Text Classification with Classifier

Name: classifier
Rating: 92
Author: cardmagic

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

•For quick classification tasks, use pre-trained models first
•For custom domains, train with representative examples from each category
•Use classifier models to discover available pre-trained models
•Balance training data across categories for best results