zhangsiyuan
bd1f51c496
Merged code repo (CompanionGuard-RL) into single project-level git. Reorganized root: docs/, reference/, experiments/, tmp/active|archives/. Gitignored: data/, checkpoints/, .venv, experiment logs, tmp/archives. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
52 lines
1.5 KiB
YAML
52 lines
1.5 KiB
YAML
model:
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name: "hfl/chinese-macbert-large"
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hidden_size: 1024
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num_heads: 8
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dropout: 0.1
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use_lora: false
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data:
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train_path: "data/processed/CompanionRisk-Bench/train.jsonl"
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val_path: "data/processed/CompanionRisk-Bench/dev.jsonl"
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test_path: "data/processed/CompanionRisk-Bench/test.jsonl"
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max_persona_len: 128
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max_context_len: 512
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max_response_len: 256
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max_history_turns: 5
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num_workers: 0 # 0 for Windows (avoids multiprocessing issues); set to 4 on Linux
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training:
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epochs: 10
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per_gpu_batch_size: 16 # single GPU: 16; 4 GPUs: use 32 (effective 128)
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gradient_accumulation_steps: 2 # effective_batch = 16 × 1 GPU × 2 = 32
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lr: 2e-5
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warmup_steps: 100
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weight_decay: 0.01
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gradient_clip: 1.0
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eval_steps: 100 # global steps between validation runs
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mixed_precision: "bf16" # RTX 5090: bf16; RTX 30xx/40xx: fp16; CPU-only: no
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seed: 42
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loss_weights:
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binary: 1.0
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level: 1.0
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primary: 1.0
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fine: 1.0 # 下次训练建议提升到 2.0，配合 fine_training 选项
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# Fine-grained label training options（下次训练时开启，当前 best.pt 不受影响）
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fine_training:
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use_pos_weight: false # 改为 true 开启 pos_weight（下次训练）
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risky_only: false # 改为 true 开启（下次训练）
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evaluation:
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binary_threshold: 0.5
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fine_threshold: 0.4
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logging:
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project: "CompanionGuard-RL"
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run_name: "detector-macbert-v1"
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use_wandb: false # set true if wandb is configured
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output:
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checkpoint_dir: "checkpoints/detector"
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