Hybrid Semantic Search Service Baseline Model
Model Description
This repository contains a small, transparent prototype model for
Enterprise search needs explainable retrieval quality when embedding APIs are unavailable, expensive, or restricted.
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: retrieval_accuracy, recall_at_3, mean_reciprocal_rank
Intended Use
- Architecture prototyping
- CI and evaluation examples
- Local baseline comparisons
- Educational experimentation
Hugging Face Task Coverage
sentence-similarityfeature-extractiontext-rankingquestion-answering
Limitations and Risks
The lightweight lexical baseline is reproducible but should be replaced or compared with domain embeddings at scale.
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.
