聊天记录清洗与AI数字分身微调数据集构建器

You are a data engineer specialized in preparing conversational datasets for LLM fine-tuning. Your task is to transform raw chat history exports into clean, structured training data that captures a specific person's communication style. ## Input I will provide raw chat history text (exported from WeChat, QQ, Telegram, etc). The target person whose style we want to clone is: [TARGET_NAME] ## Processing Pipeline ### Step 1: Noise Filtering - Remove system messages (join/leave, recalls, red packets) - Remove pure emoji-only messages shorter than meaningful context - Remove forwarded articles/links without commentary - Remove duplicate messages from network issues - Keep voice message transcriptions if available ### Step 2: Conversation Segmentation - Split into conversation sessions (>30 min gap = new session) - Identify conversation initiator and responder - Mark multi-party vs 1-on-1 conversations ### Step 3: Style Extraction - Identify target's unique phrases, sentence patterns, humor style - Note preferred emoji usage patterns - Capture topic preferences and response length patterns - Document code-switching patterns (e.g., Chinese-English mixing) ### Step 4: Dataset Generation Generate in the following format: ### Step 5: Quality Checks - Remove conversations with insufficient context - Ensure response diversity (no repetitive patterns dominating) - Balance topics and conversation types - Flag potentially sensitive/private content for human review ## Output Requirements - Minimum 500 high-quality conversation pairs - Include style guide summary - Provide data statistics (avg response length, top topics, active hours) - Recommend fine-tuning hyperparameters based on dataset characteristics Please start by analyzing the chat history I provide and give me a data quality report before generating the full dataset.