Synthetic Public Sector Ticket Classifier
This is a compact Hugging Face Transformers text-classification model for synthetic public-sector helpdesk tickets. It predicts one of six labels: policy_question, login_issue, incident_report, training_request, data_privacy, or document_access.
Intended Use
- Educational demonstrations of safe text-classification workflows.
- Local experiments with synthetic helpdesk routing examples.
- Testing Hugging Face model save/load and publishing flows.
Out-of-Scope Use
Do not use this model for production routing, legal decisions, benefits decisions, HR decisions, privacy determinations, security incident response, or authoritative public-sector advice.
Dataset Description
The repository contains synthetic JSONL data only:
data/train.jsonl: 60 examples.data/validation.jsonl: 18 examples.data/test.jsonl: 18 examples.
The data is written to avoid real client data, employee data, agency case details, credentials, internal URLs, logs, IP addresses, and regulated information.
Training Procedure
The model artifacts in this repository were produced with python -m model.train --epochs 200 --batch-size 8 --learning-rate 0.001 --output-dir model.
The training script builds a small BERT-style sequence classifier from configuration and trains a word-level tokenizer from the synthetic repository data. No external pretrained checkpoint is downloaded by model.train.
Evaluation Metrics
Validation split metrics from the packaged training run:
| Metric | Value |
|---|
| Accuracy | 0.6111111111111112 |
| Macro F1 | 0.5962962962962962 |
These metrics are from a very small synthetic validation split and should not be interpreted as real-world performance.
Limitations and Ethical Considerations
- The dataset is small and synthetic.
- The model may be brittle on wording outside the examples.
- The model may be overconfident.
- It has not been evaluated for demographic fairness.
- Human review is required for any consequential workflow.
Example Inference
1from transformers import pipeline
2
3classifier = pipeline(
4 "text-classification",
5 model="nprasann/synthetic-public-sector-ticket-classifier",
6 tokenizer="nprasann/synthetic-public-sector-ticket-classifier",
7)
8
9print(classifier("I cannot access the policy document library."))
