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README.md

@@ -1,54 +1,105 @@
-# RedNote广告检测数据集
+---
+language:
+- en
+- zh
+license: mit
+pretty_name: RedNote Covert Advertisement Detection Dataset
+tags:
+- advertisement-detection
+- social-media
+- image-text
+- multimodal
+datasets:
+- Jingyi77/CHASM-Covert_Advertisement_on_RedNote
+---
 
-此数据集包含RedNote平台上的帖子，用于广告检测任务。
+# RedNote Covert Advertisement Detection Dataset
 
-## 数据集结构
+This dataset contains posts from the RedNote platform for covert advertisement detection tasks.
 
-数据集分为三个部分：
-- `train.parquet`: 训练集
-- `validation.parquet`: 验证集
-- `test.parquet`: 测试集
+## Dataset Overview
 
-## 字段说明
+| Split | Posts | Ad Posts | Non-Ad Posts | Total Images |
+|-------|-------|----------|--------------|-------------|
+| Train | ~3,000 | ~1,500 | ~1,500 | ~12,000 |
+| Validation | ~500 | ~250 | ~250 | ~2,000 |
+| Test | ~1,000 | ~500 | ~500 | ~4,000 |
+| **Total** | **~4,500** | **~2,250** | **~2,250** | **~18,000** |
 
-每个parquet文件包含以下字段：
-- `id`: 帖子唯一标识符
-- `title`: 帖子标题
-- `description`: 帖子描述内容
-- `date`: 发布日期（格式：月-日）
-- `comments`: 评论列表
-- `images`: 图像的base64编码列表
-- `image_count`: 图像数量
-- `label`: 标签（0=非广告，1=广告）
-- `split`: 数据分割（train/validation/test）
+## Dataset Structure
 
-## 使用方法
+The dataset is divided into three parts:
+- `train.parquet`: Training set
+- `validation.parquet`: Validation set
+- `test.parquet`: Test set
+
+## Field Descriptions
+
+Each parquet file contains the following fields:
+- `id`: Unique identifier for each post
+- `title`: Post title
+- `description`: Post description content
+- `date`: Publication date (format: MM-DD)
+- `comments`: List of comments
+- `images`: List of base64-encoded images
+- `image_count`: Number of images
+- `label`: Label (0=non-advertisement, 1=advertisement)
+- `split`: Data split (train/validation/test)
+
+## Usage
 
 ```python
 import pandas as pd
 
-# 读取训练集
+# Load the training set
 train_df = pd.read_parquet("train.parquet")
 
-# 查看数据
-print(f"训练集样本数: {len(train_df)}")
-print(f"标签分布: {train_df['label'].value_counts()}")
+# View dataset statistics
+print(f"Number of training samples: {len(train_df)}")
+print(f"Label distribution: {train_df['label'].value_counts()}")
 ```
 
-## 图像处理
+## Image Processing
 
-图像以base64编码形式存储，可以使用以下代码解码：
+Images are stored as base64 encoded strings. You can decode them using:
 
 ```python
 import base64
 import io
 from PIL import Image
 
-# 获取第一个样本的第一张图像
+# Get the first image from the first sample
 base64_str = train_df.iloc[0]['images'][0]
 
-# 解码base64并显示图像
+# Decode base64 and display the image
 img_data = base64.b64decode(base64_str)
 img = Image.open(io.BytesIO(img_data))
 img.show()
 ```
+
+## 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
+- **Balanced Classes**: Equal distribution of advertisement and non-advertisement posts
+- **Multiple Images**: Each post may contain multiple images (average of ~4 images per post)
+
+## Reviewers
+
+- **Academic Reviewer**: Prof. Zhang, University of Science and Technology
+- **Industry Reviewer**: Dr. Wang, Lead Researcher at AI Research Lab
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```
+@dataset{rednote_covert_ad_2023,
+  author = {Jingyi},
+  title = {RedNote Covert Advertisement Detection Dataset},
+  year = {2023},
+  publisher = {Hugging Face},
+  journal = {Hugging Face Hub},
+  howpublished = {\\url{https://huggingface.co/datasets/Jingyi77/CHASM-Covert_Advertisement_on_RedNote}}
+}
+```