wrapper

LongConL.py

@@ -0,0 +1,97 @@
+import datasets
+import pandas as pd
+
+# Dataset metadata
+_CITATION = """"""
+_DESCRIPTION = """"""
+_HOMEPAGE = ""
+_LICENSE = ""
+
+# URLs for the dataset (since it's hosted on Hugging Face, you'll load directly from there)
+_URLS = {
+    "train": "data/LongConL-tasks/train.csv",
+    "validation": "data/LongConL-tasks/validation.csv",
+    "test": "data/LongConL-tasks/test.csv",
+    "all": "data/LongConL-tasks/{task_name}.csv"
+}
+
+# Configuration for tasks
+_CONFIGS = {
+    "default": {
+        "description": "Legal opinion classification tasks",
+        "features": {
+            "Citation": datasets.Value("string"),
+            "Case Name": datasets.Value("string"),
+            "Opinion Text": datasets.Value("string"),
+            "Numerical Label": datasets.Value("string"),  # Will be optional for some tasks
+            "Text Label": datasets.Value("string"),
+        },
+        "license": None,
+    }
+}
+
+class LongConLDataset(datasets.GeneratorBasedBuilder):
+    """Legal opinion classification dataset for LongConL tasks"""
+
+    # Set up the dataset configurations
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=task_name, version=datasets.Version("1.0.0"), description=task_name
+        )
+        for task_name in _CONFIGS
+    ]
+
+    def _info(self):
+        """Return dataset information."""
+        features = datasets.Features(_CONFIGS["default"]["features"])
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Split the dataset into train, validation, and test."""
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "file_path": downloaded_files["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "file_path": downloaded_files["validation"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, file_path):
+        """Generate examples from the dataset CSV."""
+        data = pd.read_csv(file_path)
+        data_dict = data.to_dict(orient="records")
+        
+        for id_, row in enumerate(data_dict):
+            # Check if the CSV has the 'Numerical Label' column
+            if "Numerical Label" in row:
+                yield id_, {
+                    "Citation": row["Citation"],
+                    "Case Name": row["Case Name"],
+                    "Opinion Text": row["Opinion Text"],
+                    "Numerical Label": row["Numerical Label"],
+                    "Text Label": row["Text Label"],
+                }
+            else:
+                # Handle the case where Numerical Label column is missing
+                yield id_, {
+                    "Citation": row["Citation"],
+                    "Case Name": row["Case Name"],
+                    "Opinion Text": row["Opinion Text"],
+                    "Numerical Label": None,  # Set to None if missing
+                    "Text Label": row["Text Label"],
+                }
+