classifier
# Text Classification with Classifier
What this skill does
# Text Classification with Classifier Use when: User asks to classify text, detect spam, analyze sentiment, detect emotions, or use pre-trained ML models. ## Pre-trained Models Run `classifier models` to see all available models. Common ones: | Model | Command | Use Case | |-------|---------|----------| | `sms-spam-filter` | `classifier -r sms-spam-filter "text"` | Spam detection | | `imdb-sentiment` | `classifier -r imdb-sentiment "text"` | Sentiment analysis | | `emotion-detection` | `classifier -r emotion-detection "text"` | Emotion classification | ## Quick Classification ```bash # Classify with a pre-trained model classifier -r <model-name> "text to classify" # Example: detect spam classifier -r sms-spam-filter "You won a free iPhone! Click here now!" # Example: sentiment analysis classifier -r imdb-sentiment "This movie was absolutely terrible" # Example: emotion detection classifier -r emotion-detection "I am so happy today" ``` ## Custom Training ```bash # Train from text classifier train positive "Great product, love it" classifier train negative "Terrible quality, waste of money" # Train from files classifier train positive reviews/good/*.txt classifier train negative reviews/bad/*.txt # Classify after training classifier "This product exceeded my expectations" ``` ## Model Management ```bash # List all available models classifier models # Show model details classifier info <model-name> # Save trained model classifier save my-model.json # Load saved model classifier load my-model.json ``` ## Best Practices 1. For quick classification tasks, use pre-trained models first 2. For custom domains, train with representative examples from each category 3. Use `classifier models` to discover available pre-trained models 4. Balance training data across categories for best results
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