script

LongConL.py

@@ -1,48 +1,75 @@
-# LongConL dataset script
 import datasets
 import pandas as pd
 
-
 _CITATION = """"""
 _DESCRIPTION = """"""
 _HOMEPAGE = ""
 _LICENSE = ""
 _URLS = {
-    "LongConL": {
-        "train": "data/LongConL-tasks/{task_name}/train.csv",
-        "validation": "data/LongConL-tasks/{task_name}/validation.csv",
-        "test": "data/LongConL-tasks/{task_name}/test.csv",
-    }
+    "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 = {}
-
-# Adding a config for each task in your dataset
-_CONFIGS["LongConL"] = {
-    "description": "Legal dataset containing various classification tasks.",
-    "features": {
-        "Citation": datasets.Value("string"),
-        "Case Name": datasets.Value("string"),
-        "Opinion Text": datasets.Value("string"),
-        "Numerical Label": datasets.Value("string"),  # Change to int32 if necessary
-        "Text Label": datasets.Value("string"),
+_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,
     },
-    "license": None,
 }
 
 
-class LongConL(datasets.GeneratorBasedBuilder):
-    """LongConL legal annotation dataset for multiple tasks."""
-    
+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
     ]
-    
+
     def _info(self):
-        """Returns the dataset's metadata."""
         features = _CONFIGS[self.config.name]["features"]
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -53,10 +80,10 @@ class LongConL(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        """Returns SplitGenerators for train, validation, and test sets."""
-        downloaded_file_dir = dl_manager.download_and_extract(_URLS["LongConL"])
-        
-        splits = [
+        # 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)
+        return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
@@ -79,12 +106,18 @@ class LongConL(datasets.GeneratorBasedBuilder):
                 },
             ),
         ]
-        return splits
 
     def _generate_examples(self, fpath, name):
-        """Yields examples from each split as (key, example) tuples."""
-        data = pd.read_csv(fpath)
-        data = data.to_dict(orient="records")
-        for id_line, example in enumerate(data):
-            yield id_line, example
+        """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
+
+        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