script

LongConL.py

@@ -1,105 +1,63 @@
 import datasets
+import os
 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",
-}
-
-# 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"""
+    """Legal opinion classification dataset for LongConL tasks."""
 
-    # Set up the dataset configurations
+    # List of tasks (add more as needed)
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
-            name=task_name, version=datasets.Version("1.0.0"), description=task_name
+            name=task_name, version=datasets.Version("1.0.0"), description=f"Task: {task_name}"
         )
-        for task_name in _CONFIGS  # Assuming _CONFIGS contains all task names
+        for task_name in ["ATS-Jurisdiction", "ATS-FavorableJudgment"]  
     ]
 
     def _info(self):
         """Return dataset information."""
-        features = datasets.Features(_CONFIGS["default"]["features"])
+        features = datasets.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"),
+        })
         return datasets.DatasetInfo(
-            description=_DESCRIPTION,
+            description="Legal classification tasks dataset",
             features=features,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE,
+            homepage="https://huggingface.co/datasets/LongConL",
+            citation="",
         )
 
     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)
-
+        task_name = self.config.name  # Dynamically get the current task name
+        base_dir = f"data/LongConL-tasks/{task_name}"
+        
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "file_path": downloaded_files["train"],
-                },
+                gen_kwargs={"file_path": f"{base_dir}/train.csv"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "file_path": downloaded_files["validation"],
-                },
+                gen_kwargs={"file_path": f"{base_dir}/validation.csv"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={
-                    "file_path": downloaded_files["test"],
-                },
+                gen_kwargs={"file_path": f"{base_dir}/test.csv"},
             ),
         ]
 
     def _generate_examples(self, file_path):
-        """Generate examples from the dataset CSV."""
+        """Generate examples from a CSV file."""
         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"],
-                }
+        for id_, row in data.iterrows():
+            yield id_, {
+                "Citation": row["Citation"],
+                "Case Name": row["Case Name"],
+                "Opinion Text": row["Opinion Text"],
+                "Numerical Label": row.get("Numerical Label", None),  # Optional column
+                "Text Label": row["Text Label"],
+            }
 

__init__.py

@@ -0,0 +1,2 @@
+from .LongConL import LongConLDataset
+

load-data.py

@@ -1,23 +1,22 @@
 from datasets import load_dataset
-from LongConL import LongConLDataset  # Import the dataset class
 from huggingface_hub import login
 
 # Login to Hugging Face using your token
-login(token="")  # Replace with your actual token
+login(token="")
 
 # Specify the task name you want to load
-task_name = "ATS-Jurisdiction"  # Replace with your desired task
+task_name = "ATS-Jurisdiction"  # Replace with the task you want
 
 try:
-    # Load the dataset
-    dataset = load_dataset(LongConLDataset, name=task_name)
+    # Load the dataset with the dynamic task name
+    dataset = load_dataset("reglab/LongConL", name=task_name)  # Using dynamic task name
 
     # Access train, validation, and test splits
     train_dataset = dataset['train']
     validation_dataset = dataset['validation']
     test_dataset = dataset['test']
 
-    # Now you can use these datasets as needed
+    # Use the datasets as needed
     print("Train Dataset:", train_dataset)
     print("Validation Dataset:", validation_dataset)
     print("Test Dataset:", test_dataset)