wangyu
7d4345c29d
Two-module pipeline for AI companion safety: - Module B: context-aware risk detector with CrossAttention fusion - Module C: PPO-based adaptive intervention policy Includes CompanionRisk Taxonomy (10 primary + 14 fine-grained labels), dataset generation/annotation pipeline, training scripts, and eval suite. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
41 lines
1.0 KiB
Python
41 lines
1.0 KiB
Python
"""
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Step 1: Generate companion conversation dataset using LLM.
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Usage:
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python scripts/generate_data.py --config configs/data_generation.yaml
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"""
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import argparse
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import yaml
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from pathlib import Path
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from src.data.data_generator import ConversationGenerator
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--config", default="configs/data_generation.yaml")
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args = parser.parse_args()
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with open(args.config) as f:
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cfg = yaml.safe_load(f)
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Path(cfg["output"]["raw_dir"]).mkdir(parents=True, exist_ok=True)
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generator = ConversationGenerator(
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api_type=cfg["api"]["type"],
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model=cfg["api"]["model"],
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)
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count = generator.generate_dataset(
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output_path=cfg["output"]["output_file"],
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total_samples=cfg["generation"]["total_samples"],
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samples_per_category=cfg["generation"]["samples_per_category"],
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delay=cfg["generation"]["delay"],
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)
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print(f"Generated {count} samples → {cfg['output']['output_file']}")
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if __name__ == "__main__":
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main()
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