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From classifier
Trains a text classifier by associating a category with labeled examples from text or file patterns.
How this command is triggered — by the user, by Claude, or both
Slash command
/classifier:trainThe summary Claude sees in its command listing — used to decide when to auto-load this command
# /classifier:train Train a classifier with labeled examples. ## Usage ## Examples ## Instructions Run the classifier train command: After training, inform the user they can: 1. Add more training examples with additional `/classifier:train` commands 2. Classify new text with `classifier "text to classify"` 3. Save the model with `classifier save model-name.json` For best results, recommend balanced training data across all categories.
Train a classifier with labeled examples.
/classifier:train <category> <text or file pattern>
/classifier:train spam "Buy cheap viagra now"
/classifier:train ham "Meeting scheduled for tomorrow"
/classifier:train positive reviews/good/*.txt
/classifier:train negative reviews/bad/*.txt
Run the classifier train command:
classifier train "$category" "$text_or_pattern"
After training, inform the user they can:
/classifier:train commandsclassifier "text to classify"classifier save model-name.jsonFor best results, recommend balanced training data across all categories.
npx claudepluginhub cardmagic/classifier/text-classificationGuides through an LLM-based text classification workflow covering codebook design, learning regime selection, human-LLM hybrid approaches, cross-model validation, and agreement statistics.
/trainTrains a machine learning model on a dataset with cross-validation, selecting the appropriate model type and saving the trained artifact with performance metrics.
/build-classifierBuilds a classifier for an ML task by analyzing requirements, generating validated code, providing performance metrics, and saving artifacts with documentation.
/trainTrains machine learning and deep learning models using PyTorch, TensorFlow, or Hugging Face frameworks. Supports fine-tuning, transfer learning, and checkpoint management.
/analyze-sentimentExecutes AI/ML tasks by analyzing the current context, generating code with data validation and error handling, providing performance metrics, and saving artifacts and documentation.
/analyze-textExecutes an AI/ML task by analyzing context, generating code with validation and error handling, and producing performance metrics and documentation.