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support-ticket-classifier
faysal725/support-ticket-classifier
support-ticket-classifier is a text classification model from faysal725. Use it when you need a label for a piece of text. It is set up for setfit. The card lists the license as apache-2.0.
Automatically classifies customer support tickets by category and urgency using a fine-tuned SetFit model trained on 30,000+ real support tickets.
Access
Public
Updated Jun 24, 2026
Repo size
69.6 MB
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0
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Repo makeup
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At a glance
- Task
- Text Classification
- Library
- setfit
- License
- apache-2.0
- Access
- Public
- Created
- Jun 16, 2026
- Updated
- Jun 24, 2026
- SHA
- 380617a0
Try a prompt
Model card
From the Hugging Face model README
🎫 Support Ticket Classifier
Automatically classifies customer support tickets by category and urgency using a fine-tuned SetFit model trained on 30,000+ real support tickets.
What It Does
Input: Raw support ticket text
Output: Category + confidence score + urgency level
{
"category": "billing",
"confidence": 0.79,
"urgency": "high"
}
Categories
| Category | Example ticket |
|---|---|
billing | "I was charged twice for my subscription" |
technical | "My account keeps logging me out" |
complaint | "This service is completely unacceptable" |
refund | "I want to cancel and get my money back" |
Urgency Levels
| Level | When assigned |
|---|---|
high | Fraud, service down, unauthorized charges, locked out |
medium | General issues, standard requests |
low | General questions, curiosity, minor changes |
Performance
| Metric | Score |
|---|---|
| Weighted F1 | 82% |
| Complaint F1 | 92% |
| Technical F1 | 82% |
| Billing F1 | 79% |
| Refund F1 | 68% |
Trained on 30,571 labeled tickets from Kaggle + HuggingFace datasets.
Evaluated on a held-out test set of 3,058 tickets.
Why Use This Instead of an LLM?
- ✅ 100x cheaper per call than GPT-4 at volume
- ✅ Fast — under 200ms per ticket
- ✅ Private — runs on your own server, data never leaves your infrastructure
- ✅ No vendor lock-in — no API key, no per-token billing
- ✅ GDPR friendly — fully on-premise capable
Quick Start
Install dependencies
pip install setfit==1.0.3 sentence-transformers==2.7.0 transformers==4.40.2 huggingface_hub==0.23.5 scikit-learn numpy
Run predictions
from predict import predict_ticket
result = predict_ticket("I was charged twice and need a refund immediately")
print(result)
# {"category": "billing", "confidence": 0.79, "urgency": "high"}
Files in This Repo
| File | Description |
|---|---|
predict.py | Ready-to-run prediction script |
requirements.txt | Pinned dependencies |
category_model/ | Fine-tuned SetFit classifier |
calibration.pkl | Platt scaling confidence calibration |
label_mappings.pkl | Label encoders |
Tech Stack
- Model: SetFit (Sentence Transformers fine-tuning)
- Base model:
paraphrase-MiniLM-L3-v2 - Training data: 30,571 labeled support tickets
- Confidence calibration: Platt scaling on held-out validation set
- Urgency: Keyword-rule layer (transparent and auditable)
Get the Full Docker API Version
Want a production-ready REST API you can deploy to your own server in minutes?
The Docker version includes:
- FastAPI wrapper (
POST /predictendpoint) - Dockerfile — one command to deploy anywhere
- Full setup guide
👉 Get the Docker API version on Gumroad
Sample Results
- Task
- Text Classification
- Library
- setfit
- License
- apache-2.0
- Languages
- en
- Created
- Jun 16, 2026
- Updated
- Jun 24, 2026