Update dataset.py

dataset.py

@@ -3,19 +3,7 @@ from datasets import GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split,
 import datasets
 import csv
 import pyarrow as pa
-
-DEFAULT_DATA_FILES = {
-    "Thresholding": {
-        "train": "train/train.csv",
-        "val": "validation/validation.csv",
-        "test": "test/test.csv",
-    },
-    "Contrastive Learning": {
-        "train": "train/data-00000-of-00001.arrow",
-        "dev": "validation/data-00000-of-00001.arrow",
-        "test": "test/data-00000-of-00001.arrow",
-    }
-}
+import os
 
 class CustomConfig(BuilderConfig):
     def __init__(self, **kwargs):
@@ -26,44 +14,52 @@ class MyDataset(GeneratorBasedBuilder):
         CustomConfig(
             name="Contrastive Learning",
             version=datasets.Version("1.0.0"),
-            description="Loads Arrow files for contrastive learning"
+            description="Load from Arrow files"
         ),
         CustomConfig(
             name="Thresholding",
             version=datasets.Version("1.0.0"),
-            description="Loads CSV files for thresholding task"
+            description="Load from CSV files"
         ),
     ]
 
     def _info(self):
-        return DatasetInfo()  # no schema assumptions
+        return DatasetInfo()
 
     def _split_generators(self, dl_manager):
-        # Try default files if config has no data_files
-        file_dict = DEFAULT_DATA_FILES.get(self.config.name)
-        if not file_dict:
-            raise ValueError(f"No default data files defined for config: {self.config.name}")
+        if self.config.name == "Contrastive Learning":
+            return [
+                SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": "train/data-00000-of-00001.arrow"}),
+                SplitGenerator(name=Split.VALIDATION, gen_kwargs={"filepath": "validation/data-00000-of-00001.arrow"}),
+                SplitGenerator(name=Split.TEST, gen_kwargs={"filepath": "test/data-00000-of-00001.arrow"}),
+            ]
+
+        elif self.config.name == "Thresholding":
+            return [
+                SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": "train/train.csv"}),
+                SplitGenerator(name=Split.VALIDATION, gen_kwargs={"filepath": "validation/validation.csv"}),
+                SplitGenerator(name=Split.TEST, gen_kwargs={"filepath": "test/test.csv"}),
+            ]
 
-        return [
-            SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": file_dict["train"]}),
-            SplitGenerator(name=Split.VALIDATION, gen_kwargs={"filepath": file_dict.get("val") or file_dict.get("dev")}),
-            SplitGenerator(name=Split.TEST, gen_kwargs={"filepath": file_dict["test"]}),
-        ]
+        else:
+            raise ValueError(f"Unsupported config: {self.config.name}")
 
     def _generate_examples(self, filepath):
         if filepath.endswith(".arrow"):
-            try:
-                with open(filepath, "rb") as f:
+            with open(filepath, "rb") as f:
+                try:
                     table = pa.ipc.RecordBatchFileReader(f).read_all()
-            except pa.lib.ArrowInvalid as e:
-                raise ValueError(f"Invalid Arrow file at {filepath}: {e}")
+                except Exception as e:
+                    raise ValueError(f"Could not read Arrow file: {filepath}") from e
             data = table.to_pydict()
             for i in range(len(next(iter(data.values())))):
                 yield i, {k: data[k][i] for k in data}
+
         elif filepath.endswith(".csv"):
             with open(filepath, encoding="utf-8") as f:
                 reader = csv.DictReader(f)
                 for i, row in enumerate(reader):
                     yield i, row
+
         else:
             raise ValueError(f"Unsupported file format: {filepath}")