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.
npx skills add ECNU-ICALK/AutoSkill --skill pytorch-training-configuration-and-evaluation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 th
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What does the PyTorch Training Configuration and Evaluation skill do?
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.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill pytorch-training-configuration-and-evaluation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.