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README.md

@@ -1,77 +1,73 @@
 ---
 language:
-- zh
+  - zh
 license: mit
 pretty_name: RedNote Covert Advertisement Detection Dataset
 tags:
-- covert advertisement detection
-- social-media
-- image-text
-- multimodal
-- RedNote
-- Xiaohongshu
+  - covert advertisement detection
+  - social-media
+  - image-text
+  - multimodal
+  - RedNote
+  - Xiaohongshu
 datasets:
-- Jingyi77/CHASM-Covert_Advertisement_on_RedNote
+  - Jingyi77/CHASM-Covert_Advertisement_on_RedNote
 dataset_info:
-- config_name: default
-  features:
-  - name: id
-    dtype: string
-  - name: title
-    dtype: string
-  - name: description
-    dtype: string
-  - name: date
-    dtype: string
-  - name: comments
-    sequence:
-      dtype: string
-  - name: images
-    sequence:
-      dtype: binary
-  - name: image_count
-    dtype: int32
-  - name: label
-    dtype: int8
-  - name: split
-    dtype: string
+  - config_name: default
+    features:
+      - name: id
+        dtype: string
+      - name: title
+        dtype: string
+      - name: description
+        dtype: string
+      - name: date
+        dtype: string
+      - name: comments
+        sequence:
+          dtype: string
+      - name: images
+        sequence:
+          dtype: binary
+      - name: image_count
+        dtype: int32
+      - name: label
+        dtype: int8
+      - name: split
+        dtype: string
 configs:
-- config_name: default
-  data_files:
-  - split: test
-    path: test.parquet
-  - split: train
-    path: train.parquet
-  - split: validation
-    path: validation.parquet
+  - config_name: default
+    data_files:
+      - split: test
+        path: test.parquet
+      - split: train
+        path: train.parquet
+      - split: validation
+        path: validation.parquet
 
 size_categories:
-- 1K<n<10K
+  - 1K<n<10K
 ---
 
+<!-- @format -->
+
 # RedNote Covert Advertisement Detection Dataset
 
 This dataset contains posts from the RedNote platform for covert advertisement detection tasks.
 
 ## Dataset Overview
 
-| Split | Posts | Ad Posts | Non-Ad Posts | Total Images |
-|-------|-------|----------|--------------|-------------|
-| Train | 3493 | 426 | 3067 | 18543 |
-| Validation | 499 | 57 | 442 | 2678 |
-| Test | 1000 | 130 | 870 | 5103 |
-| **Total** | **4992** | **613** | **4379** | **26324** |
-
-## Dataset Structure
-
-The dataset is divided into three parts:
-- `train.parquet`: Training set
-- `validation.parquet`: Validation set
-- `test.parquet`: Test set
+| Split      | Posts    | Ad Posts | Non-Ad Posts | Total Images |
+| ---------- | -------- | -------- | ------------ | ------------ |
+| Train      | 3493     | 426      | 3067         | 18543        |
+| Validation | 499      | 57       | 442          | 2678         |
+| Test       | 1000     | 130      | 870          | 5103         |
+| **Total**  | **4992** | **613**  | **4379**     | **26324**    |
 
 ## Field Descriptions
 
 Each parquet file contains the following fields:
+
 - `id`: Unique identifier for each post
 - `title`: Post title
 - `description`: Post description content
@@ -82,43 +78,49 @@ Each parquet file contains the following fields:
 - `label`: Label (0=non-advertisement, 1=advertisement)
 - `split`: Data split (train/validation/test)
 
-## Usage
+## Dataset Features
 
-```python
-import pandas as pd
+- **Multimodal Data**: Each post contains both text (title, description, comments) and images
+- **Real-world Data**: Collected from actual social media posts on the RedNote platform
+- **Multiple Images**: Each post may contain multiple images (average of 5.27 images per post)
 
-# Load the training set
-train_df = pd.read_parquet("train.parquet")
+## Data Format
 
-# View dataset statistics
-print(f"Number of training samples: {len(train_df)}")
-print(f"Label distribution: {train_df['label'].value_counts()}")
-```
+The dataset is stored in WebDataset format, with each sample containing:
 
-## Image Processing
+1. One or more image files (.jpg format)
+2. A JSON metadata file with the following fields:
+   - `id`: Sample ID
+   - `title`: Title
+   - `description`: Description
+   - `date`: Date
+   - `comments`: List of comments
+   - `label`: Label (0: non-advertisement, 1: advertisement)
 
-Images are stored as base64 encoded strings. You can decode them using:
+## Loading the Dataset
 
 ```python
-import base64
-import io
-from PIL import Image
+from datasets import load_dataset
 
-# Get the first image from the first sample
-base64_str = train_df.iloc[0]['images'][0]
+# Load training set
+train_dataset = load_dataset("Jingyi77/CHASM-Covert_Advertisement_on_RedNote", split="train")
 
-# Decode base64 and display the image
-img_data = base64.b64decode(base64_str)
-img = Image.open(io.BytesIO(img_data))
-img.show()
-```
+# Load validation set
+val_dataset = load_dataset("Jingyi77/CHASM-Covert_Advertisement_on_RedNote", split="validation")
 
-## Dataset Features
-
-- **Multimodal Data**: Each post contains both text (title, description, comments) and images
-- **Real-world Data**: Collected from actual social media posts on the RedNote platform
-- **Multiple Images**: Each post may contain multiple images (average of 5.27 images per post)
+# Load test set
+test_dataset = load_dataset("Jingyi77/CHASM-Covert_Advertisement_on_RedNote", split="test")
 
+# Access a sample
+example = train_dataset[0]
+metadata = {
+    "id": example["id"],
+    "title": example["title"],
+    "description": example["description"],
+    "label": example["label"]
+}
+images = example["images"]  # List of images
+```
 
 ## Citation
 
@@ -126,11 +128,11 @@ If you use this dataset in your research, please cite:
 
 ```
 @dataset{CHASM,
-  author = {Jingyi Zheng, Tianyi Hu, Yule Liu, Zhen Sun, Zongmin Zhang, Wenhan Dong, Zifan Peng},
+  author = {Jingyi Zheng, Tianyi Hu, Yule Liu, Zhen Sun, Zongmin Zhang, Wenhan Dong, Zifan Peng, Xinlei He},
   title = {CHASM: Unveiling Covert Advertisements on Chinese Social Media},
   year = {2025},
   publisher = {Hugging Face},
   journal = {Hugging Face Hub},
   howpublished = {\url{https://huggingface.co/datasets/Jingyi77/CHASM-Covert_Advertisement_on_RedNote}}
 }
-```
+```