Create load_script.py

load_script.py

@@ -0,0 +1,69 @@
+import os
+import datasets
+
+_CITATION = ""  # Add citation if available
+_DESCRIPTION = "Phantom Hallucination Detection dataset with multiple configurations based on token length and position."
+_HOMEPAGE = "https://huggingface.co/datasets/seyled/Phantom_Hallucination_Detection"
+_LICENSE = "for-review-only"
+
+# List of all configurations (filename without path or extension)
+CONFIG_NAMES = [
+    "10K_seed", "497K_seed", "8K_seed", "def14a_seed",
+    "10K_2000tokens_beginning", "10K_2000tokens_middle", "10K_2000tokens_end",
+    "10K_5000tokens_beginning", "10K_5000tokens_middle", "10K_5000tokens_end",
+    "10K_10000tokens_beginning", "10K_10000tokens_middle", "10K_10000tokens_end",
+    "10K_20000tokens_beginning", "10K_20000tokens_middle", "10K_20000tokens_end",
+    "10K_30000tokens_beginning", "10K_30000tokens_middle", "10K_30000tokens_end",
+    "def14A_2000tokens_beginning", "def14A_2000tokens_middle", "def14A_2000tokens_end",
+    "def14A_5000tokens_beginning", "def14A_5000tokens_middle", "def14A_5000tokens_end",
+    "def14A_10000tokens_beginning", "def14A_10000tokens_middle", "def14A_10000tokens_end",
+    "def14A_20000tokens_beginning", "def14A_20000tokens_middle", "def14A_20000tokens_end",
+    "def14A_30000tokens_beginning", "def14A_30000tokens_middle", "def14A_30000tokens_end"
+]
+
+# Define a config per dataset variant
+class PhantomConfig(datasets.BuilderConfig):
+    def __init__(self, data_file, **kwargs):
+        super().__init__(**kwargs)
+        self.data_file = data_file
+
+class PhantomDataset(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIG_CLASS = PhantomConfig
+    BUILDER_CONFIGS = [
+        PhantomConfig(
+            name=config_name,
+            version=datasets.Version("1.0.0"),
+            description=f"Phantom dataset variant: {config_name}",
+            data_file=f"PhantomDataset/Phantom_{config_name}.csv"
+        )
+        for config_name in CONFIG_NAMES
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "text": datasets.Value("string"),
+                # Add other fields if applicable
+            }),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_file = dl_manager.download_and_extract(self.config.data_file)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_file},
+            )
+        ]
+
+    def _generate_examples(self, filepath):
+        import csv
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for idx, row in enumerate(reader):
+                yield idx, row