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+=======
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README.md

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+---
+license: mit
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- politics
+- tweets
+pretty_name: Political Slant Tweets Dataset
+size_categories:
+- 100K<n<1M
+---
+
+## Political Slant Tweets Dataset
+
+Political slant transfer dataset. Contains two classes of political tweets between Democratic and Republican Politicans. This dataset can be used for classification tasks.
+
+This is taken directly from:
+
+    http://tts.speech.cs.cmu.edu/style_models/political_data.tar
+    tar -xvf political_data.tar
+
+I can't take credit for this dataset; this is largely the work of Prabhumoye et al. This is simply a Wrapper for that dataset. See References below.
+
+## Reference
+
+    @inproceedings{style_transfer_acl18,
+      title={Style Transfer Through Back-Translation},
+      author={Prabhumoye, Shrimai and Tsvetkov, Yulia and Salakhutdinov, Ruslan and Black, Alan W},
+      year={2018},
+      booktitle={Proc. ACL}
+      }
+    
+    @inproceedings{rtgender,
+      title={{RtGender}: A Corpus for Studying Differential Responses to Gender},
+      author={Voigt, Rob and Jurgens, David and Prabhakaran, Vinodkumar and Jurafsky, Dan and Tsvetkov, Yulia},
+      year={2018},
+      booktitle={Proc. LREC},
+      }

political.py

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+import os
+from pathlib import Path
+import datasets
+from typing import List
+
+STYLE, CLASSIFIER = "style", "classifier"
+_CITATION = """\
+@inproceedings{style_transfer_acl18,
+    title={Style Transfer Through Back-Translation},
+    author={Prabhumoye, Shrimai and Tsvetkov, Yulia and Salakhutdinov, Ruslan and Black, Alan W},
+    year={2018},
+    booktitle={Proc. ACL}
+    }
+"""
+
+_DESCRIPTION = """\
+Political slant transfer dataset. Contains two classes of political tweets between Democratic and Republican Politicans. This dataset can be used for classification tasks.
+"""
+_HOMEPAGE = "https://github.com/shrimai/Style-Transfer-Through-Back-Translation"
+_LICENSE = ""  # could not find.
+
+
+class PoliticalDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    DEFAULT_CONFIG_NAME = STYLE
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=STYLE,
+            version=VERSION,
+            description="Political Tweets Dataset, used for Style Transfer tasks.",
+        ),
+        datasets.BuilderConfig(
+            name=CLASSIFIER,
+            version=VERSION,
+            description="Political Tweets Dataset, Used for classification tasks.",
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+                "label": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("text", "label"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        data_dir = "political_data"
+
+        splits: List[datasets.SplitGenerator] = []
+        if self.config.name == STYLE:
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepaths": [
+                            os.path.join(data_dir, "republican_only.train.en"),
+                            os.path.join(data_dir, "democratic_only.train.en"),
+                        ],
+                        "split": "train",
+                    },
+                )
+            )
+        else:
+            splits.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepaths": [os.path.join(data_dir, "classtrain.txt")],
+                        "split": "train",
+                    },
+                )
+            )
+
+        splits += [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepaths": [
+                        os.path.join(data_dir, "republican_only.dev.en"),
+                        os.path.join(data_dir, "democratic_only.dev.en"),
+                    ],
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepaths": [
+                        os.path.join(data_dir, "republican_only.test.en"),
+                        os.path.join(data_dir, "democratic_only.test.en"),
+                    ],
+                    "split": "test",
+                },
+            ),
+        ]
+        return splits
+
+    def _generate_examples(self, filepaths: List[str], split: str):
+        for filepath in filepaths:
+            filename = Path(filepath).name
+            label = filename.split(".")[0].split("_")[0]
+            with open(filepath, encoding="utf-8") as f:
+                for key, row in enumerate(f):
+                    text = row.strip()
+                    if split != "test":
+                        # label only exists in train/eval files.
+                        text = text.split()
+                        label, text = text[0], text[1:]
+                        text = " ".join(text)
+
+                    yield (
+                        key,
+                        {"text": text, "label": label},
+                    )
+
+
+if __name__ == "__main__":
+    from tqdm import tqdm
+
+    dataset = PoliticalDataset(config_name="classifier")
+    dataset = dataset.as_streaming_dataset()
+    print(dataset)
+    for row in tqdm(dataset["train"]):
+        row["text"] = "hello"

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