Multimodal Document Retrieval Baseline Baseline Model
Model Description
This repository contains a small, transparent prototype model for
Business documents contain meaning in text, tables, layout, and imagery that text-only retrieval can miss.
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, modality_coverage, recall_at_3
Intended Use
- Architecture prototyping
- CI and evaluation examples
- Local baseline comparisons
- Educational experimentation
Hugging Face Task Coverage
visual-document-retrievaldocument-question-answeringimage-to-textfeature-extraction
Limitations and Risks
The starter dataset contains synthetic textual modality descriptors, not sensitive scanned documents.
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.
