script

LongConL.py

@@ -1,123 +1,78 @@
 import datasets
 import pandas as pd
 
+# Dataset metadata
 _CITATION = """"""
 _DESCRIPTION = """"""
 _HOMEPAGE = ""
 _LICENSE = ""
-_URLS = {
-    "qa": {
-        "train": "data/qa/train.csv",
-        "validation": "data/qa/validation.csv",
-        "test": "data/qa/test.csv",
-        "all": "data/qa/qa.csv",
-    },
-    "passages": {
-        "train": "data/passages/train.tsv",
-        "validation": "data/passages/validation.tsv",
-        "test": "data/passages/test.tsv",
-        "all": "data/passages/passages.tsv"
-    },
-}
 
-_CONFIGS = {
-    "qa": {
-        "description": "Answer bar exam questions",
-        "features": {
-            "idx": datasets.Value("string"),
-            "dataset": datasets.Value("string"),
-            "example_id": datasets.Value("string"),
-            "prompt_id": datasets.Value("string"),
-            "source": datasets.Value("string"),
-            "subject": datasets.Value("string"),
-            "question_number": datasets.Value("string"),
-            "prompt": datasets.Value("string"),
-            "question": datasets.Value("string"),
-            "choice_a": datasets.Value("string"),
-            "choice_b": datasets.Value("string"),
-            "choice_c": datasets.Value("string"),
-            "choice_d": datasets.Value("string"),
-            "answer": datasets.Value("string"),
-            "gold_passage": datasets.Value("string"),
-            "gold_idx": datasets.Value("string"),
-        },
-        "license": None,
-    },
-    "passages": {
-        "description": "Passage corpus of bar exam question explanations, Wex definitions and primary sources, and caselaw",
-        "features": {
-            "idx": datasets.Value("string"),
-            "source": datasets.Value("string"),
-            "faiss_id": datasets.Value("string"),
-            "case_id": datasets.Value("string"),
-            "absolute_paragraph_id": datasets.Value("string"),
-            "opinion_id": datasets.Value("string"),
-            "relative_paragraph_id": datasets.Value("string"),
-            "text": datasets.Value("string"),
-        },
-        "license": None,
-    },
+# 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",
 }
 
-
 class LongConLDataset(datasets.GeneratorBasedBuilder):
-    """Dataset for LongConL."""
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name=task, version=datasets.Version("1.0.0"), description=task,
-        )
-        for task in _CONFIGS
-    ]
+    """Legal opinion classification dataset for LongConL tasks"""
 
     def _info(self):
-        features = _CONFIGS[self.config.name]["features"]
+        """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=datasets.Features(features),
+            features=features,
             homepage=_HOMEPAGE,
             citation=_CITATION,
-            license=_CONFIGS[self.config.name]["license"],
+            license=_LICENSE,
         )
 
     def _split_generators(self, dl_manager):
-        # Added use_auth_token to download_and_extract method
-        download_options = {"use_auth_token": self._kwargs.get("use_auth_token")}
-        downloaded_file_dir = dl_manager.download_and_extract(_URLS[self.config.name], download_options=download_options)
+        """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={
-                    "fpath": downloaded_file_dir["train"],
-                    "name": self.config.name,
-                },
+                gen_kwargs={"file_path": downloaded_files["train"]},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "fpath": downloaded_file_dir["validation"],
-                    "name": self.config.name,
-                },
+                gen_kwargs={"file_path": downloaded_files["validation"]},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                gen_kwargs={
-                    "fpath": downloaded_file_dir["test"],
-                    "name": self.config.name,
-                },
+                gen_kwargs={"file_path": downloaded_files["test"]},
             ),
         ]
 
-    def _generate_examples(self, fpath, name):
-        """Yields examples as (key, example) tuples."""
-        if name in ["qa"]:
-            data = pd.read_csv(fpath)
-            data = data.to_dict(orient="records")
-            for id_line, example in enumerate(data):
-                yield id_line, example
+    def _generate_examples(self, file_path):
+        """Generate examples from the dataset CSV."""
+        data = pd.read_csv(file_path)
+        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
+    ]
 
-        if name in ["passages"]:
-            data = pd.read_csv(fpath, sep='\t')
-            data = data.to_dict(orient="records")
-            for id_line, example in enumerate(data):
-                yield id_line, example