This document provides a comprehensive guide on configuring the text classification feature within the AI Toolkit. Text classification is a crucial component for understanding and categorizing text data, enabling various applications such as sentiment analysis, spam detection, and topic modeling.

Getting Started

Before diving into the configuration details, make sure you have the AI Toolkit installed and properly set up. For more information on installation and initial setup, please refer to the AI Toolkit Installation Guide.

Configuration Steps

  1. Enable Text Classification:

    • Navigate to the AI Toolkit Configuration page.
    • Enable the Text Classification feature under the Features section.

  2. Select Model:

    • Choose the appropriate text classification model based on your requirements. The AI Toolkit offers a variety of pre-trained models, including:
      • General Model: Suitable for general text classification tasks.
      • Sentiment Analysis Model: Designed for sentiment analysis tasks.
      • Spam Detection Model: Effective for identifying spam messages.

  3. Set Labels:

    • Define the labels for your text classification task. Labels are the categories into which the text will be classified. For example, in a sentiment analysis task, the labels could be "Positive", "Negative", and "Neutral".

  4. Configure Training Data:

    • Upload your training data and configure the data preprocessing options, such as text cleaning and tokenization.

  5. Train the Model:

    • Click on the "Train" button to start the training process. The AI Toolkit will automatically train the selected model using your training data.

  6. Evaluate the Model:

    • Once the training is complete, evaluate the model's performance using the provided evaluation metrics, such as accuracy, precision, and recall.

  7. Deploy the Model:

    • After evaluating and fine-tuning the model, deploy it for real-time text classification tasks.

Additional Resources

For more detailed information on text classification and the AI Toolkit, please refer to the following resources:

Example

Here's an example of how to configure the text classification feature for sentiment analysis:

  • Step 1: Enable the Text Classification feature.
  • Step 2: Select the Sentiment Analysis Model.
  • Step 3: Set the labels as "Positive", "Negative", and "Neutral".
  • Step 4: Upload your training data and configure the preprocessing options.
  • Step 5: Train the model.
  • Step 6: Evaluate the model using the evaluation metrics.
  • Step 7: Deploy the model for real-time sentiment analysis tasks.

[center] Sentiment Analysis Model

By following these steps, you can easily configure the text classification feature within the AI Toolkit and leverage its capabilities for various text processing tasks.