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README.md

@@ -1,145 +1,54 @@
----
-language:
-  - zh
-license:
-  - mit
-multilinguality:
-  - monolingual
-size_categories:
-  - 1K<n<10K
-source_datasets:
-  - original
-task_categories:
-  - text-classification
-  - image-classification
-task_ids:
-  - sentiment-classification
-tags:
-  - advertisement
-  - social-media
-  - xiaohongshu
-  - RedNote
-pretty_name: CHASM - Covert Advertisement on RedNote
-configs:
-  - config_name: default
-    default: true
-    data_files:
-      - split: train
-        path: "train.parquet"
-      - split: validation
-        path: "validation.parquet"
-      - split: test
-        path: "test.parquet"
----
+# RedNote广告检测数据集
 
-<!-- @format -->
+此数据集包含RedNote平台上的帖子，用于广告检测任务。
 
-# CHASM Dataset
+## 数据集结构
 
-CHASM (Covert Advertisement on RedNote) is a dataset containing RedNote posts for text and image classification tasks focused on identifying covert advertisements. The dataset includes samples categorized as advertisements (label=1) and non-advertisements (label=0).
+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 Format
+## 字段说明
 
-The dataset is provided in an optimized parquet format:
+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）
 
-```
-parquet_data_optimized/
-├── train.parquet       # Training set
-├── validation.parquet  # Validation set
-└── test.parquet        # Test set
-```
-
-Images are stored in a structured directory system:
-
-```
-processed_images/
-├── images_0000/        # Image subfolder 0
-├── images_0001/        # Image subfolder 1
-└── ...                 # Additional image subfolders
-```
-
-Each image is named following the pattern `post_{id}_img_{index}.jpg`, where:
-
-- `id` is the unique numeric identifier of the post
-- `index` is the image index within that post
-
-The parquet files contain the following fields:
-
-- `id`: Numeric sample identifier (sequentially assigned)
-- `title`: Post title
-- `description`: Post description content
-- `date`: Published date and location
-- `comments`: List of comments (as array)
-- `images`: List of image paths relative to processed_images directory
-- `image_count`: Number of images in the post
-- `label`: Classification label (0: non-advertisement, 1: advertisement)
-- `split`: Dataset split (train, validation, test)
-
-## Dataset Statistics
-
-- **Total samples**: 4,992 samples
-- **Class distribution**:
-  - Non-advertisements (label=0): 4,379 samples (87.7%)
-  - Advertisements (label=1): 613 samples (12.3%)
-- **Splits**:
-  - Training set: 3,493 samples
-  - Validation set: 499 samples
-  - Test set: 1,000 samples
-- **Images**: 26,324 images in total
-- **Average images per post**: 5.27 images
-
-## Usage
-
-### Loading Parquet Data
+## 使用方法
 
 ```python
 import pandas as pd
 
-# Load training set
-train_df = pd.read_parquet("parquet_data_optimized/train.parquet")
+# 读取训练集
+train_df = pd.read_parquet("train.parquet")
 
-# View data
-print(train_df.head())
-print(f"Training samples: {len(train_df)}")
-
-# Access images
-print(f"First sample images: {train_df.iloc[0]['images']}")
-print(f"Image count: {train_df.iloc[0]['image_count']}")
+# 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()}")
 ```
 
-### Accessing Images
+## 图像处理
+
+图像以base64编码形式存储，可以使用以下代码解码：
 
 ```python
-import os
+import base64
+import io
 from PIL import Image
-import matplotlib.pyplot as plt
 
-# Function to load and display an image
-def load_and_display_image(image_path, base_dir="processed_images"):
-    full_path = os.path.join(base_dir, image_path)
-    img = Image.open(full_path)
-    plt.figure(figsize=(10, 8))
-    plt.imshow(img)
-    plt.axis('off')
-    plt.title(f"Image: {image_path}")
-    plt.show()
+# 获取第一个样本的第一张图像
+base64_str = train_df.iloc[0]['images'][0]
 
-# Example: load the first image of the first post in the training set
-if len(train_df) > 0 and len(train_df.iloc[0]['images']) > 0:
-    first_image_path = train_df.iloc[0]['images'][0]
-    load_and_display_image(first_image_path)
-```
-
-## Citation
-
-If you use the CHASM dataset, please cite:
-
-```
-@misc{CHASM2023,
-  author = {Jingyi},
-  title = {CHASM: Covert Advertisement on RedNote},
-  year = {2023},
-  publisher = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/datasets/Jingyi77/CHASM-Covert_Advertisement_on_RedNote}}
-}
+# 解码base64并显示图像
+img_data = base64.b64decode(base64_str)
+img = Image.open(io.BytesIO(img_data))
+img.show()
 ```