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 ClassificationFramework : PyTorchRepository : ZAM-ITI-110/Distil_Bert_V3 License : MIT (see YAML metadata above)Created : February 2025Creator : 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-casedFine-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 
