README.md

metadata

license: mit
library_name: custom
pipeline_tag: text-classification
datasets:
  - RKB109/support-routing-ml-20260811-dataset
tags:
  - synthetic-data
  - transparent-baseline
  - applied-machine-learning
  - text-classification
  - zero-shot-classification
  - sentence-similarity
  - summarization
metrics:
  - accuracy

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-classification
• zero-shot-classification
• sentence-similarity
• summarization

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.