Update README to specify binary classification and simplify notes

README.md

@@ -19,7 +19,7 @@ tags:
   - advertisement
   - social-media
   - xiaohongshu
-  - redbook
+  - RedNote
 pretty_name: CHASM - Covert Advertisement on RedNote
 ---
 
@@ -31,7 +31,7 @@ A dataset containing posts from Xiaohongshu (RedNote) for text classification ta
 
 ## Dataset Description
 
-- **Task Type**: Text Classification
+- **Task Type**: Text Classification (Binary Classification)
 - **Language**: Chinese
 - **License**: MIT
 - **Dataset Size**: Contains two classes (label_0 and label_1)
@@ -39,40 +39,40 @@ A dataset containing posts from Xiaohongshu (RedNote) for text classification ta
 
 ### Dataset Structure
 
-本数据集有两种格式：
+This dataset is available in two formats:
 
-#### 1. 原始目录格式 (Original Directory Structure)
+#### 1. Original Directory Structure
 
-数据按照以下目录结构组织：
+Data is organized in the following directory structure:
 
-- `label_0/`: 非广告内容
-- `label_1/`: 广告内容
+- `label_0/`: Non-advertisement content
+- `label_1/`: Advertisement content
 
-每个样本有一个独立的文件夹（如 `train_889/`），包含：
+Each sample has its own folder (e.g., `train_889/`), containing:
 
-- `data.json`: 包含文本数据
-- 多个图像文件（JPG/WEBP 格式）
+- `data.json`: Text data
+- Multiple image files (JPG/WEBP format)
 
-#### 2. Hugging Face 格式 (Hugging Face Format)
+#### 2. Hugging Face Format
 
-为了更好地与 Hugging Face 平台兼容，我们还提供了处理后的格式：
+For better compatibility with the Hugging Face platform, we also provide processed formats:
 
-- `hf_format/train.csv` 和 `hf_format/train.json`: 训练集
-- `hf_format/validation.csv` 和 `hf_format/validation.json`: 验证集
-- `hf_format/test.csv` 和 `hf_format/test.json`: 测试集
-- `hf_format/dataset_info.json`: 数据集元信息
+- `hf_format/train.csv` and `hf_format/train.json`: Training set
+- `hf_format/validation.csv` and `hf_format/validation.json`: Validation set
+- `hf_format/test.csv` and `hf_format/test.json`: Test set
+- `hf_format/dataset_info.json`: Dataset metadata
 
-### 数据字段说明
+### Data Fields
 
 Each data sample contains the following fields:
 
-- `id`: 样本唯一标识符
-- `title`: 帖子标题
-- `description`: 帖子描述
-- `date`: 发布日期和位置
-- `comments`: 评论列表
-- `images`: 图像文件名列表
-- `label`: 分类标签 (0: 非广告, 1: 广告)
+- `id`: Sample unique identifier
+- `title`: Post title
+- `description`: Post description
+- `date`: Publication date and location
+- `comments`: List of comments
+- `images`: List of image filenames
+- `label`: Classification label (0: non-advertisement, 1: advertisement)
 
 ### Data Example
 
@@ -90,56 +90,56 @@ Each data sample contains the following fields:
 
 ## Usage
 
-使用原始目录格式：
+Using the original directory structure:
 
 ```python
 import os
 import json
 from glob import glob
 
-# 遍历所有样本
+# Iterate through all samples
 for label_dir in ['label_0', 'label_1']:
     for sample_dir in glob(f"{label_dir}/*"):
         if os.path.isdir(sample_dir):
-            # 读取数据文件
+            # Read data file
             with open(os.path.join(sample_dir, 'data.json'), 'r', encoding='utf-8') as f:
                 data = json.load(f)
 
-            # 获取图像文件
+            # Get image files
             images = glob(os.path.join(sample_dir, '*.jpg'))
 
-            # 处理数据...
+            # Process data...
 ```
 
-使用 Hugging Face 格式：
+Using Hugging Face format:
 
 ```python
 import pandas as pd
 
-# 加载数据
+# Load data
 train_df = pd.read_csv('hf_format/train.csv')
 val_df = pd.read_csv('hf_format/validation.csv')
 test_df = pd.read_csv('hf_format/test.csv')
 
-# 或者使用JSON格式
+# Or use JSON format
 import json
 with open('hf_format/train.json', 'r', encoding='utf-8') as f:
     train_data = json.load(f)
 ```
 
-使用 Hugging Face datasets 库：
+Using Hugging Face datasets library:
 
 ```python
 from datasets import load_dataset
 
-# 加载数据集
+# Load dataset
 dataset = load_dataset("Jingyi77/CHASM-Covert_Advertisement_on_RedNote")
 
-# 查看数据集信息
+# View dataset information
 print(dataset)
 
-# 访问数据
-print(dataset['train'][0])  # 查看训练集的第一个数据点
+# Access data
+print(dataset['train'][0])  # View first data point in training set
 ```
 
 ## Data Preprocessing
@@ -150,10 +150,7 @@ print(dataset['train'][0])  # 查看训练集的第一个数据点
 
 ## Notes
 
-1. Image files need to be downloaded and processed separately
-2. Comments may contain sensitive content, please use with caution
-3. All temporal information has been anonymized
-4. This dataset is specifically designed for research on covert advertisement detection
+This dataset is specifically designed for research on covert advertisement detection as a binary classification task.
 
 ## Dataset Creation