add loader and readme

political_advertising_loader.py

@@ -0,0 +1,110 @@
+from pathlib import Path
+
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@inproceedings{augustyniak-etal-2020-political,
+    title = "Political Advertising Dataset: the use case of the Polish 2020 Presidential Elections",
+    author = "Augustyniak, Lukasz  and
+      Rajda, Krzysztof  and
+      Kajdanowicz, Tomasz  and
+      Bernaczyk, Micha{\l}",
+    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
+    month = jul,
+    year = "2020",
+    address = "Seattle, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.winlp-1.28",
+    pages = "110--114"
+}
+"""
+
+_DESCRIPTION = "Polish Political Advertising Dataset"
+
+_HOMEPAGE = "https://github.com/laugustyniak/misinformation"
+
+DATA_PATH = Path(".")
+
+
+class PoliticalAdvertisingConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(PoliticalAdvertisingConfig, self).__init__(**kwargs)
+
+
+class PoliticalAdvertisingDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    TRAIN_FILE = DATA_PATH / "train.json"
+    VAL_FILE = DATA_PATH / "dev.json"
+    TEST_FILE = DATA_PATH / "test.json"
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="political-advertising-pl", version=VERSION)
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "tokens": datasets.Sequence(datasets.Value("string")),
+                "tags": datasets.Sequence(
+                    datasets.features.ClassLabel(
+                        names=[
+                            "O",
+                            "B-DEFENSE_AND_SECURITY",
+                            "I-DEFENSE_AND_SECURITY",
+                            "B-EDUCATION",
+                            "I-EDUCATION",
+                            "B-FOREIGN_POLICY",
+                            "I-FOREIGN_POLICY",
+                            "B-HEALHCARE",
+                            "I-HEALHCARE",
+                            "B-IMMIGRATION",
+                            "I-IMMIGRATION",
+                            "B-INFRASTRUCTURE_AND_ENVIROMENT",
+                            "I-INFRASTRUCTURE_AND_ENVIROMENT",
+                            "B-POLITICAL_AND_LEGAL_SYSTEM",
+                            "I-POLITICAL_AND_LEGAL_SYSTEM",
+                            "B-SOCIETY",
+                            "I-SOCIETY",
+                            "B-WELFARE",
+                            "I-WELFARE",
+                        ]
+                    )
+                ),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": str(self.TRAIN_FILE)}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": str(self.TEST_FILE)}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": str(self.VAL_FILE)},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: str):
+        df = pd.read_json(filepath)
+        for row_id, row in df.iterrows():
+            yield row_id, {
+                "id": row.id,
+                "text": row.text,
+                "tags": row.tags,
+                "url": row.url,
+                "tweet_id": row.tweet_id,
+            }