---
license: mit
library_name: custom
pipeline_tag: visual-document-retrieval
datasets:
- RKB109/multimodal-document-retrieval-20260802-dataset
tags:
- synthetic-data
- transparent-baseline
- multimodal-ai
- visual-document-retrieval
- document-question-answering
- image-to-text
- feature-extraction
metrics:
- accuracy
---

# 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-retrieval`
- `document-question-answering`
- `image-to-text`
- `feature-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.