Create dataset.py

dataset.py

@@ -0,0 +1,68 @@
+import datasets
+
+_CITATION = """\
+@misc{deepurlbench2025,
+  author       = {Deep Instinct Research Team},
+  title        = {DeepURLBench: A large-scale benchmark for URL classification},
+  year         = {2025},
+  howpublished = {\\url{https://huggingface.co/datasets/DeepInstinct/DeepURLBench}}
+}
+"""
+
+_DESCRIPTION = """\
+DeepURLBench is a large-scale benchmark for real-world URL classification.
+It includes two subsets: one with DNS resolution information and one without.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/DeepInstinct/DeepURLBench"
+
+_LICENSE = "cc-by-nc-4.0"
+
+# If your files are hosted in the root of the repo
+_URLS = {
+    "with_dns": "https://huggingface.co/datasets/DeepInstinct/DeepURLBench/resolve/main/urls_with_dns.parquet",
+    "without_dns": "https://huggingface.co/datasets/DeepInstinct/DeepURLBench/resolve/main/urls_without_dns.parquet",
+}
+
+
+class DeepURLBench(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="with_dns", version=VERSION, description="URLs with DNS info"),
+        datasets.BuilderConfig(name="without_dns", version=VERSION, description="URLs without DNS info"),
+    ]
+
+    def _info(self):
+        if self.config.name == "with_dns":
+            features = datasets.Features({
+                "url": datasets.Value("string"),
+                "first_seen": datasets.Value("string"),
+                "TTL": datasets.Value("int32"),
+                "label": datasets.Value("string"),
+                "ip_address": datasets.Sequence(datasets.Value("string")),
+            })
+        else:  # without_dns
+            features = datasets.Features({
+                "url": datasets.Value("string"),
+                "first_seen": datasets.Value("string"),
+                "label": datasets.Value("string"),
+            })
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_file = _URLS[self.config.name]
+        downloaded_file = dl_manager.download_and_extract(data_file)
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file})]
+
+    def _generate_examples(self, filepath):
+        import pandas as pd
+        df = pd.read_parquet(filepath)
+        for idx, row in df.iterrows():
+            yield idx, row.to_dict()