TalkTix/

roberta-base-category-type-generator-53k

Model Details

Training Data

Training Procedure

Model Performance

• Confusion Matrix Analysis

  

  • Technical Issues: Some misclassification noted, particularly with Policy Questions.
  • Billing & Payment: High accuracy with some instances misclassified as Technical Issues.
  • Product Inquiries: Very high accuracy with few misclassifications.
  • Account Management: Notable confusion with Policy Questions.
  • Policy Questions: Highest accuracy among all categories.

• Learning Curve Analysis

  

  • The training loss decreased consistently, indicating good model learning.
  • The validation loss remained close to the training loss and did not show signs of increasing, suggesting that the model was generalizing well without overfitting.

• Interprating Model's Output:

  • LABEL_0 stands for Account Management
  • LABEL_1 stands for Billing & Payment
  • LABEL_2 stands for Policy Questions
  • LABEL_3 stands for Product Inquiries
  • LABEL_4 stands for Technical Issues