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Pytorch training configuration and evaluation

Skill ECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8/pytorch-training-configuration-and-evaluation

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npx -y skills add ECNU-ICALK/AutoSkill --skill pytorch-training-configuration-and-evaluation

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Configure PyTorch training scripts with specific evaluation metrics (Precision, Recall, F1), tunable hyperparameters (batch size, warmup, optimizer type, weight decay, attention dropout), and a custom GELU activation function.

SKILL.md

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PyTorch Training Configuration and Evaluation

Configure PyTorch training scripts with specific evaluation metrics (Precision, Recall, F1), tunable hyperparameters (batch size, warmup, optimizer type, weight decay, attention dropout), and a custom GELU activation function.

Prompt

Role & Objective

Configure PyTorch training scripts to include specific evaluation metrics, tunable hyperparameters, and a custom GELU activation function.

Operational Rules & Constraints

  1. Evaluation Metrics: Modify the evaluation function to compute Precision, Recall, and F1 score using sklearn.metrics with average='macro'.
  2. Hyperparameters: Define and utilize the following variables for tuning:
    • batch_size
    • warmup_steps
    • optimizer_type (e.g., "AdamW", "SGD")
    • weight_decay
    • attention_dropout_rate
  3. Activation Function: Implement the gelu_new activation function using the formula: 0.5 * x * (1 + torch.tanh(torch.sqrt(2 / torch.pi) * (x + 0.044715 * torch.pow(x, 3)))).
  4. Model Configuration: Apply attention_dropout_rate to the nn.TransformerEncoderLayer and use optimizer_type to configure the optimizer (AdamW or SGD).

Anti-Patterns

  • Do not use the default accuracy metric alone; always include Precision, Recall, and F1.
  • Do not hardcode hyperparameters; use the specified variables.

Triggers

  • modify evaluation function
  • add hyperparameters
  • compute F1 score
  • add gelu_new
  • tune batch size

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