DistilBERT Ticket Classifier (Distil_Bert_V3)

Model Overview

This is a fine-tuned DistilBERT model (distilbert-base-cased) designed to classify defect tickets and assign them to the appropriate team based on their text content. It cleaned the ticket data from Defect_ticket_V2.csv by fixing missing values input of ticket Description, Comment, and Summary, and predicts one of 5 team labels, each linked to a team email for automated routing.

• Model Type: DistilBERT for Sequence Classification
• Framework: PyTorch
• Repository: ZAM-ITI-110/Distil_Bert_V3
• License: MIT (see YAML metadata above)
• Created: February 2025
• Creator: AUNGHLAINGTUN/Student ID6319250G NYP

Intended Use

This model is intended for:

• Automating ticket assignment in IT support or defect tracking systems.
• Reducing manual triage time by predicting the responsible team based on ticket text.

Use Case

• Input: A ticket with fields Description, Comment, and Summary (e.g., "Urgent server crash reported in production").
• Output: A team label (0-4) mapped to a team email (e.g., team1@example.com).

Out of Scope

• Not designed for multi-label classification or sentiment analysis.
• May not generalize well to tickets outside the training domain (e.g., non-technical support tickets).

Training Data

• Dataset: Defect_ticket_v2.csv (private dataset)
• Size: Approximately 5,000 samples (70% train: ~3,504, 15% validation: ~750, 15% test: ~750).
• Features: Combined text from Description, Comment, and Summary columns.
• Labels: 5 unique team labels (encoded as 0-4), derived from the Assigned Team column.
• Preprocessing: Missing values filled with empty strings; text truncated/padded to 512 tokens.

Note: The dataset is not publicly available due to privacy constraints.

Training Procedure

• Base Model: distilbert-base-cased
• Fine-Tuning:
  • Epochs: 5
  • Batch Size: 8
  • Optimizer: AdamW (learning rate: 3e-5, weight decay: 0.01)
  • Scheduler: Linear with 10% warmup steps
• Hardware: Trained on Google Colab with a T4 GPU (~31 seconds/epoch).
• Mixed Precision: Enabled via PyTorch AMP for efficiency.
• Loss Function: CrossEntropyLoss

Training Metrics

Epoch	Train Loss	Validation Loss	Validation Accuracy
1	0.4021	0.0038	100%
2	0.0031	0.0011	100%
3	0.0013	0.0006	100%
4	0.0008	0.0004	100%
5	0.0007	0.0004	100%

• Test Accuracy: 100% (on ~750 test samples).

Evaluation

• Performance: Achieved 100% accuracy on both validation and test sets, indicating excellent fit to the provided data.
• Caveats:
  • Perfect accuracy may suggest an easy classification task, limited dataset diversity, or potential data leakage (e.g., duplicates across splits).
  • Real-world performance on new, unseen tickets should be validated.

How to Use

• Predicts the appropriate team and email for up to 6 ticket descriptions.
• Click 'Predict' for each ticket or then 'Send Tickets' to process for all .

Installation

pip install transformers torch