contextual-bandit-simulator-20260903-model

Contextual Bandit Decision Simulator Baseline Model

Model Description

This repository contains a small, transparent prototype model for Teams need to validate decision policies offline before exposing users or systems to online reinforcement learning.

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: average_reward, policy_regret, unsafe_action_block_rate

Intended Use

• Architecture prototyping
• CI and evaluation examples
• Local baseline comparisons
• Educational experimentation

Hugging Face Task Coverage

• reinforcement-learning
• text-classification
• feature-extraction
• sentence-similarity

Limitations and Risks

Offline simulated rewards cannot prove online safety or business impact. Real experiments require review and guardrails.

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.