Add Hugging Face dataset loading script

chasm.py

@@ -0,0 +1,126 @@
+import json
+import os
+import pandas as pd
+
+import datasets
+from datasets.tasks import TextClassification, ImageClassification
+
+_CITATION = """\
+@misc{CHASM2023,
+  author       = {Jingyi Zhen},
+  title        = {CHASM: Covert Advertisement on RedNote},
+  year         = {2023},
+  publisher    = {Hugging Face},
+  howpublished = {https://huggingface.co/datasets/Jingyi77/CHASM-Covert_Advertisement_on_RedNote},
+}
+"""
+
+_DESCRIPTION = """\
+A dataset containing posts from Xiaohongshu (RedNote) for text classification tasks, 
+specifically focused on identifying covert advertisements.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/Jingyi77/CHASM-Covert_Advertisement_on_RedNote"
+
+_LICENSE = "MIT"
+
+_URLS = {
+    "train": "hf_format/train.json",
+    "validation": "hf_format/validation.json",
+    "test": "hf_format/test.json",
+}
+
+_FEATURES = datasets.Features({
+    "id": datasets.Value("string"),
+    "title": datasets.Value("string"),
+    "description": datasets.Value("string"),
+    "date": datasets.Value("string"),
+    "comments": datasets.Sequence(datasets.Value("string")),
+    "images": datasets.Sequence(datasets.Value("string")),
+    "label": datasets.ClassLabel(names=["non_advertisement", "advertisement"]),
+})
+
+class CHASMConfig(datasets.BuilderConfig):
+    """BuilderConfig for CHASM dataset."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for CHASM.
+        Args:
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(CHASMConfig, self).__init__(**kwargs)
+
+
+class CHASM(datasets.GeneratorBasedBuilder):
+    """CHASM: Covert Advertisement on RedNote Dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        CHASMConfig(
+            name="chasm",
+            version=VERSION,
+            description="CHASM dataset for covert advertisement detection",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=_FEATURES,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            task_templates=[
+                TextClassification(
+                    text_column="description", 
+                    label_column="label",
+                ),
+                ImageClassification(
+                    image_column="images",
+                    label_column="label",
+                ),
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = os.path.dirname(os.path.abspath(__file__))
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, _URLS["train"]),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, _URLS["validation"]),
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, _URLS["test"]),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Generate CHASM examples."""
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for i, example in enumerate(data):
+                yield i, {
+                    "id": example["id"],
+                    "title": example["title"],
+                    "description": example["description"],
+                    "date": example["date"],
+                    "comments": example["comments"],
+                    "images": example["images"],
+                    "label": example["label"],
+                }