pre-compile dataset for convenience

README.md

@@ -10,18 +10,46 @@ tags:
 pretty_name: Political Slant Tweets Dataset
 size_categories:
 - 100K<n<1M
+configs:
+- config_name: style
+  default: true
+  data_files:
+  - split: train
+    path:
+    - "political_data/democratic_only.train.en.parquet"
+    - "political_data/republican_only.train.en.parquet"
+  - split: eval 
+    path: 
+    - "political_data/democratic_only.dev.en.parquet"
+    - "political_data/republican_only.dev.en.parquet"
+  - split: test
+    path: 
+    - "political_data/democratic_only.test.en.parquet"
+    - "political_data/republican_only.test.en.parquet" 
+- config_name: classifier
+  data_files:
+  - split: train
+    path: "political_data/classtrain.txt.parquet"
+  - split: eval 
+    path: 
+    - "political_data/democratic_only.dev.en.parquet"
+    - "political_data/republican_only.dev.en.parquet"
+  - split: test
+    path: 
+    - "political_data/democratic_only.test.en.parquet"
+    - "political_data/republican_only.test.en.parquet"
 ---
 
-## Political Slant Tweets Dataset
+# 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:
+This is taken directly from the below:
 
     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.
+I can't take credit for this dataset; this is largely the work of Prabhumoye et al. (See References below). I have detokenized (via MosesDetokenizer), and added relevant labels for creature comforts.
 
 ## Reference
 

political.py

@@ -1,134 +0,0 @@
-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"

political_data/{republican_only.test.en → classtrain.txt.parquet}

@@ -1,3 +1,3 @@
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-size 2292494
+oid sha256:a8423df1e42eddeecc99746cd80147a53c879162b9b0a66b3be29b68ea1c490e
+size 9286667

political_data/{republican_only.dev.en → democratic_only.dev.en.parquet}

@@ -1,3 +1,3 @@
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+size 237800

political_data/{democratic_only.test.en → democratic_only.test.en.parquet}

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+size 3258396

political_data/{democratic_only.train.en → democratic_only.train.en.parquet}

@@ -1,3 +1,3 @@
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-size 25559362
+oid sha256:401e69d0a996fd5e3388fb32bdec193ea690a03efabb451cbe956fb1141ee58b
+size 30826508

political_data/{democratic_only.dev.en → republican_only.dev.en.parquet}

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+size 231669

political_data/republican_only.test.en.parquet

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+size 3117740

political_data/republican_only.train.en

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-size 25145176

political_data/republican_only.train.en.parquet

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+size 29691039