Update dataset.py

dataset.py

@@ -1,26 +1,57 @@
+""" generated with GPT-4o """
 from datasets import GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split, BuilderConfig
 import datasets
 import csv
 import pyarrow as pa
 
+# Default data files for the Hub viewer (when no data_files passed)
+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",
+    }
+}
+
 class CustomConfig(BuilderConfig):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
 
 class MyDataset(GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
-        CustomConfig(name="Contrastive Learning", version=datasets.Version("1.0.0"),
-                     description="Loads Arrow files for contrastive learning"),
-        CustomConfig(name="Thresholding", version=datasets.Version("1.0.0"),
-                     description="Loads CSV files for thresholding"),
+        CustomConfig(
+            name="Contrastive Learning",
+            version=datasets.Version("1.0.0"),
+            description="Loads Arrow files for contrastive learning"
+        ),
+        CustomConfig(
+            name="Thresholding",
+            version=datasets.Version("1.0.0"),
+            description="Loads CSV files for thresholding task"
+        ),
     ]
 
     def _info(self):
-        # No features defined — schema will be inferred
+        # Let Hugging Face infer features from data (no hard-coded schema)
         return DatasetInfo()
 
     def _split_generators(self, dl_manager):
-        data_files = self.config.data_files
+        # Try to get data_files passed in the config
+        data_files = getattr(self.config, "data_files", None)
+
+        # Fallback to default if not passed
+        if not data_files:
+            if self.config.name not in DEFAULT_DATA_FILES:
+                raise ValueError(f"No default files for config {self.config.name}")
+            data_files = [
+                {"split": k, "path": v}
+                for k, v in DEFAULT_DATA_FILES[self.config.name].items()
+            ]
 
         def get_path(split_name, fallback=None):
             for entry in data_files:
@@ -37,14 +68,14 @@ class MyDataset(GeneratorBasedBuilder):
     def _generate_examples(self, filepath):
         if filepath.endswith(".arrow"):
             table = pa.ipc.RecordBatchFileReader(filepath).read_all()
-            records = table.to_pydict()
-            keys = list(records.keys())
-            for i in range(len(records[keys[0]])):
-                yield i, {k: records[k][i] for k in keys}
+            data = table.to_pydict()
+            keys = list(data.keys())
+            for i in range(len(data[keys[0]])):
+                yield i, {k: data[k][i] for k in keys}
         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 for file: {filepath}")
+            raise ValueError(f"Unsupported file format: {filepath}")