Abhimanyu345/ticket-classifier

Customer Support Ticket Classifier

Fine-tuned DistilBERT model for classifying customer support tickets into 5 categories.

Model Description

This model is a fine-tuned version of distilbert-base-uncased trained on real customer support tweets from the Customer Support on Twitter dataset.

Developed as part of the MLDLOps Course Project at IIT Rajasthan by Abhimanyu Gupta (B22BB001).

Labels

ID	Label
0	Billing inquiry
1	Cancellation request
2	Product inquiry
3	Refund request
4	Technical issue

Performance

Metric	Value
Test Accuracy	99.0%
Macro F1	0.989
Training Time	~4.5 min (T4 GPU)
Inference Latency	~60ms (CPU)

Usage

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="abhimanyu345/ticket-classifier"
)

result = classifier("I was charged twice for my subscription this month")
print(result)
# [{'label': 'Billing inquiry', 'score': 0.9996}]

Training Details

• Base model: distilbert-base-uncased
• Learning rate: 3e-5
• Batch size: 32
• Epochs: 4
• Max sequence length: 128
• Training platform: Google Colab T4 GPU
• Experiment tracking: WandB Project

Dataset

• Source: Twitter Customer Support dataset (2.8M tweets)
• After filtering: 658,787 labeled examples
• After balancing: 25,000 examples (5,000 per class)
• Split: 70% train / 15% val / 15% test

MLOps Pipeline

Full production pipeline including:

• DVC — data versioning
• WandB — experiment tracking
• FastAPI — model serving
• Docker — containerization
• Prometheus — metrics monitoring
• Evidently AI — drift detection
• GitHub Actions — CI/CD

GitHub Repository: https://github.com/abhimanyu345/ticket-classifier

Citation

@misc{gupta2026ticketclassifier,
  author = {Abhimanyu Gupta},
  title = {Customer Support Ticket Classifier with MLOps Pipeline},
  year = {2026},
  publisher = {HuggingFace},
  journal = {HuggingFace Model Hub},
  howpublished = {\url{https://huggingface.co/abhimanyu345/ticket-classifier}}
}