(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("0.1.0")
+
+    DEFAULT_CONFIG_NAME = "default"
+
+    BUILDER_CONFIGS = [FakeNewsConfig(
+            name=DEFAULT_CONFIG_NAME,
+            description=_DESCRIPTION,
+            citation=textwrap.dedent(_CITATION),
+            text_features=_FEATURES,
+            data_url="https://gitlab.com/datasciencesociety/case_fake_news/-/blob/master/data/main_data_fake_news.csv",
+            url="https://gitlab.com/datasciencesociety/case_fake_news/-/blob/master/data/main_data_fake_news.csv",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=_FEATURES,
+            supervised_keys=None,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager=None, config=None):
+        data_dir = dl_manager.download(self.config.data_url)
+        split_filenames = {
+            datasets.Split.TRAIN: "train.jsonl",
+            datasets.Split.VALIDATION: "dev.jsonl",
+            datasets.Split.TEST: "test.jsonl",
+        }
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={
+                    "filepath": dl_manager.iter_archive(data_dir),
+                    "filename": split_filenames[split],
+                },
+            )
+            for split in split_filenames
+        ]
+
+    def _generate_examples(self, filepath=None, filename=None):
+        idx = 0
+
+        for path, file in filepath:
+            if path.endswith(filename):
+                lines = (line.decode("utf-8") for line in file)
+                for line in lines:
+                    idx += 1
+                    example = json.loads(line)
+
+                    yield idx, example