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. : Implement the activation function using the formula: .