longcon

LongConL.py

@@ -78,7 +78,107 @@ class LongConLDataset(datasets.GeneratorBasedBuilder):
         print(data.head())  # Display first few rows
 
         # Check if the DataFrame is empty or has the correct columns
-        if data.empty:
+        if data.empty:import datasets
+import pandas as pd
+
+# Dataset metadata
+_CITATION = """"""
+_DESCRIPTION = """"""
+_HOMEPAGE = ""
+_LICENSE = ""
+
+# Updated URLs to dynamically handle task names
+_URLS = {
+    "train": "data/LongConL-tasks/{task_name}/train.csv",
+    "validation": "data/LongConL-tasks/{task_name}/validation.csv",
+    "test": "data/LongConL-tasks/{task_name}/test.csv",
+}
+
+TASK_NAMES = ["ATS-Jurisdiction", "ATS-FavorableJudgment"]  # Add more task names as needed
+
+_CONFIGS = {
+    task_name: {
+        "description": f"{task_name} specific legal opinions",  # Dynamic description based on task name
+        "features": {
+            "Citation": datasets.Value("string"),
+            "Full 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"),
+        },
+    }
+    for task_name in TASK_NAMES
+}
+
+
+
+class LongConLDataset(datasets.GeneratorBasedBuilder):
+    """Legal opinion classification dataset for LongConL tasks"""
+
+    def _info(self):
+        """Return dataset information."""
+        features = datasets.Features({
+            "Citation": datasets.Value("string"),
+            "Full 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"),
+        })
+        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."""
+        task_name = self.config.name  # Get the current task name from the config
+        urls = {key: val.format(task_name=task_name) for key, val in _URLS.items()}  # Update URLs with the task name
+        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"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"file_path": downloaded_files["test"]},
+            ),
+        ]
+
+    def _generate_examples(self, file_path):
+        """Generate examples from the dataset CSV."""
+        data = pd.read_csv(file_path)
+        print("Data loaded from file:", file_path)
+        print(data.head())  # Display first few rows
+        data_dict = data.to_dict(orient="records")
+        print(f"Number of examples to generate: {len(data_dict)}")
+
+        for id_, row in enumerate(data_dict):
+            yield id_, {
+                "Citation": row["Citation"],
+                "Full Case Name": row["Full Case Name"],
+                "Opinion Text": row["Opinion Text"],
+                "Numerical Label": row.get("Numerical Label", None),  # Use .get() to handle missing keys
+                "Text Label": row["Text Label"],
+            }
+
+
+
+    # Use a dynamic config
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name=task_name, version=datasets.Version("1.0.0"), description=task_name)
+        for task_name in ["ATS-Jurisdiction", "ATS-FavorableJudgment","JRC-AREA1","DC-category", "SC-issueArea","SSC-ca_disp", "Chevron-Agency","CoA-geniss"]  # Add your task names here
+    ]
+
+
             print(f"Warning: No data found in {file_path}")
             return  # Early exit if there's no data