Applied ML Support Router Baseline Model
Model Description
This repository contains a small, transparent prototype model for
Support operations need reproducible routing models that expose confidence and defer uncertain cases.
The model combines per-label token weights with IDF-weighted evidence
retrieval. It was generated for reproducible architecture demonstrations and
does not call a hosted LLM.
Evaluation
- Held-out synthetic examples: 4
- Accuracy: 1
- Intended metrics: classification_accuracy, automation_coverage, escalation_precision
Intended Use
- Architecture prototyping
- CI and evaluation examples
- Local baseline comparisons
- Educational experimentation
Hugging Face Task Coverage
text-classificationzero-shot-classificationsentence-similaritysummarization
Limitations and Risks
Synthetic tickets do not represent every user population or language. Production training data needs consent and bias analysis.
The dataset is synthetic and small. Do not use this model for consequential
decisions without representative data, expert review, and production-grade
evaluation.
Reproducibility
The linked GitHub repository includes train.py, the exact dataset split,
evaluation code, and the model JSON format.
