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README.md
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language:
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- zh
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license:
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- mit
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multilinguality:
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- monolingual
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size_categories:
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- 1K<n<10K
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source_datasets:
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- original
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task_categories:
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- text-classification
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- image-classification
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task_ids:
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- sentiment-classification
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tags:
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- advertisement
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- social-media
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- xiaohongshu
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- RedNote
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pretty_name: CHASM - Covert Advertisement on RedNote
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configs:
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- config_name: default
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default: true
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data_files:
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- split: train
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path: "train.parquet"
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- split: validation
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path: "validation.parquet"
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- split: test
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path: "test.parquet"
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---
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##
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parquet_data_optimized/
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├── train.parquet # Training set
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├── validation.parquet # Validation set
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└── test.parquet # Test set
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```
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Images are stored in a structured directory system:
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```
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processed_images/
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├── images_0000/ # Image subfolder 0
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├── images_0001/ # Image subfolder 1
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└── ... # Additional image subfolders
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```
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Each image is named following the pattern `post_{id}_img_{index}.jpg`, where:
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- `id` is the unique numeric identifier of the post
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- `index` is the image index within that post
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The parquet files contain the following fields:
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- `id`: Numeric sample identifier (sequentially assigned)
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- `title`: Post title
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- `description`: Post description content
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- `date`: Published date and location
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- `comments`: List of comments (as array)
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- `images`: List of image paths relative to processed_images directory
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- `image_count`: Number of images in the post
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- `label`: Classification label (0: non-advertisement, 1: advertisement)
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- `split`: Dataset split (train, validation, test)
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## Dataset Statistics
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- **Total samples**: 4,992 samples
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- **Class distribution**:
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- Non-advertisements (label=0): 4,379 samples (87.7%)
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- Advertisements (label=1): 613 samples (12.3%)
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- **Splits**:
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- Training set: 3,493 samples
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- Validation set: 499 samples
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- Test set: 1,000 samples
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- **Images**: 26,324 images in total
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- **Average images per post**: 5.27 images
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## Usage
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### Loading Parquet Data
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```python
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import pandas as pd
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#
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train_df = pd.read_parquet("
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#
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print(train_df
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print(f"
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# Access images
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print(f"First sample images: {train_df.iloc[0]['images']}")
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print(f"Image count: {train_df.iloc[0]['image_count']}")
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```
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```python
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import
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from PIL import Image
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import matplotlib.pyplot as plt
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#
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full_path = os.path.join(base_dir, image_path)
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img = Image.open(full_path)
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plt.figure(figsize=(10, 8))
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plt.imshow(img)
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plt.axis('off')
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plt.title(f"Image: {image_path}")
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plt.show()
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#
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```
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## Citation
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If you use the CHASM dataset, please cite:
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```
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@misc{CHASM2023,
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author = {Jingyi},
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title = {CHASM: Covert Advertisement on RedNote},
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year = {2023},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/datasets/Jingyi77/CHASM-Covert_Advertisement_on_RedNote}}
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}
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```
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# RedNote广告检测数据集
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此数据集包含RedNote平台上的帖子,用于广告检测任务。
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## 数据集结构
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数据集分为三个部分:
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- `train.parquet`: 训练集
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- `validation.parquet`: 验证集
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- `test.parquet`: 测试集
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## 字段说明
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每个parquet文件包含以下字段:
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- `id`: 帖子唯一标识符
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- `title`: 帖子标题
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- `description`: 帖子描述内容
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- `date`: 发布日期(格式:月-日)
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- `comments`: 评论列表
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- `images`: 图像的base64编码列表
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- `image_count`: 图像数量
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- `label`: 标签(0=非广告,1=广告)
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- `split`: 数据分割(train/validation/test)
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## 使用方法
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```python
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import pandas as pd
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# 读取训练集
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train_df = pd.read_parquet("train.parquet")
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# 查看数据
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print(f"训练集样本数: {len(train_df)}")
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print(f"标签分布: {train_df['label'].value_counts()}")
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```
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## 图像处理
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图像以base64编码形式存储,可以使用以下代码解码:
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```python
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import base64
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import io
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from PIL import Image
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# 获取第一个样本的第一张图像
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base64_str = train_df.iloc[0]['images'][0]
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# 解码base64并显示图像
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img_data = base64.b64decode(base64_str)
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img = Image.open(io.BytesIO(img_data))
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img.show()
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```
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