LongConL.py

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",
}

_CONFIGS = {
    "ATS-Jurisdiction": {
        "description": "Jurisdiction-specific legal opinions",
        "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"),
        },
    },
    "ATS-FavorableJudgment": {
        "description": "Description for other task 1",
        "features": {
            "Citation": datasets.Value("string"),
            "Case Name": datasets.Value("string"),
            "Opinion Text": datasets.Value("string"),
            "Numerical Label": datasets.Value("string"),
            "Text Label": datasets.Value("string"),
        },
    }
}


class LongConLDataset(datasets.GeneratorBasedBuilder):
    """Legal opinion classification dataset for LongConL tasks"""

    def _info(self):
        """Return dataset information."""
        features = datasets.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"),
        })
        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")
        
        for id_, row in enumerate(data_dict):
            yield id_, {
                "Citation": row["Citation"],
                "Case Name": row["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", "Other-Task-Name-1", "Other-Task-Name-2"]  # Add your task names here
    ]