Create LongConL.py

LongConL.py

@@ -0,0 +1,118 @@
+import datasets
+import pandas as pd
+
+# Dataset metadata
+_CITATION = """"""
+_DESCRIPTION = """"""
+_HOMEPAGE = ""
+_LICENSE = ""
+
+# Updated URLs to dynamically handle task names
+_URLS = {
+    "train": "data/automated-tasks-subsample/{task_name}/{task_name}_subsample_train.csv",
+    "validation": "data/automated-tasks-subsample/{task_name}/{task_name}_subsample_val.csv",
+    "test": "data/automated-tasks-subsample/{task_name}/{task_name}_subsample_test.csv",
+}
+
+TASK_NAMES = [
+    "ATS-Jurisdiction", "ATS-FavorableJudgment", "Chevron-Agency", "Chevron-ChevCited", 
+    "Chevron-Dec.Ov.", "Chevron-Deference", "Chevron-Outcome", "Chevron-Subject", 
+    "CoA-casetyp1", "CoA-direct1", "CoA-geniss", "CoA-typeiss", 
+    "DC-casetype", "DC-category", "DC-libcon", 
+    "JRC-AREA1", "JRC-CERT", "JRC-REVERSD", 
+    "SC-decisionDirection", "SC-issueArea", "SC-partyWinning", 
+    "SC-petitioner", "SC-precedentAlteration", 
+    "SSC-ca_disp", "SSC-ca_uscty", "SSC-death_c", "SSC-p1_persn"
+]
+
+_CONFIGS = {
+    task_name: {
+        "description": f"{task_name} specific legal opinions",  # Dynamic description based on task name
+        "features": {
+            "idx": datasets.Value("string"),
+            "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"), # Will be optional for some tasks
+            #"DC Numerical Label": datasets.Value("string")
+            #"Syllabus": datasets.Value("string") # Will be optional for some tasks
+        },
+    }
+    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({
+            "idx": datasets.Value("string"),
+            "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"), # Will be optional for some tasks
+            #"DC Numerical Label": datasets.Value("string")
+            #"Syllabus": datasets.Value("string") # Will be optional for some tasks
+        })
+        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
+        valid_task_name = task_name.replace("-", "-")  # Replace hyphens with underscores
+
+        # Update URLs with the valid task name
+        urls = {key: val.format(task_name=valid_task_name) for key, val in _URLS.items()}  
+        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_, {
+                "idx": row["idx"],
+                "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"],
+                #"DC Numerical Label": row["DC Numerical Label"]
+                #"Syllabus": row["Syllabus"]
+            }
+
+    # Use a dynamic config
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name=task_name, version=datasets.Version("1.0.0"), description=task_name)
+        for task_name in TASK_NAMES
+    ]
+