longcon

LongConL.py

@@ -30,86 +30,6 @@ _CONFIGS = {
     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
-
-        # Check if the DataFrame is empty or has the correct columns
-        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):
@@ -179,29 +99,3 @@ class LongConLDataset(datasets.GeneratorBasedBuilder):
     ]
 
 
-            print(f"Warning: No data found in {file_path}")
-            return  # Early exit if there's no data
-        
-        expected_columns = {"Citation", "Full Case Name", "Opinion Text", "Numerical Label", "Text Label"}
-        if not expected_columns.issubset(data.columns):
-            print(f"Warning: Missing columns in {file_path}. Expected columns: {expected_columns}. Found columns: {set(data.columns)}")
-            return  # Early exit if columns are missing
-
-        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),
-                "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
-]
-